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— Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However,(More)
In this paper, we aim to provide a point-of-interests (POI) recommendation service for the rapid growing location-based social networks (LBSNs), e.g., Foursquare, Whrrl, etc. Our idea is to explore user preference, social influence and geographical influence for POI recommendations. In addition to deriving user preference based on user-based collaborative(More)
Data gathering is a common but critical operation in many applications of wireless SenSOT networks. Innovative techniques that improve energy eficiency to prolong the network lifetime are highly required. Clustering is an eflective topology control approach in wireless sensor networks, which can increase network scal-ability and lifetime. In this paper, we(More)
Clustering provides an effective way for prolong-5 ing the lifetime of a wireless sensor network. Current clus-6 tering algorithms usually utilize two techniques, selecting 7 cluster heads with more residual energy and rotating clus-8 ter heads periodically, to distribute the energy consumption 9 among nodes in each cluster and extend the network lifetime.(More)
In this paper, we study the research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the <i>social</i> and <i>geographical</i> characteristics of users and locations/places. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we(More)
Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if the user is traveling out of town. In this paper, we study the issues in making location recommendations for(More)
In this paper we present a novel real-time algorithm for simultaneous pose and shape estimation for articulated objects, such as human beings and animals. The key of our pose estimation component is to embed the articulated deformation model with exponential-maps-based parametrization into a Gaussian Mixture Model. Benefiting from this probabilistic(More)
In this paper, we develop a semantic annotation technique for location-based social networks to automatically annotate all places with category tags which are a crucial prerequisite for location search, recommendation services, or data cleaning. Our annotation algorithm learns a binary support vector machine (SVM) classifier for each tag in the tag space to(More)
This paper presents a novel system to estimate body pose configuration from a single depth map. It combines both pose detection and pose refinement. The input depth map is matched with a set of pre-captured motion exemplars to generate a body configuration estimation, as well as semantic labeling of the input point cloud. The initial estimation is then(More)