Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices

We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs

Volume rendering is a technique for visualizing 3D arrays of sampled data. It has applications in areas such as medical imaging and scientific visualization, but its use has been limited by its high computational expense. Early implementations of volume rendering used brute-force techniques

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Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course traveled by research ideas as they grow into production systems.

feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified

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Trust is a judgement of unquestionable utility — as humans we use it every day of our lives. However, trust has suffered from an imperfect understanding, a plethora of definitions, and informal use in the literature and in everyday life. It is common to say “I trust you, ” but what does that mean? This thesis provides a clarification of trust. We present a formalism for trust which provides us with a tool for precise discussion. The formalism is implementable: it can be embedded in an artificial agent, enabling the agent to make trust-based decisions. Its applicability in the domain of Distributed Artificial Intelligence (DAI) is raised. The thesis presents a testbed populated by simple trusting agents which substantiates the utility of the formalism. The formalism provides a step in the direction of a proper understanding and definition of human trust. A contribution of the thesis is its detailed exploration of the possibilities of future work in the area. Summary 1. Overview This thesis presents an overview of trust as a social phenomenon and discusses it formally. It argues that trust is: • A means for understanding and adapting to the complexity of the environment. • A means of providing added robustness to independent agents. • A useful judgement in the light of experience of the behaviour of others. • Applicable to inanimate others. The thesis argues these points from the point of view of artificial agents. Trust in an artificial agent is a means of providing an additional tool for the consideration of other agents and the environment in which it exists. Moreover, a formalisation of trust enables the embedding of the concept into an artificial agent. This has been done, and is documented in the thesis. 2. Exposition There are places in the thesis where it is necessary to give a broad outline before going deeper. In consequence it may seem that the subject is not receiving a thorough treatment, or that too much is being discussed at one time! (This is particularly apparent in the first and second chapters.) To present a thorough understanding of trust, we have proceeded breadth first in the introductory chapters. Chapter 3 expands, depth first, presenting critical views of established researchers.