Catálogo de publicaciones - revistas

<jats:p>Pointwise anomaly detection and change detection focus on the study of individual data instances; however, an emerging area of research involves groups or collections of observations. From applications of high-energy particle physics to health care collusion, group deviation detection techniques result in novel research discoveries, mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights. In particular, static group anomaly detection is the process of identifying groups that are not consistent with regular group patterns, while dynamic group change detection assesses significant differences in the state of a group over a period of time. Since both group anomaly detection and group change detection share fundamental ideas, this survey article provides a clearer and deeper understanding of group deviation detection research in static and dynamic situations.</jats:p>

<jats:p>Dynamic and partial reconfiguration are key differentiating capabilities of field programmable gate arrays (FPGAs). While they have been studied extensively in academic literature, they find limited use in deployed systems. We review FPGA reconfiguration, looking at architectures built for the purpose, and the properties of modern commercial architectures. We then investigate design flows and identify the key challenges in making reconfigurable FPGA systems easier to design. Finally, we look at applications where reconfiguration has found use, as well as proposing new areas where this capability places FPGAs in a unique position for adoption.</jats:p>

<jats:p>Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differ from relational data for which computational approaches are developed in the data-mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data-mining community. In this article, we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data-mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data-mining problems in each of these categories.</jats:p>

<jats:p>Articular cartilage (AC) is a flexible and soft yet stiff tissue that can be visualized and interpreted using magnetic resonance (MR) imaging for the assessment of knee osteoarthritis. Segmentation of AC from MR images is a challenging task that has been investigated widely. The development of computational methods to segment AC is highly dependent on various image parameters, quality, tissue structure, and acquisition protocol involved. This review focuses on the challenges faced during AC segmentation from MR images followed by the discussion on computational methods for semi/fully automated approaches, whilst performances parameters and their significances have also been explored. Furthermore, hybrid approaches used to segment AC are reviewed. This review indicates that despite the challenges in AC segmentation, the semi-automated method utilizing advanced computational methods such as active contour and clustering have shown significant accuracy. Fully automated AC segmentation methods have obtained moderate accuracy and show suitability for extensive clinical studies whilst advanced methods are being investigated that have led to achieving significantly better sensitivity. In conclusion, this review indicates that research in AC segmentation from MR images is moving towards the development of fully automated methods using advanced multi-level, multi-data, and multi-approach techniques to provide assistance in clinical studies.</jats:p>

<jats:p>Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. However, the efficiency of an IDS depends primarily on both its configuration and its precision. The large amount of network traffic that needs to be analyzed, in addition to the increase in attacks’ sophistication, renders the optimization of intrusion detection an important requirement for infrastructure security, and a very active research subject. In the state of the art, a number of approaches have been proposed to improve the efficiency of intrusion detection and response systems. In this article, we review the works relying on decision-making techniques focused on game theory and Markov decision processes to analyze the interactions between the attacker and the defender, and classify them according to the type of the optimization problem they address. While these works provide valuable insights for decision-making, we discuss the limitations of these solutions as a whole, in particular regarding the hypotheses in the models and the validation methods. We also propose future research directions to improve the integration of game-theoretic approaches into IDS optimization techniques.</jats:p>

<jats:p>Petri nets slicing is a technique that aims to improve the verification of systems modeled in Petri nets. Petri nets slicing was first developed to facilitate debugging, but then was used for the alleviation of the state space explosion problem for the model checking of Petri nets. In this article, different slicing techniques are studied along with their algorithms introducing: (i) a classification of Petri nets slicing algorithms based on their construction methodology and objective (such as improving state space analysis or testing); (ii) a qualitative and quantitative discussion and comparison of major differences such as accuracy and efficiency: (iii) a syntactic unification of slicing algorithms that improve state space analysis for easy and clear understanding; and (iv) applications of slicing for multiple perspectives. Furthermore, some recent improvements to slicing algorithms are presented, which can certainly reduce the slice size even for strongly connected nets. A noteworthy use of this survey is for the selection and improvement of slicing techniques for optimizing the verification of state event models.</jats:p>

<jats:p>Complex networks are ubiquitous to several computer science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore is a robust, effective alternative in real-world applications. The methodology and proposed machine-learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.</jats:p>

<jats:p>The cloud-computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT)--based applications, the use of cloud services is increasing exponentially. The next generation of cloud computing must be energy efficient and sustainable to fulfill end-user requirements, which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The use of a large number of cloud datacenters increases cost as well as carbon footprints, which further affects the sustainability of cloud services. In this article, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. The current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy, and waste heat utilization. The existing techniques have been compared and categorized based on common characteristics and properties. A conceptual model for sustainable cloud computing has been presented along with a discussion on future research directions.</jats:p>

<jats:p>Distance-bounding protocols allow a verifier to both authenticate a prover and evaluate whether the latter is located in his vicinity. These protocols are of particular interest in contactless systems, e.g., electronic payment or access control systems, which are vulnerable to distance-based frauds. This survey analyzes and compares in a unified manner many existing distance-bounding protocols with respect to several key security and complexity features.</jats:p>

<jats:p>Developing applications targeting mobile devices is a complex task involving numerous options, technologies, and trade-offs, mostly due to the proliferation and fragmentation of devices and platforms. As a result of this, cross-platform app development has enjoyed the attention of practitioners and academia for the previous decade. Throughout this review, we assess the academic body of knowledge and report on the state of research on the field. We do so with a particular emphasis on core concepts, including those of user experience, device features, performance, and security. Our findings illustrate that the state of research demand for empirical verification of an array of unbacked claims, and that a particular focus on qualitative user-oriented research is essential. Through our outlined taxonomy and state of research overview, we identify research gaps and challenges, and provide numerous suggestions for further research.</jats:p>