Catálogo de publicaciones - revistas

<jats:p> <jats:bold>Context</jats:bold> : Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for building a body of knowledge on this important topic. <jats:bold>Objective</jats:bold> : This article aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures. This review also aims at identifying gaps in research on ML-based data exfiltration countermeasures. <jats:bold>Method</jats:bold> : We used Systematic Literature Review (SLR) method to select and review 92 papers. <jats:bold>Results</jats:bold> : The review has enabled us to: (a) classify the ML approaches used in the countermeasures into data-driven, and behavior-driven approaches; (b) categorize features into six types: behavioral, content-based, statistical, syntactical, spatial, and temporal; (c) classify the evaluation datasets into simulated, synthesized, and real datasets; and (d) identify 11 performance measures used by these studies. <jats:bold>Conclusion</jats:bold> : We conclude that: (i) The integration of data-driven and behavior-driven approaches should be explored; (ii) There is a need of developing high quality and large size evaluation datasets; (iii) Incremental ML model training should be incorporated in countermeasures; (iv) Resilience to adversarial learning should be considered and explored during the development of countermeasures to avoid poisoning attacks; and (v) The use of automated feature engineering should be encouraged for efficiently detecting data exfiltration attacks. </jats:p>

<jats:p> Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain Based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from the perspective of the software engineering discipline. We characterize BBS engineering based on the key aspects of <jats:italic>theoretical foundations, processes, models</jats:italic> , and <jats:italic>roles</jats:italic> . Based on these aspects, we present a rich repertoire of development tasks, design principles, models, roles, challenges, and resolution techniques. The focus and depth of this survey not only give software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpin a starting point for further research in this field. </jats:p>

<jats:p>With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS.</jats:p>

<jats:p> Remote collaboration systems have become increasingly important in today’s society, especially during times where physical distancing is advised. Industry, research and individuals face the challenging task of collaborating and networking over long distances. While video and teleconferencing are already widespread, collaboration systems in augmented, virtual, and mixed reality are still a niche technology. We provide an overview of recent developments of synchronous remote collaboration systems and create a taxonomy by dividing them into three main components that form such systems: <jats:italic>Environment</jats:italic> , <jats:italic>Avatars</jats:italic> , and <jats:italic>Interaction</jats:italic> . A thorough overview of existing systems is given, categorising their main contributions in order to help researchers working in different fields by providing concise information about specific topics such as avatars, virtual environment, visualisation styles and interaction. The focus of this work is clearly on synchronised collaboration from a distance. A total of 87 unique systems for remote collaboration are discussed, including more than 100 publications and 25 commercial systems. </jats:p>

<jats:p>The adoption of immersive virtual experiences (IVEs) opened new research lines where the impact of realism is being studied, allowing developers to focus resources on realism factors proven to improve the user experience the most. We analysed papers that compared different levels of realism and evaluated their impact on user experience. Exploratorily, we also synthesised the realism terms used by authors. From 1300 initial documents, 79 met the eligibility criteria. Overall, most of the studies reported that higher realism has a positive impact on user experience. These data allow a better understanding of realism in IVEs, guiding future R&amp;D.</jats:p>

<jats:p>In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs.</jats:p> <jats:p>In the first part of this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking. In this second part of this survey, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods.</jats:p>

<jats:p> Software security defenses are routinely broken by the persistence of both security researchers and attackers. Hardware solutions based on tagging are emerging as a promising technique that provides strong security guarantees ( <jats:italic>e.g.,</jats:italic> memory safety) while incurring minimal runtime overheads and maintaining compatibility with existing codebases. Such schemes extend every word in memory with a tag and enforce security policies across them. This paper provides a survey of existing work on tagged architectures and describe the types of attacks such architectures aim to prevent as well as the guarantees they provide. It highlights the main distinguishing factors among tagged architectures and presents the diversity of designs and implementations that have been proposed. The survey reveals several real-world challenges have been neglected relating to both security and practical deployment. The challenges relate to the provisioning and enforcement phases of tagged architectures, and various overheads they incur. This work identifies these challenges as open research problems and provides suggestions for improving their security and practicality. </jats:p>

<jats:p> Bayesian data analysis is gaining traction in many fields, including empirical studies in software engineering. Bayesian approaches provide many advantages over traditional, or frequentist, data analysis, but the mechanics often remain opaque to beginners due to the underlying computational complexity. Introductory articles, while successful in explaining the theory and principles, fail to provide a totally transparent operationalization. To address this gap, this tutorial provides a step-by-step illustration of Bayesian hypothesis testing in the context of software engineering research using a fully-developed example and in comparison to the frequentist hypothesis testing approach. It shows how Bayesian analysis can help build evidence over time incrementally through a family of experiments. It also discusses chief advantages and disadvantages in an applied manner. A <jats:italic>figshare</jats:italic> package is provided for reproducing all calculations. </jats:p>

<jats:p>In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities. In this survey we give a systematic overview of this work, offering a broad perspective that connects formalizations and algorithmic approaches across subfields. An important contribution of our work is in developing a common narrative around the value frameworks that motivate specific fairness-enhancing interventions in ranking. This allows us to unify the presentation of mitigation objectives and of algorithmic techniques to help meet those objectives or identify trade-offs.</jats:p> <jats:p>In this first part of this survey, we describe four classification frameworks for fairness-enhancing interventions, along which we relate the technical methods surveyed in this paper, discuss evaluation datasets, and present technical work on fairness in score-based ranking. In the second part of this survey, we present methods that incorporate fairness in supervised learning, and also give representative examples of recent work on fairness in recommendation and matchmaking systems. We also discuss evaluation frameworks for fair score-based ranking and fair learning-to-rank, and draw a set of recommendations for the evaluation of fair ranking methods.</jats:p>

<jats:p>Movement dataset reviews exist but are limited in coverage, both in terms of size and research discipline. While topic-specific reviews clearly have their merit, it is critical to have a comprehensive overview based on a systematic survey across disciplines. This enables higher visibility of datasets available to the research communities and can foster interdisciplinary collaborations. We present a catalogue of 704 open datasets described by 10 variables that can be valuable to researchers searching for secondary data: name and reference, creation purpose, data type, annotations, source, population groups, ordinal size of people captured simultaneously, URL, motion capture sensor, and funders. The catalogue is available in the supplementary materials. We provide an analysis of the datasets and further review them under the themes of human diversity, ecological validity, and data recorded. The resulting 12-dimension framework can guide researchers in planning the creation of open movement datasets. This work has been the interdisciplinary effort of researchers across affective computing, clinical psychology, disability innovation, ethnomusicology, human-computer interaction, machine learning, music cognition, music computing, and movement neuroscience.</jats:p>