Xingshe Zhou

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Since today’s television can receive more and more programs, and televisions are often viewed by groups of people, such as a family or a student dormitory, this paper proposes a TV program recommendation strategy for multiple viewers based on user profile merging. This paper first introduces three alternative strategies to achieve program recommendation for(More)
The traditional view of Internet of Things (IoT) attempts to connect all the physical objects to build a global, infrastructure-based IoT. In this paper, however, we will present opportunistic IoT, which is formed based on the ad hoc, opportunistic networking of devices (e.g., mobile phones, smart vehicles) using short-range radio techniques (e.g.,(More)
With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and(More)
The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory(More)
The abundance of DTV (Digital Television) programs precipitates a need for new tools to help people personalize interesting TV content. We developed an adaptive assistant: TV3P (TV Program Personalization for PDR), which observes users’ viewing behaviors in the background, updates users’ profiles continuously and autonomously, and then filters and(More)
The event-driven nature of wireless sensor networks (WSNs) leads to unpredictable network load. As a result, congestion may occur at sensors that receive more data than they can forward, which causes energy waste, throughput reduction, and packet loss. In this paper, we propose a rate-based fairness-aware congestion control (FACC) protocol, which controls(More)
Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a(More)
A context-aware media recommendation platform uses an NtimesM-dimensional model and a hybrid processing approach to support media recommendation, adaptation, and delivery for smart phones. To provide media recommendations for smart phones based on all three context categories, we present a generic and flexible NtimesM-dimensional (N2M) recommendation model.(More)