Searching social networks
A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.

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: An evidential model of distributed reputation management
abstract: For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy correspondents is by collaborating with others to identify those whose past behavior has been untrustworthy. In other words, finding trustworthy correspondents reduces to the problem of distributed reputation management.Our approach adapts the mathematical theory of evidence to represent and propagate the ratings that agents give to their correspondents. When evaluating the trustworthiness of a correspondent, an agent combines its local evidence (based on direct prior interactions with the correspondent) with the testimonies of other agents regarding the same correspondent. We experimentally studied this approach to establish that some important properties of trust are captured by it.

title: A Social Mechanism of Reputation Management in Electronic Communities
abstract: Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.

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: Dynamic Splitting Policies of the Adaptive 3DR-Tree for Indexing Continuously Moving Objects
abstract: Moving-objects databases need a spatio-temporal indexing scheme for moving objects to efficiently process queries over continuously changing locations of the objects. A simple extension of the R-tree that employs time as the third dimension of the data universe shows low space utilization and poor search performance because of overlapping index regions. In this paper, we propose a variant of the 3-dimensional R-tree called the Adaptive 3DR-tree. The dynamic splitting policies of the Adaptive 3DR-tree significantly reduce the overlap rate, and this, in turn, results in improved query performance. The results of our extensive experiments show that the Adaptive 3DR-tree outperforms the original 3D R-tree and the TB-tree typically by a big margin.

title: A scalable overlay video mixing service model
abstract: Information disparity over time and between streams

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: The Minimum Description Length Principle in Coding and Modeling
abstract: We review the principles of minimum description length and stochastic complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon's basic source coding theorem. The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to within asymptotically vanishing terms. We assess the performance of the minimum description length criterion both from the vantage point of quality of data compression and accuracy of statistical inference. Context tree modeling, density estimation, and model selection in Gaussian linear regression serve as examples.