SpiderNet: an integrated peer-to-peer service composition framework
Service composition is highly desirable in peer-to-peer (P2P) systems where application services are naturally dispersed on distributed peers. However, it is challenging to provide high quality and failure resilient service composition in P2P systems due to the decentralization requirement and dynamic peer arrivals/departures. We present an integrated P2P service composition framework called SpiderNet to address the challenges. At service setup phase, SpiderNet performs a novel bounded composition probing protocol to provide scalable quality-aware and resource-efficient sendee composition in a fully distributed fashion. Moreover, SpiderNet supports directed acyclic graph composition topologies and explores exchangeable composition orders for enhanced service quality. During service runtime, SpiderNet provides proactive failure recovery to overcome dynamic changes (e.g., peer departures) in P2P systems. The proactive failure recovery scheme maintains a small number of dynamically selected backup compositions to achieve quick failure recovery for soft realtime streaming applications. We have implemented a prototype of SpiderNet and conducted extensive experiments using both large-scale simulations and wide-area network testbed. Experimental results show the feasibility and efficiency of the SpiderNet service composition solution for P2P systems.

title: Intelligent online BGP-4 analyzer
abstract: Border Gateway Protocol 4 (BGP-4) is one of the widely used routing protocols. This paper presents the design and implementation of an intelligent BGP-4 analyzer working online. Serving in the study and test of BGP-4, the analyzer can capture all the BGP-4 traffic and further exploit "state synchronization" algorithm to analyze the behavior of the BGP-4 state machine by observing the interaction between BGP-4 peers. As to the routing information manipulation, the analyzer can also verify its correctness and efficiency through internal process simulation and statistical computation. And the interested sessions and routes are traced and analyzed. This analyzer possesses many other beneficial features and plays an important role both in the study and test of BGP-4 and the network management.

title: Video summarization based on user log enhanced link analysis
abstract: Efficient video data management calls for intelligent video summarization tools that automatically generate concise video summaries for fast skimming and browsing. Traditional video summarization techniques are based on low-level feature analysis, which generally fails to capture the semantics of video content. Our vision is that users unintentionally embed their understanding of the video content in their interaction with computers. This valuable knowledge, which is difficult for computers to learn autonomously, can be utilized for video summarization process. In this paper, we present an intelligent video browsing and summarization system that utilizes previous viewers' browsing log to facilitate future viewers. Specifically, a novel ShotRank notion is proposed as a measure of the subjective interestingness and importance of each video shot. A ShotRank computation framework is constructed to seamlessly unify low-level video analysis and user browsing log mining. The resulting ShotRank is used to organize the presentation of video shots and generate video skims. Experimental results from user studies have strongly confirmed that ShotRank indeed represents the subjective notion of interestingness and importance of each video shot, and it significantly improves future viewers' browsing experience.

title: Internet-based interactive HDTV
abstract: Traditional interactive TV systems depend on expensive hardware, proprietary formats, and a closed-loop end-to-end approach, which greatly limits scalability and extensibility of TV services. In this paper, we present the HDControl interactive Internet TV architecture that achieves an open service model and combines high-quality video with flexible user control using two key software real-time algorithms: visual information embedding (VIE) algorithm and resynchronization algorithm. Experimental results in our HDTV testbed have confirmed the feasibility and efficiency of our proposed algorithms.

title: Scalable and reliable data delivery in mobile ad hoc sensor networks
abstract: This paper studies scalable data delivery algorithms in mobile ad hoc sensor networks with node and link failures. Many algorithms have been developed for data delivery and fusion in static microsensor networks, but most of them are not appropriate for mobile sensor networks due to their heavy traffic and long latency. In this paper we propose an efficient and robust data delivery algorithm for distributed data fusion in mobile ad hoc sensor networks, where each node controls its data flows and learns routing decisions solely based on their local knowledge. We analyze the localized algorithm in a formal model and validate our model using simulations. The experiments indicate that controlled data delivery processes significantly increase the probability of relevant data being fused in the network even with limited local knowledge of each node and relatively small hops of data delivery.

title: Synergistic Integration of Agent Technologies for Military Simulation
abstract: To perform large-scale coordination in real-world environments requires that many individually complex technologies come together to form integrated solutions. In this paper, we present an application where several key technologies are integrated into a unified system via a multiagent infrastructure. We show how the synergistic behavior among heterogeneous technologies results in a significant improvement over the performance of the individual technologies acting alone. Critical extensions were required to the language describing required behavior to allow the pieces to work together. Initial experimental results show system performance on a task of coordinating a military convoy in an adversarial environment was significantly improved when all technologies worked together. However, experiments with a human user in the loop showed that significant advances must still be made before such systems can be fielded in the real-world

title: Community-based service location
abstract: lthough both computing and communications capabilities are improving exponentially, there has been a more rapid increase in available bandwidth than in computing power over recent years . Naturally enough, while the network-related improvements create new possibilities for applications, the improvements and especially their differential, render obsolete traditional ways of thinking about telecommunications and computing. There is an ongoing debate occurring among two main factions within the telecommunications industry [1, 4]. On one side are traditional telephone companies, who advocate intelligent networks with increased intelligence embedded within the network and controlled by the telecommunications providers. The intelligence would be reflected in features such as for caller identification or call forwarding. On the other side are the proponents of (what may be called) stupid networks, who take their inspiration from the Internet. In this view, as bandwidth becomes plentiful, intelligence will propagate to the edges of the network and the network itself will provide no more than bit transport. The overwhelming advantage of stupid networks is that, like the Internet, they naturally support heterogeneity and extensibility. End users can choose whichever applications they like and invoke whichever services they prefer without requiring consistent changes throughout a large network. Consider telephone directories. White pages and yellow pages are essential for making a telecommunications system practical, but are classical centralized functionalities. Internet portals are a close analogue of telephone directories, and provide a one-size-fits-all solution to users’ information needs. However, just as advances in computing are driving manufacturing from mass production to mass customization, advances in communication are driving information access from large portals toward personal contacts. Large portals won’t be eliminated, but will increasingly be supplemented and superseded by personalized sources of information as people increasingly want to receive information and advice from those whom they know and trust. Accordingly, in this article we consider the problem of service location. We study an approach that places the intelligence on the endpoints, enabling the users to locate desirable services based on trustworthy, personalized recommendations of their peers. The task is not only to locate a particular service, but also to locate a service that is rated highly by one’s friends and associ-

title: Implementing KDB-trees to support high-dimensional data
abstract: The problem of retrieving large volumes of high dimensional data is an important and timely issue in the area of database management. The guiding idea of the paper is to develop a general-purpose point access method that attacks the limitations of KDB-trees in high-dimensional spaces, while preserving their relatively good performance in low-dimensional situations. The proposed structure, called high-dimensional KDB-tree, eliminates downward propagation of splits associated with the original KDB-tree structure, which results in low storage utilization and rapid deterioration of the retrieval performance. Additional improvements in the storage and retrieval performance are achieved by removing certain redundant information from the interior nodes. Experimental results show that, in high-dimensional spaces, the proposed structure outperforms the original KDB-trees by a significant margin, while incurring no loss of performance in low-dimensional spaces. The structure also outperforms two other variants of KDB-trees investigated in the paper.

title: Density estimation by stochastic complexity
abstract: The results by P. Hall and E.J. Hannan (1988) on optimization of histogram density estimators with equal bin widths by minimization of the stochastic complexity are extended and sharpened in two separate ways. As the first contribution, two generalized histogram estimators are constructed. The first has unequal bin widths which, together with the number of the bins, are determined by minimization of the stochastic complexity using dynamic programming. The other estimator consists of a mixture of equal bin width estimators, each of which is defined by the associated stochastic complexity. As the main contribution in the present work, two theorems are proved, which together extend the universal coding theorems to a large class of data generating densities. The first gives an asymptotic upper bound for the code redundancy in the order of magnitude, achieved with a special predictive type of histogram estimator, which sharpens a related bound. The second theorem states that this bound cannot be improved upon by any code whatsoever.