Catálogo de publicaciones - revistas

<jats:p>Quantum computing is the latest revolution in computing and will probably come to be seen an advance as important as the steam engine or the information society. In the last few decades, our understanding of quantum computers has expanded and multiple efforts have been made to create languages, libraries, tools, and environments to facilitate their programming. Nonetheless, quantum computers are complex systems at the bottom of a stack of layers that programmers need to understand. Hence, efforts towards creating quantum programming languages and computing environments that can abstract low-level technology details have become crucial steps to achieve a useful quantum computing technology. However, most of these environments still lack many of the features that would be desirable, such as those outlined in The Talavera Manifesto for Quantum Software Engineering and Programming. For advancing quantum computing, we will need to develop quantum software engineering techniques and tools to ensure the feasibility of this new type of quantum software. To contribute to this goal, this paper provides a review of the main quantum software components and platforms. We also propose a set of quality requirements for the development of quantum software platforms and the conduct of their quality assessment.</jats:p>

<jats:p>The outstanding performance of deep neural networks has promoted deep learning applications in a broad set of domains. However, the potential risks caused by adversarial samples have hindered the large-scale deployment of deep learning. In these scenarios, adversarial perturbations, imperceptible to human eyes, significantly decrease the model’s final performance. Many papers have been published on adversarial attacks and their countermeasures in the realm of deep learning. Most focus on evasion attacks, where the adversarial examples are found at test time, as opposed to poisoning attacks where poisoned data is inserted into the training data. Further, it is difficult to evaluate the real threat of adversarial attacks or the robustness of a deep learning model, as there are no standard evaluation methods. Hence, with this paper, we review the literature to date. Additionally, we attempt to offer the first analysis framework for a systematic understanding of adversarial attacks. The framework is built from the perspective of cybersecurity so as to provide a lifecycle for adversarial attacks and defenses.</jats:p>

<jats:p> Despite their success, deep networks are used as black-box models with outputs that are not easily explainable during the learning and the prediction phases. This lack of interpretability is significantly limiting the adoption of such models in domains where decisions are critical such as the medical and legal fields. Recently, researchers have been interested in developing methods that help explain individual decisions and decipher the hidden representations of machine learning models in general and deep networks specifically. While there has been a recent explosion of work on <jats:bold>Ex</jats:bold> plainable <jats:bold>A</jats:bold> rtificial <jats:bold>I</jats:bold> ntelligence ( <jats:bold>ExAI</jats:bold> ) on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI community. Such challenges can be attributed to the lack of input structure in textual data, the use of word embeddings that add to the opacity of the models and the difficulty of the visualization of the inner workings of deep models when they are trained on textual data. </jats:p> <jats:p>Lately, methods have been developed to address the aforementioned challenges and present satisfactory explanations on Natural Language Processing (NLP) models. However, such methods are yet to be studied in a comprehensive framework where common challenges are properly stated and rigorous evaluation practices and metrics are proposed.</jats:p> <jats:p> Motivated to democratize ExAI methods in the NLP field, we present in this work a survey that studies <jats:italic>model-agnostic</jats:italic> as well as <jats:italic>model-specific</jats:italic> explainability methods on NLP models. Such methods can either develop <jats:italic>inherently</jats:italic> interpretable NLP models or operate on pre-trained models in a <jats:italic>post-hoc</jats:italic> manner. We make this distinction and we further decompose the methods into three categories according to what they explain: (1) word embeddings (input-level), (2) inner workings of NLP models (processing-level) and (3) models’ decisions (output-level). We also detail the different evaluation approaches interpretability methods in the NLP field. Finally, we present a case-study on the well-known neural machine translation in an appendix and we propose promising future research directions for ExAI in the NLP field. </jats:p>

<jats:p>Graphics Processing Unit (GPU) devices and their associated software programming languages and frameworks can deliver the computing performance required to facilitate the development of next-generation high-performance safety-critical systems such as autonomous driving systems. However, the integration of complex, parallel and computationally demanding software functions with different safety-criticality levels on GPU devices with shared hardware resources contributes to several safety certification challenges. This survey categorizes and provides an overview of research contributions that address GPU devices’ random hardware failures, systematic failures and independence of execution.</jats:p>

<jats:p>Humans are increasingly integrated with devices that enable the collection of vast unstructured opinionated data. Accurately analysing subjective information from this data is the task of sentiment analysis (an actively researched area in NLP). Deep learning provides a diverse selection of architectures to model sentiment analysis tasks and has surpassed other machine learning methods as the foremast approach for performing sentiment analysis tasks. Recent developments in deep learning architectures represent a shift away from Recurrent and Convolutional neural networks and the increasing adoption of Transformer language models. Utilising pre-trained Transformer language models to transfer knowledge to downstream tasks has been a breakthrough in NLP.</jats:p> <jats:p>This survey applies a task-oriented taxonomy to recent trends in architectures with a focus on the theory, design and implementation. To the best of our knowledge, this is the only survey to cover state-of-the-art Transformer-based language models and their performance on the most widely used benchmark datasets. This survey paper provides a discussion of the open challenges in NLP and sentiment analysis. The survey covers five years from 1st Jul 2017 to 1st Jul 2022.</jats:p>

<jats:p> <jats:styled-content style="color:colid318">X-ray imagery systems have enabled security personnel to identify potential threats contained within the baggage and cargo since the early 1970s. However, the manual process of screening the threatening items is time-consuming and vulnerable to human error. Hence, researchers have utilized recent advancements in computer vision techniques, revolutionized by machine learning models, to aid in baggage security threat identification via 2D X-ray and 3D CT imagery. However, the performance of these approaches is severely affected by heavy occlusion, class imbalance, and limited labeled data, further complicated by ingeniously concealed emerging threats. Hence, the research community must devise suitable approaches by leveraging the findings from existing literature to move in new directions. Towards that goal, we present a structured survey providing systematic insight into state-of-the-art advances in baggage threat detection. Furthermore, we also present a comprehensible understanding of X-ray based imaging systems and the challenges faced within the threat identification domain. We include a taxonomy to classify the approaches proposed within the context of 2D and 3D CT X-ray based baggage security threat screening and provide a comparative analysis of the performance of the methods evaluated on four benchmarks. Besides, we also discuss current open challenges and potential future research avenues.</jats:styled-content> </jats:p>

<jats:p>Remote electronic voting, often called online or Internet voting, has been subject to research for the last four decades. It is regularly discussed in public debates, especially in the context of enabling voters to conveniently cast their ballot from home using their personal devices. Since these devices are not under the control of the electoral authority and could be potentially compromised, this setting is referred to as an “uncontrolled environment” for which special security assumptions have to be considered.</jats:p> <jats:p>This paper employs general election principles to derive cryptographic, technical, and organizational requirements for remote electronic voting. Based on these requirements, we have extended an existing methodology to assess online voting schemes and develop a corresponding reference attacker model to support the preparation of tailored protection profiles for different levels of elections. After presenting a broad survey of different voting schemes, we use this methodology to assess and classify those schemes comparatively by leveraging four election-specific attacker models.</jats:p>

<jats:p> The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations. Research explores different approaches to improve ML dependability by proposing new models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. However, there is a missing connection between ongoing ML research and well-established safety principles. In this paper, we present a structured and comprehensive review of ML techniques to improve the dependability of ML algorithms in uncontrolled open-world settings. From this review, we propose the <jats:italic>Taxonomy of ML Safety</jats:italic> that maps state-of-the-art ML techniques to key engineering safety strategies. Our taxonomy of ML safety presents a safety-oriented categorization of ML techniques to provide guidance for improving dependability of the ML design and development. The proposed taxonomy can serve as a safety checklist to aid designers in improving coverage and diversity of safety strategies employed in any given ML system. </jats:p>

<jats:p>Security attacks abuse software vulnerabilities of IoT devices; hence, detecting and eliminating these vulnerabilities immediately are crucial. Fuzzing is an efficient method to identify vulnerabilities automatically, and many publications have been released to date. However, fuzzing for embedded systems has not been studied extensively owing to various obstacles, such as multi-architecture support, crash detection difficulties, and limited resources. Thus, the paper introduces fuzzing techniques for embedded systems and the fuzzing differences for desktop and embedded systems. Further, we collect state-of-the-art technologies, discuss their advantages and disadvantages, and classify embedded system fuzzing tools. Finally, future directions for fuzzing research of embedded systems are predicted and discussed.</jats:p>

<jats:p>In the last 35 years, parallel computing has drawn increasing interest from the academic community, especially in solving complex optimization problems that require large amounts of computational power. The use of parallel (multi-core and distributed) architectures is a natural and effective alternative to speeding up search methods, such as metaheuristics, and to enhancing the quality of the solutions. This survey focuses particularly on studies that adopt high-performance computing techniques to design, implement, and experiment trajectory-based metaheuristics, which pose a great challenge to high-performance computing and represent a large gap in the operations research literature. We outline the contributions from 1987 to the present, and the result is a complete overview of the current state of the art with respect to multi-core and distributed trajectory-based metaheuristics. Basic notions of high-performance computing are introduced, and different taxonomies for multi-core and distributed architectures and metaheuristics are reviewed. A comprehensive list of 127 publications is summarized and classified according to taxonomies and application types. Furthermore, past and future trends are indicated, and open research gaps are identified.</jats:p>