Catálogo de publicaciones - revistas

<jats:p>An enormous amount of research has been conducted in the area of positioning systems and thus it calls for a detailed literature review of recent localization systems. This article focuses on recent developments of non-Global Positioning System (GPS) localization/positioning systems. We have presented a new hierarchical method to classify various positioning systems. A comprehensive performance comparison of the techniques and technologies against multiple performance metrics along with the limitations is presented. A few indoor positioning systems have emerged as more successful in particular application environments than others, which are presented at the end.</jats:p>

<jats:p> JavaScript has been a <jats:italic>de facto</jats:italic> standard language for client-side web programs, and now it is expanding its territory to general purpose programs. In this article, we classify the client-side JavaScript research for the last decade or so into six topics: static analysis, dynamic analysis, formalization and reasoning, type safety and JIT optimization, security for web applications, and empirical studies. Because the majority of the research has focused on static and dynamic analyses of JavaScript, we evaluate research trends in the analysis of JavaScript first and then the other topics. Finally, we discuss possible future research directions with open challenges. </jats:p>

<jats:p>Collaborative Information Retrieval (CIR) is a well-known setting in which explicit collaboration occurs among a group of users working together to solve a shared information need. This type of collaboration has been deemed as beneficial for complex or exploratory search tasks. With the multiplicity of factors impacting on the search effectiveness (e.g., collaborators’ interactions or the individual perception of the shared information need), CIR gives rise to several challenges in terms of collaboration support through algorithmic approaches. More particularly, CIR should allow us to satisfy the shared information need by optimizing the collaboration within the search session over all collaborators, while ensuring that both mutually beneficial goals are reached and that the cognitive cost of the collaboration does not impact the search effectiveness. In this survey, we propose an overview of CIR with a particular focus on the collaboration support through algorithmic approaches. The objective of this article is (a) to organize previous empirical studies analyzing collaborative search with the goal to provide useful design implications for CIR models, (b) to give a picture of the CIR area by distinguishing two main categories of models using the collaboration mediation axis, and (c) to point out potential perspectives in the domain.</jats:p>

<jats:p>Technological advances allow more physical objects to connect to the Internet and provide their services on the Web as resources. Search engines are the key to fully utilize this emerging Web of Things, as they bridge users and applications with resources needed for their operation. Developing these systems is a challenging and diverse endeavor due to the diversity of Web of Things resources that they work with. Each combination of resources in query resolution process requires a different type of search engine with its own technical challenges and usage scenarios. This diversity complicates both the development of new systems and assessment of the state of the art. In this article, we present a systematic survey on Web of Things Search Engines (WoTSE), focusing on the diversity in forms of these systems. We collect and analyze over 200 related academic works to build a flexible conceptual model for WoTSE. We develop an analytical framework on this model to review the development of the field and its current status, reflected by 30 representative works in the area. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and an ideal WoTSE.</jats:p>

<jats:p>Algorithmic debugging is a technique proposed in 1982 by E. Y. Shapiro in the context of logic programming. This survey shows how the initial ideas have been developed to become a widespread debugging schema fitting many different programming paradigms and with applications out of the program debugging field. We describe the general framework and the main issues related to the implementations in different programming paradigms and discuss several proposed improvements and optimizations. We also review the main algorithmic debugger tools that have been implemented so far and compare their features. From this comparison, we elaborate a summary of desirable characteristics that should be considered when implementing future algorithmic debuggers.</jats:p>

<jats:p>Software security vulnerabilities are one of the critical issues in the realm of computer security. Due to their potential high severity impacts, many different approaches have been proposed in the past decades to mitigate the damages of software vulnerabilities. Machine-learning and data-mining techniques are also among the many approaches to address this issue. In this article, we provide an extensive review of the many different works in the field of software vulnerability analysis and discovery that utilize machine-learning and data-mining techniques. We review different categories of works in this domain, discuss both advantages and shortcomings, and point out challenges and some uncharted territories in the field.</jats:p>

<jats:p>Trust allows the behavior evaluation of individuals by setting confidence values, which are used in decisions about whether or not to interact. They have been used in several fields, and many trust and reputation models were developed recently. We perceived that most of them were built upon the numeric and cognitive paradigms, which do not use affective aspects to build trust or help in decision making. Studies in psychology argued that personality, emotions, and mood are important in decision making and can change the behaviors of individuals. Based on that, we present links between trust and affective computing, showing relations of trust dimensions found in trust models with affective aspects, and presenting why affective computing approaches fit trust issues often addressed by research in this field. We also discuss trust relationships and decision making, emotions, and personality. Affective computing concepts have been used in a dispersed way and specifically in some models, so we aim to bring them together to encourage the growth of fuller trust models similar to those used by humans. We aim to find relations between both fields so it will be possible to employ such concepts to develop trust models using this new paradigm, defined as the affective paradigm.</jats:p>

<jats:p> Detecting and analyzing dense groups or <jats:italic>communities</jats:italic> from social and information networks has attracted immense attention over the last decade due to its enormous applicability in different domains. Community detection is an <jats:italic>ill-defined problem</jats:italic> , as the nature of the communities is not known in advance. The problem has turned even more complicated due to the fact that communities emerge in the network in various forms such as disjoint, overlapping, and hierarchical. Various heuristics have been proposed to address these challenges, depending on the application in hand. All these heuristics have been materialized in the form of new <jats:italic>metrics</jats:italic> , which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. Over the last decade, a large number of such metrics have been proposed. Thus, there arises a need for an organized and detailed survey of the metrics proposed for community detection and evaluation. Here, we present a survey of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure. </jats:p>

<jats:p>Limiting the knowledge of individual nodes is a major concern for the design of distributed algorithms. With the LOCAL model, theoretical research already established a common model of locality that has gained little practical relevance. As a result, practical research de facto lacks any common locality model. The only common denominator among practitioners is that a local algorithm is distributed with a restricted scope of interaction. This article closes the gap by introducing four practically motivated classes of locality that successively weaken the strict requirements of the LOCAL model. These classes are applied to categorize and survey 36 local algorithms from 12 different application domains. A detailed comparison shows the practicality of the classification and provides interesting insights. For example, the majority of algorithms limit the scope of interaction to at most two hops, independent of their locality class. Moreover, the application domain of algorithms tends to influence their degree of locality.</jats:p>

<jats:p>Nanoscale technology nodes bring reliability concerns back to the center stage of digital system design. A systematic classification of approaches that increase system resilience in the presence of functional hardware (HW)-induced errors is presented, dealing with higher system abstractions, such as the (micro)architecture, the mapping, and platform software (SW). The field is surveyed in a systematic way based on nonoverlapping categories, which add insight into the ongoing work by exposing similarities and differences. HW and SW solutions are discussed in a similar fashion so that interrelationships become apparent. The presented categories are illustrated by representative literature examples to illustrate their properties. Moreover, it is demonstrated how hybrid schemes can be decomposed into their primitive components.</jats:p>