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Abstract. In this paper, an adaptive news event detection method is proposed. We consider a news event as a life form and propose an aging theory to model its life span. A news event becomes popular with a burst of news reports, and it fades away with time. We incorporate the proposed aging theory into the traditional single-pass clustering algorithm to model life spans of news events. Experiment results show that the proposed method has fairly good performance for both long-running and short-term events compared to other approaches.

Abstract-The goal of online event analysis is to detect events and track their associated documents in real time from a continuous stream of documents generated by multiple information sources. Unlike traditional text categorization methods, event analysis approaches consider the temporal relations among documents. However, such methods suffer from the threshold-dependency problem, so they only perform well for a narrow range of thresholds. In addition, if the contents of a document stream change, the optimal threshold (that is, the threshold that yields the best performance) often changes as well. In this paper, we propose a thresholdresilient online algorithm, called the Incremental Probabilistic Latent Semantic Indexing (IPLSI) algorithm, which alleviates the threshold-dependency problem and simultaneously maintains the continuity of the latent semantics to better capture the story line development of events. The IPLSI algorithm is theoretically sound and empirically efficient and effective for event analysis. The results of the performance evaluation performed on the Topic Detection and Tracking (TDT)-4 corpus show that the algorithm reduces the cost of event analysis by as much as 15 percent $ 20 percent and increases the acceptable threshold range by 200 percent to 300 percent over the baseline.

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Abstract-With the rapid developments in vehicular communication technology, academics and industry researchers are paying increasing attention to vehicular ad hoc networks (VANETs). In VANETs, dissemination delay and reliability are important criteria for many applications, especially for emergency messages. Existing approaches have difficulty satisfying both requirements simultaneously because they conflict with one another. In this paper, we propose a novel mechanism, called the Density-aware Emergency message Extension Protocol (DEEP) to disseminate emergency messages in VANETs. DEEP resolves the broadcast storm problem, achieves low dissemination delay, and provides high reliability over a realistic multi-lane freeway scenario. The mechanism delivers emergency messages to a specific area (e.g., the area before the exit) in a timely manner and guarantees that all relevant vehicles in that area will receive the messages. Drivers can then change their routes and avoid getting caught in a traffic jam. Performance evaluations via NS-2 simulations demonstrate that DEEP achieves both lower dissemination delay and higher reliability than existing approaches.

Abstract-High-speed rail systems are becoming increasingly popular among long-distance travelers. With the explosive growth in the number of mobile devices, the provision of high quality telecommunication and Internet access services on high-speed trains is now a pressing problem. Network mobility (NEMO) has been proposed to enable a large number of mobile devices on a vehicle to access the Internet; however, several issues must be solved before it can be put into practice, e.g., frequent handovers, long handover latency, and poor quality of service. To resolve the above problems, we propose an LTE femtocell-based network mobility scheme that uses Multiple Egress Network interfaces to support seamless handover for high-speed rail systems, called MEN-NEMO. The results of simulations show that the proposed MEN-NEMO scheme reduces the handover latency and transmission overhead of handover signaling substantially.

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