Catálogo de publicaciones - revistas

<jats:p>Cloud computing has been regarded as an emerging approach to provisioning resources and managing applications. It provides attractive features, such as an on-demand model, scalability enhancement, and management cost reduction. However, cloud computing systems continue to face problems such as hardware failures, overloads caused by unexpected workloads, or the waste of energy due to inefficient resource utilization, which all result in resource shortages and application issues such as delays or saturation. A paradigm, the brownout, has been applied to handle these issues by adaptively activating or deactivating optional parts of applications or services to manage resource usage in cloud computing system. Brownout has successfully shown that it can avoid overloads due to changes in workload and achieve better load balancing and energy saving effects. This article proposes a taxonomy of the brownout approach for managing resources and applications adaptively in cloud computing systems and carries out a comprehensive survey. It identifies open challenges and offers future research directions.</jats:p>

<jats:p> The notion of <jats:italic>L</jats:italic> <jats:sub> <jats:italic>p</jats:italic> </jats:sub> sampling, and corresponding algorithms known as <jats:italic>L</jats:italic> <jats:sub> <jats:italic>p</jats:italic> </jats:sub> samplers, has found a wide range of applications in the design of data stream algorithms and beyond. In this survey, we present some of the core algorithms to achieve this sampling distribution based on ideas from hashing, sampling, and sketching. We give results for the special cases of insertion-only inputs, lower bounds for the sampling problems, and ways to efficiently sample multiple elements. We describe a range of applications of <jats:italic>L</jats:italic> <jats:sub> <jats:italic>p</jats:italic> </jats:sub> sampling, drawing on problems across the domain of computer science, from matrix and graph computations, as well as to geometric and vector streaming problems. </jats:p>

<jats:p>Power management for data centers has been extensively studied in the past 10 years. Most research has focused on owner-operated data centers with less focus on Multi-Tenant Data Centers (MTDC) or colocation data centers. In an MTDC, an operator owns the building and leases out space, power, and cooling to tenants to install their own IT equipment. MTDC’s present new challenges for data center power management due to an inherent lack of coordination between the operator and tenants. In this article, we conduct a comprehensive survey of existing MTDC power management techniques for demand response programs, sustainability, and/or power hierarchy oversubscription. Power oversubscription is of particular interest, as it can maximize resource utilization, increase operator profit, and reduce tenant costs. We create a taxonomy to classify and compare key works. Our taxonomy and review differ from existing works in that our emphasis is on safe power oversubscription, which has been neglected in previous surveys. We propose future research for prediction and control of power overload events in an oversubscribed MTDC.</jats:p>

<jats:p>A brain-computer interface (BCI) provides a way to develop interaction between a brain and a computer. The communication is developed as a result of neural responses generated in the brain because of motor movements or cognitive activities. The means of communication here includes muscular and non-muscular actions. These actions generate brain activities or brain waves that are directed to a hardware device to perform a specific task. BCI initially was developed as the communication device for patients suffering from neuromuscular disorders. Owing to recent advancements in BCI devices—such as passive electrodes, wireless headsets, adaptive software, and decreased costs—it is also being used for developing communication between the general public. The BCI device records brain responses using various invasive and non-invasive acquisition techniques such as electrocorticography (ECoG), electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI). In this article, a survey on these techniques has been provided. The brain response needs to be translated using machine learning and pattern recognition methods to control any application. A brief review of various existing feature extraction techniques and classification algorithms applied on data recorded from the brain has been included in this article. A significant comparative analysis of popular existing BCI techniques is presented and possible future directives are provided.</jats:p>

<jats:p>Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.</jats:p>

<jats:p> Top- <jats:italic>k</jats:italic> nodes are the important actors for a subjectively determined topic in a social network. To some extent, a topic is taken as a ranking criteria for identifying top- <jats:italic>k</jats:italic> nodes. Within a viral marketing network, subjectively selected topics can include the following: Who can promote a new product to the largest number of people, and who are the highest spending customers? Based on these questions, there has been a growing interest in top- <jats:italic>k</jats:italic> nodes research to effectively identify key players. In this article, we review and classify existing literature on top- <jats:italic>k</jats:italic> nodes identification into two major categories: top- <jats:italic>k</jats:italic> influential nodes and top- <jats:italic>k</jats:italic> significant nodes. We survey both theoretical and applied work in the field and describe promising research directions based on our review. This research area has proven to be beneficial for data analysis on online social networks as well as practical applications on real-life networks. </jats:p>

<jats:p>The advent of the Android system has brought smartphone technology to the doorsteps of the masses. The latest technologies have made it affordable for every section of the society. However, the emergence of the Android platform has also escalated the growth of cybercrime through the mobile platform. Its open source operating system has made it a center of attraction for the attackers. This article provides a comprehensive study of the state of the Android Security domain. This article classifies the attacks on the Android system in four categories (i) hardware-based attacks, (ii) kernel-based attacks, (iii) hardware abstraction layer-based attacks, and (iv) application-based attacks. The study deals with various threats and security measures relating to these categories and presents an in-depth analysis of the underlying problems in the Android security domain. The article also stresses the role of Android application developers in realizing a more secure Android environment. This article attempts to provide a comparative analysis of various malware detection techniques concerning their methods and limitations. The study can help researchers gain knowledge of the Android security domain from various aspects and build a more comprehensive, robust, and efficient solution to the threats that Android is facing.</jats:p>

<jats:p>The unabated increase in the complexity of the hardware and software components of modern embedded real-time systems has given momentum to a host of research in the use of probabilistic and statistical techniques for timing analysis. In the last few years, that front of investigation has yielded a body of scientific literature vast enough to warrant some comprehensive taxonomy of motivations, strategies of application, and directions of research. This survey addresses this very need, singling out the principal techniques in the state of the art of timing analysis that employ probabilistic reasoning at some level, building a taxonomy of them, discussing their relative merit and limitations, and the relations among them. In addition to offering a comprehensive foundation to savvy probabilistic timing analysis, this article also identifies the key challenges to be addressed to consolidate the scientific soundness and industrial viability of this emerging field.</jats:p>

<jats:p>Code authorship attribution is the process of identifying the author of a given code. With increasing numbers of malware and advanced mutation techniques, the authors of malware are creating a large number of malware variants. To better deal with this problem, methods for examining the authorship of malicious code are necessary. Code authorship attribution techniques can thus be utilized to identify and categorize the authors of malware. This information can help predict the types of tools and techniques that the author of a specific malware uses, as well as the manner in which the malware spreads and evolves. In this article, we present the first comprehensive review of research on code authorship attribution. The article summarizes various methods of authorship attribution and highlights challenges in the field.</jats:p>

<jats:p>Bayesian (machine) learning has been playing a significant role in machine learning for a long time due to its particular ability to embrace uncertainty, encode prior knowledge, and endow interpretability. On the back of Bayesian learning’s great success, Bayesian nonparametric learning (BNL) has emerged as a force for further advances in this field due to its greater modelling flexibility and representation power. Instead of playing with the fixed-dimensional probabilistic distributions of Bayesian learning, BNL creates a new “game” with infinite-dimensional stochastic processes. BNL has long been recognised as a research subject in statistics, and, to date, several state-of-the-art pilot studies have demonstrated that BNL has a great deal of potential to solve real-world machine-learning tasks. However, despite these promising results, BNL has not created a huge wave in the machine-learning community. Esotericism may account for this. The books and surveys on BNL written by statisticians are overcomplicated and filled with tedious theories and proofs. Each is certainly meaningful but may scare away new researchers, especially those with computer science backgrounds. Hence, the aim of this article is to provide a plain-spoken, yet comprehensive, theoretical survey of BNL in terms that researchers in the machine-learning community can understand. It is hoped this survey will serve as a starting point for understanding and exploiting the benefits of BNL in our current scholarly endeavours. To achieve this goal, we have collated the extant studies in this field and aligned them with the steps of a standard BNL procedure—from selecting the appropriate stochastic processes through manipulation to executing the model inference algorithms. At each step, past efforts have been thoroughly summarised and discussed. In addition, we have reviewed the common methods for implementing BNL in various machine-learning tasks along with its diverse applications in the real world as examples to motivate future studies.</jats:p>