Catálogo de publicaciones - revistas

<jats:p>The emergent context-aware applications in ubiquitous computing demands for obtaining accurate location information of humans or objects in real-time. Indoor location-based services can be delivered through implementing different types of technology, among which is a recent approach that utilizes LED lighting as a medium for Visible Light Communication (VLC). The ongoing development of solid-state lighting (SSL) is resulting in the wide increase of using LED lights and thereby building the ground for a ubiquitous wireless communication network from lighting systems. Considering the recent advances in implementing Visible Light Positioning (VLP) systems, this article presents a review of VLP systems and focuses on the performance evaluation of experimental achievements on location sensing through LED lights. We have outlined the performance evaluation of different prototypes by introducing new performance metrics, their underlying principles, and their notable findings. Furthermore, the study synthesizes the fundamental characteristics of VLC-based positioning systems that need to be considered, presents several technology gaps based on the current state-of-the-art for future research endeavors, and summarizes our lessons learned towards the standardization of the performance evaluation.</jats:p>

<jats:p>In recent years, visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real-world problems such as human-computer interaction, autonomous vehicles, robotics, surveillance, and security just to name a few. In the current study, we review latest trends and advances in the tracking area and evaluate the robustness of different trackers based on the feature extraction methods. The first part of this work includes a comprehensive survey of the recently proposed trackers. We broadly categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs. Each category is further classified into various types based on the architecture and the tracking mechanism. In the second part of this work, we experimentally evaluated 24 recent trackers for robustness and compared handcrafted and deep feature based trackers. We observe that trackers using deep features performed better, though in some cases a fusion of both increased performance significantly. To overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms. We analyze the performance of trackers over 11 different challenges in OTTC and 3 other benchmarks. Our study concludes that Discriminative Correlation Filter (DCF) based trackers perform better than the others. Our study also reveals that inclusion of different types of regularizations over DCF often results in boosted tracking performance. Finally, we sum up our study by pointing out some insights and indicating future trends in the visual object tracking field.</jats:p>

<jats:p>Organizations use diverse types of security solutions to prevent cyber-attacks. Multiple vendors provide security solutions developed using heterogeneous technologies and paradigms. Hence, it is a challenging rather impossible to easily make security solutions to work an integrated fashion. Security orchestration aims at smoothly integrating multivendor security tools that can effectively and efficiently interoperate to support security staff of a Security Operation Centre (SOC). Given the increasing role and importance of security orchestration, there has been an increasing amount of literature on different aspects of security orchestration solutions. However, there has been no effort to systematically review and analyze the reported solutions. We report a Multivocal Literature Review that has systematically selected and reviewed both academic and grey (blogs, web pages, white papers) literature on different aspects of security orchestration published from January 2007 until July 2017. The review has enabled us to provide a working definition of security orchestration and classify the main functionalities of security orchestration into three main areas—unification, orchestration, and automation. We have also identified the core components of a security orchestration platform and categorized the drivers of security orchestration based on technical and socio-technical aspects. We also provide a taxonomy of security orchestration based on the execution environment, automation strategy, deployment type, mode of task and resource type. This review has helped us to reveal several areas of further research and development in security orchestration.</jats:p>

<jats:p>Humans are able to identify other people’s voices even in voice disguise conditions. However, we are not immune to all voice changes when trying to identify people from voice. Likewise, automatic speaker recognition systems can also be deceived by voice imitation and other types of disguise. Taking into account the voice disguise classification into the combination of two different categories (deliberate/non-deliberate and electronic/non-electronic), this survey provides a literature review on the influence of voice disguise in the automatic speaker recognition task and the robustness of these systems to such voice changes. Additionally, the survey addresses existing applications dealing with voice disguise and analyzes some issues for future research.</jats:p>

<jats:p>Testing is one of the most important phases in the development of any product or software. Various types of software testing exist that have to be done to meet the need of the software. Regression testing is one of the crucial phases of testing where testing of a program is done for the original test build along with the modifications. In this article, various studies proposed by the authors have been analysed focusing on test cases generation and their approach toward web application. A detailed study was conducted on Regression Test Case Generation and its approaches toward web application. From our detailed study, we have found that very few approaches and methodologies have been found that provide the real tool for test case generation. There is a need of an automated regression testing tool to generate the regression test cases directly based on user requirements. These test cases have to be generated and implemented by the tool so that the reduction in the overall effort and cost can be achieved. From our study, we have also found that regression testing for web applications was not investigated much, but in today's scenario web applications are an integral part of our daily life and so that needs to be tested for regression testing.</jats:p>

<jats:p>The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.</jats:p>

<jats:p>Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.</jats:p>

<jats:p>Malicious software, a threat users face on a daily basis, have evolved from simple bankers based on social engineering to advanced persistent threats. Recent research and discoveries reveal that malware developers have been using a wide range of anti-analysis and evasion techniques, in-memory attacks, and system subversion, including BIOS and hypervisors. In addition, code-reuse attacks like Returned Oriented Programming emerge as highly potential remote code execution threats. To counteract the broadness of malicious codes, distinct techniques and tools have been proposed, such as transparent malware tracers, system-wide debuggers, live forensics tools, and isolated execution rings. In this work, we present a survey on state-of-the-art techniques that detect, mitigate, and analyze the aforementioned attacks. We show approaches based on Hardware Virtual Machines introspection, System Management Mode instrumentation, Hardware Performance Counters, isolated rings (e.g., Software Guard eXtensions), as well as others based on external hardware. We also discuss upcoming threats based on the very same technologies used for defense. Our main goal is to provide the reader with a broader, more comprehensive understanding of recently surfaced tools and techniques aiming at binary analysis for modern platforms.</jats:p>

<jats:p>Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ontologies, automatic matchers become an inevitable solution. However, the process of matching large ontologies has high space and time complexities. Therefore, for a tool to efficiently and accurately match these large ontologies within the limited computing resources, it must have techniques that can significantly reduce the high space and time complexities associated with the ontology matching process. This article provides a review of the state-of-the-art techniques being applied by ontology matching tools to achieve scalability and produce high-quality mappings when matching large ontologies. In addition, we provide a direct comparison of the techniques to gauge their effectiveness in achieving scalability. A review of the state-of-the-art ontology matching tools that employ each strategy is also provided. We also evaluate the state-of-the-art tools to gauge the progress they have made over the years in improving alignment’s quality and reduction of execution time when matching large ontologies.</jats:p>

<jats:p>Iris recognition is increasingly used in large-scale applications. As a result, presentation attack detection for iris recognition takes on fundamental importance. This survey covers the diverse research literature on this topic. Different categories of presentation attack are described and placed in an application-relevant framework, and the state of the art in detecting each category of attack is summarized. One conclusion from this is that presentation attack detection for iris recognition is not yet a solved problem. Datasets available for research are described, research directions for the near- and medium-term future are outlined, and a short list of recommended readings is suggested.</jats:p>