Catálogo de publicaciones - revistas

<jats:p>Recognizing people by their gait has become more and more popular nowadays due to the following reasons. First, gait recognition can work well remotely. Second, gait recognition can be done from low-resolution videos and with simple instrumentation. Third, gait recognition can be done without the cooperation of individuals. Fourth, gait recognition can work well while other features such as faces and fingerprints are hidden. Finally, gait features are typically difficult to be impersonated.</jats:p> <jats:p>Recent ubiquity of smartphones that capture gait patterns through accelerometers and gyroscope and advances in machine learning have opened new research directions and applications in gait recognition. A timely survey that addresses current advances is missing.</jats:p> <jats:p>In this article, we survey research works in gait recognition. In addition to recognition based on video, we address new modalities, such as recognition based on floor sensors, radars, and accelerometers; new approaches that include machine learning methods; and examine challenges and vulnerabilities in this field. In addition, we propose a set of future research directions. Our review reveals the current state-of-art and can be helpful to both experts and newcomers of gait recognition. Moreover, it lists future works and publicly available databases in gait recognition for researchers.</jats:p>

<jats:p> Relation extraction is a subtask of information extraction where <jats:italic>semantic relationships</jats:italic> are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text. In this article, we present a survey of relation extraction methods that leverage pre-existing structured or semi-structured data to guide the extraction process. We introduce a taxonomy of existing methods and describe distant supervision approaches in detail. We describe, in addition, the evaluation methodologies and the datasets commonly used for quality assessment. Finally, we give a high-level outlook on the field, highlighting open problems as well as the most promising research directions. </jats:p>

<jats:p>Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions.</jats:p>

<jats:p> Mulsemedia—multiple sensorial media—makes possible the inclusion of layered sensory stimulation and interaction through multiple sensory channels. The recent upsurge in technology and wearables provides mulsemedia researchers a vehicle for potentially boundless choice. However, in order to build systems that integrate various senses, there are still some issues that need to be addressed. This review deals with mulsemedia topics that remain insufficiently explored by previous work, with a focus on the <jats:italic>multi-multi</jats:italic> (multiple media-multiple senses) perspective, where multiple types of media engage multiple senses. Moreover, it addresses the evolution of previously identified challenges in this area and formulates new exploration directions. </jats:p>

<jats:p>The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.</jats:p>

<jats:p>In a contemporary data center, Linux applications often generate a large quantity of real-time system call traces, which are not suitable for traditional host-based intrusion detection systems deployed on every single host. Training data mining models with system calls on a single host that has static computing and storage capacity is time-consuming, and intermediate datasets are not capable of being efficiently handled. It is cumbersome for the maintenance and updating of host-based intrusion detection systems (HIDS) installed on every physical or virtual host, and comprehensive system call analysis can hardly be performed to detect complex and distributed attacks among multiple hosts. Considering these limitations of current system-call-based HIDS, in this article, we provide a review of the development of system-call-based HIDS and future research trends. Algorithms and techniques relevant to system-call-based HIDS are investigated, including feature extraction methods and various data mining algorithms. The HIDS dataset issues are discussed, including currently available datasets with system calls and approaches for researchers to generate new datasets. The application of system-call-based HIDS on current embedded systems is studied, and related works are investigated. Finally, future research trends are forecast regarding three aspects, namely, the reduction of the false-positive rate, the improvement of detection efficiency, and the enhancement of collaborative security.</jats:p>

<jats:p>The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.</jats:p>

<jats:p>Engaging users is a priority for designers of products and services of every kind. The need to understand users’ experiences has motivated a focus on user engagement across computer science. However, to date, there has been limited review of how Human--Computer Interaction and computer science research interprets and employs the concept. Questions persist concerning its conception, abstraction, and measurement. This article presents a systematic review of engagement spanning a corpus of 351 articles and 102 definitions. We map the current state of engagement research, including the diverse interpretation, theory, and measurement of the concept. We describe the ecology of engagement and strategies for the design of engaging experiences, discuss the value of the concept and its relationship to other terms, and present a set of guidelines and opportunities for future research.</jats:p>

<jats:p>Over the recent years, computational trust and reputation models have become an invaluable method to improve computer-computer and human-computer interaction. As a result, a considerable amount of research has been published trying to solve open problems and improving existing models. This survey will bring additional structure into the already conducted research on both topics. After recapitulating the major underlying concepts, a new integrated review and analysis scheme for reputation and trust models is put forward. Using highly recognized review papers in this domain as a basis, this article will also introduce additional evaluation metrics to account for characteristics so far unstudied. A subsequent application of the new review schema on 40 top recent publications in this scientific field revealed interesting insights. While the area of computational trust and reputation models is still a very active research branch, the analysis carried out here was able to show that some aspects have already started to converge, whereas others are still subject to vivid discussions.</jats:p>

<jats:p>Smart cities (SCs) are becoming highly sophisticated ecosystems at which innovative solutions and smart services are being deployed. These ecosystems consider SCs as data production and sharing engines, setting new challenges for building effective SC architectures and novel services. The aim of this article is to “connect the pieces” among Data Science and SC domains, with a systematic literature review which identifies the core topics, services, and methods applied in SC data monitoring. The survey focuses on data harvesting and data mining processes over repeated SC data cycles. A survey protocol is followed to reach both quantitative and semantically important entities. The review results generate useful taxonomies for data scientists in the SC context, which offers clear guidelines for corresponding future works. In particular, a taxonomy is proposed for each of the main SC data entities, namely, the “D Taxonomy” for the data production, the “M Taxonomy” for data analytics methods, and the “S Taxonomy” for smart services. Each of these taxonomies clearly places entities in a classification which is beneficial for multiple stakeholders and for multiple domains in urban smartness targeting. Such indicative scenarios are outlined and conclusions are quite promising for systemizing.</jats:p>