Catálogo de publicaciones - revistas

<jats:p>Power and energy efficiency are now critical concerns in extreme-scale high-performance scientific computing. Many extreme-scale computing systems today (for example: Top500) have tight integration of multicore CPU processors and accelerators (mix of Graphical Processing Units, Intel Xeon Phis, or Field Programmable Gate Arrays) empowering them to provide not just unprecedented computational power but also to address these concerns. However, such integration renders these systems highly heterogeneous and hierarchical, thereby necessitating design of novel performance, power, and energy models to accurately capture these inherent characteristics.</jats:p> <jats:p>There are now several extensive research efforts focusing exclusively on power and energy efficiency models and techniques for the processors composing these extreme-scale computing systems. This article synthesizes these research efforts with absolute concentration on predictive power and energy models and prime emphasis on node architecture. Through this survey, we also intend to highlight the shortcomings of these models to correctly and comprehensively predict the power and energy consumptions by taking into account the hierarchical and heterogeneous nature of these tightly integrated high-performance computing systems.</jats:p>

<jats:p> This note corrects a technical error in the <jats:italic>ACM Computing Surveys</jats:italic> article mentioned in the title. The flaw involved constructions for showing that timed automata with urgent locations have the same expressiveness as timed automata that allow false location invariants. Corrected constructions are presented in this note, and the affected results are reproved. </jats:p>

<jats:p>Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, automatic sarcasm detection has witnessed great interest from the sentiment analysis community. This article is a compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and incorporation of context beyond target text. In this article, we describe datasets, approaches, trends, and issues in sarcasm detection. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. In terms of resources to understand the state-of-the-art, the survey presents several useful illustrations—most prominently, a table that summarizes past papers along different dimensions such as the types of features, annotation techniques, and datasets used.</jats:p>

<jats:p>The task of quantification consists in providing an aggregate estimation (e.g., the class distribution in a classification problem) for unseen test sets, applying a model that is trained using a training set with a different data distribution. Several real-world applications demand this kind of method that does not require predictions for individual examples and just focuses on obtaining accurate estimates at an aggregate level. During the past few years, several quantification methods have been proposed from different perspectives and with different goals. This article presents a unified review of the main approaches with the aim of serving as an introductory tutorial for newcomers in the field.</jats:p>

<jats:p>Cybercriminal activity has exploded in the past decade, with diverse threats ranging from phishing attacks to botnets and drive-by-downloads afflicting millions of computers worldwide. In response, a volunteer defense has emerged, led by security companies, infrastructure operators, and vigilantes. This reactionary force does not concern itself with making proactive upgrades to the cyber infrastructure. Instead, it operates on the front lines by remediating infections as they appear. We construct a model of the abuse reporting infrastructure in order to explain how voluntary action against cybercrime functions today, in hopes of improving our understanding of what works and how to make remediation more effective in the future. We examine the incentives to participate among data contributors, affected resource owners, and intermediaries. Finally, we present a series of key attributes that differ among voluntary actions to investigate further through experimentation, pointing toward a research agenda that could establish causality between interventions and outcomes.</jats:p>

<jats:p>Networks have become increasingly important to model complex systems composed of interacting elements. Network data mining has a large number of applications in many disciplines including protein-protein interaction networks, social networks, transportation networks, and telecommunication networks. Different empirical studies have shown that it is possible to predict new relationships between elements attending to the topology of the network and the properties of its elements. The problem of predicting new relationships in networks is called link prediction. Link prediction aims to infer the behavior of the network link formation process by predicting missed or future relationships based on currently observed connections. It has become an attractive area of study since it allows us to predict how networks will evolve. In this survey, we will review the general-purpose techniques at the heart of the link prediction problem, which can be complemented by domain-specific heuristic methods in practice.</jats:p>

<jats:p> Security metrics have received significant attention. However, they have not been systematically explored based on the understanding of attack-defense interactions, which are affected by various factors, including the degree of system vulnerabilities, the power of system defense mechanisms, attack (or threat) severity, and situations a system at risk faces. This survey particularly focuses on how a system security state can evolve as an outcome of cyber attack-defense interactions. This survey concerns how to measure system-level security by proposing a security metrics framework based on the following four sub-metrics: (1) metrics of <jats:italic>system vulnerabilities</jats:italic> , (2) metrics of <jats:italic>defense power</jats:italic> , (3) metrics of <jats:italic>attack or threat severity</jats:italic> , and (4) metrics of <jats:italic>situations</jats:italic> . To investigate the relationships among these four sub-metrics, we propose a hierarchical ontology with four sub-ontologies corresponding to the four sub-metrics and discuss how they are related to each other. Using the four sub-metrics, we discuss the state-of-art existing security metrics and their advantages and disadvantages (or limitations) to obtain lessons and insight in order to achieve an ideal goal in developing security metrics. Finally, we discuss open research questions in the security metrics research domain and we suggest key factors to enhance security metrics from a system security perspective. </jats:p>

<jats:p>A survey was conducted to provide a state of the art of online banking authentication and communications security implementations. Between global regions the applied (single or multifactor) authentication schemes differ greatly, as well as the security of SSL/TLS implementations. Three phases for online banking development are identified. It is predicted that mobile banking will enter a third phase, characterized by the use of standard web technologies to develop mobile banking applications for different platforms. This has the potential to make mobile banking a target for attacks in a similar manner that home banking currently is.</jats:p>

<jats:p>Data deduplication has attracted many cloud service providers (CSPs) as a way to reduce storage costs. Even though the general deduplication approach has been increasingly accepted, it comes with many security and privacy problems due to the outsourced data delivery models of cloud storage. To deal with specific security and privacy issues, secure deduplication techniques have been proposed for cloud data, leading to a diverse range of solutions and trade-offs. Hence, in this article, we discuss ongoing research on secure deduplication for cloud data in consideration of the attack scenarios exploited most widely in cloud storage. On the basis of classification of deduplication system, we explore security risks and attack scenarios from both inside and outside adversaries. We then describe state-of-the-art secure deduplication techniques for each approach that deal with different security issues under specific or combined threat models, which include both cryptographic and protocol solutions. We discuss and compare each scheme in terms of security and efficiency specific to different security goals. Finally, we identify and discuss unresolved issues and further research challenges for secure deduplication in cloud storage.</jats:p>

<jats:p>Shared memory architectures have recently experienced a large increase in thread-level parallelism, leading to complex memory hierarchies with multiple cache memory levels and memory controllers. These new designs created a Non-Uniform Memory Access (NUMA) behavior, where the performance and energy consumption of memory accesses depend on the place where the data is located in the memory hierarchy. Accesses to local caches or memory controllers are generally more efficient than accesses to remote ones. A common way to improve the locality and balance of memory accesses is to determine the mapping of threads to cores and data to memory controllers based on the affinity between threads and data. Such mapping techniques can operate at different hardware and software levels, which impacts their complexity, applicability, and the resulting performance and energy consumption gains. In this article, we introduce a taxonomy to classify different mapping mechanisms and provide a comprehensive overview of existing solutions.</jats:p>