Catálogo de publicaciones - revistas

<jats:p>Most contemporary Web frameworks may be classified as server-centric. An overview of such Web frameworks is presented. It is based on information gleaned from surveying 80 server-centric Web frameworks, as well as from popular related specifications. Requirements typically expected of a server-centric Web framework are discussed. Two Web framework taxonomies are proposed, reflecting two orthogonal ways of characterizing a framework: the way in which the markup language content of a browser-destined document is specified in the framework (presentation concerns); and the framework's facilities for the user to control the flow of events between browser and server (control concerns).</jats:p>

<jats:p>Structures for the expression of fault-tolerance provisions in application software comprise the central topic of this article. Structuring techniques answer questions as to how to incorporate fault tolerance in the application layer of a computer program and how to manage the fault-tolerant code. As such, they provide the means to control complexity, the latter being a relevant factor for the introduction of design faults. This fact and the ever-increasing complexity of today's distributed software justify the need for simple, coherent, and effective structures for the expression of fault-tolerance in the application software. In this text we first define a “base” of structural attributes with which application-level fault-tolerance structures can be qualitatively assessed and compared with each other and with respect to the aforementioned needs. This result is then used to provide an elaborated survey of the state-of-the-art of application-level fault-tolerance structures.</jats:p>

<jats:p>We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.</jats:p>

<jats:p>Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed ‘hype’ and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering.</jats:p>

<jats:p>The Internet enables global sharing of data across organizational boundaries. Distributed file systems facilitate data sharing in the form of remote file access. However, traditional access control mechanisms used in distributed file systems are intended for machines under common administrative control, and rely on maintaining a centralized database of user identities. They fail to scale to a large user base distributed across multiple organizations. We provide a survey of decentralized access control mechanisms in distributed file systems intended for large scale, in both administrative domains and users. We identify essential properties of such access control mechanisms. We analyze both popular production and experimental distributed file systems in the context of our survey.</jats:p>

<jats:p>Trust management systems are frameworks for authorization in modern distributed systems, allowing remotely accessible resources to be protected by providers. By allowing providers to specify policy, and access requesters to possess certain access rights, trust management automates the process of determining whether access should be allowed on the basis of policy, rights, and an authorization semantics. In this paper we survey modern state-of-the-art in trust management authorization, focusing on features of policy and rights languages that provide the necessary expressiveness for modern practice. We characterize systems in light of a generic structure that takes into account components of practical implementations. We emphasize systems that have a formal foundation, since security properties of them can be rigorously guaranteed. Underlying formalisms are reviewed to provide necessary background.</jats:p>

<jats:p>Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and new ideas are constantly introduced. This survey presents a tutorial overview of the state of the art. We describe the context of the current research and then move to a formal problem description and an overview of the main subproblems: translation modeling, parameter estimation, and decoding. Along the way, we present a taxonomy of some different approaches within these areas. We conclude with an overview of evaluation and a discussion of future directions.</jats:p>

<jats:p> Efficient processing of top- <jats:italic>k</jats:italic> queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top- <jats:italic>k</jats:italic> processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top- <jats:italic>k</jats:italic> processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top- <jats:italic>k</jats:italic> queries in XML domain, and show their connections to relational approaches. </jats:p>

<jats:p> Differential topology, and specifically Morse theory, provide a suitable setting for formalizing and solving several problems related to shape analysis. The fundamental idea behind Morse theory is that of combining the topological exploration of a shape with quantitative measurement of geometrical properties provided by a real function defined on the shape. The added value of approaches based on Morse theory is in the possibility of adopting different functions as shape descriptors according to the properties and invariants that one wishes to analyze. In this sense, Morse theory allows one to construct a <jats:italic>general framework</jats:italic> for shape characterization, parametrized with respect to the mapping function used, and possibly the space associated with the shape. The mapping function plays the role of a <jats:italic>lens</jats:italic> through which we look at the properties of the shape, and different functions provide different insights. </jats:p> <jats:p>In the last decade, an increasing number of methods that are rooted in Morse theory and make use of properties of real-valued functions for describing shapes have been proposed in the literature. The methods proposed range from approaches which use the configuration of contours for encoding topographic surfaces to more recent work on size theory and persistent homology. All these have been developed over the years with a specific target domain and it is not trivial to systematize this work and understand the links, similarities, and differences among the different methods. Moreover, different terms have been used to denote the same mathematical constructs, which often overwhelm the understanding of the underlying common framework.</jats:p> <jats:p> The aim of this survey is to provide a clear vision of what has been developed so far, focusing on methods that make use of theoretical frameworks that are developed for <jats:italic>classes</jats:italic> of real functions rather than for a single function, even if they are applied in a restricted manner. The term <jats:italic>geometrical-topological</jats:italic> used in the title is meant to underline that both levels of information content are relevant for the applications of shape descriptions: geometrical, or metrical, properties and attributes are crucial for characterizing specific instances of features, while topological properties are necessary to abstract and classify shapes according to invariant aspects of their geometry. The approaches surveyed will be discussed in detail, with respect to theory, computation, and application. Several properties of the shape descriptors will be analyzed and compared. We believe this is a crucial step to exploit fully the potential of such approaches in many applications, as well as to identify important areas of future research. </jats:p>

<jats:p> The algorithm selection problem [Rice 1976] seeks to answer the question: <jats:italic>Which algorithm is likely to perform best for my problem?</jats:italic> Recognizing the problem as a learning task in the early 1990's, the machine learning community has developed the field of meta-learning, focused on learning about learning algorithm performance on classification problems. But there has been only limited generalization of these ideas beyond classification, and many related attempts have been made in other disciplines (such as AI and operations research) to tackle the algorithm selection problem in different ways, introducing different terminology, and overlooking the similarities of approaches. In this sense, there is much to be gained from a greater awareness of developments in meta-learning, and how these ideas can be generalized to learn about the behaviors of other (nonlearning) algorithms. In this article we present a unified framework for considering the algorithm selection problem as a learning problem, and use this framework to tie together the crossdisciplinary developments in tackling the algorithm selection problem. We discuss the generalization of meta-learning concepts to algorithms focused on tasks including sorting, forecasting, constraint satisfaction, and optimization, and the extension of these ideas to bioinformatics, cryptography, and other fields. </jats:p>