Catálogo de publicaciones - revistas

<jats:p> Survival analysis is a subfield of statistics where the goal is to analyze and model data where the outcome is the time until an event of interest occurs. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. This so-called <jats:italic>censoring</jats:italic> can be handled most effectively using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome the issue of censoring. In addition, many machine learning algorithms have been adapted to deal with such censored data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the statistical methods typically used and the machine learning techniques developed for survival analysis, along with a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and describe several successful applications in a variety of real-world application domains. We hope that this article will give readers a more comprehensive understanding of recent advances in survival analysis and offer some guidelines for applying these approaches to solve new problems arising in applications involving censored data. </jats:p>

<jats:p>Cloud computing emerged as a centralised paradigm that made “infinite” computing resources available on demand. Nevertheless, the ever-increasing computing capacities present on smart connected things and devices calls for the decentralisation of Cloud computing to avoid unnecessary latencies and fully exploit accessible computing capacities at the edges of the network. Whilst these decentralised Cloud models represent a significant breakthrough from a Cloud perspective, they are rooted in existing research areas such as Mobile Cloud Computing, Mobile Ad hoc Computing, and Edge computing. This article analyses the pre-existing works to determine their role in Decentralised Cloud and future computing development.</jats:p>

<jats:p>Generating a description of an image is called image captioning. Image captioning requires recognizing the important objects, their attributes, and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep-learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey article, we aim to present a comprehensive review of existing deep-learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths, and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep-learning-based automatic image captioning.</jats:p>

<jats:p>Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability.</jats:p>

<jats:p>Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are new paradigms in the move towards open software and network hardware. While NFV aims to virtualize network functions and deploy them into general purpose hardware, SDN makes networks programmable by separating the control and data planes. NFV and SDN are complementary technologies capable of providing one network solution. SDN can provide connectivity between Virtual Network Functions (VNFs) in a flexible and automated way, whereas NFV can use SDN as part of a service function chain. There are many studies designing NFV/SDN architectures in different environments. Researchers have been trying to address reliability, performance, and scalability problems using different architectural designs. This Systematic Literature Review (SLR) focuses on integrated NFV/SDN architectures, with the following goals: (i) to investigate and provide an in-depth review of the state of the art of NFV/SDN architectures, (ii) to synthesize their architectural designs, and (iii) to identify areas for further improvements. Broadly, this SLR will encourage researchers to advance the current stage of development (i.e., the state of the practice) of integrated NFV/SDN architectures and shed some light on future research efforts and the challenges faced.</jats:p>

<jats:p>Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.</jats:p>

<jats:p>Various system metrics have been proposed for measuring the quality of computer-based systems, such as dependability and security metrics for estimating their performance and security characteristics. As computer-based systems grow in complexity with many subsystems or components, measuring their quality in multiple dimensions is a challenging task. In this work, we tackle the problem of measuring the quality of computer-based systems based on the four key attributes of trustworthiness we developed: security, trust, resilience, and agility. In addition to conducting a systematic survey on metrics, measurements, attributes of metrics, and associated ontologies, we propose a system-level trustworthiness metric framework that accommodates four submetrics, called STRAM (&lt;u&gt;S&lt;/u&gt;ecurity, &lt;u&gt;T&lt;/u&gt;rust, &lt;u&gt;R&lt;/u&gt;esilience, and &lt;u&gt;A&lt;/u&gt;gility &lt;u&gt;M&lt;/u&gt;etrics). The proposed STRAM framework offers a hierarchical ontology structure where each submetric is defined as a sub-ontology. Moreover, this work proposes developing and incorporating metrics describing key assessment tools, including vulnerability assessment, risk assessment, and red teaming, to provide additional evidence in the measurement and quality of trustworthy systems. We further discuss how assessment tools are related to measuring the quality of computer-based systems and the limitations of the state-of-the-art metrics and measurements. Finally, we suggest future research directions for system-level metrics research toward measuring fundamental attributes of the quality of computer-based systems and improving the current metric and measurement methodologies.</jats:p>

<jats:p>Mixed reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. With most of the research and development focused on delivering the promise of MR, the privacy and security implications of this technology are yet to be thoroughly investigated. This survey article aims to put in to light these risks and to look into the latest security and privacy work on MR. Specifically, we list and review the different protection approaches that have been proposed to ensure user and data security and privacy in MR. We extend the scope to include work on related technologies such as augmented reality, virtual reality, and human-computer interaction as crucial components, if not the origins, of MR, as well as numerous related work from the larger area of mobile devices, wearables, and Internet-of-Things. We highlight the lack of investigation, implementation, and evaluation of data protection approaches in MR. Further challenges and directions on MR security and privacy are also discussed.</jats:p>

<jats:p>Mobile devices supporting the “Internet of Things” often have limited capabilities in computation, battery energy, and storage space, especially to support resource-intensive applications involving virtual reality, augmented reality, multimedia delivery, and artificial intelligence, which could require broad bandwidth, low response latency, and large computational power. Edge cloud or edge computing is an emerging topic and a technology that can tackle the deficiencies of the currently centralized-only cloud computing model and move the computation and storage resources closer to the devices in support of the above-mentioned applications. To make this happen, efficient coordination mechanisms and “offloading” algorithms are needed to allow mobile devices and the edge cloud to work together smoothly. In this survey article, we investigate the key issues, methods, and various state-of-the-art efforts related to the offloading problem. We adopt a new characterizing model to study the whole process of offloading from mobile devices to the edge cloud. Through comprehensive discussions, we aim to draw an overall “big picture” on the existing efforts and research directions. Our study also indicates that the offloading algorithms in the edge cloud have demonstrated profound potentials for future technology and application development.</jats:p>

<jats:p>In machine learning, Reinforcement Learning (RL) is an important tool for creating intelligent agents that learn solely through experience. One particular subarea within the RL domain that has received great attention is how to define macro-actions, which are temporal abstractions composed of a sequence of primitive actions. This subarea, loosely called skill acquisition, has been under development for several years and has led to better results in a diversity of RL problems. Among the many skill acquisition approaches, graph-based methods have received considerable attention. This survey presents an overview of graph-based skill acquisition methods for RL. We cover a diversity of these approaches and discuss how they evolved throughout the years. Finally, we also discuss the current challenges and open issues in the area of graph-based skill acquisition for RL.</jats:p>