Catálogo de publicaciones - revistas

<jats:p>The dynamic nature of the cloud environment has made the distributed resource management process a challenge for cloud service providers. The importance of maintaining quality of service in accordance with customer expectations and the highly dynamic nature of cloud-hosted applications add new levels of complexity to the process. Advances in big-data learning approaches have shifted conventional static capacity planning solutions to complex performance-aware resource management methods. It is shown that the process of decision-making for resource adjustment is closely related to the behavior of the system, including the utilization of resources and application components. Therefore, a continuous monitoring of system attributes and performance metrics provides the raw data for the analysis of problems affecting the performance of the application. Data analytic methods, such as statistical and machine-learning approaches, offer the required concepts, models, and tools to dig into the data and find general rules, patterns, and characteristics that define the functionality of the system. Obtained knowledge from the data analysis process helps to determine the changes in the workloads, faulty components, or problems that can cause system performance to degrade. A timely reaction to performance degradation can avoid violations of service level agreements, including performing proper corrective actions such as auto-scaling or other resource adjustment solutions. In this article, we investigate the main requirements and limitations of cloud resource management, including a study of the approaches to workload and anomaly analysis in the context of performance management in the cloud. A taxonomy of the works on this problem is presented that identifies main approaches in existing research from the data analysis side to resource adjustment techniques. Finally, considering the observed gaps in the general direction of the reviewed works, a list of these gaps is proposed for future researchers to pursue.</jats:p>

<jats:p>Color is a powerful communication component, not only as part of the message meaning but also as a way of discriminating contents therein. However, 5% of the world’s population suffers from color vision deficiency (CVD), commonly known as colorblindness. This handicap adulterates the way the color is perceived, compromising the reading and understanding of the message contents. This issue becomes even more pertinent due to the increasing availability of multimedia contents in computational environments (e.g., web browsers). Aware of this problem, a significant number of CVD research works came up in the literature in the past two decades to improve color perception in text documents, still images, video, and so forth. This survey mainly addresses recoloring algorithms toward still images for colorblind people, including the current trends in the field of color adaptation.</jats:p>

<jats:p>Fairness assumptions are a valuable tool when reasoning about systems. In this article, we classify several fairness properties found in the literature and argue that most of them are too restrictive for many applications. As an alternative, we introduce the concept of justness.</jats:p>

<jats:p>Phase Change Memory (PCM) is an emerging memory technology that has the capability to address the growing demand for memory capacity and bridge the gap between the main memory and the secondary storage. As a resistive memory, PCM is able to store data based on its resistance values. The wide resistance range of PCM makes it possible to store even multiple bits per cell (MLC) rather than a single bit per cell (SLC). Unfortunately, PCM cells suffer from short lifetime. That means PCM cells could tolerate a limited number of write operations, and afterward they tend to permanently stick at a constant value.</jats:p> <jats:p>Limited lifetime is an issue related to PCM memory; hence, in recent years, many studies have been conducted to prolong PCM lifetime. These schemes have vast variety and are applied at different architectural levels. In this survey, we review the important works of such schemes to give insights to those starting to research on non-volatile memories (NVMs). These schemes are not limited to PCM and are applicable on other NVM technologies due to the similarities between them and the generality of lifetime-prolonging schemes.</jats:p>

<jats:p>Gait is a biometric trait that can allow user authentication, though it is classified as a “soft” one due to a certain lack in permanence and to sensibility to specific conditions. The earliest research relies on computer vision, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, has spurred a different research line. In fact, they are able to capture the dynamics of the walking pattern through simpler one-dimensional signals. This capture modality can avoid some problems related to computer vision-based techniques but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, many factors - the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques - contribute to making this biometrics attractive and suggest continuing investigating. This survey provides interested readers with a reasoned and systematic overview of problems, approaches, and available benchmarks.</jats:p>

<jats:p>Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning.</jats:p>

<jats:p>The number of applications being developed that require access to knowledge about the real world has increased rapidly over the past two decades. Domain ontologies, which formalize the terms being used in a discipline, have become essential for research in areas such as Machine Learning, the Internet of Things, Robotics, and Natural Language Processing, because they enable separate systems to exchange information. The quality of these domain ontologies, however, must be ensured for meaningful communication. Assessing the quality of domain ontologies for their suitability to potential applications remains difficult, even though a variety of frameworks and metrics have been developed for doing so. This article reviews domain ontology assessment efforts to highlight the work that has been carried out and to clarify the important issues that remain. These assessment efforts are classified into five distinct evaluation approaches and the state of the art of each described. Challenges associated with domain ontology assessment are outlined and recommendations are made for future research and applications.</jats:p>

<jats:p>Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web’s real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have?</jats:p> <jats:p>In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools’ degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented.</jats:p>

<jats:p>Understanding people’s expertise is not a trivial task since it is time-consuming when manually executed. Automated approaches have become a topic of research in recent years in various scientific fields, such as information retrieval, databases, and machine learning. This article carries out a survey on automated expertise retrieval, i.e., finding data linked to a person that describes the person’s expertise, which allows tasks such as profiling or finding people with a certain expertise. A faceted taxonomy is introduced that covers many of the existing approaches and classifies them on the basis of features chosen from studying the state-of-the-art. A list of open issues, with suggestions for future research topics, is introduced as well. It is hoped that our taxonomy and review of related works on expertise retrieval will be useful when analyzing different proposals and will allow a better understanding of existing work and a systematic classification of future work on the topic.</jats:p>

<jats:p>The recent advances in DNA sequencing technology, from first-generation sequencing (FGS) to third-generation sequencing (TGS), have constantly transformed the genome research landscape. Its data throughput is unprecedented and severalfold as compared with past technologies. DNA sequencing technologies generate sequencing data that are big, sparse, and heterogeneous. This results in the rapid development of various data protocols and bioinformatics tools for handling sequencing data.</jats:p> <jats:p>In this review, a historical snapshot of DNA sequencing is taken with an emphasis on data manipulation and tools. The technological history of DNA sequencing is described and reviewed in thorough detail. To manipulate the sequencing data generated, different data protocols are introduced and reviewed. In particular, data compression methods are highlighted and discussed to provide readers a practical perspective in the real-world setting. A large variety of bioinformatics tools are also reviewed to help readers extract the most from their sequencing data in different aspects, such as sequencing quality control, genomic visualization, single-nucleotide variant calling, INDEL calling, structural variation calling, and integrative analysis. Toward the end of the article, we critically discuss the existing DNA sequencing technologies for their pitfalls and potential solutions.</jats:p>