<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhe Wang</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Ruoxi Jia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Buildings.Occupants: a Modelica package for modelling occupant behaviour in buildings</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Building Performance Simulation</style></secondary-title><short-title><style face="normal" font="default" size="100%">Journal of Building Performance Simulation</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">modelica</style></keyword><keyword><style  face="normal" font="default" size="100%">Modelica Buildings Library</style></keyword><keyword><style  face="normal" font="default" size="100%">Modelica Occupants Package</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupant Behaviour</style></keyword><keyword><style  face="normal" font="default" size="100%">Occupant behaviour modelling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/full/10.1080/19401493.2018.1543352https://www.tandfonline.com/doi/pdf/10.1080/19401493.2018.1543352</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1 - 12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Energy-related occupant behaviour is crucial to design and operation of energy and control systems in buildings. Occupant behaviours are often oversimplified as static schedules or settings in building performance simulation ignoring their stochastic nature. The continuous and dynamic interaction between occupants and building systems motivates their simultaneous simulation in an efficient manner. In the past, simultaneous simulation has relied on co-simulation approaches or customized source code changes to building simulation programmes. This paper presents Buildings. Occupants, an open-source package implemented in Modelica, for the simulation of occupant behaviours of lighting, windows, blinds, heating and air conditioning systems in office and residential buildings. Examples were presented to illustrate how the models in the Occupants package are capable to simulate stochastic occupant behaviours. The major contribution of this work is to introduce the equation-based modelling approach to simulate occupant behaviours in buildings and to develop an open-source Occupants package in the Modelica language&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fisayo Caleb Sangogboye</style></author><author><style face="normal" font="default" size="100%">Ruoxi Jia</style></author><author><style face="normal" font="default" size="100%">Tianzhen Hong</style></author><author><style face="normal" font="default" size="100%">Costas Spanos</style></author><author><style face="normal" font="default" size="100%">Mikkel Baun Kjærgaard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Transactions on Sensor Networks</style></secondary-title><short-title><style face="normal" font="default" size="100%">ACM Trans. Sen. Netw.TOSN</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cyber physical systems</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">k-anonymity</style></keyword><keyword><style  face="normal" font="default" size="100%">Privacy preservation</style></keyword><keyword><style  face="normal" font="default" size="100%">Smart buildings</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2018</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">1 - 22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research, which, in turn, helps improve overall system efficiency and resiliency. The main challenge in data publishing is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work (Jia et al. 2017a), we presented a privacy-preserving data publishing framework (referred to as PAD hereinafter), which can guarantee k-anonymity while achieving better data utility than traditional anonymization techniques. PAD learns the information of interest to data users or features from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work is only applicable to the case where the desired features are linear in the original data record. In this article, we extend PAD to nonlinear features. Our experiments demonstrate that for various data-driven applications, PAD can achieve enhanced utility while remaining highly resilient to privacy threats.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3-4</style></issue></record></records></xml>