Detecting Stress Based on Social Interactions in Social Networks

Detecting Stress Based on Social Interactions in Social Networks Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. It is find that users stress state is closely related to that of his/her friends in social media, and a large-scale dataset from real-world social platforms is employed to systematically study…

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Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories

Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories We study the problem of preserving user privacy in the publication of location sequences. Consider a database of trajectories, corresponding to movements of people, captured by their transactions when they use credit cards, RFID debit cards, or NFC compliant devices. We show that, if such trajectories are published exactly (by only hiding the identities of persons that followed them), one can use partial trajectory knowledge as a quasi-identifier for the remaining locations in the sequence. We devise four intuitive…

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Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Personal Web Revisitation by Context and Content Keywords with Relevance Feedback Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human’s natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique called WebPagePrev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism…

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Crowdsourced Coverage as a Service: Two-Level Composition of Sensor Cloud Services

Crowdsourced Coverage as a Service: Two-Level Composition of Sensor Cloud Services A new two-level composition model for crowdsourced Sensor-Cloud services is proposed based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of…

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Energy-efficient Query Processing in Web Search Engines

Energy-efficient Query Processing in Web Search Engines Web search engines are composed by thousands of query processing nodes, i.e., servers dedicated to process user queries. Such many servers consume a significant amount of energy, mostly accountable to their CPUs, but they are necessary to ensure low latencies, since users expect sub-second response times . However, users can hardly notice response times that are faster than their expectations. Hence, we propose the Predictive Energy Saving Online Scheduling Algorithm (PESOS ) to select the most appropriate CPU frequency to process a query…

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Efficient and Exact Local Search for Random Walk Based Top-K Proximity Query in Large Graphs

Efficient and Exact Local Search for Random Walk Based Top-K Proximity Query in Large Graphs DOWNLOAD PROJECT SYNOPSIS Top- k proximity query in large graphs is a fundamental problem with a wide range of applications. Various random walk based measures have been proposed to measure the proximity between different nodes. Although these measures are effective, efficiently computing them on large graphs is a challenging task. An efficient and exact local search method is developed, FLoS (Fast Local Search), for top- k proximity query in large graphs. FLoS guarantees the exactness…

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Exploit Every Bit: Effective Caching for High-Dimensional Nearest Neighbor Search

Exploit Every Bit: Effective Caching for High-Dimensional Nearest Neighbor Search DOWNLOAD PROJECT SYNOPSIS High-dimensional k nearest neighbor (kNN) search has a wide range of applications in multimedia information retrieval. Existing disk-based kNN search methods incur significant I/O costs in the candidate refinement phase.  Propose to cache compact approximate representations of data points in main memory in order to reduce the candidate refinement time during kNN search. This problem raises two challenging issues: (i) which is the most effective encoding scheme for data points to support kNN search? and (ii) what…

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Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences

Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences DOWNLOAD PROJECT SYNOPSIS Frequent sequence mining is well known and well studied problem in datamining. The output of the algorithm is used in many other areas like bioinformatics, chemistry, and market basket analysis. Unfortunately the frequent sequence mining is computationally quite expensive. In this paper we present a novel parallel algorithm for mining of frequent sequences based on a static load-balancing. The static load-balancing is done by measuring the computational time using a probabilistic algorithm. For reasonable size of instance, the…

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RSkNN: kNN Search on Road Networks by Incorporating Social Influence

RSkNN: kNN Search on Road Networks by Incorporating Social Influence DOWNLOAD PROJECT SYNOPSIS Although kNN search on a road network Gr, i.e., finding k nearest objects to a query user q on Gr, has been extensively studied, existing works neglected the fact that the q’s social information can play an important role in this kNN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper we study a new problem: kNN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art…

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Efficient R-Tree Based Indexing Scheme for Server-Centric Cloud Storage System

Efficient R-Tree Based Indexing Scheme for Server-Centric Cloud Storage System   DOWNLOAD PROJECT SYNOPSIS Cloud storage system poses new challenges to the community to support efficient concurrent querying tasks for various data-intensive applications, where indexes always hold important positions. In this paper, we explore a practical method to construct a two-layer indexing scheme for multi-dimensional data in diverse server-centric cloud storage system. RT-HCN is proposed, which is an indexing scheme integrating R-tree based indexing structure and HCN-based routing protocol. RT-HCN organizes storage and compute nodes into an HCN overlay, one…

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