Catálogo de publicaciones - revistas

<jats:p>Blockchain is a disruptive technology that has attracted the attention of the scientific community and companies, as proven by the exponential growth of publications on this topic in recent years. This growing interest is mainly due to the promise that the use of blockchain enables it to be verified, without including any trusted intermediaries, that the information received from the network is authentic and up-to-date. In this respect, blockchain is a distributed database that can be seen as a ledger that records all transactions that have ever been executed. In this context, smart contracts are pieces of software used to facilitate, verify, and enforce the negotiation of a transaction on a blockchain platform. These pieces of software are implemented by using programming languages, which are sometimes provided by the blockchain platforms themselves. This study aims to (1) identify and categorise the state-of-the-art related to smart contract languages, in terms of the existing languages and their main features, and (2) identify new research opportunities. The review has been conducted as a multivocal mapping study that follows the guidelines proposed by Garousi et al. for conducting multivocal literature reviews, as well as the guidelines proposed by Kitchenham and Charters for conducting mapping studies. As a result of the implementation of the review protocol, 4,119 papers were gathered, and 109 of them were selected for extraction. The contributions of this article are twofold: (1) 101 different smart contract languages have been identified and classified according to a variety of criteria; (2) a discussion on the findings and their implications for future research have been outlined. As a conclusion, it could be stated that a rigorous and replicable overview of the state-of-the-art of smart contract languages has been provided that can benefit not only researchers but also practitioners in the field, thanks to its multivocal nature.</jats:p>

<jats:p>We conduct a comprehensive review covering academic publications and industry products relating to tools for cybersecurity awareness and education aimed at non-expert end-users developed in the past 20 years. Through our search criteria, we identified 119 tools that we cataloged into five broad media categories. We explore current trends, assess their use of relevant instructional design principles, and review empirical evidence of the tools’ effectiveness. From our review, we provide an evaluation checklist and suggest that a more systematic approach to the design and evaluation of cybersecurity educational tools would be beneficial.</jats:p>

<jats:p>The phenomenon of Big Data continues to present moving targets for the scientific and technological state-of-the-art. This work demonstrates that the solution space of these challenges has expanded with deep learning now moving beyond traditional applications in computer vision and natural language processing to diverse and core machine learning tasks such as learning with streaming and non-iid-data, partial supervision, and large volumes of distributed data while preserving privacy. We present a framework coalescing multiple deep methods and corresponding models as responses to specific Big Data challenges. First, we perform a detailed per-challenge review of existing techniques, with benchmarks and usage advice, and subsequently synthesize them together into one organic construct that we discover principally uses extensions of one underlying model, the autoencoder. This work therefore provides a synthesis where challenges at scale across the Vs of Big Data could be addressed by new algorithms and architectures being proposed in the deep learning community. The value being proposed to the reader from either community in terms of nomenclature, concepts, and techniques of the other would advance the cause of multi-disciplinary, transversal research and accelerate the advance of technology in both domains.</jats:p>

<jats:p>Public-key cryptography is an indispensable component used in almost all of our present-day digital infrastructure. However, most if not all of it is predominantly built upon hardness guarantees of number theoretic problems that can be broken by large-scale quantum computers in the future. Sensing the imminent threat from continued advances in quantum computing, NIST has recently initiated a global-level standardization process for quantum resistant public-key cryptographic primitives such as public-key encryption, digital signatures, and key encapsulation mechanisms. While the process received proposals from various categories of post-quantum cryptography, lattice-based cryptography features most prominently among all the submissions. Lattice-based cryptography offers a very attractive alternative to traditional public-key cryptography mainly due to the variety of lattice-based schemes offering varying flavors of security and efficiency guarantees. In this article, we survey the evolution of lattice-based key-sharing schemes (public-key encryption and key encapsulation schemes) and cover various aspects ranging from theoretical security guarantees, general algorithmic frameworks, practical implementation aspects, and physical attack security, with special focus on lattice-based key-sharing schemes competing in the NIST’s standardization process.</jats:p>

<jats:p>Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these “deepfakes” have advanced significantly.</jats:p> <jats:p>In this article, we explore the creation and detection of deepfakes and provide an in-depth view as to how these architectures work. The purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas that require further research and attention.</jats:p>

<jats:p>Cyber-aggression, cyberbullying, and cyber-grooming are distinctive and similar phenomena that represent the objectionable content appearing on online social media. Timely detection of the objectionable content is very important for its prevention and reduction. This article explores and spotlights diversity of definitions of cyber-aggression, cyberbulling, and cyber-grooming; analyzes current categorization systems and taxonomies; identifies the targets, target categories, and subcategories of the subjects of the objectionable content research; analyzes the ambiguity of the linguistic terms in the domain; reviews present databases gathered for researching the field; explores types of features used for modeling systems for automatic detection; and examines methods for automatic detection and/or prediction of the objectionable content. The results point to directions of system development for tracing transformations of objectionable content over time on different online social platforms.</jats:p>

<jats:p>The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.</jats:p>

<jats:p> <jats:italic>Verifiable Secret-Sharing</jats:italic> (VSS) is a fundamental primitive in secure distributed computing. It is used as a building block in several distributed computing tasks, such as Byzantine agreement and secure multi-party computation. In this article, we consider VSS schemes with <jats:italic>perfect</jats:italic> security, tolerating <jats:italic>computationally unbounded</jats:italic> adversaries. We comprehensively survey the existing perfectly-secure VSS schemes in three different communication settings, namely synchronous, asynchronous and hybrid setting and provide full details of the existing schemes in these settings. The aim of this survey is to provide a clear knowledge and foundation to researchers who are interested in knowing and extending the state-of-the-art perfectly-secure VSS schemes. </jats:p>

<jats:p>The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, etc. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services like storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The paper is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey paper is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.</jats:p>

<jats:p>Data Stream Processing (DSP) has emerged over the years as the reference paradigm for the analysis of continuous and fast information flows, which have often to be processed with low-latency requirements to extract insights and knowledge from raw data. Dealing with unbounded data flows, DSP applications are typically long-running and, thus, likely experience varying workloads and working conditions over time. To keep a consistent service level in face of such variability, a lot of effort has been spent studying strategies for run-time adaptation of DSP systems and applications. In this survey, we review the most relevant approaches from the literature, presenting a taxonomy to characterize the state of the art along several key dimensions. Our analysis allows us to identify current research trends as well as open challenges that will motivate further investigations in this field.</jats:p>