Catálogo de publicaciones - revistas

<jats:p>Over the last 20 years research has been done on the question of how egocentric distances, i.e., the subjectively reported distance from a human observer to an object, are perceived in virtual environments. This review surveys the existing literature on empirical user studies on this topic. In summary, there is a mean estimation of egocentric distances in virtual environments of about 74% of the modeled distances. Many factors possibly influencing distance estimates were reported in the literature. We arranged these factors into four groups, namely measurement methods, technical factors, compositional factors, and human factors. The research on these factors is summarized, conclusions are drawn, and promising areas for future research are outlined.</jats:p>

<jats:p> Increasingly, one of the most prominent ways to disseminate information on the Web is through “notifications” (also known as alerts), and as such they are at the core of many large-scale applications. For instance, users are notified of articles in which they are interested through RSS feeds, of posts from their friends through social networks, or of recommendation generated by various sites. Event notification usually relies on the so-called Publish-Subscribe (P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> ) communication paradigm. In P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> systems, subscribers sign up for events or classes of events in order to be asynchronously notified afterward by the system. The size of such systems (with respect to events and subscriptions) keeps growing, and providing scalable implementations of P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> systems is extremely challenging. Although there exist popular examples of centralized P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> systems that currently support a large number of subscribers, such as online social networks, they periodically face formidable challenges due to peak loads and do not always offer a support for fine-grain subscriptions. In fact, providing scalability along with expressiveness in the subscription patterns calls for distributed implementations of P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> systems. In parallel, peer-to-peer (P2P) overlay networks have emerged, providing a sound and highly scalable network foundation upon which to build distributed applications including P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> systems. </jats:p> <jats:p> In this article, we focus on fully decentralized (P2P), highly scalable, P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> systems. More specifically, we investigate how P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> and P2P research can be integrated. We define the design space and explore it in a systematic way. We expose an understanding of available design choices; provide a comprehensive classification and understanding of prominent P2P P <jats:sc>ub</jats:sc> /S <jats:sc>ub</jats:sc> systems, positioning them against the design dimensions; and highlight correlations between and implications, benefits, and shortcomings of design alternatives. </jats:p>

<jats:p> Machine translation can be considered a highly interdisciplinary and multidisciplinary field because it is approached from the point of view of human translators, engineers, computer scientists, mathematicians, and linguists. One of the most popular approaches is the Statistical Machine Translation ( <jats:sc>smt</jats:sc> ) approach, which tries to cover translation in a holistic manner by learning from parallel corpus aligned at the sentence level. However, with this basic approach, there are some issues at each written linguistic level (i.e., orthographic, morphological, lexical, syntactic and semantic) that remain unsolved. Research in <jats:sc>smt</jats:sc> has continuously been focused on solving the different linguistic levels challenges. This article represents a survey of how the <jats:sc>smt</jats:sc> has been enhanced to perform translation correctly at all linguistic levels. </jats:p>

<jats:p>Traditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents, and assemble information that fulfill his/her information need.<jats:italic>Aggregated search</jats:italic>represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, etc.) and relational content (similar entities, features) are included in search results.</jats:p><jats:p>In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation, and question answering. Then we focus on more recent trends, namely<jats:italic>cross vertical aggregated search</jats:italic>and<jats:italic>relational aggregated search,</jats:italic>which are already present in current Web search.</jats:p>

<jats:p>In the virtualization area, replication has been considered as a mechanism to provide high availability. A high-availability system should be active most of the time, and this is the reason that its design should consider almost zero downtime and a minimal human intervention if a recovery process is demanded. Several migration and replication mechanisms have been developed to provide high availability inside virtualized environments. In this article, a survey of migration mechanisms is reported. These approaches are classified in three main classes: process migration, memory migration, and suspend/resume migration.</jats:p>

<jats:p>The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.</jats:p>

<jats:p>Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the capital growth theory so as to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state of the art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research.</jats:p>

<jats:p>The explosive growth and widespread accessibility of community-contributed media content on the Internet have led to a surge of research activity in multimedia search. Approaches that apply text search techniques for multimedia search have achieved limited success as they entirely ignore visual content as a ranking signal. Multimedia search reranking, which reorders visual documents based on multimodal cues to improve initial text-only searches, has received increasing attention in recent years. Such a problem is challenging because the initial search results often have a great deal of noise. Discovering knowledge or visual patterns from such a noisy ranked list to guide the reranking process is difficult. Numerous techniques have been developed for visual search re-ranking. The purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. We conclude with several promising directions for future research.</jats:p>

<jats:p>Algorithms that solve Dynamic Multi-Objective Optimisation Problems (DMOOPs) should be tested on benchmark functions to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for Dynamic Multi-Objective Optimisation (DMOO), no standard benchmark functions are used. A number of DMOOPs have been proposed in recent years. However, no comprehensive overview of DMOOPs exist in the literature. Therefore, choosing which benchmark functions to use is not a trivial task. This article seeks to address this gap in the DMOO literature by providing a comprehensive overview of proposed DMOOPs, and proposing characteristics that an ideal DMOO benchmark function suite should exhibit. In addition, DMOOPs are proposed for each characteristic. Shortcomings of current DMOOPs that do not address certain characteristics of an ideal benchmark suite are highlighted. These identified shortcomings are addressed by proposing new DMOO benchmark functions with complicated Pareto-Optimal Sets (POSs), and approaches to develop DMOOPs with either an isolated or deceptive Pareto-Optimal Front (POF). In addition, DMOO application areas and real-world DMOOPs are discussed.</jats:p>

<jats:p>This article is devoted to the survey, analysis, and classification of operational profiles (OP) that characterize the type and frequency of software inputs and are used in software testing techniques. The survey follows a mixed method based on systematic maps and qualitative analysis. This article is articulated around a main dimension, that is, OP classes, which are a characterization of the OP model and the basis for generating test cases. The classes are organized as a taxonomy composed of common OP features (e.g., profiles, structure, and scenarios), software boundaries (which define the scope of the OP), OP dependencies (such as those of the code or in the field of interest), and OP development (which specifies when and how an OP is developed). To facilitate understanding of the relationships between OP classes and their elements, a meta-model was developed that can be used to support OP standardization. Many open research questions related to OP definition and development are identified based on the survey and classification.</jats:p>