Catálogo de publicaciones - revistas

<jats:p>With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.</jats:p>

<jats:p>An increased number of bio-inspired face recognition systems have emerged in recent decades owing to their intelligent problem-solving ability, flexibility, scalability, and adaptive nature. Hence, this survey aims to present a detailed overview of bio-inspired approaches pertaining to the advancement of face recognition. Based on a well-classified taxonomy, relevant bio-inspired techniques and their merits and demerits in countering potential problems vital to face recognition are analyzed. A synthesis of various approaches in terms of key governing principles and their associated performance analysis are systematically portrayed. Finally, some intuitive future directions are suggested on how bio-inspired approaches can contribute to the advancement of face biometrics in the years to come.</jats:p>

<jats:p>When designing computer monitoring systems, one goal has always been to have a complete view of the monitored target and at the same time stealthily protect the monitor itself. One way to achieve this is to use hypervisor-based, or more generally out of virtual machine (VM)-based, monitoring. There are, however, challenges that limit the use of this mechanism; the most significant of these is the semantic gap problem. Over the past decade, a considerable amount of research has been carried out to bridge the semantic gap and develop all kinds of out-of-VM monitoring techniques and applications. By tracing the evolution of out-of-VM security solutions, this article examines and classifies different approaches that have been proposed to overcome the semantic gap—the fundamental challenge in hypervisor-based monitoring—and how they have been used to develop various security applications. In particular, we review how the past approaches address different constraints, such as practicality, flexibility, coverage, and automation, while bridging the semantic gap; how they have developed different monitoring systems; and how the monitoring systems have been applied and deployed. In addition to systematizing all of the proposed techniques, we also discuss the remaining research problems and shed light on the future directions of hypervisor-based monitoring.</jats:p>

<jats:p>Digital video enables manifold ways of multimedia content interaction. Over the last decade, many proposals for improving and enhancing video content interaction were published. More recent work particularly leverages on highly capable devices such as smartphones and tablets that embrace novel interaction paradigms, for example, touch, gesture-based or physical content interaction. In this article, we survey literature at the intersection of Human-Computer Interaction and Multimedia. We integrate literature from video browsing and navigation, direct video manipulation, video content visualization, as well as interactive video summarization and interactive video retrieval. We classify the reviewed works by the underlying interaction method and discuss the achieved improvements so far. We also depict a set of open problems that the video interaction community should address in future.</jats:p>

<jats:p>Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and environmental impact. Therefore, cloud providers must optimize the use of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This article surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, and pointing out areas that need further research.</jats:p>

<jats:p>In order to meet stringent performance requirements, system administrators must effectively detect undesirable performance behaviours, identify potential root causes, and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in different system and application domains is well studied. In order to assess progress, research trends, and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods.</jats:p>

<jats:p>Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.</jats:p>

<jats:p>Networking at any time and any place paves the way for a large number of possible applications in ad hoc networks, from disaster relief in remote areas to network extension. Thus, for the past decades, many works have been proposed trying to make ad hoc networks a reality. The importance of broadcasting in networking and the broadcast nature of the wireless medium have encouraged researchers to join their efforts on designing efficient dissemination algorithms for Mobile Ad Hoc Networks (MANETs). The many different challenges that MANETs face, such as limited network resources, network partitions, or energy restrictions, gave rise to many different approaches to overcome one or more of those problems. Therefore, literature reveals a huge variety of techniques that have been proposed for efficient message dissemination. In this article, we make an in-depth review of the existing state-of-the-art techniques, as well as propose a new taxonomy that provides a global overview of the most relevant existing algorithms.</jats:p>

<jats:p>Autonomous agents that operate as components of dynamic spatial systems are becoming increasingly popular and mainstream. Applications can be found in consumer robotics, in road, rail, and air transportation, manufacturing, and military operations. Unfortunately, the approaches to modeling and analyzing the behavior of dynamic spatial systems are just as diverse as these application domains. In this article, we discuss reasoning approaches for the medium-term control of autonomous agents in dynamic spatial systems, which requires a sufficiently detailed description of the agent’s behavior and environment but may still be conducted in a qualitative manner. We survey logic-based qualitative and hybrid modeling and commonsense reasoning approaches with respect to their features for describing and analyzing dynamic spatial systems in general, and the actions of autonomous agents operating therein in particular. We introduce a conceptual reference model, which summarizes the current understanding of the characteristics of dynamic spatial systems based on a catalog of evaluation criteria derived from the model. We assess the modeling features provided by logic-based qualitative commonsense and hybrid approaches for projection, planning, simulation, and verification of dynamic spatial systems. We provide a comparative summary of the modeling features, discuss lessons learned, and introduce a research roadmap for integrating different approaches of dynamic spatial system analysis to achieve coverage of all required features.</jats:p>

<jats:p>The evaluation of computer intrusion detection systems (which we refer to as intrusion detection systems) is an active research area. In this article, we survey and systematize common practices in the area of evaluation of such systems. For this purpose, we define a design space structured into three parts: workload, metrics, and measurement methodology. We then provide an overview of the common practices in evaluation of intrusion detection systems by surveying evaluation approaches and methods related to each part of the design space. Finally, we discuss open issues and challenges focusing on evaluation methodologies for novel intrusion detection systems.</jats:p>