{"id":74,"date":"2016-09-15T10:42:27","date_gmt":"2016-09-15T08:42:27","guid":{"rendered":"http:\/\/127.0.0.1\/~veipone\/wordpress\/?page_id=74"},"modified":"2024-02-06T09:36:15","modified_gmt":"2024-02-06T09:36:15","slug":"pubblicazioni","status":"publish","type":"page","link":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/","title":{"rendered":"Pubblicazioni"},"content":{"rendered":"\n<div class=\"wp-block-group alignwide is-layout-flow wp-block-group-is-layout-flow\"><div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><div class=\"teachpress_cloud\"><span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=47&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"3 Publications\" class=\"\">activity recognition<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=84&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"2 Publications\" class=\"\">Agent Computing<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=2&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"2 Publications\" class=\"\">Artificial Intelligence<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=87&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"4 Publications\" class=\"\">cloud computing<\/a><\/span> <span style=\"font-size:12px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=103&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"6 Publications\" class=\"\">cogito<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=80&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"3 Publications\" class=\"\">combined sewer overflow<\/a><\/span> <span style=\"font-size:24px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=78&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"12 Publications\" class=\"\">cyber physical systems<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=40&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"2 Publications\" class=\"\">Data Mining for Energy Efficiency<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=93&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"3 Publications\" class=\"\">data stream mining<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=18&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"3 Publications\" class=\"\">Deep Reinforcement Learning<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=102&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"5 Publications\" class=\"\">domus<\/a><\/span> <span style=\"font-size:12px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=48&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"6 Publications\" class=\"\">Edge and cloud computing<\/a><\/span> <span style=\"font-size:22px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=32&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"11 Publications\" class=\"\">Edge computing<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=81&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"4 Publications\" class=\"\">flooding<\/a><\/span> <span style=\"font-size:28px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"14 Publications\" class=\"\">insider<\/a><\/span> <span style=\"font-size:35px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=34&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"17 Publications\" class=\"\">internet of things<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=57&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"4 Publications\" class=\"\">modeling<\/a><\/span> <span style=\"font-size:35px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=24&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"17 Publications\" class=\"\">multi-agent systems<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=107&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"5 Publications\" class=\"\">quantum computing<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=82&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"4 Publications\" class=\"\">real-time control<\/a><\/span> <span style=\"font-size:16px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=101&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"8 Publications\" class=\"\">res-novae<\/a><\/span> <span style=\"font-size:28px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=15&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"14 Publications\" class=\"\">smart city<\/a><\/span> <span style=\"font-size:28px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=19&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"14 Publications\" class=\"\">smart environments<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=50&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"3 Publications\" class=\"\">Smart Office<\/a><\/span> <span style=\"font-size:18px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"9 Publications\" class=\"\">sobigdata.it<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=54&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"3 Publications\" class=\"\">social internet of things<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=98&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"5 Publications\" class=\"\">swarm intelligence<\/a><\/span> <span style=\"font-size:12px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=16&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"6 Publications\" class=\"\">Urban computing<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=83&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"4 Publications\" class=\"\">urban drainage system<\/a><\/span> <span style=\"font-size:11px;\"><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=90&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\" title=\"3 Publications\" class=\"\">wireless sensor and actuator networks<\/a><\/span> <\/div><div class=\"teachpress_filter\"><select class=\"default\" name=\"yr\" id=\"yr\" tabindex=\"2\" onchange=\"teachpress_jumpMenu('parent',this, 'https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?')\">\r\n                   <option value=\"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=#tppubs\">All years<\/option>\r\n                   <option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2026#tppubs\" >2026<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2025#tppubs\" >2025<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2024#tppubs\" >2024<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2023#tppubs\" >2023<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2022#tppubs\" >2022<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2021#tppubs\" >2021<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2020#tppubs\" >2020<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2019#tppubs\" >2019<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2018#tppubs\" >2018<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2017#tppubs\" >2017<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2016#tppubs\" >2016<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2015#tppubs\" >2015<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2014#tppubs\" >2014<\/option><option value = \"tgid=&amp;type=&amp;auth=&amp;usr=&amp;yr=2013#tppubs\" >2013<\/option>\r\n                <\/select><select class=\"default\" name=\"type\" id=\"type\" tabindex=\"3\" onchange=\"teachpress_jumpMenu('parent',this, 'https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?')\">\r\n                   <option value=\"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=#tppubs\">All types<\/option>\r\n                   <option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=article#tppubs\" >Journal Articles<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=book#tppubs\" >Books<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=inbook#tppubs\" >Book Chapters<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=inproceedings#tppubs\" >Proceedings Articles<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=proceedings#tppubs\" >Proceedings<\/option><option value = \"tgid=&amp;yr=&amp;auth=&amp;usr=&amp;type=techreport#tppubs\" >Technical Reports<\/option>\r\n                <\/select><select class=\"default\" name=\"auth\" id=\"auth\" tabindex=\"5\" onchange=\"teachpress_jumpMenu('parent',this, 'https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?')\">\r\n                   <option value=\"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=#tppubs\">All authors<\/option>\r\n                   <option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=44#tppubs\" > Altomare, A.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=6#tppubs\" > Amadeo, Marica<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=91#tppubs\" > Belcastro, Loris  Marozzo, Fabrizio  Presta, Aleandro  Varchera, Rosa  Vinci, Andrea<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=74#tppubs\" > Belcastro, Loris<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=48#tppubs\" > Briante, O.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=57#tppubs\" > Briante, Orazio<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=35#tppubs\" > Brusco, A. C.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=17#tppubs\" > Buono, Michele De<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=22#tppubs\" > Canino, M. P.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=15#tppubs\" > Canino, Maria Pia<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=116#tppubs\" > Capalbo, Santina<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=45#tppubs\" > Catlett, C.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=23#tppubs\" > Cesario, E.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=11#tppubs\" > Cesario, Eugenio<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=111#tppubs\" > Cicero, Simona  Guarascio, Massimo  Guerrieri, Antonio  Mungari, Simone  Vinci, Andrea<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=28#tppubs\" > Cicirelli, F.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=7#tppubs\" > Cicirelli, Franco<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=20#tppubs\" > Colace, Simone<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=98#tppubs\" > D&#039;Amore, Francesco<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=117#tppubs\" > Folino, Gianluigi<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=53#tppubs\" > Fortino, G.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=51#tppubs\" > Fortino, Giancarlo<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=54#tppubs\" > Garofalo, Giuseppina<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=41#tppubs\" > Gentile, A. F.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=101#tppubs\" > Gentile, Antonio Francesco<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=55#tppubs\" > Giordano, Andrea<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=42#tppubs\" > Greco, E.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=13#tppubs\" > Greco, Emilio<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=29#tppubs\" > Guerrieri, A.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=8#tppubs\" > Guerrieri, Antonio<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=18#tppubs\" > Gullo, Nicola<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=49#tppubs\" > Iera, A.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=58#tppubs\" > Iera, Antonio<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=14#tppubs\" > Khan, Irfanullah<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=94#tppubs\" > Khan, Irfanullah  Cicirelli, Franco  Greco, Emilio  Guerrieri, Antonio  Mastroianni, Carlo  Scarcello, Luigi  Spezzano, Giandomenico  Vinci, Andrea<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=21#tppubs\" > Laurita, Sara<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=112#tppubs\" > Li, Qimeng<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=12#tppubs\" > Lindia, Paolo<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=115#tppubs\" > Lobello, Federica<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=33#tppubs\" > Maiolo, M.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=99#tppubs\" > Mariani, Luca<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=81#tppubs\" > Marozzo, Fabrizio<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=30#tppubs\" > Mastroianni, C.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=1#tppubs\" > Mastroianni, Carlo<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=47#tppubs\" > Mercuri, A.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=52#tppubs\" > Mercuri, Alessandro<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=97#tppubs\" > Micieli, Massimo<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=100#tppubs\" > Orsino, Alessio<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=34#tppubs\" > Palermo, S. A.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=43#tppubs\" > Palopoli, F.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=38#tppubs\" > Piro, P.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=56#tppubs\" > Piro, Patrizia<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=36#tppubs\" > Pirouz, B.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=2#tppubs\" > Plastina, Francesco<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=92#tppubs\" > Presta, Aleandro<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=113#tppubs\" > Qi, Wen<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=95#tppubs\" > Rizzo, Luigi<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=50#tppubs\" > Ruggeri, G.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=9#tppubs\" > Ruggeri, Giuseppe<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=31#tppubs\" > Scarcello, L.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=3#tppubs\" > Scarcello, Luigi<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=4#tppubs\" > Settino, Jacopo<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=32#tppubs\" > Spezzano, G.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=10#tppubs\" > Spezzano, Giandomenico<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=66#tppubs\" > Sunarsa, Hanry<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=46#tppubs\" > Talia, D.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=37#tppubs\" > Turco, M.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=26#tppubs\" > Uchubilo, P. I.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=93#tppubs\" > Varchera, Rosa<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=19#tppubs\" > Vennera, Andrea<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=24#tppubs\" > Vinci, A.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=5#tppubs\" > Vinci, Andrea<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=25#tppubs\" > Zarin, S.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=16#tppubs\" > Zarin, Shabnam<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=27#tppubs\" > Zhu, X.<\/option><option value = \"tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=96#tppubs\" > Zicari, Paolo<\/option>\r\n                <\/select><select class=\"default\" name=\"usr\" id=\"usr\" tabindex=\"6\" onchange=\"teachpress_jumpMenu('parent',this, 'https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?')\">\r\n                   <option value=\"tgid=&amp;yr=&amp;type=&amp;auth=&amp;usr=#tppubs\">All users<\/option>\r\n                   <option value = \"tgid=&amp;yr=&amp;type=&amp;auth=&amp;usr=1#tppubs\" >vinci<\/option>\r\n                <\/select><\/div><\/form><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">84 entries<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 of 2 <a href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"next page\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"last page\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><div class=\"teachpress_publication_list\"><h3 class=\"tp_h3\" id=\"tp_h3_2026\">2026<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, Eugenio;  Lindia, Paolo;  Lobello, Federica;  Vinci, Andrea;  Capalbo, Santina<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('89','tp_links')\" style=\"cursor:pointer;\">Enhancing energy efficiency in cloud computing through regression models: A data-driven approach with experimental validation<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Future Generation Computer Systems, <\/span><span class=\"tp_pub_additional_volume\">vol. 177, <\/span><span class=\"tp_pub_additional_pages\">pp. 108245, <\/span><span class=\"tp_pub_additional_year\">2026<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 0167-739X<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_89\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('89','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_89\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('89','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_89\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{CESARIO2026108245,<br \/>\r\ntitle = {Enhancing energy efficiency in cloud computing through regression models: A data-driven approach with experimental validation},<br \/>\r\nauthor = {Eugenio Cesario and Paolo Lindia and Federica Lobello and Andrea Vinci and Santina Capalbo},<br \/>\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X25005394},<br \/>\r\ndoi = {https:\/\/doi.org\/10.1016\/j.future.2025.108245},<br \/>\r\nissn = {0167-739X},<br \/>\r\nyear  = {2026},<br \/>\r\ndate = {2026-01-01},<br \/>\r\nurldate = {2026-01-01},<br \/>\r\njournal = {Future Generation Computer Systems},<br \/>\r\nvolume = {177},<br \/>\r\npages = {108245},<br \/>\r\nkeywords = {insider},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('89','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_89\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X25005394\" title=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X25005394\" target=\"_blank\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X25005394<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1016\/j.future.2025.108245\" title=\"Follow DOI:https:\/\/doi.org\/10.1016\/j.future.2025.108245\" target=\"_blank\">doi:https:\/\/doi.org\/10.1016\/j.future.2025.108245<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('89','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2025\">2025<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, Eugenio;  Lindia, Paolo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('85','tp_links')\" style=\"cursor:pointer;\">Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What\u2019s the Best Approach for Crime Forecasting?<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">IEEE Access, <\/span><span class=\"tp_pub_additional_volume\">vol. 13, <\/span><span class=\"tp_pub_additional_pages\">pp. 133053-133077, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 2169-3536<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_85\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('85','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_85\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('85','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_85\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{11096550b,<br \/>\r\ntitle = {Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What\u2019s the Best Approach for Crime Forecasting?},<br \/>\r\nauthor = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/ACCESS.2025.3592668},<br \/>\r\nisbn = {2169-3536},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-07-25},<br \/>\r\nurldate = {2025-01-01},<br \/>\r\njournal = {IEEE Access},<br \/>\r\nvolume = {13},<br \/>\r\npages = {133053-133077},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('85','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_85\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ACCESS.2025.3592668\" title=\"Follow DOI:10.1109\/ACCESS.2025.3592668\" target=\"_blank\">doi:10.1109\/ACCESS.2025.3592668<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('85','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, Eugenio;  Lindia, Paolo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('80','tp_links')\" style=\"cursor:pointer;\">How to\u00a0Deal with\u00a0Different Densities of\u00a0Urban Spatial Data? A Comparison of\u00a0Clustering Approaches to\u00a0Detect City Hotspots<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span> Sergeyev, Yaroslav D.;  Kvasov, Dmitri E.;  Astorino, Annabella (Ed.): <span class=\"tp_pub_additional_booktitle\">Numerical Computations: Theory and Algorithms, <\/span><span class=\"tp_pub_additional_pages\">pp. 248\u2013253, <\/span><span class=\"tp_pub_additional_publisher\">Springer Nature Switzerland, <\/span><span class=\"tp_pub_additional_address\">Cham, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-3-031-81247-7<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_80\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('80','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_80\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('80','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_80\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('80','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_80\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10.1007\/978-3-031-81247-7_20,<br \/>\r\ntitle = {How to\u00a0Deal with\u00a0Different Densities of\u00a0Urban Spatial Data? A Comparison of\u00a0Clustering Approaches to\u00a0Detect City Hotspots},<br \/>\r\nauthor = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},<br \/>\r\neditor = {Yaroslav D. Sergeyev and Dmitri E. Kvasov and Annabella Astorino},<br \/>\r\ndoi = {10.1007\/978-3-031-81247-7_20},<br \/>\r\nisbn = {978-3-031-81247-7},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nurldate = {2025-01-01},<br \/>\r\nbooktitle = {Numerical Computations: Theory and Algorithms},<br \/>\r\npages = {248\u2013253},<br \/>\r\npublisher = {Springer Nature Switzerland},<br \/>\r\naddress = {Cham},<br \/>\r\nabstract = {In the field of urban data analysis, the detection of city hotspots is becoming a fundamental activity aimed at showing functions and roles played by each city area and providing valuable support for policymakers, scientists, and planners. However, since metropolitan cities are heavily characterized by variable densities, multi-density clustering algorithms might be more reliable than classic approaches to discover proper hotspots from urban data. This paper presents a study on hotspots detection in urban environments, by comparing two approaches, i.e., single-threshold and multi-density threshold ones, for clustering urban data. The experimental evaluation, carried out on a synthetic state-of-the-art multi-density dataset, shows that a multi-density approach achieves higher clustering quality than classic techniques.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('80','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_80\" style=\"display:none;\"><div class=\"tp_abstract_entry\">In the field of urban data analysis, the detection of city hotspots is becoming a fundamental activity aimed at showing functions and roles played by each city area and providing valuable support for policymakers, scientists, and planners. However, since metropolitan cities are heavily characterized by variable densities, multi-density clustering algorithms might be more reliable than classic approaches to discover proper hotspots from urban data. This paper presents a study on hotspots detection in urban environments, by comparing two approaches, i.e., single-threshold and multi-density threshold ones, for clustering urban data. The experimental evaluation, carried out on a synthetic state-of-the-art multi-density dataset, shows that a multi-density approach achieves higher clustering quality than classic techniques.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('80','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_80\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-81247-7_20\" title=\"Follow DOI:10.1007\/978-3-031-81247-7_20\" target=\"_blank\">doi:10.1007\/978-3-031-81247-7_20<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('80','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Li, Qimeng;  Cicirelli, Franco;  Vinci, Andrea;  Guerrieri, Antonio;  Qi, Wen;  Fortino, Giancarlo<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('82','tp_links')\" style=\"cursor:pointer;\">Quadruped Robots: Bridging Mechanical Design, Control, and Applications<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Robotics, <\/span><span class=\"tp_pub_additional_volume\">vol. 14, <\/span><span class=\"tp_pub_additional_number\">no. 5, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2218-6581<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_82\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('82','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_82\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('82','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_82\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('82','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_82\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{robotics14050057,<br \/>\r\ntitle = {Quadruped Robots: Bridging Mechanical Design, Control, and Applications},<br \/>\r\nauthor = {Qimeng Li and Franco Cicirelli and Andrea Vinci and Antonio Guerrieri and Wen Qi and Giancarlo Fortino},<br \/>\r\nurl = {https:\/\/www.mdpi.com\/2218-6581\/14\/5\/57},<br \/>\r\ndoi = {10.3390\/robotics14050057},<br \/>\r\nissn = {2218-6581},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nurldate = {2025-01-01},<br \/>\r\njournal = {Robotics},<br \/>\r\nvolume = {14},<br \/>\r\nnumber = {5},<br \/>\r\nabstract = {Quadruped robots have emerged as a prominent field of research due to their exceptional mobility and adaptability in complex terrains. This paper presents an overview of quadruped robots, encompassing their design principles, control mechanisms, perception systems, and applications across various industries. We review the historical evolution and technological milestones that have shaped quadruped robotics. To understand their impact on performance and functionality, key aspects of mechanical design are analyzed, including leg configurations, actuation systems, and material selection. Control strategies for locomotion, balance, and navigation are all examined, highlighting the integration of artificial intelligence and machine learning to enhance adaptability and autonomy. This review also explores perception and sensing technologies that enable environmental interaction and decision-making capabilities. Furthermore, we systematically examine the diverse applications of quadruped robots in sectors including the military, search and rescue, industrial inspection, agriculture, and entertainment. Finally, we address challenges and limitations, including technical hurdles, ethical considerations, and regulatory issues, and propose future research directions to advance the field. By structuring this review as a systematic study, we ensure clarity and a comprehensive understanding of the domain, making it a valuable resource for researchers and engineers in quadruped robotics.},<br \/>\r\nkeywords = {sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('82','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_82\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Quadruped robots have emerged as a prominent field of research due to their exceptional mobility and adaptability in complex terrains. This paper presents an overview of quadruped robots, encompassing their design principles, control mechanisms, perception systems, and applications across various industries. We review the historical evolution and technological milestones that have shaped quadruped robotics. To understand their impact on performance and functionality, key aspects of mechanical design are analyzed, including leg configurations, actuation systems, and material selection. Control strategies for locomotion, balance, and navigation are all examined, highlighting the integration of artificial intelligence and machine learning to enhance adaptability and autonomy. This review also explores perception and sensing technologies that enable environmental interaction and decision-making capabilities. Furthermore, we systematically examine the diverse applications of quadruped robots in sectors including the military, search and rescue, industrial inspection, agriculture, and entertainment. Finally, we address challenges and limitations, including technical hurdles, ethical considerations, and regulatory issues, and propose future research directions to advance the field. By structuring this review as a systematic study, we ensure clarity and a comprehensive understanding of the domain, making it a valuable resource for researchers and engineers in quadruped robotics.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('82','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_82\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.mdpi.com\/2218-6581\/14\/5\/57\" title=\"https:\/\/www.mdpi.com\/2218-6581\/14\/5\/57\" target=\"_blank\">https:\/\/www.mdpi.com\/2218-6581\/14\/5\/57<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3390\/robotics14050057\" title=\"Follow DOI:10.3390\/robotics14050057\" target=\"_blank\">doi:10.3390\/robotics14050057<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('82','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, Eugenio;  Lindia, Paolo;  Lobello, Federica;  Vinci, Andrea;  Zarin, Shabnam;  Capalbo, Santina<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('83','tp_links')\" style=\"cursor:pointer;\">Improving Cloud Energy Efficiency through Machine Learning Models<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), <\/span><span class=\"tp_pub_additional_pages\">pp. 247-251, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_83\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('83','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_83\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('83','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_83\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10974836,<br \/>\r\ntitle = {Improving Cloud Energy Efficiency through Machine Learning Models},<br \/>\r\nauthor = {Eugenio Cesario and Paolo Lindia and Federica Lobello and Andrea Vinci and Shabnam Zarin and Santina Capalbo},<br \/>\r\ndoi = {10.1109\/PDP66500.2025.00041},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nurldate = {2025-01-01},<br \/>\r\nbooktitle = {2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},<br \/>\r\npages = {247-251},<br \/>\r\nkeywords = {insider},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('83','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_83\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/PDP66500.2025.00041\" title=\"Follow DOI:10.1109\/PDP66500.2025.00041\" target=\"_blank\">doi:10.1109\/PDP66500.2025.00041<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('83','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Micieli, Massimo;  Cicirelli, Franco;  Guerrieri, Antonio;  Rizzo, Luigi;  Vinci, Andrea;  Zicari, Paolo<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('86','tp_links')\" style=\"cursor:pointer;\">Leveraging Hyperspectral Data in Cognitive Environments<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">IEEE Access, <\/span><span class=\"tp_pub_additional_pages\">pp. 1-1, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_86\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('86','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_86\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('86','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_86\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{11134376,<br \/>\r\ntitle = {Leveraging Hyperspectral Data in Cognitive Environments},<br \/>\r\nauthor = {Massimo Micieli and Franco Cicirelli and Antonio Guerrieri and Luigi Rizzo and Andrea Vinci and Paolo Zicari},<br \/>\r\ndoi = {10.1109\/ACCESS.2025.3601840},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\njournal = {IEEE Access},<br \/>\r\npages = {1-1},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('86','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_86\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ACCESS.2025.3601840\" title=\"Follow DOI:10.1109\/ACCESS.2025.3601840\" target=\"_blank\">doi:10.1109\/ACCESS.2025.3601840<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('86','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Folino, Gianluigi;  Gentile, Antonio Francesco;  Varchera, Rosa;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('88','tp_links')\" style=\"cursor:pointer;\">Applying Multi-Objective Differential Evolution for IoT Application Design in the Edge-Cloud Continuum<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the Genetic and Evolutionary Computation Conference Companion, <\/span><span class=\"tp_pub_additional_pages\">pp. 95\u201396, <\/span><span class=\"tp_pub_additional_publisher\">Association for Computing Machinery, <\/span><span class=\"tp_pub_additional_address\">NH Malaga Hotel, Malaga, Spain, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9798400714641<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_88\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('88','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_88\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('88','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_88\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('88','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_88\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10.1145\/3712255.3734265,<br \/>\r\ntitle = {Applying Multi-Objective Differential Evolution for IoT Application Design in the Edge-Cloud Continuum},<br \/>\r\nauthor = {Gianluigi Folino and Antonio Francesco Gentile and Rosa Varchera and Andrea Vinci},<br \/>\r\nurl = {https:\/\/biblioproxy.cnr.it:2481\/10.1145\/3712255.3734265},<br \/>\r\ndoi = {10.1145\/3712255.3734265},<br \/>\r\nisbn = {9798400714641},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-01-01},<br \/>\r\nurldate = {2025-01-01},<br \/>\r\nbooktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},<br \/>\r\npages = {95\u201396},<br \/>\r\npublisher = {Association for Computing Machinery},<br \/>\r\naddress = {NH Malaga Hotel, Malaga, Spain},<br \/>\r\nseries = {GECCO &#039;25 Companion},<br \/>\r\nabstract = {The edge-cloud continuum&#039;s lack of unified standards often limits application development to the initially chosen platform. This challenges designers and developers to select the computation resources available in the edge\/cloud continuum to optimize application performances while containing execution costs. In this context, this work proposes a methodology for assessing distributed application deployment configurations through simulation based on the Petri-net formalism. Furthermore, it investigates using the Differential Evolution (DE) algorithm to identify optimal configurations, considering multiple and potentially conflicting objectives.},<br \/>\r\nkeywords = {insider},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('88','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_88\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The edge-cloud continuum&#039;s lack of unified standards often limits application development to the initially chosen platform. This challenges designers and developers to select the computation resources available in the edge\/cloud continuum to optimize application performances while containing execution costs. In this context, this work proposes a methodology for assessing distributed application deployment configurations through simulation based on the Petri-net formalism. Furthermore, it investigates using the Differential Evolution (DE) algorithm to identify optimal configurations, considering multiple and potentially conflicting objectives.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('88','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_88\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/biblioproxy.cnr.it:2481\/10.1145\/3712255.3734265\" title=\"https:\/\/biblioproxy.cnr.it:2481\/10.1145\/3712255.3734265\" target=\"_blank\">https:\/\/biblioproxy.cnr.it:2481\/10.1145\/3712255.3734265<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3712255.3734265\" title=\"Follow DOI:10.1145\/3712255.3734265\" target=\"_blank\">doi:10.1145\/3712255.3734265<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('88','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Belcastro, Loris;  Marozzo, Fabrizio;  Presta, Aleandro;  Varchera, Rosa;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('74','tp_links')\" style=\"cursor:pointer;\">Developing Platform-Agnostic IIoT Applications in Edge-Cloud Environments<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span> Solina, Vittorio;  Longo, Francesco;  Romero, David (Ed.): <span class=\"tp_pub_additional_booktitle\">6th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2024), <\/span><span class=\"tp_pub_additional_pages\">pp. 2016-2115, <\/span><span class=\"tp_pub_additional_publisher\">Elsevier, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1877-0509<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_74\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('74','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_74\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('74','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_74\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{BELCASTRO20252106,<br \/>\r\ntitle = {Developing Platform-Agnostic IIoT Applications in Edge-Cloud Environments},<br \/>\r\nauthor = {Belcastro, Loris AND Marozzo, Fabrizio AND Presta, Aleandro AND Varchera, Rosa AND Vinci, Andrea},<br \/>\r\neditor = {Vittorio Solina and Francesco Longo and David Romero},<br \/>\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050925002790},<br \/>\r\ndoi = {10.1016\/j.procs.2025.01.271},<br \/>\r\nissn = {1877-0509},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-00-00},<br \/>\r\nurldate = {2025-00-00},<br \/>\r\nbooktitle = {6th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2024)},<br \/>\r\nvolume = {253},<br \/>\r\npages = {2016-2115},<br \/>\r\npublisher = {Elsevier},<br \/>\r\nseries = {Procedia Computer Science},<br \/>\r\nkeywords = {insider},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('74','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_74\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050925002790\" title=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050925002790\" target=\"_blank\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050925002790<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.procs.2025.01.271\" title=\"Follow DOI:10.1016\/j.procs.2025.01.271\" target=\"_blank\">doi:10.1016\/j.procs.2025.01.271<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('74','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicero, Simona;  Guarascio, Massimo;  Guerrieri, Antonio;  Mungari, Simone;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('81','tp_links')\" style=\"cursor:pointer;\">A Deep Neural Framework for Fault Detection in IoT-based Sensor Networks<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span> Ting, I-Hsien;  Alhajj, Reda;  Karampelas, Panagiotis;  Day, Min-Yuh (Ed.): <span class=\"tp_pub_additional_booktitle\"> Advances in Social Networks Analysis and Mining. ASONAM 2024., <\/span><span class=\"tp_pub_additional_pages\">pp. 129\u2013145, <\/span><span class=\"tp_pub_additional_publisher\">Springer Nature Switzerland, <\/span><span class=\"tp_pub_additional_address\">Cham, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-3-031-85386-9<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_81\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('81','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_81\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('81','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_81\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{CiceroDNFFDIOT,<br \/>\r\ntitle = {A Deep Neural Framework for Fault Detection in IoT-based Sensor Networks},<br \/>\r\nauthor = {Simona Cicero AND Massimo Guarascio AND Antonio Guerrieri AND Simone Mungari AND Andrea Vinci},<br \/>\r\neditor = {Ting, I-Hsien<br \/>\r\nand Alhajj, Reda<br \/>\r\nand Karampelas, Panagiotis<br \/>\r\nand Day, Min-Yuh},<br \/>\r\ndoi = {https:\/\/doi.org\/10.1007\/978-3-031-85386-9_10},<br \/>\r\nisbn = {978-3-031-85386-9},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-00-00},<br \/>\r\nurldate = {2025-00-00},<br \/>\r\nbooktitle = { Advances in Social Networks Analysis and Mining. ASONAM 2024.},<br \/>\r\npages = {129--145},<br \/>\r\npublisher = {Springer Nature Switzerland},<br \/>\r\naddress = {Cham},<br \/>\r\nkeywords = {insider, sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('81','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_81\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1007\/978-3-031-85386-9_10\" title=\"Follow DOI:https:\/\/doi.org\/10.1007\/978-3-031-85386-9_10\" target=\"_blank\">doi:https:\/\/doi.org\/10.1007\/978-3-031-85386-9_10<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('81','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2024\">2024<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Marozzo, Fabrizio;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('69','tp_links')\" style=\"cursor:pointer;\">Design of Platform-Independent IoT Applications in the Edge-Cloud Continuum<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), <\/span><span class=\"tp_pub_additional_pages\">pp. 589-594, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_69\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('69','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_69\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('69','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=48#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge and cloud computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_69\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{marozzo2024,<br \/>\r\ntitle = {Design of Platform-Independent IoT Applications in the Edge-Cloud Continuum},<br \/>\r\nauthor = {Fabrizio Marozzo and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/DCOSS-IoT61029.2024.00092},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-08-12},<br \/>\r\nurldate = {2024-12-31},<br \/>\r\nbooktitle = {2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)},<br \/>\r\npages = {589-594},<br \/>\r\nkeywords = {Edge and cloud computing, insider},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('69','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_69\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/DCOSS-IoT61029.2024.00092\" title=\"Follow DOI:10.1109\/DCOSS-IoT61029.2024.00092\" target=\"_blank\">doi:10.1109\/DCOSS-IoT61029.2024.00092<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('69','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, Eugenio;  Lindia, Paolo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('72','tp_links')\" style=\"cursor:pointer;\">Multi-Density Crime Predictor: an approach to forecast criminal activities in multi-density crime hotspots<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Journal of Big Data, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 21961115<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_72\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('72','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_72\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('72','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_72\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{ceslinvin2024jbd,<br \/>\r\ntitle = {Multi-Density Crime Predictor: an approach to forecast criminal activities in multi-density crime hotspots},<br \/>\r\nauthor = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},<br \/>\r\ndoi = {https:\/\/doi.org\/10.1186\/s40537-024-00935-4},<br \/>\r\nissn = {21961115},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-05-09},<br \/>\r\nurldate = {2024-05-09},<br \/>\r\njournal = {Journal of Big Data},<br \/>\r\nkeywords = {insider, sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('72','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_72\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1186\/s40537-024-00935-4\" title=\"Follow DOI:https:\/\/doi.org\/10.1186\/s40537-024-00935-4\" target=\"_blank\">doi:https:\/\/doi.org\/10.1186\/s40537-024-00935-4<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('72','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Mastroianni, Carlo;  Plastina, Francesco;  Settino, Jacopo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('68','tp_links')\" style=\"cursor:pointer;\">Variational Quantum Algorithms for the Allocation of Resources in a Cloud\/Edge Architecture<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">IEEE Transactions on Quantum Engineering, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 26891808<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_68\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('68','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_68\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('68','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=48#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge and cloud computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=107#tppubs\" title=\"Show all publications which have a relationship to this tag\">quantum computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_68\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{mastroianni2024quantum,<br \/>\r\ntitle = {Variational Quantum Algorithms for the Allocation of Resources in a Cloud\/Edge Architecture},<br \/>\r\nauthor = {Carlo Mastroianni and Francesco Plastina and Jacopo Settino and Andrea Vinci},<br \/>\r\nurl = {https:\/\/ieeexplore.ieee.org\/document\/10522849},<br \/>\r\ndoi = {https:\/\/doi.org\/10.1109\/TQE.2024.3398410},<br \/>\r\nissn = {26891808},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-05-08},<br \/>\r\nurldate = {2024-05-08},<br \/>\r\njournal = {IEEE Transactions on Quantum Engineering},<br \/>\r\nkeywords = {Edge and cloud computing, insider, quantum computing, sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('68','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_68\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/ieeexplore.ieee.org\/document\/10522849\" title=\"https:\/\/ieeexplore.ieee.org\/document\/10522849\" target=\"_blank\">https:\/\/ieeexplore.ieee.org\/document\/10522849<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1109\/TQE.2024.3398410\" title=\"Follow DOI:https:\/\/doi.org\/10.1109\/TQE.2024.3398410\" target=\"_blank\">doi:https:\/\/doi.org\/10.1109\/TQE.2024.3398410<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('68','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Khan, Irfanullah;  Cicirelli, Franco;  Greco, Emilio;  Guerrieri, Antonio;  Mastroianni, Carlo;  Scarcello, Luigi;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('71','tp_links')\" style=\"cursor:pointer;\">Leveraging Distributed AI for Multi-Occupancy Prediction in Cognitive Buildings<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Internet Of Things, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2542-6605<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_71\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('71','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_71\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('71','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=48#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge and cloud computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_71\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Khan2024,<br \/>\r\ntitle = {Leveraging Distributed AI for Multi-Occupancy Prediction in Cognitive Buildings},<br \/>\r\nauthor = {Irfanullah Khan AND Franco Cicirelli AND Emilio Greco AND Antonio Guerrieri AND Carlo Mastroianni AND Luigi Scarcello AND Giandomenico Spezzano AND Andrea Vinci},<br \/>\r\ndoi = {10.1016\/j.iot.2024.101181},<br \/>\r\nissn = {2542-6605},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-04-07},<br \/>\r\nurldate = {2024-04-07},<br \/>\r\njournal = {Internet Of Things},<br \/>\r\npublisher = {Elsevier},<br \/>\r\nkeywords = {Edge and cloud computing, insider, sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('71','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_71\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.iot.2024.101181\" title=\"Follow DOI:10.1016\/j.iot.2024.101181\" target=\"_blank\">doi:10.1016\/j.iot.2024.101181<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('71','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, Eugenio;  Lindia, Paolo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('70','tp_links')\" style=\"cursor:pointer;\">A scalable multi-density clustering approach to detect city hotspots in a smart city<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Future Generation Computer Systems, <\/span><span class=\"tp_pub_additional_volume\">vol. 157, <\/span><span class=\"tp_pub_additional_pages\">pp. 226-236, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 0167-739X<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_70\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('70','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_70\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('70','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_70\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('70','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=108#tppubs\" title=\"Show all publications which have a relationship to this tag\">Multi density-based clustering<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=109#tppubs\" title=\"Show all publications which have a relationship to this tag\">Parallel data mining<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=15#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart city<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_70\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{CESARIO2024,<br \/>\r\ntitle = {A scalable multi-density clustering approach to detect city hotspots in a smart city},<br \/>\r\nauthor = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},<br \/>\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X24001122},<br \/>\r\ndoi = {https:\/\/doi.org\/10.1016\/j.future.2024.03.042},<br \/>\r\nissn = {0167-739X},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nurldate = {2024-01-01},<br \/>\r\njournal = {Future Generation Computer Systems},<br \/>\r\nvolume = {157},<br \/>\r\npages = {226-236},<br \/>\r\nabstract = {In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.},<br \/>\r\nkeywords = {insider, Multi density-based clustering, Parallel data mining, smart city, sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('70','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_70\" style=\"display:none;\"><div class=\"tp_abstract_entry\">In the field of Smart City applications, the analysis of urban data to detect city hotspots, i.e., regions where urban events (such as pollution peaks, virus infections, traffic spikes, and crimes) occur at a higher density than in the rest of the dataset, is becoming a common task. The detection of such hotspots can serve as a valuable organizational technique for framing detailed information about a metropolitan area, providing high-level spatial knowledge for planners, scientists, and policymakers. From the algorithmic viewpoint, classic density-based clustering algorithms are very effective in discovering hotspots characterized by homogeneous density; however, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are characterized by areas with significantly variable densities, multi-density clustering approaches are more effective in discovering city hotspots. Moreover, the growing volumes of data collected in urban environments require the development of parallel approaches, in order to take advantage of scalable executions offered by Edge and Cloud environments. This paper describes the design and implementation of a parallel multi-density clustering algorithm aimed at analyzing high volumes of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time, speedup, and efficiency.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('70','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_70\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X24001122\" title=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X24001122\" target=\"_blank\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X24001122<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1016\/j.future.2024.03.042\" title=\"Follow DOI:https:\/\/doi.org\/10.1016\/j.future.2024.03.042\" target=\"_blank\">doi:https:\/\/doi.org\/10.1016\/j.future.2024.03.042<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('70','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Mastroianni, Carlo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('73','tp_links')\" style=\"cursor:pointer;\">Tutorial on Variational Quantum Algorithms for Resource Management in Cloud\/Edge Architectures<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing, <\/span><span class=\"tp_pub_additional_pages\">pp. 350\u2013351, <\/span><span class=\"tp_pub_additional_publisher\">Association for Computing Machinery, <\/span><span class=\"tp_pub_additional_address\">Pisa, Italy, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9798400704130<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_73\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('73','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_73\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('73','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=48#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge and cloud computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=107#tppubs\" title=\"Show all publications which have a relationship to this tag\">quantum computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_73\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10.1145\/3625549.3660508,<br \/>\r\ntitle = {Tutorial on Variational Quantum Algorithms for Resource Management in Cloud\/Edge Architectures},<br \/>\r\nauthor = {Carlo Mastroianni and Andrea Vinci},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3625549.3660508},<br \/>\r\ndoi = {10.1145\/3625549.3660508},<br \/>\r\nisbn = {9798400704130},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nurldate = {2024-01-01},<br \/>\r\nbooktitle = {Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing},<br \/>\r\npages = {350\u2013351},<br \/>\r\npublisher = {Association for Computing Machinery},<br \/>\r\naddress = {Pisa, Italy},<br \/>\r\nseries = {HPDC &#039;24},<br \/>\r\nkeywords = {Edge and cloud computing, insider, quantum computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('73','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_73\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3625549.3660508\" title=\"https:\/\/doi.org\/10.1145\/3625549.3660508\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3625549.3660508<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3625549.3660508\" title=\"Follow DOI:10.1145\/3625549.3660508\" target=\"_blank\">doi:10.1145\/3625549.3660508<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('73','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Marozzo, Fabrizio;  Presta, Aleandro;  Varchera, Rosa;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('75','tp_links')\" style=\"cursor:pointer;\">Estimating Performances of Application Deployment on Distributed IoT-Edge-Cloud Infrastructures<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2024 IEEE Conference on Pervasive and Intelligent Computing (PICom), <\/span><span class=\"tp_pub_additional_pages\">pp. 156-161, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_75\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('75','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_75\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('75','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_75\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10795415,<br \/>\r\ntitle = {Estimating Performances of Application Deployment on Distributed IoT-Edge-Cloud Infrastructures},<br \/>\r\nauthor = {Fabrizio Marozzo and Aleandro Presta and Rosa Varchera and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/PICom64201.2024.00029},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nurldate = {2024-01-01},<br \/>\r\nbooktitle = {2024 IEEE Conference on Pervasive and Intelligent Computing (PICom)},<br \/>\r\npages = {156-161},<br \/>\r\nkeywords = {insider},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('75','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_75\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/PICom64201.2024.00029\" title=\"Follow DOI:10.1109\/PICom64201.2024.00029\" target=\"_blank\">doi:10.1109\/PICom64201.2024.00029<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('75','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Rizzo, Luigi;  Zicari, Paolo;  Cicirelli, Franco;  Guerrieri, Antonio;  Micieli, Massimo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('76','tp_links')\" style=\"cursor:pointer;\">A Study on Consumer-Grade EEG Headsets in BCI Applications<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2024 IEEE Conference on Pervasive and Intelligent Computing (PICom), <\/span><span class=\"tp_pub_additional_pages\">pp. 67-74, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_76\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('76','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_76\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('76','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_76\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10795393,<br \/>\r\ntitle = {A Study on Consumer-Grade EEG Headsets in BCI Applications},<br \/>\r\nauthor = {Luigi Rizzo and Paolo Zicari and Franco Cicirelli and Antonio Guerrieri and Massimo Micieli and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/PICom64201.2024.00016},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nurldate = {2024-01-01},<br \/>\r\nbooktitle = {2024 IEEE Conference on Pervasive and Intelligent Computing (PICom)},<br \/>\r\npages = {67-74},<br \/>\r\nkeywords = {sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('76','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_76\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/PICom64201.2024.00016\" title=\"Follow DOI:10.1109\/PICom64201.2024.00016\" target=\"_blank\">doi:10.1109\/PICom64201.2024.00016<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('76','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> D&#039;Amore, Francesco;  Mariani, Luca;  Mastroianni, Carlo;  Settino, Jacopo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('77','tp_links')\" style=\"cursor:pointer;\">Projected Quantum Kernel for IoT Data Analysis<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2024 IEEE Conference on Pervasive and Intelligent Computing (PICom), <\/span><span class=\"tp_pub_additional_pages\">pp. 173-177, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_77\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('77','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_77\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('77','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=107#tppubs\" title=\"Show all publications which have a relationship to this tag\">quantum computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_77\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10795417,<br \/>\r\ntitle = {Projected Quantum Kernel for IoT Data Analysis},<br \/>\r\nauthor = {Francesco D&#039;Amore and Luca Mariani and Carlo Mastroianni and Jacopo Settino and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/PICom64201.2024.00032},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nurldate = {2024-01-01},<br \/>\r\nbooktitle = {2024 IEEE Conference on Pervasive and Intelligent Computing (PICom)},<br \/>\r\npages = {173-177},<br \/>\r\nkeywords = {quantum computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('77','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_77\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/PICom64201.2024.00032\" title=\"Follow DOI:10.1109\/PICom64201.2024.00032\" target=\"_blank\">doi:10.1109\/PICom64201.2024.00032<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('77','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Belcastro, Loris;  Marozzo, Fabrizio;  Orsino, Alessio;  Presta, Aleandro;  Vinci, Andrea<\/p><p class=\"tp_pub_title\">Developing Cross-Platform and Fast-Responsive Applications on the Edge-Cloud Continuum <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2024 15th IFIP Wireless and Mobile Networking Conference (WMNC), <\/span><span class=\"tp_pub_additional_pages\">pp. 88-93, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_78\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('78','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_78\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10767092,<br \/>\r\ntitle = {Developing Cross-Platform and Fast-Responsive Applications on the Edge-Cloud Continuum},<br \/>\r\nauthor = {Loris Belcastro and Fabrizio Marozzo and Alessio Orsino and Aleandro Presta and Andrea Vinci},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nurldate = {2024-01-01},<br \/>\r\nbooktitle = {2024 15th IFIP Wireless and Mobile Networking Conference (WMNC)},<br \/>\r\npages = {88-93},<br \/>\r\nkeywords = {insider},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('78','tp_bibtex')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, Franco;  Greco, Emilio;  Guerrieri, Antonio;  Gentile, Antonio Francesco;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\">Blockchain-Empowered PSO for\u00a0Scalable Swarm Robotics <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span> Villani, Marco;  Cagnoni, Stefano;  Serra, Roberto (Ed.): <span class=\"tp_pub_additional_booktitle\">Artificial Life and Evolutionary Computation, <\/span><span class=\"tp_pub_additional_pages\">pp. 214\u2013227, <\/span><span class=\"tp_pub_additional_publisher\">Springer Nature Switzerland, <\/span><span class=\"tp_pub_additional_address\">Cham, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-3-031-57430-6<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_79\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('79','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_79\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('79','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_79\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{10.1007\/978-3-031-57430-6_17,<br \/>\r\ntitle = {Blockchain-Empowered PSO for\u00a0Scalable Swarm Robotics},<br \/>\r\nauthor = {Franco Cicirelli and Emilio Greco and Antonio Guerrieri and Antonio Francesco Gentile and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\neditor = {Marco Villani and Stefano Cagnoni and Roberto Serra},<br \/>\r\nisbn = {978-3-031-57430-6},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\nbooktitle = {Artificial Life and Evolutionary Computation},<br \/>\r\npages = {214\u2013227},<br \/>\r\npublisher = {Springer Nature Switzerland},<br \/>\r\naddress = {Cham},<br \/>\r\nabstract = {Swarm robotics is an innovative field that utilizes collective behavior principles to design systems where multiple robots coordinate through simple rules and interactions. It faces the challenges of decentralized governance, security, and scalability. Due to its decentralized optimization capabilities, Particle Swarm Optimization (PSO) has shown promise for controlling robot swarms. However, implementing PSO in a distributed manner still poses problems in achieving full scalability and fault-tolerant operation. Blockchain, a decentralized system that securely stores and distributes data, enables transparent and autonomous communication among robots. Integrating blockchain with PSO can potentially revolutionize swarm robotics by providing secure and decentralized coordination through Decentralized applications (Dapps). The work proposed here demonstrates the application of blockchain technology, utilizing ad-hoc techniques, to manage a swarm of robots in conjunction with particle swarm optimization for solving navigation paths. In particular, the emergent Tendermint platform is exploited as a lean blockchain infrastructure for supporting asynchronous swarm robotics applications by showing its main advantages compared to a more traditional blockchain platform.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('79','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_79\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Swarm robotics is an innovative field that utilizes collective behavior principles to design systems where multiple robots coordinate through simple rules and interactions. It faces the challenges of decentralized governance, security, and scalability. Due to its decentralized optimization capabilities, Particle Swarm Optimization (PSO) has shown promise for controlling robot swarms. However, implementing PSO in a distributed manner still poses problems in achieving full scalability and fault-tolerant operation. Blockchain, a decentralized system that securely stores and distributes data, enables transparent and autonomous communication among robots. Integrating blockchain with PSO can potentially revolutionize swarm robotics by providing secure and decentralized coordination through Decentralized applications (Dapps). The work proposed here demonstrates the application of blockchain technology, utilizing ad-hoc techniques, to manage a swarm of robots in conjunction with particle swarm optimization for solving navigation paths. In particular, the emergent Tendermint platform is exploited as a lean blockchain infrastructure for supporting asynchronous swarm robotics applications by showing its main advantages compared to a more traditional blockchain platform.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('79','tp_abstract')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Mastroianni, Carlo;  Plastina, Francesco;  Scarcello, Luigi;  Settino, Jacopo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1','tp_links')\" style=\"cursor:pointer;\">Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">IEEE Trans. Smart Grid, <\/span><span class=\"tp_pub_additional_volume\">vol. 15, <\/span><span class=\"tp_pub_additional_number\">no. 1, <\/span><span class=\"tp_pub_additional_pages\">pp. 444\u2013456, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1949-3061<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=107#tppubs\" title=\"Show all publications which have a relationship to this tag\">quantum computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Mastroianni2024,<br \/>\r\ntitle = {Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community},<br \/>\r\nauthor = {Carlo Mastroianni and Francesco Plastina and Luigi Scarcello and Jacopo Settino and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/tsg.2023.3286106},<br \/>\r\nissn = {1949-3061},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-00},<br \/>\r\nurldate = {2024-01-00},<br \/>\r\njournal = {IEEE Trans. Smart Grid},<br \/>\r\nvolume = {15},<br \/>\r\nnumber = {1},<br \/>\r\npages = {444\u2013456},<br \/>\r\npublisher = {Institute of Electrical and Electronics Engineers (IEEE)},<br \/>\r\nkeywords = {quantum computing, sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/tsg.2023.3286106\" title=\"Follow DOI:10.1109\/tsg.2023.3286106\" target=\"_blank\">doi:10.1109\/tsg.2023.3286106<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2023\">2023<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Amadeo, Marica;  Cicirelli, Franco;  Guerrieri, Antonio;  Ruggeri, Giuseppe;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('2','tp_links')\" style=\"cursor:pointer;\">When edge intelligence meets cognitive buildings: The COGITO platform<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Internet of Things, <\/span><span class=\"tp_pub_additional_volume\">vol. 24, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2542-6605<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_2\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('2','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_2\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('2','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=2#tppubs\" title=\"Show all publications which have a relationship to this tag\">Artificial Intelligence<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=103#tppubs\" title=\"Show all publications which have a relationship to this tag\">cogito<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=3#tppubs\" title=\"Show all publications which have a relationship to this tag\">Computer Science (miscellaneous)<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=4#tppubs\" title=\"Show all publications which have a relationship to this tag\">Computer Science Applications<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=5#tppubs\" title=\"Show all publications which have a relationship to this tag\">Engineering (miscellaneous)<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=6#tppubs\" title=\"Show all publications which have a relationship to this tag\">Hardware and Architecture<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=7#tppubs\" title=\"Show all publications which have a relationship to this tag\">Information Systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=8#tppubs\" title=\"Show all publications which have a relationship to this tag\">Management of Technology and Innovation<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=9#tppubs\" title=\"Show all publications which have a relationship to this tag\">Software<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_2\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Amadeo2023,<br \/>\r\ntitle = {When edge intelligence meets cognitive buildings: The COGITO platform},<br \/>\r\nauthor = {Marica Amadeo and Franco Cicirelli and Antonio Guerrieri and Giuseppe Ruggeri and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\ndoi = {10.1016\/j.iot.2023.100908},<br \/>\r\nissn = {2542-6605},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-12-00},<br \/>\r\nurldate = {2023-12-00},<br \/>\r\njournal = {Internet of Things},<br \/>\r\nvolume = {24},<br \/>\r\npublisher = {Elsevier BV},<br \/>\r\nkeywords = {Artificial Intelligence, cogito, Computer Science (miscellaneous), Computer Science Applications, Engineering (miscellaneous), Hardware and Architecture, Information Systems, Management of Technology and Innovation, Software},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('2','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_2\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.iot.2023.100908\" title=\"Follow DOI:10.1016\/j.iot.2023.100908\" target=\"_blank\">doi:10.1016\/j.iot.2023.100908<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('2','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, Eugenio;  Lindia, Paolo;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('4','tp_links')\" style=\"cursor:pointer;\">Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Big Data and Cognitive Computing, <\/span><span class=\"tp_pub_additional_volume\">vol. 7, <\/span><span class=\"tp_pub_additional_number\">no. 1, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 2504-2289<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_4\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('4','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_4\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('4','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_4\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('4','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=2#tppubs\" title=\"Show all publications which have a relationship to this tag\">Artificial Intelligence<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=4#tppubs\" title=\"Show all publications which have a relationship to this tag\">Computer Science Applications<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=7#tppubs\" title=\"Show all publications which have a relationship to this tag\">Information Systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=10#tppubs\" title=\"Show all publications which have a relationship to this tag\">Management Information Systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=105#tppubs\" title=\"Show all publications which have a relationship to this tag\">sobigdata.it<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_4\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Cesario2023,<br \/>\r\ntitle = {Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications},<br \/>\r\nauthor = {Eugenio Cesario and Paolo Lindia and Andrea Vinci},<br \/>\r\ndoi = {10.3390\/bdcc7010029},<br \/>\r\nissn = {2504-2289},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-03-00},<br \/>\r\nurldate = {2023-03-00},<br \/>\r\njournal = {Big Data and Cognitive Computing},<br \/>\r\nvolume = {7},<br \/>\r\nnumber = {1},<br \/>\r\npublisher = {MDPI AG},<br \/>\r\nabstract = {&lt;jats:p&gt;Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.&lt;\/jats:p&gt;},<br \/>\r\nkeywords = {Artificial Intelligence, Computer Science Applications, Information Systems, Management Information Systems, sobigdata.it},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('4','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_4\" style=\"display:none;\"><div class=\"tp_abstract_entry\">&lt;jats:p&gt;Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.&lt;\/jats:p&gt;<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('4','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_4\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3390\/bdcc7010029\" title=\"Follow DOI:10.3390\/bdcc7010029\" target=\"_blank\">doi:10.3390\/bdcc7010029<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('4','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Scarcello, Luigi;  Cicirelli, Franco;  Guerrieri, Antonio;  Mastroianni, Carlo;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('64','tp_links')\" style=\"cursor:pointer;\">Pursuing Energy Saving and Thermal Comfort With a Human-Driven DRL Approach<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">IEEE Transactions on Human-Machine Systems, <\/span><span class=\"tp_pub_additional_volume\">vol. 53, <\/span><span class=\"tp_pub_additional_number\">no. 4, <\/span><span class=\"tp_pub_additional_pages\">pp. 707-719, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_64\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('64','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_64\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('64','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=103#tppubs\" title=\"Show all publications which have a relationship to this tag\">cogito<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=104#tppubs\" title=\"Show all publications which have a relationship to this tag\">HVAC;Energy consumption;Thermal management;Process control;Behavioral sciences;Training;Temperature distribution;Cognitive buildings;deep reinforcement learning (DRL);energy saving;human-in-the-loop;thermal comfort<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_64\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{9940565,<br \/>\r\ntitle = {Pursuing Energy Saving and Thermal Comfort With a Human-Driven DRL Approach},<br \/>\r\nauthor = {Luigi Scarcello and Franco Cicirelli and Antonio Guerrieri and Carlo Mastroianni and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/THMS.2022.3216365},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nurldate = {2023-01-01},<br \/>\r\njournal = {IEEE Transactions on Human-Machine Systems},<br \/>\r\nvolume = {53},<br \/>\r\nnumber = {4},<br \/>\r\npages = {707-719},<br \/>\r\nkeywords = {cogito, HVAC;Energy consumption;Thermal management;Process control;Behavioral sciences;Training;Temperature distribution;Cognitive buildings;deep reinforcement learning (DRL);energy saving;human-in-the-loop;thermal comfort},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('64','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_64\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/THMS.2022.3216365\" title=\"Follow DOI:10.1109\/THMS.2022.3216365\" target=\"_blank\">doi:10.1109\/THMS.2022.3216365<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('64','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Mastroianni, Carlo;  Scarcello, Luigi;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('67','tp_links')\" style=\"cursor:pointer;\">Quantum Computing Management of a Cloud\/Edge Architecture<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span> Bartolini, Andrea;  Rietveld, Kristian F. D.;  Schuman, Catherine D.;  Moreira, Jose (Ed.): <span class=\"tp_pub_additional_booktitle\">Proceedings of the 20th ACM International Conference on Computing Frontiers, CF 2023, <\/span><span class=\"tp_pub_additional_pages\">pp. 193\u2013196, <\/span><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_67\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('67','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_67\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('67','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=48#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge and cloud computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=106#tppubs\" title=\"Show all publications which have a relationship to this tag\">insider<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=107#tppubs\" title=\"Show all publications which have a relationship to this tag\">quantum computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_67\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{MastroianniSV23,<br \/>\r\ntitle = {Quantum Computing Management of a Cloud\/Edge Architecture},<br \/>\r\nauthor = {Carlo Mastroianni and Luigi Scarcello and Andrea Vinci},<br \/>\r\neditor = {Andrea Bartolini and Kristian F. D. Rietveld and Catherine D. Schuman and Jose Moreira},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3587135.3592190},<br \/>\r\ndoi = {10.1145\/3587135.3592190},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\nurldate = {2023-01-01},<br \/>\r\nbooktitle = {Proceedings of the 20th ACM International Conference on Computing Frontiers, CF 2023},<br \/>\r\npages = {193\u2013196},<br \/>\r\npublisher = {ACM},<br \/>\r\nkeywords = {Edge and cloud computing, insider, quantum computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('67','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_67\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3587135.3592190\" title=\"https:\/\/doi.org\/10.1145\/3587135.3592190\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3587135.3592190<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3587135.3592190\" title=\"Follow DOI:10.1145\/3587135.3592190\" target=\"_blank\">doi:10.1145\/3587135.3592190<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('67','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inbook\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, Franco;  Greco, Emilio;  Guerrieri, Antonio;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('5','tp_links')\" style=\"cursor:pointer;\">Collaborative Learning over\u00a0Cellular Automata<\/a> <span class=\"tp_pub_type tp_  inbook\">Book Chapter<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Communications in Computer and Information Science, <\/span><span class=\"tp_pub_additional_pages\">pp. 3\u201314, <\/span><span class=\"tp_pub_additional_publisher\">Springer Nature Switzerland, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9783031311833<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_5\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('5','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_5\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('5','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_5\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inbook{Cicirelli2023,<br \/>\r\ntitle = {Collaborative Learning over\u00a0Cellular Automata},<br \/>\r\nauthor = {Franco Cicirelli and Emilio Greco and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\ndoi = {10.1007\/978-3-031-31183-3_1},<br \/>\r\nisbn = {9783031311833},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-00-00},<br \/>\r\nbooktitle = {Communications in Computer and Information Science},<br \/>\r\npages = {3\u201314},<br \/>\r\npublisher = {Springer Nature Switzerland},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inbook}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('5','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_5\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-31183-3_1\" title=\"Follow DOI:10.1007\/978-3-031-31183-3_1\" target=\"_blank\">doi:10.1007\/978-3-031-31183-3_1<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('5','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_book\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, Franco;  Guerrieri, Antonio;  Vinci, Andrea;  Spezzano, Giandomenico (Ed.)<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('6','tp_links')\" style=\"cursor:pointer;\">IoT Edge Solutions for Cognitive Buildings<\/a> <span class=\"tp_pub_type tp_  book\">Book<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Springer International Publishing, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9783031151606<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_6\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('6','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_6\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('6','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=103#tppubs\" title=\"Show all publications which have a relationship to this tag\">cogito<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_6\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@book{2023,<br \/>\r\ntitle = {IoT Edge Solutions for Cognitive Buildings},<br \/>\r\neditor = {Franco Cicirelli and Antonio Guerrieri and Andrea Vinci and Giandomenico Spezzano},<br \/>\r\ndoi = {10.1007\/978-3-031-15160-6},<br \/>\r\nisbn = {9783031151606},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-00-00},<br \/>\r\nurldate = {2023-00-00},<br \/>\r\npublisher = {Springer International Publishing},<br \/>\r\nkeywords = {cogito},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {book}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('6','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_6\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-15160-6\" title=\"Follow DOI:10.1007\/978-3-031-15160-6\" target=\"_blank\">doi:10.1007\/978-3-031-15160-6<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('6','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2022\">2022<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Khan, Irfanullah;  Guerrieri, Antonio;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('7','tp_links')\" style=\"cursor:pointer;\">Occupancy Prediction in Buildings: An approach leveraging LSTM and Federated Learning<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_7\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('7','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_7\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('7','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=103#tppubs\" title=\"Show all publications which have a relationship to this tag\">cogito<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_7\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Khan2022,<br \/>\r\ntitle = {Occupancy Prediction in Buildings: An approach leveraging LSTM and Federated Learning},<br \/>\r\nauthor = {Irfanullah Khan and Antonio Guerrieri and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\ndoi = {10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927838},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-09-12},<br \/>\r\nurldate = {2022-09-12},<br \/>\r\nbooktitle = {2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech)},<br \/>\r\npublisher = {IEEE},<br \/>\r\nkeywords = {cogito},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('7','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_7\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927838\" title=\"Follow DOI:10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927838\" target=\"_blank\">doi:10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927838<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('7','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Canino, Maria Pia;  Cesario, Eugenio;  Vinci, Andrea;  Zarin, Shabnam<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('8','tp_links')\" style=\"cursor:pointer;\">Exploiting mobility data to forecast Covid-19 spread<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_8\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('8','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_8\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('8','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_8\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Canino2022b,<br \/>\r\ntitle = {Exploiting mobility data to forecast Covid-19 spread},<br \/>\r\nauthor = {Maria Pia Canino and Eugenio Cesario and Andrea Vinci and Shabnam Zarin},<br \/>\r\ndoi = {10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927898},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-09-12},<br \/>\r\nurldate = {2022-09-12},<br \/>\r\nbooktitle = {2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC\/PiCom\/CBDCom\/CyberSciTech)},<br \/>\r\npublisher = {IEEE},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('8','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_8\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927898\" title=\"Follow DOI:10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927898\" target=\"_blank\">doi:10.1109\/dasc\/picom\/cbdcom\/cy55231.2022.9927898<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('8','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inbook\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Buono, Michele De;  Gullo, Nicola;  Spezzano, Giandomenico;  Vennera, Andrea;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('9','tp_links')\" style=\"cursor:pointer;\">Development of Indoor Smart Environments Leveraging the Internet of Things and Artificial Intelligence: A Case Study<\/a> <span class=\"tp_pub_type tp_  inbook\">Book Chapter<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Internet of Things, <\/span><span class=\"tp_pub_additional_pages\">pp. 263\u2013284, <\/span><span class=\"tp_pub_additional_publisher\">Springer International Publishing, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9783031151606<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_9\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('9','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_9\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('9','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=103#tppubs\" title=\"Show all publications which have a relationship to this tag\">cogito<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_9\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inbook{Buono2022,<br \/>\r\ntitle = {Development of Indoor Smart Environments Leveraging the Internet of Things and Artificial Intelligence: A Case Study},<br \/>\r\nauthor = {Michele De Buono and Nicola Gullo and Giandomenico Spezzano and Andrea Vennera and Andrea Vinci},<br \/>\r\ndoi = {10.1007\/978-3-031-15160-6_12},<br \/>\r\nisbn = {9783031151606},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-08-09},<br \/>\r\nurldate = {2022-08-09},<br \/>\r\nbooktitle = {Internet of Things},<br \/>\r\npages = {263\u2013284},<br \/>\r\npublisher = {Springer International Publishing},<br \/>\r\nkeywords = {cogito},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inbook}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('9','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_9\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-15160-6_12\" title=\"Follow DOI:10.1007\/978-3-031-15160-6_12\" target=\"_blank\">doi:10.1007\/978-3-031-15160-6_12<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('9','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inbook\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Colace, Simone;  Laurita, Sara;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('10','tp_links')\" style=\"cursor:pointer;\">Room Occupancy Prediction Leveraging LSTM: An Approach for Cognitive and Self-Adapting Buildings<\/a> <span class=\"tp_pub_type tp_  inbook\">Book Chapter<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Internet of Things, <\/span><span class=\"tp_pub_additional_pages\">pp. 197\u2013219, <\/span><span class=\"tp_pub_additional_publisher\">Springer International Publishing, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9783031151606<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_10\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('10','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_10\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('10','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=103#tppubs\" title=\"Show all publications which have a relationship to this tag\">cogito<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_10\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inbook{Colace2022,<br \/>\r\ntitle = {Room Occupancy Prediction Leveraging LSTM: An Approach for Cognitive and Self-Adapting Buildings},<br \/>\r\nauthor = {Simone Colace and Sara Laurita and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\ndoi = {10.1007\/978-3-031-15160-6_9},<br \/>\r\nisbn = {9783031151606},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-08-09},<br \/>\r\nurldate = {2022-08-09},<br \/>\r\nbooktitle = {Internet of Things},<br \/>\r\npages = {197\u2013219},<br \/>\r\npublisher = {Springer International Publishing},<br \/>\r\nkeywords = {cogito},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inbook}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('10','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_10\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-15160-6_9\" title=\"Follow DOI:10.1007\/978-3-031-15160-6_9\" target=\"_blank\">doi:10.1007\/978-3-031-15160-6_9<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('10','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inbook\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Amadeo, Marica;  Cicirelli, Franco;  Guerrieri, Antonio;  Ruggeri, Giuseppe;  Spezzano, Giandomenico;  Vinci, Andrea<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('11','tp_links')\" style=\"cursor:pointer;\">COGITO: A Platform for Developing Cognitive Environments<\/a> <span class=\"tp_pub_type tp_  inbook\">Book Chapter<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">Internet of Things, <\/span><span class=\"tp_pub_additional_pages\">pp. 1\u201322, <\/span><span class=\"tp_pub_additional_publisher\">Springer International Publishing, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9783031151606<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_11\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('11','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_11\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('11','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_11\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inbook{Amadeo2022,<br \/>\r\ntitle = {COGITO: A Platform for Developing Cognitive Environments},<br \/>\r\nauthor = {Marica Amadeo and Franco Cicirelli and Antonio Guerrieri and Giuseppe Ruggeri and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\ndoi = {10.1007\/978-3-031-15160-6_1},<br \/>\r\nisbn = {9783031151606},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-08-09},<br \/>\r\nbooktitle = {Internet of Things},<br \/>\r\npages = {1\u201322},<br \/>\r\npublisher = {Springer International Publishing},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inbook}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('11','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_11\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-15160-6_1\" title=\"Follow DOI:10.1007\/978-3-031-15160-6_1\" target=\"_blank\">doi:10.1007\/978-3-031-15160-6_1<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('11','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Canino, M. P.;  Cesario, E.;  Vinci, A.;  Zarin, S.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('12','tp_links')\" style=\"cursor:pointer;\">Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Social Network Analysis and Mining, <\/span><span class=\"tp_pub_additional_volume\">vol. 12, <\/span><span class=\"tp_pub_additional_issue\">iss. 1, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 18695469<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_12\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('12','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_12\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('12','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_12\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('12','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=11#tppubs\" title=\"Show all publications which have a relationship to this tag\">COVID-19<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=12#tppubs\" title=\"Show all publications which have a relationship to this tag\">Epidemic forecasting<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=13#tppubs\" title=\"Show all publications which have a relationship to this tag\">Predictive models<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_12\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Canino2022,<br \/>\r\ntitle = {Epidemic forecasting based on mobility patterns: an approach and experimental evaluation on COVID-19 Data},<br \/>\r\nauthor = {M. P. Canino and E. Cesario and A. Vinci and S. Zarin},<br \/>\r\ndoi = {10.1007\/s13278-022-00932-6},<br \/>\r\nissn = {18695469},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\njournal = {Social Network Analysis and Mining},<br \/>\r\nvolume = {12},<br \/>\r\nissue = {1},<br \/>\r\nabstract = {During an epidemic, decision-makers in public health need accurate predictions of the future case numbers, in order to control the spread of new cases and allow efficient resource planning for hospital needs and capacities. In particular, considering that infectious diseases are spread through human-human transmissions, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents the design and implementation of a predictive approach, based on spatial analysis and regressive models, to discover spatio-temporal predictive epidemic patterns from mobility and infection data. The experimental evaluation, performed on mobility and COVID-19 data collected in the city of Chicago, is aimed to assess the effectiveness of the approach in a real-world scenario.},<br \/>\r\nkeywords = {COVID-19, Epidemic forecasting, Predictive models},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('12','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_12\" style=\"display:none;\"><div class=\"tp_abstract_entry\">During an epidemic, decision-makers in public health need accurate predictions of the future case numbers, in order to control the spread of new cases and allow efficient resource planning for hospital needs and capacities. In particular, considering that infectious diseases are spread through human-human transmissions, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents the design and implementation of a predictive approach, based on spatial analysis and regressive models, to discover spatio-temporal predictive epidemic patterns from mobility and infection data. The experimental evaluation, performed on mobility and COVID-19 data collected in the city of Chicago, is aimed to assess the effectiveness of the approach in a real-world scenario.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('12','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_12\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/s13278-022-00932-6\" title=\"Follow DOI:10.1007\/s13278-022-00932-6\" target=\"_blank\">doi:10.1007\/s13278-022-00932-6<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('12','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, E.;  Uchubilo, P. I.;  Vinci, A.;  Zhu, X.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('13','tp_links')\" style=\"cursor:pointer;\">Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Pervasive and Mobile Computing, <\/span><span class=\"tp_pub_additional_volume\">vol. 86, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 15741192<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_13\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('13','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_13\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('13','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_13\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('13','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=14#tppubs\" title=\"Show all publications which have a relationship to this tag\">Multi-density city hotspots<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=15#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart city<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=16#tppubs\" title=\"Show all publications which have a relationship to this tag\">Urban computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_13\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Cesario2022,<br \/>\r\ntitle = {Multi-density urban hotspots detection in smart cities: A data-driven approach and experiments},<br \/>\r\nauthor = {E. Cesario and P. I. Uchubilo and A. Vinci and X. Zhu},<br \/>\r\ndoi = {10.1016\/j.pmcj.2022.101687},<br \/>\r\nissn = {15741192},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\njournal = {Pervasive and Mobile Computing},<br \/>\r\nvolume = {86},<br \/>\r\nabstract = {The detection of city hotspots from geo-referenced urban data is a valuable knowledge support for planners, scientists, and policymakers. However, the application of classic density-based clustering algorithms on multi-density data can produce inaccurate results. Since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents CHD (City Hotspot Detector), a multi-density approach to discover urban hotspots in a city, by reporting an extensive comparative analysis with three classic density-based clustering algorithms, on both state-of-the-art and real-world datasets. The comparative experimental evaluation in an urban scenario shows that the proposed multi-density algorithm, enhanced by an additional rolling moving average technique, detects higher quality city hotspots than other classic density-based approaches proposed in literature.},<br \/>\r\nkeywords = {Multi-density city hotspots, smart city, Urban computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('13','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_13\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The detection of city hotspots from geo-referenced urban data is a valuable knowledge support for planners, scientists, and policymakers. However, the application of classic density-based clustering algorithms on multi-density data can produce inaccurate results. Since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents CHD (City Hotspot Detector), a multi-density approach to discover urban hotspots in a city, by reporting an extensive comparative analysis with three classic density-based clustering algorithms, on both state-of-the-art and real-world datasets. The comparative experimental evaluation in an urban scenario shows that the proposed multi-density algorithm, enhanced by an additional rolling moving average technique, detects higher quality city hotspots than other classic density-based approaches proposed in literature.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('13','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_13\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.pmcj.2022.101687\" title=\"Follow DOI:10.1016\/j.pmcj.2022.101687\" target=\"_blank\">doi:10.1016\/j.pmcj.2022.101687<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('13','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Guerrieri, A.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('14','tp_links')\" style=\"cursor:pointer;\">Smart monitoring and control in the future internet of things<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Sensors, <\/span><span class=\"tp_pub_additional_volume\">vol. 22, <\/span><span class=\"tp_pub_additional_issue\">iss. 1, <\/span><span class=\"tp_pub_additional_year\">2022<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 14248220<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_14\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('14','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_14\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('14','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_14\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Cicirelli2022,<br \/>\r\ntitle = {Smart monitoring and control in the future internet of things},<br \/>\r\nauthor = {F. Cicirelli and A. Guerrieri and A. Vinci},<br \/>\r\ndoi = {10.3390\/s22010027},<br \/>\r\nissn = {14248220},<br \/>\r\nyear  = {2022},<br \/>\r\ndate = {2022-01-01},<br \/>\r\njournal = {Sensors},<br \/>\r\nvolume = {22},<br \/>\r\nissue = {1},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('14','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_14\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3390\/s22010027\" title=\"Follow DOI:10.3390\/s22010027\" target=\"_blank\">doi:10.3390\/s22010027<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('14','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2021\">2021<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Guerrieri, A.;  Mastroianni, C.;  Scarcello, L.;  Spezzano, G.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('15','tp_links')\" style=\"cursor:pointer;\">Balancing Energy Consumption and Thermal Comfort with Deep Reinforcement Learning<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2021<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781665401708<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_15\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('15','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_15\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('15','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_15\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('15','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=17#tppubs\" title=\"Show all publications which have a relationship to this tag\">Cognitive Buildings<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=18#tppubs\" title=\"Show all publications which have a relationship to this tag\">Deep Reinforcement Learning<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=19#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart environments<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=20#tppubs\" title=\"Show all publications which have a relationship to this tag\">Thermal Comfort<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_15\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cicirelli2021,<br \/>\r\ntitle = {Balancing Energy Consumption and Thermal Comfort with Deep Reinforcement Learning},<br \/>\r\nauthor = {F. Cicirelli and A. Guerrieri and C. Mastroianni and L. Scarcello and G. Spezzano and A. Vinci},<br \/>\r\ndoi = {10.1109\/ICHMS53169.2021.9582638},<br \/>\r\nisbn = {9781665401708},<br \/>\r\nyear  = {2021},<br \/>\r\ndate = {2021-01-01},<br \/>\r\nurldate = {2021-01-01},<br \/>\r\nbooktitle = {2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS)},<br \/>\r\njournal = {Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {The management of thermal comfort in a building is a challenging and multi-faced problem because it requires considering both objective and subjective parameters that are often in contrast. Subjective parameters are tied to reaching and maintaining an adequate user comfort by considering human preferences and behaviours, while objective parameters can be related to other important aspects like the reduction of energy consumption. This paper exploits cognitive technologies, based on Deep Reinforcement Learning (DRL), for automatically learning how to control the HVAC system in an office. The goal is to develop a cyber-controller able to minimize both the perceived thermal discomfort and the needed energy. The learning process is driven through the definition of a cumulative reward, which includes and combines two reward components that consider, respectively, user comfort and energy consumption. Simulation experiments show that the adopted approach is able to affect the behaviour of the DRL controller and the learning process and therefore to balance the two objectives by weighing the two components of the reward.},<br \/>\r\nkeywords = {Cognitive Buildings, Deep Reinforcement Learning, smart environments, Thermal Comfort},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('15','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_15\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The management of thermal comfort in a building is a challenging and multi-faced problem because it requires considering both objective and subjective parameters that are often in contrast. Subjective parameters are tied to reaching and maintaining an adequate user comfort by considering human preferences and behaviours, while objective parameters can be related to other important aspects like the reduction of energy consumption. This paper exploits cognitive technologies, based on Deep Reinforcement Learning (DRL), for automatically learning how to control the HVAC system in an office. The goal is to develop a cyber-controller able to minimize both the perceived thermal discomfort and the needed energy. The learning process is driven through the definition of a cumulative reward, which includes and combines two reward components that consider, respectively, user comfort and energy consumption. Simulation experiments show that the adopted approach is able to affect the behaviour of the DRL controller and the learning process and therefore to balance the two objectives by weighing the two components of the reward.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('15','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_15\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICHMS53169.2021.9582638\" title=\"Follow DOI:10.1109\/ICHMS53169.2021.9582638\" target=\"_blank\">doi:10.1109\/ICHMS53169.2021.9582638<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('15','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, E.;  Vinci, A.;  Zarin, S.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('16','tp_links')\" style=\"cursor:pointer;\">Towards Parallel Multi-density Clustering for Urban Hotspots Detection<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2021<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781665414555<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_16\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('16','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_16\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('16','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_16\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('16','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=21#tppubs\" title=\"Show all publications which have a relationship to this tag\">Multi density clustering<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=15#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart city<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=16#tppubs\" title=\"Show all publications which have a relationship to this tag\">Urban computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_16\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cesario2021,<br \/>\r\ntitle = {Towards Parallel Multi-density Clustering for Urban Hotspots Detection},<br \/>\r\nauthor = {E. Cesario and A. Vinci and S. Zarin},<br \/>\r\ndoi = {10.1109\/PDP52278.2021.00046},<br \/>\r\nisbn = {9781665414555},<br \/>\r\nyear  = {2021},<br \/>\r\ndate = {2021-01-01},<br \/>\r\nurldate = {2021-01-01},<br \/>\r\nbooktitle = {2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},<br \/>\r\njournal = {Proceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker&#039;s decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.},<br \/>\r\nkeywords = {Multi density clustering, smart city, Urban computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('16','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_16\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker&#039;s decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('16','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_16\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/PDP52278.2021.00046\" title=\"Follow DOI:10.1109\/PDP52278.2021.00046\" target=\"_blank\">doi:10.1109\/PDP52278.2021.00046<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('16','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Guerrieri, A.;  Mastroianni, C.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('17','tp_links')\" style=\"cursor:pointer;\">Emerging internet of things solutions and technologies<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Electronics (Switzerland), <\/span><span class=\"tp_pub_additional_volume\">vol. 10, <\/span><span class=\"tp_pub_additional_issue\">iss. 16, <\/span><span class=\"tp_pub_additional_year\">2021<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 20799292<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_17\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('17','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_17\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('17','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_17\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Cicirelli2021b,<br \/>\r\ntitle = {Emerging internet of things solutions and technologies},<br \/>\r\nauthor = {F. Cicirelli and A. Guerrieri and C. Mastroianni and A. Vinci},<br \/>\r\ndoi = {10.3390\/electronics10161928},<br \/>\r\nissn = {20799292},<br \/>\r\nyear  = {2021},<br \/>\r\ndate = {2021-01-01},<br \/>\r\njournal = {Electronics (Switzerland)},<br \/>\r\nvolume = {10},<br \/>\r\nissue = {16},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('17','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_17\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3390\/electronics10161928\" title=\"Follow DOI:10.3390\/electronics10161928\" target=\"_blank\">doi:10.3390\/electronics10161928<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('17','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2020\">2020<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Maiolo, M.;  Palermo, S. A.;  Brusco, A. C.;  Pirouz, B.;  Turco, M.;  Vinci, A.;  Spezzano, G.;  Piro, P.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('18','tp_links')\" style=\"cursor:pointer;\">On the use of a real-time control approach for urban stormwater management<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Water (Switzerland), <\/span><span class=\"tp_pub_additional_volume\">vol. 12, <\/span><span class=\"tp_pub_additional_issue\">iss. 10, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 20734441<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_18\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('18','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_18\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('18','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_18\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('18','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=22#tppubs\" title=\"Show all publications which have a relationship to this tag\">Distributed real-time system<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=23#tppubs\" title=\"Show all publications which have a relationship to this tag\">Gossip-based algorithm<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=24#tppubs\" title=\"Show all publications which have a relationship to this tag\">multi-agent systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=25#tppubs\" title=\"Show all publications which have a relationship to this tag\">PID<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=26#tppubs\" title=\"Show all publications which have a relationship to this tag\">Rainfall-runoff<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=27#tppubs\" title=\"Show all publications which have a relationship to this tag\">Sewer system<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=28#tppubs\" title=\"Show all publications which have a relationship to this tag\">SWMM<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_18\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Maiolo2020,<br \/>\r\ntitle = {On the use of a real-time control approach for urban stormwater management},<br \/>\r\nauthor = {M. Maiolo and S. A. Palermo and A. C. Brusco and B. Pirouz and M. Turco and A. Vinci and G. Spezzano and P. Piro},<br \/>\r\ndoi = {10.3390\/w12102842},<br \/>\r\nissn = {20734441},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\njournal = {Water (Switzerland)},<br \/>\r\nvolume = {12},<br \/>\r\nissue = {10},<br \/>\r\nabstract = {The real-time control (RTC) system is a valid and cost-effective solution for urban stormwater management. This paper aims to evaluate the beneficial effect on urban flooding risk mitigation produced by applying RTC techniques to an urban drainage network by considering different control configuration scenarios. To achieve the aim, a distributed real-time system, validated in previous studies, was considered. This approach uses a smart moveable gates system, controlled by software agents, managed by a swarm intelligence algorithm. By running the different scenarios by a customized version of the Storm Water Management Model (SWMM), the findings obtained show a redistribution of conduits filling degrees, exploiting the whole system storage capacity, with a significant reduction of node flooding and total flood volume.},<br \/>\r\nkeywords = {Distributed real-time system, Gossip-based algorithm, multi-agent systems, PID, Rainfall-runoff, Sewer system, SWMM},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('18','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_18\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The real-time control (RTC) system is a valid and cost-effective solution for urban stormwater management. This paper aims to evaluate the beneficial effect on urban flooding risk mitigation produced by applying RTC techniques to an urban drainage network by considering different control configuration scenarios. To achieve the aim, a distributed real-time system, validated in previous studies, was considered. This approach uses a smart moveable gates system, controlled by software agents, managed by a swarm intelligence algorithm. By running the different scenarios by a customized version of the Storm Water Management Model (SWMM), the findings obtained show a redistribution of conduits filling degrees, exploiting the whole system storage capacity, with a significant reduction of node flooding and total flood volume.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('18','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_18\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3390\/w12102842\" title=\"Follow DOI:10.3390\/w12102842\" target=\"_blank\">doi:10.3390\/w12102842<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('18','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, E.;  Uchubilo, P. I.;  Vinci, A.;  Zhu, X.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('19','tp_links')\" style=\"cursor:pointer;\">Discovering Multi-density Urban Hotspots in a Smart City<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2020 IEEE International Conference on Smart Computing (SMARTCOMP), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781728169972<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_19\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('19','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_19\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('19','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_19\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('19','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=29#tppubs\" title=\"Show all publications which have a relationship to this tag\">Crime Data Analysis<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=30#tppubs\" title=\"Show all publications which have a relationship to this tag\">Data Mining<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=15#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart city<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_19\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cesario2020,<br \/>\r\ntitle = {Discovering Multi-density Urban Hotspots in a Smart City},<br \/>\r\nauthor = {E. Cesario and P. I. Uchubilo and A. Vinci and X. Zhu},<br \/>\r\ndoi = {10.1109\/SMARTCOMP50058.2020.00073},<br \/>\r\nisbn = {9781728169972},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\nurldate = {2020-01-01},<br \/>\r\nbooktitle = {2020 IEEE International Conference on Smart Computing (SMARTCOMP)},<br \/>\r\njournal = {Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows. In particular, the detection of city hotspots is becoming a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are valuable for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents a study about how density-based clustering algorithms are suitable for discovering urban hotspots in a city, by showing a comparative analysis of single-density and multi-density clustering on both state-of-the-art data and real-world data. The experimental evaluation shows that, in an urban scenario, multi-density clustering achieves higher quality hotspots than a single-density approach.},<br \/>\r\nkeywords = {Crime Data Analysis, Data Mining, smart city},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('19','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_19\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, traffic flows. In particular, the detection of city hotspots is becoming a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are valuable for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate to discover city hotspots. This paper presents a study about how density-based clustering algorithms are suitable for discovering urban hotspots in a city, by showing a comparative analysis of single-density and multi-density clustering on both state-of-the-art data and real-world data. The experimental evaluation shows that, in an urban scenario, multi-density clustering achieves higher quality hotspots than a single-density approach.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('19','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_19\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/SMARTCOMP50058.2020.00073\" title=\"Follow DOI:10.1109\/SMARTCOMP50058.2020.00073\" target=\"_blank\">doi:10.1109\/SMARTCOMP50058.2020.00073<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('19','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Guerrieri, A.;  Mastroianni, C.;  Spezzano, G.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('20','tp_links')\" style=\"cursor:pointer;\">Thermal comfort management leveraging deep reinforcement learning and human-in-The-loop<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2020 IEEE International Conference on Human-Machine Systems (ICHMS), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781728158716<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_20\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('20','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_20\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('20','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_20\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('20','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=31#tppubs\" title=\"Show all publications which have a relationship to this tag\">Cognitive Building.<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=18#tppubs\" title=\"Show all publications which have a relationship to this tag\">Deep Reinforcement Learning<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=19#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart environments<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=20#tppubs\" title=\"Show all publications which have a relationship to this tag\">Thermal Comfort<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_20\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cicirelli2020,<br \/>\r\ntitle = {Thermal comfort management leveraging deep reinforcement learning and human-in-The-loop},<br \/>\r\nauthor = {F. Cicirelli and A. Guerrieri and C. Mastroianni and G. Spezzano and A. Vinci},<br \/>\r\ndoi = {10.1109\/ICHMS49158.2020.9209555},<br \/>\r\nisbn = {9781728158716},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\nurldate = {2020-01-01},<br \/>\r\nbooktitle = {2020 IEEE International Conference on Human-Machine Systems (ICHMS)},<br \/>\r\njournal = {Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {The design and implementation of effective systems devoted to the thermal comfort management in a building is a challenging task because they require to consider both objective and subjective parameters, tied for instance to human profile and behavior. This paper presents a novel approach for the management of thermal comfort in buildings by leveraging cognitive technologies, namely the Deep Reinforcement Learning paradigm. The approach is able to learn how to automatically control the HVAC system and improve people&#039;s comfort. The learning process is driven by a reward that includes and combines an environmental reward, related to objective environmental parameters, with a human reward, related to subjective human perceptions that are implicitly inferred by the way people interact with the HVAC system. Simulation results aim to assess the impact of the two types of reward on the achieved comfort level.},<br \/>\r\nkeywords = {Cognitive Building., Deep Reinforcement Learning, smart environments, Thermal Comfort},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('20','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_20\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The design and implementation of effective systems devoted to the thermal comfort management in a building is a challenging task because they require to consider both objective and subjective parameters, tied for instance to human profile and behavior. This paper presents a novel approach for the management of thermal comfort in buildings by leveraging cognitive technologies, namely the Deep Reinforcement Learning paradigm. The approach is able to learn how to automatically control the HVAC system and improve people&#039;s comfort. The learning process is driven by a reward that includes and combines an environmental reward, related to objective environmental parameters, with a human reward, related to subjective human perceptions that are implicitly inferred by the way people interact with the HVAC system. Simulation results aim to assess the impact of the two types of reward on the achieved comfort level.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('20','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_20\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICHMS49158.2020.9209555\" title=\"Follow DOI:10.1109\/ICHMS49158.2020.9209555\" target=\"_blank\">doi:10.1109\/ICHMS49158.2020.9209555<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('20','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, E.;  Vinci, A.;  Zhu, X.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('22','tp_links')\" style=\"cursor:pointer;\">Hierarchical Clustering of Spatial Urban Data<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span>Y. Sergeyev, Kvasov (Ed.): <span class=\"tp_pub_additional_booktitle\">Numerical Computations: Theory and Algorithms. NUMTA 2019., <\/span><span class=\"tp_pub_additional_publisher\">Springer, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 16113349<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_22\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('22','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_22\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('22','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_22\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('22','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_22\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cesario2020b,<br \/>\r\ntitle = {Hierarchical Clustering of Spatial Urban Data},<br \/>\r\nauthor = {E. Cesario and A. Vinci and X. Zhu},<br \/>\r\neditor = {Sergeyev, Y., Kvasov, D. },<br \/>\r\ndoi = {10.1007\/978-3-030-39081-5_20},<br \/>\r\nissn = {16113349},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\nurldate = {2020-01-01},<br \/>\r\nbooktitle = {Numerical Computations: Theory and Algorithms. NUMTA 2019.},<br \/>\r\njournal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},<br \/>\r\nvolume = {11973 LNCS},<br \/>\r\npublisher = {Springer},<br \/>\r\nseries = {Lecture Notes in Computer Science},<br \/>\r\nabstract = {The growth of data volume collected in urban contexts opens up\u00a0to their exploitation for improving citizens\u2019 quality-of-life and city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services. Moreover, due to the large-scale diffusion of GPS and scanning devices, most of the available data are geo-referenced. Considering such an abundance of data, a very desirable and common task is to identify homogeneous regions in spatial data by partitioning a city into uniform regions based on pollution density, mobility spikes, crimes, or on other characteristics. Density-based clustering algorithms have been shown to be very suitable to detect density-based regions, i.e. areas in which urban events occur with higher density than the remainder of the dataset. Nevertheless, an important issue of such algorithms is that, due to the adoption of global parameters, they fail to identify clusters with varied densities, unless the clusters are clearly separated by sparse regions. In this paper we provide a preliminary analysis about how hierarchical clustering can be used to discover spatial clusters of different densities, in spatial urban data. The algorithm can automatically estimate the area of data having different densities, it can automatically estimate parameters for each cluster so as to reduce the requirement for human intervention or domain knowledge.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('22','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_22\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The growth of data volume collected in urban contexts opens up\u00a0to their exploitation for improving citizens\u2019 quality-of-life and city management issues, like resource planning (water, electricity), traffic, air and water quality, public policy and public safety services. Moreover, due to the large-scale diffusion of GPS and scanning devices, most of the available data are geo-referenced. Considering such an abundance of data, a very desirable and common task is to identify homogeneous regions in spatial data by partitioning a city into uniform regions based on pollution density, mobility spikes, crimes, or on other characteristics. Density-based clustering algorithms have been shown to be very suitable to detect density-based regions, i.e. areas in which urban events occur with higher density than the remainder of the dataset. Nevertheless, an important issue of such algorithms is that, due to the adoption of global parameters, they fail to identify clusters with varied densities, unless the clusters are clearly separated by sparse regions. In this paper we provide a preliminary analysis about how hierarchical clustering can be used to discover spatial clusters of different densities, in spatial urban data. The algorithm can automatically estimate the area of data having different densities, it can automatically estimate parameters for each cluster so as to reduce the requirement for human intervention or domain knowledge.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('22','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_22\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-030-39081-5_20\" title=\"Follow DOI:10.1007\/978-3-030-39081-5_20\" target=\"_blank\">doi:10.1007\/978-3-030-39081-5_20<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('22','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Gentile, A. F.;  Greco, E.;  Guerrieri, A.;  Spezzano, G.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('23','tp_links')\" style=\"cursor:pointer;\">An Energy Management System at the Edge based on Reinforcement Learning<\/a> <span class=\"tp_pub_label_award\" title=\"Best Paper\"><i class=\"fas fa-trophy\"><\/i> Best Paper<\/span> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2020 IEEE\/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781728173436<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_23\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('23','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_23\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('23','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_23\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('23','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=32#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=33#tppubs\" title=\"Show all publications which have a relationship to this tag\">Energy Management Systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=34#tppubs\" title=\"Show all publications which have a relationship to this tag\">internet of things<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=24#tppubs\" title=\"Show all publications which have a relationship to this tag\">multi-agent systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=35#tppubs\" title=\"Show all publications which have a relationship to this tag\">Reinforcement Learning<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_23\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cicirelli2020c,<br \/>\r\ntitle = {An Energy Management System at the Edge based on Reinforcement Learning},<br \/>\r\nauthor = {F. Cicirelli and A. F. Gentile and E. Greco and A. Guerrieri and G. Spezzano and A. Vinci},<br \/>\r\ndoi = {10.1109\/DS-RT50469.2020.9213697},<br \/>\r\nisbn = {9781728173436},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\nurldate = {2020-01-01},<br \/>\r\nbooktitle = {2020 IEEE\/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},<br \/>\r\njournal = {Proceedings of the 2020 IEEE\/ACM 24th International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2020},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {In this work, we propose an IoT edge-based energy management system devoted to minimizing the energy cost for the daily-use of in-home appliances. The proposed approach employs a load scheduling based on a load shifting technique, and it is designed to operate in an edge-computing environment naturally. The scheduling considers all together time-variable profiles for energy cost, energy production, and energy consumption for each shiftable appliance. Deadlines for load termination can also be expressed. In order to address these goals, the scheduling problem is formulated as a Markov decision process and then processed through a reinforcement learning technique. The approach is validated by the development of an agent-based real-world test case deployed in an edge context.},<br \/>\r\nkeywords = {Edge computing, Energy Management Systems, internet of things, multi-agent systems, Reinforcement Learning},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('23','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_23\" style=\"display:none;\"><div class=\"tp_abstract_entry\">In this work, we propose an IoT edge-based energy management system devoted to minimizing the energy cost for the daily-use of in-home appliances. The proposed approach employs a load scheduling based on a load shifting technique, and it is designed to operate in an edge-computing environment naturally. The scheduling considers all together time-variable profiles for energy cost, energy production, and energy consumption for each shiftable appliance. Deadlines for load termination can also be expressed. In order to address these goals, the scheduling problem is formulated as a Markov decision process and then processed through a reinforcement learning technique. The approach is validated by the development of an agent-based real-world test case deployed in an edge context.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('23','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_23\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/DS-RT50469.2020.9213697\" title=\"Follow DOI:10.1109\/DS-RT50469.2020.9213697\" target=\"_blank\">doi:10.1109\/DS-RT50469.2020.9213697<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('23','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_proceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, Franco;  Guerrieri, Antonio;  Pizzuti, Clara;  Socievole, Annalisa;  Spezzano, Giandomenico;  Vinci, Andrea (Ed.)<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('66','tp_links')\" style=\"cursor:pointer;\">Artificial Life and Evolutionary Computation - 14th Italian Workshop,  WIVACE 2019, Rende, Italy, September 18-20, 2019, Revised Selected  Papers<\/a> <span class=\"tp_pub_type tp_  proceedings\">Proceedings<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">Springer, <\/span><span class=\"tp_pub_additional_volume\">vol. 1200, <\/span><span class=\"tp_pub_additional_year\">2020<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 978-3-030-45015-1<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_66\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('66','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_66\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('66','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_66\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@proceedings{wivace\/2019,<br \/>\r\ntitle = {Artificial Life and Evolutionary Computation - 14th Italian Workshop,  WIVACE 2019, Rende, Italy, September 18-20, 2019, Revised Selected  Papers},<br \/>\r\neditor = {Franco Cicirelli and Antonio Guerrieri and Clara Pizzuti and Annalisa Socievole and Giandomenico Spezzano and Andrea Vinci},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-3-030-45016-8},<br \/>\r\ndoi = {10.1007\/978-3-030-45016-8},<br \/>\r\nisbn = {978-3-030-45015-1},<br \/>\r\nyear  = {2020},<br \/>\r\ndate = {2020-01-01},<br \/>\r\nurldate = {2020-01-01},<br \/>\r\nvolume = {1200},<br \/>\r\npublisher = {Springer},<br \/>\r\nseries = {Communications in Computer and Information Science},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {proceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('66','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_66\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-3-030-45016-8\" title=\"https:\/\/doi.org\/10.1007\/978-3-030-45016-8\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-030-45016-8<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-030-45016-8\" title=\"Follow DOI:10.1007\/978-3-030-45016-8\" target=\"_blank\">doi:10.1007\/978-3-030-45016-8<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('66','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2019\">2019<\/h3><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Guerrieri, A.;  Mastroianni, C.;  Palopoli, F.;  Spezzano, G.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('24','tp_links')\" style=\"cursor:pointer;\">Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2019 IEEE\/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781728129235<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_24\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('24','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_24\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('24','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_24\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('24','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=36#tppubs\" title=\"Show all publications which have a relationship to this tag\">Cognitive Systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=18#tppubs\" title=\"Show all publications which have a relationship to this tag\">Deep Reinforcement Learning<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=37#tppubs\" title=\"Show all publications which have a relationship to this tag\">Energy Saving<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=38#tppubs\" title=\"Show all publications which have a relationship to this tag\">Simulation<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=39#tppubs\" title=\"Show all publications which have a relationship to this tag\">Smart Buildings<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_24\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cicirelli2019,<br \/>\r\ntitle = {Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning},<br \/>\r\nauthor = {F. Cicirelli and A. Guerrieri and C. Mastroianni and F. Palopoli and G. Spezzano and A. Vinci},<br \/>\r\ndoi = {10.1109\/DS-RT47707.2019.8958661},<br \/>\r\nisbn = {9781728129235},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\nurldate = {2019-01-01},<br \/>\r\nbooktitle = {2019 IEEE\/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT)},<br \/>\r\njournal = {Proceedings - 2019 IEEE\/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2019},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.},<br \/>\r\nkeywords = {Cognitive Systems, Deep Reinforcement Learning, Energy Saving, Simulation, Smart Buildings},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('24','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_24\" style=\"display:none;\"><div class=\"tp_abstract_entry\">This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('24','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_24\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/DS-RT47707.2019.8958661\" title=\"Follow DOI:10.1109\/DS-RT47707.2019.8958661\" target=\"_blank\">doi:10.1109\/DS-RT47707.2019.8958661<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('24','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cesario, E.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('25','tp_links')\" style=\"cursor:pointer;\">A comparative analysis of classification and regression models for energy-efficient clouds<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781728100838<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_25\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('25','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_25\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('25','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_25\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('25','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=40#tppubs\" title=\"Show all publications which have a relationship to this tag\">Data Mining for Energy Efficiency<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=41#tppubs\" title=\"Show all publications which have a relationship to this tag\">Energy-aware Clouds<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=42#tppubs\" title=\"Show all publications which have a relationship to this tag\">Green Computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_25\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cesario2019,<br \/>\r\ntitle = {A comparative analysis of classification and regression models for energy-efficient clouds},<br \/>\r\nauthor = {E. Cesario and A. Vinci},<br \/>\r\ndoi = {10.1109\/ICNSC.2019.8743292},<br \/>\r\nisbn = {9781728100838},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\nurldate = {2019-01-01},<br \/>\r\nbooktitle = {2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC)},<br \/>\r\njournal = {Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a con-solidation strategy strongly depends on the forecast of the VMs resource needs. This paper presents the experimental evaluation of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. Migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting classification and regression models and shows good benefits in terms of energy saving.},<br \/>\r\nkeywords = {Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('25','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_25\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a con-solidation strategy strongly depends on the forecast of the VMs resource needs. This paper presents the experimental evaluation of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. Migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting classification and regression models and shows good benefits in terms of energy saving.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('25','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_25\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/ICNSC.2019.8743292\" title=\"Follow DOI:10.1109\/ICNSC.2019.8743292\" target=\"_blank\">doi:10.1109\/ICNSC.2019.8743292<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('25','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_inproceedings\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Guerrieri, A.;  Spezzano, G.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('26','tp_links')\" style=\"cursor:pointer;\">A Cognitive Enabled, Edge-Computing Architecture for Future Generation IoT Environments<\/a> <span class=\"tp_pub_type tp_  inproceedings\">Proceedings Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_booktitle\">2019 IEEE 5th World Forum on Internet of Things (WF-IoT), <\/span><span class=\"tp_pub_additional_publisher\">IEEE, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9781538649800<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_26\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('26','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_26\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('26','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_26\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('26','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=43#tppubs\" title=\"Show all publications which have a relationship to this tag\">Architectures<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=44#tppubs\" title=\"Show all publications which have a relationship to this tag\">Cognitive Internet of Things<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=32#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=19#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart environments<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_26\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@inproceedings{Cicirelli2019b,<br \/>\r\ntitle = {A Cognitive Enabled, Edge-Computing Architecture for Future Generation IoT Environments},<br \/>\r\nauthor = {F. Cicirelli and A. Guerrieri and G. Spezzano and A. Vinci},<br \/>\r\ndoi = {10.1109\/WF-IoT.2019.8767246},<br \/>\r\nisbn = {9781538649800},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\nurldate = {2019-01-01},<br \/>\r\nbooktitle = {2019 IEEE 5th World Forum on Internet of Things (WF-IoT)},<br \/>\r\njournal = {IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings},<br \/>\r\npublisher = {IEEE},<br \/>\r\nabstract = {Nowadays, Smart Environments (SEs) are pervasively deployed in buildings (e.g., houses, schools, and offices) and outdoor environments with the goal of improving the quality of life of their inhabitants. SEs are usually designed and developed by using well-suited architectures and platforms having the aim of simplifying and making straightforward the SE implementation. Up to now, SEs are mostly reactive and, in some ways, proactive. Current research efforts are devoted to making such environments cognitive, i.e., able to automatically adapt and adhere to the possible changes in users&#039; needs and behaviors. Anyway, in this field, the development of SEs is still in its infancy. In this direction, the paper proposes a novel Cognitive-enabled, Edge-based Internet of Things (CEIoT) architecture, purposely designed to develop cognitive IoT-based SEs. Such architecture wants to overcome some limitations arising during the usage of common SE platforms and architectures. CEIoT introduces some abstractions ranging from the \"in-platform\" implementation of decentralized cognitive algorithms to the realization of smart data aggregations.},<br \/>\r\nkeywords = {Architectures, Cognitive Internet of Things, Edge computing, smart environments},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {inproceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('26','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_26\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Nowadays, Smart Environments (SEs) are pervasively deployed in buildings (e.g., houses, schools, and offices) and outdoor environments with the goal of improving the quality of life of their inhabitants. SEs are usually designed and developed by using well-suited architectures and platforms having the aim of simplifying and making straightforward the SE implementation. Up to now, SEs are mostly reactive and, in some ways, proactive. Current research efforts are devoted to making such environments cognitive, i.e., able to automatically adapt and adhere to the possible changes in users&#039; needs and behaviors. Anyway, in this field, the development of SEs is still in its infancy. In this direction, the paper proposes a novel Cognitive-enabled, Edge-based Internet of Things (CEIoT) architecture, purposely designed to develop cognitive IoT-based SEs. Such architecture wants to overcome some limitations arising during the usage of common SE platforms and architectures. CEIoT introduces some abstractions ranging from the &quot;in-platform&quot; implementation of decentralized cognitive algorithms to the realization of smart data aggregations.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('26','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_26\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/WF-IoT.2019.8767246\" title=\"Follow DOI:10.1109\/WF-IoT.2019.8767246\" target=\"_blank\">doi:10.1109\/WF-IoT.2019.8767246<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('26','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Altomare, A.;  Cesario, E.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('27','tp_links')\" style=\"cursor:pointer;\">Data analytics for energy-efficient clouds: design, implementation and evaluation<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">International Journal of Parallel, Emergent and Distributed Systems, <\/span><span class=\"tp_pub_additional_volume\">vol. 34, <\/span><span class=\"tp_pub_additional_issue\">iss. 6, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 17445779<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_27\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('27','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_27\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('27','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_27\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('27','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=40#tppubs\" title=\"Show all publications which have a relationship to this tag\">Data Mining for Energy Efficiency<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=41#tppubs\" title=\"Show all publications which have a relationship to this tag\">Energy-aware Clouds<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=42#tppubs\" title=\"Show all publications which have a relationship to this tag\">Green Computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_27\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Altomare2019,<br \/>\r\ntitle = {Data analytics for energy-efficient clouds: design, implementation and evaluation},<br \/>\r\nauthor = {A. Altomare and E. Cesario and A. Vinci},<br \/>\r\ndoi = {10.1080\/17445760.2018.1448931},<br \/>\r\nissn = {17445779},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\njournal = {International Journal of Parallel, Emergent and Distributed Systems},<br \/>\r\nvolume = {34},<br \/>\r\nissue = {6},<br \/>\r\nabstract = {The success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. Predictive data mining models can be exploited for this purpose. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving.},<br \/>\r\nkeywords = {Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('27','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_27\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. Predictive data mining models can be exploited for this purpose. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('27','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_27\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1080\/17445760.2018.1448931\" title=\"Follow DOI:10.1080\/17445760.2018.1448931\" target=\"_blank\">doi:10.1080\/17445760.2018.1448931<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('27','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Catlett, C.;  Cesario, E.;  Talia, D.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('28','tp_links')\" style=\"cursor:pointer;\">Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Pervasive and Mobile Computing, <\/span><span class=\"tp_pub_additional_volume\">vol. 53, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 15741192<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_28\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('28','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_28\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('28','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_28\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('28','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=45#tppubs\" title=\"Show all publications which have a relationship to this tag\">Crime prediction<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=46#tppubs\" title=\"Show all publications which have a relationship to this tag\">Data analytics<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=15#tppubs\" title=\"Show all publications which have a relationship to this tag\">smart city<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=16#tppubs\" title=\"Show all publications which have a relationship to this tag\">Urban computing<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_28\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Catlett2019,<br \/>\r\ntitle = {Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments},<br \/>\r\nauthor = {C. Catlett and E. Cesario and D. Talia and A. Vinci},<br \/>\r\ndoi = {10.1016\/j.pmcj.2019.01.003},<br \/>\r\nissn = {15741192},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\njournal = {Pervasive and Mobile Computing},<br \/>\r\nvolume = {53},<br \/>\r\nabstract = {Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.},<br \/>\r\nkeywords = {Crime prediction, Data analytics, smart city, Urban computing},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('28','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_28\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Steadily increasing urbanization is causing significant economic and social transformations in urban areas, posing several challenges related to city management and services. In particular, in cities with higher crime rates, effectively providing for public safety is an increasingly complex undertaking. To handle this complexity, new technologies are enabling police departments to access growing volumes of crime-related data that can be analyzed to understand patterns and trends. These technologies have potentially to increase the efficient deployment of police resources within a given territory and ultimately support more effective crime prevention. This paper presents a predictive approach based on spatial analysis and auto-regressive models to automatically detect high-risk crime regions in urban areas and to reliably forecast crime trends in each region. The algorithm result is a spatio-temporal crime forecasting model, composed of a set of crime-dense regions with associated crime predictors, each one representing a predictive model for estimating the number of crimes likely to occur in its associated region. The experimental evaluation was performed on two real-world datasets collected in the cities of Chicago and New York City. This evaluation shows that the proposed approach achieves good accuracy in spatial and temporal crime forecasting over rolling time horizons.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('28','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_28\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.pmcj.2019.01.003\" title=\"Follow DOI:10.1016\/j.pmcj.2019.01.003\" target=\"_blank\">doi:10.1016\/j.pmcj.2019.01.003<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('28','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Cicirelli, F.;  Guerrieri, A.;  Mercuri, A.;  Spezzano, G.;  Vinci, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('29','tp_links')\" style=\"cursor:pointer;\">ITEMa: A methodological approach for cognitive edge computing IoT ecosystems<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Future Generation Computer Systems, <\/span><span class=\"tp_pub_additional_volume\">vol. 92, <\/span><span class=\"tp_pub_additional_year\">2019<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 0167739X<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_29\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('29','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_29\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('29','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_29\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('29','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span> | <span class=\"tp_pub_tags_label\">Tags: <\/span><a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=47#tppubs\" title=\"Show all publications which have a relationship to this tag\">activity recognition<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=36#tppubs\" title=\"Show all publications which have a relationship to this tag\">Cognitive Systems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=102#tppubs\" title=\"Show all publications which have a relationship to this tag\">domus<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=48#tppubs\" title=\"Show all publications which have a relationship to this tag\">Edge and cloud computing<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=49#tppubs\" title=\"Show all publications which have a relationship to this tag\">IoT-based ecosystems<\/a>, <a rel=\"nofollow\" href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?tgid=50#tppubs\" title=\"Show all publications which have a relationship to this tag\">Smart Office<\/a><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_29\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{Cicirelli2019c,<br \/>\r\ntitle = {ITEMa: A methodological approach for cognitive edge computing IoT ecosystems},<br \/>\r\nauthor = {F. Cicirelli and A. Guerrieri and A. Mercuri and G. Spezzano and A. Vinci},<br \/>\r\ndoi = {10.1016\/j.future.2018.10.003},<br \/>\r\nissn = {0167739X},<br \/>\r\nyear  = {2019},<br \/>\r\ndate = {2019-01-01},<br \/>\r\nurldate = {2019-01-01},<br \/>\r\njournal = {Future Generation Computer Systems},<br \/>\r\nvolume = {92},<br \/>\r\nabstract = {The ever-increasing spread of Internet of Things (IoT)-based technologies paired with the diffusion of the edge-based computing boosts the development of pervasive cyber ecosystems having the goal of improving the life quality of people and assisting them in daily activities. In this context, cognitive behaviors are purposely required to make such ecosystems able to adapt to people needs and to envisage their behaviors. Despite the growing interest in cognitive ecosystems, still there is a lack of methodological approaches devoted to supporting the design and implementation of such complex systems. This paper proposes ITEMa, an Iot-based smarT Ecosystem Modeling Approach based on a three-layered architecture offering some well-suited abstractions tailored to the development of IoT-based ecosystems which exhibit cognitive behaviors and are able to exploit computational resources located either at the edge of the network or in the Cloud. The effectiveness of the approach is demonstrated through a case study concerning the development of a Smart Office devoted to forecast some usual office activities and to properly adapt the office environmental conditions to them.},<br \/>\r\nkeywords = {activity recognition, Cognitive Systems, domus, Edge and cloud computing, IoT-based ecosystems, Smart Office},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('29','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_29\" style=\"display:none;\"><div class=\"tp_abstract_entry\">The ever-increasing spread of Internet of Things (IoT)-based technologies paired with the diffusion of the edge-based computing boosts the development of pervasive cyber ecosystems having the goal of improving the life quality of people and assisting them in daily activities. In this context, cognitive behaviors are purposely required to make such ecosystems able to adapt to people needs and to envisage their behaviors. Despite the growing interest in cognitive ecosystems, still there is a lack of methodological approaches devoted to supporting the design and implementation of such complex systems. This paper proposes ITEMa, an Iot-based smarT Ecosystem Modeling Approach based on a three-layered architecture offering some well-suited abstractions tailored to the development of IoT-based ecosystems which exhibit cognitive behaviors and are able to exploit computational resources located either at the edge of the network or in the Cloud. The effectiveness of the approach is demonstrated through a case study concerning the development of a Smart Office devoted to forecast some usual office activities and to properly adapt the office environmental conditions to them.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('29','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_29\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.future.2018.10.003\" title=\"Follow DOI:10.1016\/j.future.2018.10.003\" target=\"_blank\">doi:10.1016\/j.future.2018.10.003<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('29','tp_links')\">Close<\/a><\/p><\/div><\/div><\/div><\/div><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">84 entries<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 of 2 <a href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"next page\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/it\/pubblicazioni\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"last page\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":30,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-74","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/pages\/74","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/comments?post=74"}],"version-history":[{"count":2,"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/pages\/74\/revisions"}],"predecessor-version":[{"id":757,"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/pages\/74\/revisions\/757"}],"wp:attachment":[{"href":"https:\/\/staff.icar.cnr.it\/vinci\/wordpress\/index.php\/wp-json\/wp\/v2\/media?parent=74"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}