Catálogo de publicaciones - revistas

<jats:p>CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers’ attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.</jats:p>

<jats:p> Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores to provide more responsive services. Therefore, it is envisioned that a large-scale, geographically dispersed, and resource-rich distributed system will emerge and play a key role in the future Internet. However, given the loosely coupled nature of such complex systems, their operational conditions are expected to change significantly over time. In this context, the performance characteristics of such systems will need to be captured rapidly, which is referred to as performance benchmarking, for application deployment, resource orchestration, and adaptive decision-making. <jats:italic>Edge performance benchmarking</jats:italic> is a nascent research avenue that has started gaining momentum over the past five years. This article first reviews articles published over the past three decades to trace the history of performance benchmarking from tightly coupled to loosely coupled systems. It then systematically classifies previous research to identify the system under test, techniques analyzed, and benchmark runtime in edge performance benchmarking. </jats:p>

<jats:p>Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.</jats:p>

<jats:p>Social media have been growing rapidly and become essential elements of many people’s lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey analyzes various identity deception attacks, which can be categorized into fake profile, identity theft, and identity cloning. This survey provides a detailed review of social media identity deception detection techniques. It also identifies primary research challenges and issues in the existing detection techniques. This article is expected to benefit both researchers and social media providers.</jats:p>

<jats:p> Information-Centric Networking (ICN) has recently emerged as a prominent candidate for the Future Internet Architecture (FIA) that addresses existing issues with the host-centric communication model of the current TCP/IP-based Internet. Named Data Networking (NDN) is one of the most recent and active ICN architectures that provides a clean-slate approach for Internet communication. NDN provides intrinsic content security where security is directly provided to the content instead of communication channel. Among other security aspects, Access Control (AC) rules specify the privileges for the entities that can access the content. In TCP/IP-based AC systems, due to the client-server communication model, the servers control which client can access a particular content. In contrast, ICN-based networks use content names to drive communication and decouple the content from its original location. This phenomenon leads to the loss of control over the content, causing different challenges for the realization of efficient AC mechanisms. To date, considerable efforts have been made to develop various AC mechanisms in NDN. In this article, we provide a detailed and comprehensive survey of the AC mechanisms in NDN. We follow a holistic approach towards AC in NDN where we first summarize the ICN paradigm, describe the changes from channel-based security to content-based security, and highlight different cryptographic algorithms and security protocols in NDN. We then classify the existing AC mechanisms into two main categories: <jats:italic>Encryption-based AC</jats:italic> and <jats:italic>Encryption-independent AC</jats:italic> . Each category has different classes based on the working principle of AC (e.g., Attribute-based AC, Name-based AC, Identity-based AC). Finally, we present the lessons learned from the existing AC mechanisms and identify the challenges of NDN-based AC at large, highlighting future research directions for the community. </jats:p>

<jats:p>Nowadays, robots are dominating the manufacturing, entertainment, and healthcare industries. Robot vision aims to equip robots with the capabilities to discover information, understand it, and interact with the environment, which require an agent to effectively understand object affordances and functions in complex visual domains. In this literature survey, first, “visual affordances” are focused on and current state-of-the-art approaches for solving relevant problems as well as open problems and research gaps are summarized. Then, sub-problems, such as affordance detection, categorization, segmentation, and high-level affordance reasoning, are specifically discussed. Furthermore, functional scene understanding and its prevalent descriptors used in the literature are covered. This survey also provides the necessary background to the problem, sheds light on its significance, and highlights the existing challenges for affordance and functionality learning.</jats:p>

<jats:p>A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.</jats:p>

<jats:p>Using techniques derived from the syntactic methods for visual pattern recognition is not new and was much explored in the area called syntactical or structural pattern recognition. Syntactic methods have been useful because they are intuitively simple to understand and have transparent, interpretable, and elegant representations. Their capacity to represent patterns in a semantic, hierarchical, compositional, spatial, and temporal way have made them very popular in the research community. In this article, we try to give an overview of how syntactic methods have been employed for computer vision tasks. We conduct a systematic literature review to survey the most relevant studies that use syntactic methods for pattern recognition tasks in images and videos. Our search returned 597 papers, of which 71 papers were selected for analysis. The results indicated that in most of the studies surveyed, the syntactic methods were used as a high-level structure that makes the hierarchical or semantic relationship among objects or actions to perform the most diverse tasks.</jats:p>

<jats:p>Cyber threats have been growing tremendously in recent years. There are significant advancements in the threat space that have led towards an essential need for the strengthening of digital infrastructure security. Better security can be achieved by fine-tuning system parameters to the best and optimized security levels. For the protection of infrastructure and information systems, several guidelines have been provided by well-known organizations in the form of cybersecurity standards. Since security vulnerabilities incur a very high degree of financial, reputational, informational, and organizational security compromise, it is imperative that a baseline for standard compliance be established. The selection of security standards and extracting requirements from those standards in an organizational context is a tedious task. This article presents a detailed literature review, a comprehensive analysis of various cybersecurity standards, and statistics of cyber-attacks related to operating systems (OS). In addition to that, an explicit comparison between the frameworks, tools, and software available for OS compliance testing is provided. An in-depth analysis of the most common software solutions ensuring compliance with certain cybersecurity standards is also presented. Finally, based on the cybersecurity standards under consideration, a comprehensive set of minimum requirements is proposed for OS hardening and a few open research challenges are discussed.</jats:p>

<jats:p>Transient execution attacks, also known as speculative execution attacks, have drawn much interest in the last few years as they can cause critical data leakage. Since the first disclosure of Spectre and Meltdown attacks in January 2018, a number of new transient execution attack types have been demonstrated targeting different processors. A transient execution attack consists of two main components: transient execution itself and a covert channel that is used to actually exfiltrate the information.Transient execution is a result of the fundamental features of modern processors that are designed to boost performance and efficiency, while covert channels are unintended information leakage channels that result from temporal and spatial sharing of the micro-architectural components. Given the severity of the transient execution attacks, they have motivated computer architects in both industry and academia to rethink the design of the processors and to propose hardware defenses. To help understand the transient execution attacks, this survey summarizes the phases of the attacks and the security boundaries across which the information is leaked in different attacks.This survey further analyzes the causes of transient execution as well as the different types of covert channels and presents a taxonomy of the attacks based on the causes and types. This survey in addition presents metrics for comparing different aspects of the transient execution attacks and uses them to evaluate the feasibility of the different attacks. This survey especially considers both existing attacks and potential new attacks suggested by our analysis. This survey finishes by discussing different mitigations that have so far been proposed at the micro-architecture level and discusses their benefits and limitations.</jats:p>