Catálogo de publicaciones - revistas

<jats:p> The emerging field of autonomous UAV cinematography is examined through a tutorial for non-experts, which also presents the required underlying technologies and connections with different UAV application domains. Current industry practices are formalized by presenting a UAV shot-type taxonomy composed of framing shot types, single-UAV camera motion types, and multiple-UAV camera motion types. Visually pleasing combinations of framing shot types and camera motion types are identified, while the presented camera motion types are modeled geometrically and graded into distinct energy consumption classes and required technology complexity levels for autonomous capture. Two specific strategies are prescribed, namely <jats:italic>focal length compensation</jats:italic> and <jats:italic>multidrone compensation</jats:italic> , for partially overcoming a number of issues arising in UAV live outdoor event coverage, deemed as the most complex UAV cinematography scenario. Finally, the shot types compatible with each compensation strategy are explicitly identified. Overall, this tutorial both familiarizes readers coming from different backgrounds with the topic in a structured manner and lays necessary groundwork for future advancements. </jats:p>

<jats:p>With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.</jats:p>

<jats:p>Systems deployed in regulated safety-critical domains (e.g., the medical, nuclear, and automotive domains) are often required to undergo a stringent safety assessment procedure, as prescribed by a certification body, to demonstrate their compliance to one or more certification standards. Assurance cases are an emerging way of communicating safety, security, and dependability, as well as other properties of safety-critical systems in a structured and comprehensive manner. The significant size and complexity of these documents, however, makes the process of evaluating and assessing their validity a non-trivial task and an active area of research. Due to this, efforts have been made to develop and utilize software tools for the purpose of aiding developers and third party assessors in the act of assessing and analyzing assurance cases. This article presents a survey of the various assurance case assessment features contained in 10 assurance case software tools, all of which identified and selected by us via a previously conducted systematic literature review. We describe the various assessment techniques implemented, discuss their strengths and weaknesses, and identify possible areas in need of further research.</jats:p>

<jats:p>Autonomous systems will soon be integrating into our lives as home assistants, delivery drones, and driverless cars. The implementation of the level of automation in these systems from being manually controlled to fully autonomous would depend upon the autonomy approach chosen to design these systems. This article reviews the historical evolution of autonomy, its approaches, and the current trends in related fields to build robust autonomous systems. Toward such a goal and with the increased number of cyberattacks, the security of these systems needs special attention from the research community. To gauge the extent to which research has been done in this area, we discuss the cybersecurity of these systems. It is essential to model the system from a security perspective, identify the threats and vulnerabilities, and then model the attacks. A survey in this direction explores the theoretical/analytical system and attack models that have been proposed over the years and identifies the research gap that needs to be addressed by the research community.</jats:p>

<jats:p>Characteristics extracted from the training datasets of classification problems have proven to be effective predictors in a number of meta-analyses. Among them, measures of classification complexity can be used to estimate the difficulty in separating the data points into their expected classes. Descriptors of the spatial distribution of the data and estimates of the shape and size of the decision boundary are among the known measures for this characterization. This information can support the formulation of new data-driven pre-processing and pattern recognition techniques, which can in turn be focused on challenges highlighted by such characteristics of the problems. This article surveys and analyzes measures that can be extracted from the training datasets to characterize the complexity of the respective classification problems. Their use in recent literature is also reviewed and discussed, allowing to prospect opportunities for future work in the area. Finally, descriptions are given on an R package named Extended Complexity Library (ECoL) that implements a set of complexity measures and is made publicly available.</jats:p>

<jats:p>Food is essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding human behavior, improving human health, and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from different sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food-related issues in medicine, biology, gastronomy, and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, a series of works has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews that shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this article, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.</jats:p>

<jats:p>The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.</jats:p>

<jats:p>Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.</jats:p>

<jats:p>With the widespread of computing and mobile devices, authentication using biometrics has received greater attention. Although biometric systems usually provide good solutions, the recognition performance tends to be affected over time due to changing conditions and aging of biometric data, which results in intra-class variability. Adaptive biometric systems, which adapt the biometric reference over time, have been proposed to deal with such intra-class variability. This article provides the most up-to-date and complete discussion on adaptive biometrics systems we are aware of, including formalization, terminology, sources or variations that motivates the use of adaptation, adaptation strategies, evaluation methodology, and open challenges. This field of research is sometimes referred to as template update.</jats:p>

<jats:p>Video skimming, also known as dynamic video summarization, generates a temporally abridged version of a given video. Skimming can be achieved by identifying significant components either in uni-modal or multi-modal features extracted from the video. Being dynamic in nature, video skimming, through temporal connectivity, allows better understanding of the video from its summary. Having this obvious advantage, recently, video skimming has drawn the focus of many researchers benefiting from the easy availability of the required computing resources. In this article, we provide a comprehensive survey on video skimming focusing on the substantial amount of literature from the past decade. We present a taxonomy of video skimming approaches and discuss their evolution highlighting key advances. We also provide a study on the components required for the evaluation of a video skimming performance.</jats:p>