Catálogo de publicaciones - revistas

<jats:p>Routing Protocol for Low-Power and Lossy Networks (RPL) is a standard routing protocol for the Low Power and Lossy Networks (LLNs). It is a part of the IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) protocol stack. Features such as energy-efficient mechanisms and availability of the secure modes of operations make RPL suitable for the constrained Internet of Things (IoT) devices. Hence, the majority of IoT applications rely on RPL for data communication. However, routing security in RPL-based IoT networks is a significant concern, motivating us to study and analyze routing attacks and suggested countermeasures against them. To this end, we provide a comprehensive survey on the state-of-the-art security threats and their corresponding countermeasures in RPL-based IoT networks. Based on our study, we propose a novel classification scheme that uses a mapping between RPL attacks and their countermeasure techniques to the RPL control messages used to develop these techniques. Furthermore, we provide an in-depth statistical analysis that includes analysis of routing attacks through the RPL control messages, distribution of various mitigation techniques as per the method used, RPL control messages involved in the mitigation techniques, and details of the tools used by multiple researchers. In the end, we highlight some open challenges and future research opportunities on this topic. We believe that this survey will be beneficial to researchers and practitioners working in the area of RPL security.</jats:p>

<jats:p>Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access, and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into five distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base, and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and we discuss some potential future directions to explore.</jats:p>

<jats:p>Portfolio optimization can be roughly categorized as the mean-variance approach and the exponential growth rate approach based on different theoretical foundations, trading logics, optimization objectives, and methodologies. The former and the latter are often used in long-term and short-term portfolio optimizations, respectively. Although the mean-variance approach could be applied to short-term portfolio optimization, the performance may not be satisfactory (same with the exponential growth rate approach to the long-term portfolio optimization). This survey mainly explores the gaps between these two approaches, and investigates what common ideas or mechanisms are beneficial. Besides, the evaluating framework of this field and some unsolved problems are also discussed.</jats:p>

<jats:p>The purpose of a reputation system is to hold the users of a distributed application accountable for their behavior. The reputation of a user is computed as an aggregate of the feedback provided by fellow users in the system. Truthful feedback is clearly a prerequisite for computing a reputation score that accurately represents the behavior of a user. However, it has been observed that users can hesitate in providing truthful feedback because, for example, of fear of retaliation. Privacy-preserving reputation systems enable users to provide feedback in a private and thus uninhibited manner. In this survey, we propose analysis frameworks for privacy-preserving reputation systems. We use these analysis frameworks to review and compare the existing approaches. Emphasis is placed on blockchain-based systems as they are a recent significant development in the area. Blockchain-based privacy-preserving reputation systems have properties, such as trustlessness, transparency, and immutability, which prior systems do not have. Our analysis provides several insights and directions for future research. These include leveraging blockchain to its full potential in order to develop truly trustless systems, to achieve some important security properties, and to include defenses against common attacks that have so far not been addressed by most current systems.</jats:p>

<jats:p>Artificial intelligence (AI) and algorithmic decision making are having a profound impact on our daily lives. These systems are vastly used in different high-stakes applications like healthcare, business, government, education, and justice, moving us toward a more algorithmic society. However, despite so many advantages of these systems, they sometimes directly or indirectly cause harm to the users and society. Therefore, it has become essential to make these systems safe, reliable, and trustworthy. Several requirements, such as fairness, explainability, accountability, reliability, and acceptance, have been proposed in this direction to make these systems trustworthy. This survey analyzes all of these different requirements through the lens of the literature. It provides an overview of different approaches that can help mitigate AI risks and increase trust and acceptance of the systems by utilizing the users and society. It also discusses existing strategies for validating and verifying these systems and the current standardization efforts for trustworthy AI. Finally, we present a holistic view of the recent advancements in trustworthy AI to help the interested researchers grasp the crucial facets of the topic efficiently and offer possible future research directions.</jats:p>

<jats:p>Autonomous vehicles are revolutionizing transport and next-generation autonomous mobility. Such vehicles are promising to increase road safety, improve traffic efficiency, reduce vehicle emission, and improve mobility. However, for these vehicles to live up to their full potentials, there are significant research, technological and urgent organizational issues that need to be addressed to reach the highest level of automation, i.e., level 5. Sensors, communication, mobile edge computing, machine learning, data analytic, distributed learning, and so on, are examples of the building blocks technologies and concepts constituting the end-to-end solution. This survey discusses these technologies and concepts and maps their roles to the end-to-end solution. It highlights the challenges for each technology. Moreover, this survey provides an analysis of different solution approaches proposed by relevant stakeholders, utilizing these technologies aiming to achieve level-5 autonomy. Finally, the article details two use cases to present the interplay of the building blocks technologies.</jats:p>

<jats:p>Mapping of spatial hotspots, i.e., regions with significantly higher rates of generating cases of certain events (e.g., disease or crime cases), is an important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, and so on. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs of spurious results (e.g., false alarms of crime clusters). As a result, statistical rigor is needed explicitly to control the rate of spurious detections. To address this challenge, techniques for statistically-robust clustering (e.g., scan statistics) have been extensively studied by the data mining and statistics communities. In this survey, we present an up-to-date and detailed review of the models and algorithms developed by this field. We first present a general taxonomy for statistically-robust clustering, covering key steps of data and statistical modeling, region enumeration and maximization, and significance testing. We further discuss different paradigms and methods within each of the key steps. Finally, we highlight research gaps and potential future directions, which may serve as a stepping stone in generating new ideas and thoughts in this growing field and beyond.</jats:p>

<jats:p> In a dynamic stream there is an assumption that the underlying process generating the stream is non-stationary and that concepts within the stream will drift and change as the stream progresses. Concepts learned by a classification model are prone to change and non-adaptive models are likely to deteriorate and become ineffective over time. The challenge of recognising and reacting to change in a stream is compounded by the <jats:italic>scarcity of labels</jats:italic> problem. This refers to the very realistic situation in which the true class label of an incoming point is not immediately available (or might never be available) or in situations where manually annotating data points are prohibitively expensive. In a high-velocity stream, it is perhaps impossible to manually label every incoming point and pursue a fully supervised approach. In this article, we formally describe the types of change, which can occur in a data-stream and then catalogue the methods for dealing with change when there is limited access to labels. We present an overview of the most influential ideas in the field along with recent advancements and we highlight trends, research gaps, and future research directions. </jats:p>

<jats:p>In the last few years, a large number of automatic evaluation metrics have been proposed for evaluating Natural Language Generation (NLG) systems. The rapid development and adoption of such automatic evaluation metrics in a relatively short time has created the need for a survey of these metrics. In this survey, we (i) highlight the challenges in automatically evaluating NLG systems, (ii) propose a coherent taxonomy for organising existing evaluation metrics, (iii) briefly describe different existing metrics, and finally (iv) discuss studies criticising the use of automatic evaluation metrics. We then conclude the article highlighting promising future directions of research.</jats:p>

<jats:p>Software-intensive systems in most domains, from autonomous vehicles to health, are becoming predominantly parallel to efficiently manage large amount of data in short (even real-) time. There is an incredibly rich literature on languages for parallel computing, thus it is difficult for researchers and practitioners, even experienced in this very field, to get a grasp on them. With this work we provide a comprehensive, structured, and detailed snapshot of documented research on those languages to identify trends, technical characteristics, open challenges, and research directions. In this article, we report on planning, execution, and results of our systematic peer-reviewed as well as grey literature review, which aimed at providing such a snapshot by analysing 225 studies.</jats:p>