Community-based service location
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 Large portals won’t be eliminated, but will increasingly be supplemented and superseded by personalized sources of information as people increasingly want to receive information and advice from those whom they know and trust.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: 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: Distributed Reputation Management for Electronic Commerce
abstract: One of the major challenges for electronic commerce is how to establish a relationship of trust between different parties. Establishing trust is nontrivial, because the traditional physical or social means of trust cannot apply directly in virtual settings. In many cases, the parties involved may not ever have interacted before. Reputation systems seek to address the development of trust by recording the reputations of different parties. However, most existing reputation systems are restricted to individual market websites. Further, relevant information about a party may come from several websites and from interactions that were not mediated by any website. 
 
 
 
This paper considers the problem of automatically collecting ratings about a given party from others. Our approach involves a distributed agent architecture and adapts the mathematical theory of evidence to represent and propagate the ratings that participants give to each other. When evaluating the trustworthiness of a given party, a peer combines its local evidence (based on direct prior interactions with the party) with the testimonies of others regarding the same party. This approach satisfies certain important properties of distributed reputation management and is experimentally evaluated through simulations.

title: Detecting deception in reputation management
abstract: We previously developed a social mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper focuses on the problem of deception in testimony propagation and aggregation. We introduce some models of deception and study how to efficiently detect deceptive agents following those models. Our approach involves a novel application of the well-known weighted majority technique to belief function and their aggregation. We describe simulation experiments to study the number of apparently accurate witnesses found in different settings, the number of witnesses on prediction accuracy, and the evolution of trust networks.

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.

title: Spatially adaptive wavelet thresholding with context modeling for image denoising
abstract: The method of wavelet thresholding for removing noise, or denoising, has been researched extensively due to its effectiveness and simplicity. Much of the work has been concentrated on finding the best uniform threshold or best basis. However, not much has been done to make this method adaptive to spatially changing statistics which is typical of a large class of images. This work proposes a spatially adaptive wavelet thresholding method based on context modeling, a common technique used in image compression to adapt the coder to the non-stationarity of images. We model each coefficient as a random variable with the generalized Gaussian prior with unknown parameters. Context modeling is used to estimate the parameters for each coefficient, which are then used to adapt the thresholding strategy. Experimental results show that spatially adaptive wavelet thresholding yields significantly superior image quality and lower MSE than optimal uniform thresholding.

title: Interpolating and Using Most Likely Trajectories in Moving-Objects Databases
abstract: In recent years, many emerging database applications deal with large sets of continuously moving data objects. Since no computer system can commit continuously occurring infinitesimal changes to the database, related data management techniques view a moving object's trajectory as a sequence of discretely reported spatiotemporal points. For each pair of consecutive committed trajectory points, a spatiotemporal uncertainty region representing all possible in-between trajectory points is defined. To support trajectory queries with a non-uniform probability distribution model, the query system needs to compute (interpolate) the “most likely” trajectories in the uncertainty regions to determine the peak points of the probability distributions. This paper proposes a generalized trajectory interpolation model using parametric trajectory representations. In addition, the paper expands and investigates three practical specializations of our proposed model using a moving object with momentum, i.e., a vehicle, as the exemplar.

title: Scalable QSF-Trees: Retrieving Regional Objects in High-Dimensional Spaces
abstract: Many database applications require effective representation of regional objects in high-dimensional spaces. By applying an original query transformation, a recently proposed access method for regional data, called the simple QSF-tree (sQSF-tree), effectively attacks the limitations of traditional spatial access methods in spaces with many dimensions. Nevertheless, sQSF-trees are not immune to all problems associated with high data dimensionality. Based on the analysis of sQSF-trees, this paper presents a new variant of sQSF-trees, called the scalable QSF-tree (cQSF-tree), which relies on a heuristic optimization to reduce the number of false drops into pages that contain no object satisfying the query. By increasing the selectivity of search predicates, cQSF-trees improve the performance of multi-dimensional selections. Experimental evidence shows that cQSF-trees are more scalable than sQSF-trees to the growing data dimensionality. The performance improvements also increase with more skewed data distribution.

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.