Catálogo de publicaciones - revistas

<jats:p>Adaptive user interfaces (UIs) were introduced to address some of the usability problems that plague many software applications. Model-driven engineering formed the basis for most of the systems targeting the development of such UIs. An overview of these systems is presented and a set of criteria is established to evaluate the strengths and shortcomings of the state of the art, which is categorized under architectures, techniques, and tools. A summary of the evaluation is presented in tables that visually illustrate the fulfillment of each criterion by each system. The evaluation identified several gaps in the existing art and highlighted the areas of promising improvement.</jats:p>

<jats:p>Evolutionary network analysis has found an increasing interest in the literature because of the importance of different kinds of dynamic social networks, email networks, biological networks, and social streams. When a network evolves, the results of data mining algorithms such as community detection need to be correspondingly updated. Furthermore, the specific kinds of changes to the structure of the network, such as the impact on community structure or the impact on network structural parameters, such as node degrees, also needs to be analyzed. Some dynamic networks have a much faster rate of edge arrival and are referred to as network streams or graph streams. The analysis of such networks is especially challenging, because it needs to be performed with an online approach, under the one-pass constraint of data streams. The incorporation of content can add further complexity to the evolution analysis process. This survey provides an overview of the vast literature on graph evolution analysis and the numerous applications that arise in different contexts.</jats:p>

<jats:p>Wireless sensor networks (WSNs) provide a convenient way to monitor the physical environment. They consist of a large number of sensors that have sensing, computing, and communication abilities. In the past, sensors were considered as static, but the network functionality would degrade when some sensors were broken. Today, the emerging hardware techniques have promoted the development of mobile sensors. Introducing mobility to sensors not only improves their capability but also gives them flexibility to deal with node failure. The article studies the research progress of mobile sensor networks, which embraces both system hardware and dispatch software. For system hardware, we review two popular types of mobile sensor platforms. One is to integrate mobile robots with sensors, whereas the other is to use existing conveyances to carry sensors. Dispatch software includes two topics. We first address how to solve different coverage problems by using a purely mobile WSN and then investigate how to dispatch mobile sensors in a hybrid WSN to perform various missions including data collection, faulty recovery, and event analysis. A discussion about research challenges in mobile sensor networks is also presented in the article.</jats:p>

<jats:p> In recent years, reduction of energy consumption in buildings has increasingly gained interest among researchers mainly due to practical reasons, such as economic advantages and long-term environmental sustainability. Many solutions have been proposed in the literature to address this important issue from complementary perspectives, which are often hard to capture in a comprehensive manner. This survey article aims at providing a structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established <jats:italic>smart home</jats:italic> vision, in favor of the novel Ambient Intelligence paradigm. Our exposition will cover the main architectural components of such systems, beginning with the basic sensory infrastructure, moving on to the data processing engine where energy-saving strategies may be enacted, to the user interaction interface subsystem, and finally to the actuation infrastructure necessary to transfer the planned modifications to the environment. For each component, we will analyze different solutions, and we will provide qualitative comparisons, also highlighting the impact that a single design choice can have on the rest of the system. </jats:p>

<jats:p> In a wireless world, users can establish ad hoc virtual connections between devices that are unhampered by cables. This process is known as <jats:italic>spontaneous device association</jats:italic> . A wide range of interactive protocols and techniques have been demonstrated in both research and practice, predominantly with a focus on security aspects. In this article, we survey spontaneous device association with respect to the user interaction it involves. We use a novel taxonomy to structure the survey with respect to the different conceptual models and types of user action employed for device association. Within this framework, we provide an in-depth survey of existing techniques discussing their individual characteristics, benefits, and issues. </jats:p>

<jats:p>Languages that combine aspects of probabilistic representations with aspects of first-order logic are referred to as first-order probabilistic languages (FOPLs). FOPLs can be divided into three categories: rule-based, procedural-based and entity-relation--based languages. This article presents a survey of directed entity-relation--based FOPLs and their associated model construction and inference algorithms.</jats:p>

<jats:p>Visual sensor networks (VSNs) are receiving a lot of attention in research, and at the same time, commercial applications are starting to emerge. VSN devices come with image sensors, adequate processing power, and memory. They use wireless communication interfaces to collaborate and jointly solve tasks such as tracking persons within the network. VSNs are expected to replace not only many traditional, closed-circuit surveillance systems but also to enable emerging applications in scenarios such as elderly care, home monitoring, or entertainment. In all of these applications, VSNs monitor a potentially large group of people and record sensitive image data that might contain identities of persons, their behavior, interaction patterns, or personal preferences. These intimate details can be easily abused, for example, to derive personal profiles.</jats:p> <jats:p>The highly sensitive nature of images makes security and privacy in VSNs even more important than in most other sensor and data networks. However, the direct use of security techniques developed for related domains might be misleading due to the different requirements and design challenges. This is especially true for aspects such as data confidentiality and privacy protection against insiders, generating awareness among monitored people, and giving trustworthy feedback about recorded personal data—all of these aspects go beyond what is typically required in other applications.</jats:p> <jats:p>In this survey, we present an overview of the characteristics of VSN applications, the involved security threats and attack scenarios, and the major security challenges. A central contribution of this survey is our classification of VSN security aspects into data-centric, node-centric, network-centric, and user-centric security. We identify and discuss the individual security requirements and present a profound overview of related work for each class. We then discuss privacy protection techniques and identify recent trends in VSN security and privacy. A discussion of open research issues concludes this survey.</jats:p>

<jats:p> We have had to wait over 30 years since the naive Bayes model was first introduced in 1960 for the so-called Bayesian network classifiers to resurge. Based on Bayesian networks, these classifiers have many strengths, like model interpretability, accommodation to complex data and classification problem settings, existence of efficient algorithms for learning and classification tasks, and successful applicability in real-world problems. In this article, we survey the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Bayes, seminaive Bayes, one-dependence Bayesian classifiers, <jats:italic>k</jats:italic> -dependence Bayesian classifiers, Bayesian network-augmented naive Bayes, Markov blanket-based Bayesian classifier, unrestricted Bayesian classifiers, and Bayesian multinets. Issues of feature subset selection and generative and discriminative structure and parameter learning are also covered. </jats:p>

<jats:p>Maps depict natural and human-induced changes on earth at a fine resolution for large areas and over long periods of time. In addition, maps—especially historical maps—are often the only information source about the earth as surveyed using geodetic techniques. In order to preserve these unique documents, increasing numbers of digital map archives have been established, driven by advances in software and hardware technologies. Since the early 1980s, researchers from a variety of disciplines, including computer science and geography, have been working on computational methods for the extraction and recognition of geographic features from archived images of maps (digital map processing). The typical result from map processing is geographic information that can be used in spatial and spatiotemporal analyses in a Geographic Information System environment, which benefits numerous research fields in the spatial, social, environmental, and health sciences. However, map processing literature is spread across a broad range of disciplines in which maps are included as a special type of image. This article presents an overview of existing map processing techniques, with the goal of bringing together the past and current research efforts in this interdisciplinary field, to characterize the advances that have been made, and to identify future research directions and opportunities.</jats:p>

<jats:p>Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses.</jats:p>