Catálogo de publicaciones - revistas

<jats:p>Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing deep learning techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on deep learning approaches for Android malware defenses exists. In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014-2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.</jats:p>

<jats:p>Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. In order to determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by the academia or industry to drive further research and development activities in the area of custom scheduling in Kubernetes.</jats:p>

<jats:p>Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An automatic summarization system that can effectively condense long documents into short and concise texts to encapsulate the most important information would thus be significant in aiding the reader’s comprehension. Recently, with the advent of neural architectures, significant research efforts have been made to advance automatic text summarization systems, and numerous studies on the challenges of extending these systems to the long document domain have emerged. In this survey, we provide a comprehensive overview of the research on long document summarization and a systematic evaluation across the three principal components of its research setting: benchmark datasets, summarization models, and evaluation metrics. For each component, we organize the literature within the context of long document summarization and conduct an empirical analysis to broaden the perspective on current research progress. The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics. Based on the overall findings, we conclude by proposing possible directions for future exploration in this rapidly growing field.</jats:p>

<jats:p>Caching is an important technique to speed-up execution and its implementation and use cases vary. When applied specifically to the memory hierarchy, caching is used to speed up memory accesses and memory translations. Different cache implementations are considered microarchitectural secrets and oftentimes change between generations. The integration of caches in hardware greatly influence security policy enforcement in the platform since caches maintain copies of code and data and their security properties. Examples of attacks due to the existence of caches are side-channels against cryptographic software, recent speculative execution abuses to leak secret data, and usages of cache-based manipulations (e.g., forcing cache splits/incoherence) to hide from security software detection. This survey examines the security issues due to different cache usages in a microarchitecture. The survey also explains the most complicated caching features and their impact on the security of the platform in different scenarios.</jats:p>

<jats:p>While compilers generally support parallel programming languages and APIs, their internal program representations are mostly designed from the sequential programs standpoint (exceptions include source-to-source parallel compilers, for instance). This makes the integration of compilation techniques dedicated to parallel programs more challenging. In addition, parallelism has various levels and different targets, each of them with specific characteristics and constraints. With the advent of multi-core processors and general purpose accelerators, parallel computing is now a common and pervasive consideration. Thus, software support to parallel programming activities is essential to make this technical transition more realistic and beneficial. The case of compilers is fundamental as they deal with (parallel) programs at a structural level, thus the need for intermediate representations. This article surveys and discusses attempts to provide intermediate representations for the proper support of explicitly parallel programs. We highlight the gap between available contributions and their concrete implementation in compilers and then exhibit possible future research directions.</jats:p>

<jats:p>As one of the most promising lab-on-a-chip systems, flow-based microfluidic biochips are being increasingly used for automatically executing various laboratory procedures in biology and biochemistry, such as enzyme-linked immunosorbent assay, point-of-care diagnosis, and so on. As manufacturing technology advances, the characteristic dimensions of biochip systems keep shrinking, and tens of thousands of microvalves can now be integrated into a coin-sized microfluidic platform, making the conventional manual-based chip design no longer applicable. Accordingly, computer-aided design (CAD) of microfluidics has attracted considerable research interest in the EDA community over the past decade. This review article presents recent advances in the design automation of biochips, involving CAD techniques for architectural synthesis, wash optimization, testing, fault diagnosis, and fault-tolerant design. With the help of these CAD tools, chip designers can be released from the burden of complex, large-scale design tasks. Meanwhile, new chip architectures can be explored automatically to open new doors to meet requirements from future large-scale biological experiments and medical diagnosis. We discuss key trends and directions for future research that are related to enable microfluidics to reach its full potential, thus further advancing the development and progression of the microfluidics industry.</jats:p>

<jats:p>Blockchains, in general, and cryptocurrencies such as Bitcoin, in particular, are realized using distributed systems and hence critically rely on the performance and security of the interconnecting network. The requirements on these networks and their usage, however, can differ significantly from traditional communication networks, with implications on all layers of the protocol stack. This article is motivated by these differences and, in particular, by the observation that many fundamental design aspects of these networks are not well-understood today. To support the networking community to contribute to this emerging application domain, we present a structured overview of the field, from topology and neighbor discovery, over block and transaction propagation, to sharding and off-chain networks, also reviewing existing empirical results from different measurement studies. In particular, for each of these domains, we provide the context, highlighting differences and commonalities with traditional networks, review the state-of-the-art, and identify open research challenges. Our article can hence also be seen as a call-to-arms to improve the foundation on top of which blockchains are built.</jats:p>

<jats:p>Smart contracts are a key component of today’s blockchains. They are critical in controlling decentralized autonomous organizations (DAO). However, smart contracts are not yet legally binding nor enforceable; this makes it difficult for businesses to adopt the DAO paradigm. Therefore, this study reviews existing Smart Contract Languages (SCL) and identifies properties that are critical to any future SCL for drafting legally binding contracts. This is achieved by conducting a Systematic Literature Review (SLR) of white- and grey literature published between 2015 and 2019. Using the SLR methodology, 45 Selected and 28 Supporting Studies detailing 45 state-of-the-art SCLs are selected. Finally, 10 SCL properties that enable legally compliant DAOs are discovered, and specifications for developing SCLs are explored.</jats:p>

<jats:p>Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.</jats:p>

<jats:p>A major determinant of the quality of software systems is the quality of their requirements, which should be both understandable and precise. Most requirements are written in natural language, which is good for understandability but lacks precision.</jats:p> <jats:p>To make requirements precise, researchers have for years advocated the use of mathematics-based notations and methods, known as “formal.” Many exist, differing in their style, scope, and applicability. The present survey discusses some of the main formal approaches and compares them to informal methods.</jats:p> <jats:p>The analysis uses a set of nine complementary criteria, such as level of abstraction, tool availability, and traceability support. It classifies the approaches into five categories based on their principal style for specifying requirements: natural-language, semi-formal, automata/graphs, mathematical, and seamless (programming-language-based). It includes examples from all of these categories, altogether 21 different approaches, including for example SysML, Relax, Eiffel, Event-B, and Alloy.</jats:p> <jats:p>The review discusses a number of open questions, including seamlessness, the role of tools and education, and how to make industrial applications benefit more from the contributions of formal approaches.</jats:p>