Catálogo de publicaciones - revistas

<jats:p>Vehicular network technology is frequently used to provide several services and applications for drivers on road networks. The proposed applications in the environment of road networks are classified into three main categories based on their functions: safety, traffic efficiency, and entertainment. The traffic efficiency services are designed to enhance the moving fluency and smoothness of traveling vehicles over the road network. The grid layout architecture of the downtown areas provides several routes toward any targeted destination. Moreover, since several conflicted traffic flows compete at the road intersections, many vehicles have to stop and wait for safe situations to pass the road intersection without coming into conflict with other vehicles. The traffic efficiency applications in this scenario are designed to select the most efficient path for vehicles traveling toward their targeted destination/destinations. Moreover, other applications aimed to decrease the queuing delay time for vehicles at road intersections. In this article, we review several recently proposed mechanisms that worked to enhance the fluency of traffic over downtown road networks and point to the expected future trends in this field.</jats:p>

<jats:p>The increasing availability of vehicular trajectory data is at the core of smart mobility solutions. Such data offer us unprecedented information for the development of trajectory data mining-based applications. An essential task of trajectory analysis is the employment of efficient and accurate methods to compare trajectories. This work presents a systematic survey of vehicular trajectory similarity measures and provides a panorama of the research field. First, we show an overview of vehicle trajectory data, including the models and some preprocessing techniques. Then, we give a comprehensive review of methods to compare trajectories and their intrinsic properties. We classify the methods according to the trajectory representation and features such as metricity, computational complexity, and robustness to noise and local time shift. Last, we discuss the applications of vehicular trajectory similarity measures and some open research problems.</jats:p>

<jats:p>Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.</jats:p>

<jats:p>We perform a comprehensive analysis and comparison of 14 web single sign-on (SSO) systems proposed and/or deployed over the past decade, including federated identity and credential/password management schemes. We identify common design properties and use them to develop a taxonomy for SSO schemes, highlighting the associated tradeoffs in benefits (positive attributes) offered. We develop a framework to evaluate the schemes, in which we identify 14 security, usability, deployability, and privacy benefits. We also discuss how differences in priorities between users, service providers, and identity providers impact the design and deployment of SSO schemes.</jats:p>

<jats:p> The <jats:italic>predecessor</jats:italic> problem is a key component of the fundamental sorting-and-searching core of algorithmic problems. While binary search is the optimal solution in the comparison model, more realistic machine models on integer sets open the door to a rich universe of data structures, algorithms, and lower bounds. In this article, we review the evolution of the solutions to the predecessor problem, focusing on the important algorithmic ideas, from the famous data structure of van Emde Boas to the optimal results of Patrascu and Thorup. We also consider lower bounds, variants, and special cases, as well as the remaining open questions. </jats:p>

<jats:p>Human Energy Expenditure (EE) is a valuable tool for measuring physical activity and its impact on our body in an objective way. To accurately measure the EE, there are methods such as doubly labeled water and direct and indirect calorimetry, but their cost and practical limitations make them suitable only for research and professional sports. This situation, combined with the proliferation of commercial activity monitors, has stimulated the research of EE estimation (EEE) using machine learning on multimodal data from wearable devices. The article provides an overview of existing work in this evolving field, categorizes it, and makes publicly available an EEE dataset. Such a dataset is one of the most valuable resources for the development of the field but is generally not provided by researchers due to the high cost of collection. Finally, the article highlights best practices and promising future direction for designing EEE methods.</jats:p>

<jats:p>Over the past few years, the next generation of vehicular networks is envisioned to play an essential part in autonomous driving, traffic management, and infotainment applications. The next generation of intelligent vehicular networks enabled by 5G systems will integrate various heterogeneous wireless techniques to enable time-sensitive services with guaranteed quality of service and ultimate bandwidth usage. However, to allow the dense diversity of wireless technologies, seamless and reliable wireless communication protocols need to be thoroughly investigated in vehicular networks environment. Henceforth, efficient mobility management protocols that mitigate the challenges of vehicles’ mobility is essential to support massive data loads throughout various applications. In this article, we review different mobility management protocols and their ability to address issues related to 5G-enabled vehicular networks within the related works. First, we provide a broad view of existing models of vehicular networks and their applicability to the next generation of wireless networks. Next, we propose a classification of several vehicular network models that suit the 5G wireless network, followed by a thorough discussion of the mobility management challenges in each of these network models that need to be addressed and then discuss each of their benefits and drawbacks accordingly.</jats:p>

<jats:p>Cryptographic hash functions are widely used primitives with a purpose to ensure the integrity of data. Hash functions are also utilized in conjunction with digital signatures to provide authentication and non-repudiation services. The SHA has been developed over time by the National Institute of Standards and Technology for security, optimal performance, and robustness. The best-known hash standards are SHA-1, SHA-2, and SHA-3. Security is the most notable criterion for evaluating the hash functions. However, the hardware performance of an algorithm serves as a tiebreaker among the contestants when all other parameters (security, software performance, and flexibility) have equal strength. Field Programmable Gateway Array (FPGA) is a reconfigurable hardware that supports a variety of design options, making it the best choice for implementing the hash standards. In this survey, particular attention is devoted to the FPGA optimization techniques for the three hash standards. The study covers several types of optimization techniques and their contributions to the performance of FPGAs. Moreover, the article highlights the strengths and weaknesses of each of the optimization methods and their influence on performance. We are optimistic that the study will be a useful resource encompassing the efforts carried out on the SHAs and FPGA optimization techniques in a consolidated form.</jats:p>

<jats:p>Trust evaluation is the process of quantifying trust with attributes that influence trust. It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation. In order to overcome these problems and intelligently and automatically evaluate trust, machine learning has been applied into trust evaluation. Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic. In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning. Then, we justify a number of requirements that a sound trust evaluation method should satisfy, and propose them as evaluation criteria to assess the performance of trust evaluation methods. Furthermore, we systematically organize existing methods according to application scenarios and provide a comprehensive literature review on trust evaluation from the perspective of machine learning’s function in trust evaluation and evaluation granularity. Finally, according to the completed review and evaluation, we explore some open research problems and suggest the directions that are worth our research effort in the future.</jats:p>

<jats:p>Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this article, we present a comprehensive review of recent advances in causality-based feature selection. To facilitate the development of new algorithms in the research area and make it easy for the comparisons between new methods and existing ones, we develop the first open-source package, called CausalFS, which consists of most of the representative causality-based feature selection algorithms (available at https://github.com/kuiy/CausalFS). Using CausalFS, we conduct extensive experiments to compare the representative algorithms with both synthetic and real-world datasets. Finally, we discuss some challenging problems to be tackled in future research.</jats:p>