In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been...In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been addressed to unify tagging information to reveal user behaviors and preferences,extract the latent semantic relations among items,make recommendations,and so on.Specifically,this article summarizes recent progress about tag-aware recommender systems,emphasizing on the contributions from three mainstream perspectives and approaches：network-based methods,tensor-based methods,and the topic-based methods.Finally,we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.展开更多
Automation has arrived in the low voltage grid domain. In the next few years, the secondary substation—at the barriers of medium and low voltage grids—will thus be upgraded to enable novel functions. In this paper, ...Automation has arrived in the low voltage grid domain. In the next few years, the secondary substation—at the barriers of medium and low voltage grids—will thus be upgraded to enable novel functions. In this paper, we present various smart grid applications running on such intelligent secondary substations(iSSN) including their interaction with each other. We integrate energy consumption and production data, as well as forecasts, sensed from the smart buildings’ energy management systems(BEMSs) into the operation of the low voltage grid. A suitable framework for those modular applications includes features to initiate their installation, update, removal, the remote operator site, and not requiring staff on-site for such typical reappearing maintenance tasks.展开更多
In the era of abundant location-based data,an increasing number of mobile users seek access to timely local information tailored to their individual interests.Consequently,the development of an efficient publish/subsc...In the era of abundant location-based data,an increasing number of mobile users seek access to timely local information tailored to their individual interests.Consequently,the development of an efficient publish/subscribe system becomes pivotal,allowing a vast user base to seamlessly receive geo-textual objects based on their specific preferences from data streams.However,a notable challenge arises as mobile users often hesitate to disclose their personal interests,requirements,and locations to service providers in location-based publish/subscribe systems,giving rise to substantial data privacy concerns.In this light,we propose a privacy-preserving publish/subscribe framework,which not only facilitates real-time delivery of geo-textual objects to a large-scale audience of location-based subscribers,but also ensures the utmost privacy of subscribers'locations and query keywords.Through experiments conducted on two real-life datasets,our proposed privacy-preserving publish/subscribe system demonstrates its capability to produce real-time matching results.The system can simultaneously handle millions of privacy-enhanced subscription queries over a stream of geo-textual objects.展开更多
基金supported by the Future and Emerging Technologies (FET) Programs of the European Commission FP7-COSI-ICT(QLectives with Grant No.231200 and Liquid Pub with Grant No.213360)Z.-K.Zhang and T.Zhou acknowledge the National Natural Science Foundation of China under Grant Nos.11105024,60973069,61103109,and 90924011the Science and Technology Department of Sichuan Province under Grant No.2010HH0002
文摘In the past decade,Social Tagging Systems have attracted increasing attention from both physical and computer science communities.Besides the underlying structure and dynamics of tagging systems,many efforts have been addressed to unify tagging information to reveal user behaviors and preferences,extract the latent semantic relations among items,make recommendations,and so on.Specifically,this article summarizes recent progress about tag-aware recommender systems,emphasizing on the contributions from three mainstream perspectives and approaches：network-based methods,tensor-based methods,and the topic-based methods.Finally,we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.
基金supported by the Austrian Ministry for Transport,Innovation and Technology(BMVIT)the Austrian Research Promotion Agency(FFG)under Grant No.849902the Austrian Climate and Energy Fund(KLIEN)under Grant No.846141
文摘Automation has arrived in the low voltage grid domain. In the next few years, the secondary substation—at the barriers of medium and low voltage grids—will thus be upgraded to enable novel functions. In this paper, we present various smart grid applications running on such intelligent secondary substations(iSSN) including their interaction with each other. We integrate energy consumption and production data, as well as forecasts, sensed from the smart buildings’ energy management systems(BEMSs) into the operation of the low voltage grid. A suitable framework for those modular applications includes features to initiate their installation, update, removal, the remote operator site, and not requiring staff on-site for such typical reappearing maintenance tasks.
基金supported by National Natural Science Foundation of China under Grant No.72131001
文摘In the era of abundant location-based data,an increasing number of mobile users seek access to timely local information tailored to their individual interests.Consequently,the development of an efficient publish/subscribe system becomes pivotal,allowing a vast user base to seamlessly receive geo-textual objects based on their specific preferences from data streams.However,a notable challenge arises as mobile users often hesitate to disclose their personal interests,requirements,and locations to service providers in location-based publish/subscribe systems,giving rise to substantial data privacy concerns.In this light,we propose a privacy-preserving publish/subscribe framework,which not only facilitates real-time delivery of geo-textual objects to a large-scale audience of location-based subscribers,but also ensures the utmost privacy of subscribers'locations and query keywords.Through experiments conducted on two real-life datasets,our proposed privacy-preserving publish/subscribe system demonstrates its capability to produce real-time matching results.The system can simultaneously handle millions of privacy-enhanced subscription queries over a stream of geo-textual objects.
