Catálogo de publicaciones - revistas

<jats:p>The latest techniques in video motion magnification and relevant small motion analysis are surveyed. The main motion magnification techniques are discussed in chronological fashion, highlighting the inherent limitations of predecessor techniques in comparison with subsequent variants. The focus is then shifted to the specific stages within the motion magnification framework to discuss advancements that have been proposed in the literature, namely for spatial decomposition and for emphasizing, representing, and distinguishing different motion signals. The survey concludes with a treatment of different problems in varying application contexts that have benefited from motion magnification and small motion analysis.</jats:p>

<jats:p>The fireworks algorithm, which is inspired from the phenomenon of fireworks explosion, is a special kind of swarm intelligence algorithm proposed in 2010. Since then, it has been attracting more and more research interest and has been widely employed in many real-world problems due to its unique search manner and high efficiency. In this article, we present a comprehensive review of its advances and applications. We begin with an introduction to the original fireworks algorithm. Then we review its algorithmic research work for single objective and multi-objective optimization problems. After that, we present the theoretical analyses of the fireworks algorithm. Finally, we give a brief overview of its applications and implementations. Hopefully, this article could provide a useful road map for researchers and practitioners who are interested in this algorithm and inspire new ideas for its further development.</jats:p>

<jats:p>The cyber world is plagued with ever-evolving malware that readily infiltrate all defense mechanisms, operate viciously unbeknownst to the user, and surreptitiously exfiltrate sensitive data. Understanding the inner workings of such malware provides a leverage to effectively combat them. This understanding is pursued often through dynamic analysis which is conducted manually or automatically. Malware authors accordingly, have devised and advanced evasion techniques to thwart or evade these analyses. In this article, we present a comprehensive survey on malware dynamic analysis evasion techniques. In addition, we propose a detailed classification of these techniques and further demonstrate how their efficacy holds against different types of detection and analysis approaches.</jats:p> <jats:p>Our observations attest that evasive behavior is mostly concerned with detecting and evading sandboxes. The primary tactic of such malware we argue is fingerprinting followed by new trends for reverse Turing test tactic which aims at detecting human interaction. Furthermore, we will posit that the current defensive strategies, beginning with reactive methods to endeavors for more transparent analysis systems, are readily foiled by zero-day fingerprinting techniques or other evasion tactics such as stalling. Accordingly, we would recommend the pursuit of more generic defensive strategies with an emphasis on path exploration techniques that has the potential to thwart all the evasive tactics.</jats:p>

<jats:p>In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.</jats:p>

<jats:p>There has been a large amount of work on smart classrooms spanning over a wide range of research areas including information communication technology, machine learning, sensor networks, mobile computing, and hardware. Consequently, there have been several disparate reviews on various aspects of smart classrooms. Such piecemeal development is not sufficient for a pragmatic smart classroom solution. This article complements the literature by providing a consolidated review of interdisciplinary works under a common nomenclature and taxonomy. This multi-field review has exposed new research opportunities and challenges that need to be addressed for the synergistic integration of interdisciplinary works.</jats:p>

<jats:p>Video summarization is the method of extracting key frames or clips from a video to generate a synopsis of the content of the video. Generally, video is compressed before storing or transmitting it in most of the practical applications. Traditional techniques require the videos to be decoded to summarize them, which is a tedious job. Instead, compressed domain video processing can be used for summarizing videos by partially decoding them. A classification and analysis of various summarization techniques are presented in this article with special focus on compressed domain techniques along with a discussion on machine-learning-based techniques that can be applied to summarize the videos.&lt;?vsp -1.2pt?&gt;</jats:p>

<jats:p>Model-driven game development (MDGD) introduces model-driven methodology to the computer game domain, shifting the focus of game development from coding to modeling to make game development faster and easier. The research on MDGD is concerned with both the general model-driven software development methodology and the particular characteristics of game development. People in the MDGD community have proposed several approaches in the past decades, addressing both the technology and the development process in the context of MDGD. This article presents the state-of-art of MDGD research based on a literature review of 26 approaches in the field. The review is structured around five perspectives: target game domains, domain frameworks, modelling languages, tooling, and evaluation methods. The article also includes reflections and a discussion of the challenges within MDGD.</jats:p>

<jats:p>Distributed Ledger Technologies (DLTs) and blockchain systems have received enormous academic, government, and commercial interest in recent years. This article surveys the integration of DLTs within another life-changing technology, the Internet of Things (IoT). IoT-based applications, such as smart home, smart transport, supply chain, smart healthcare, and smart energy, promise to boost the efficiency of existing infrastructures and change every facet of our daily life. This article looks into the challenges faced by such applications and reviews a comprehensive selection of existing DLT solutions to those challenges. We also identify issues for future research, including DLT security and scalability, multi-DLT applications, and survival of DLT in the post-quantum world.</jats:p>

<jats:p> The vast nature of scientific publications brings out the importance of <jats:italic>Literature-Based Discovery (LBD)</jats:italic> research that is highly beneficial to accelerate knowledge acquisition and the research development process. LBD is a knowledge discovery workflow that automatically detects significant, implicit knowledge associations hidden in fragmented knowledge areas by analysing existing scientific literature. Therefore, the LBD output not only assists in formulating scientifically sensible, novel research hypotheses but also encourages the development of cross-disciplinary research. In this systematic review, we provide an in-depth analysis of the computational techniques used in the LBD process using a novel, up-to-date, and detailed classification. Moreover, we also summarise the key milestones of the discipline through a timeline of topics. To provide a general overview of the discipline, the review outlines LBD validation checks, major LBD tools, application areas, domains, and generalisability of LBD methodologies. We also outline the insights gathered through our statistical analysis that capture the trends in LBD literature. To conclude, we discuss the prevailing research deficiencies in the discipline by highlighting the challenges and opportunities of future LBD research. </jats:p>

<jats:p>DevOpsis a collaborative and multidisciplinary organizational effort to automate continuous delivery of new software updates while guaranteeing their correctness and reliability. The present survey investigates and discusses DevOps challenges from the perspective of engineers, managers, and researchers. We review the literature and develop a DevOps conceptual map, correlating the DevOps automation tools with these concepts. We then discuss their practical implications for engineers, managers, and researchers. Finally, we critically explore some of the most relevant DevOps challenges reported by the literature.</jats:p>