Catálogo de publicaciones - revistas

<jats:p>The use of robots in our daily lives is increasing. Different types of robots perform different tasks that are too dangerous or too dull to be done by humans. These sophisticated machines are susceptible to different types of faults. These faults have to be detected and diagnosed in time to allow recovery and continuous operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches and techniques that are applicable to different types of physical machines. Yet the domain of robotics poses unique requirements that are very challenging for traditional FDD approaches. The study of FDD for robotics is relatively new, and only few surveys were presented. These surveys have focused on traditional FDD approaches and how these approaches may broadly apply to a generic type of robot. Yet robotic systems can be identified by fundamental characteristics, which pose different constraints and requirements from FDD. In this article, we aim to provide the reader with useful insights regarding the use of FDD approaches that best suit the different characteristics of robotic systems. We elaborate on the advantages these approaches have and the challenges they must face. To meet this aim, we use two perspectives: (1) we elaborate on FDD from the perspective of the different characteristics a robotic system may have and give examples of successful FDD approaches, and (2) we elaborate on FDD from the perspective of the different FDD approaches and analyze the advantages and disadvantages of each approach with respect to robotic systems. Finally, we describe research opportunities for robotic systems’ FDD. With these three contributions, readers from the FDD research communities are introduced to FDD for robotic systems, and the robotics research community is introduced to the field of FDD.</jats:p>

<jats:p>This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature.</jats:p>

<jats:p>Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this article, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.</jats:p>

<jats:p>Because of the widespread adoption of GPS-enabled devices, such as smartphones and GPS navigation devices, more and more location information is being collected and available. Compared with traditional ones (e.g., Amazon, Taobao, and Dangdang), recommender systems built on location-based social networks (LBSNs) have received much attention. The former mine users’ preferences through the relationship between users and items, e.g., online commodity, movies and music. The latter add location information as a new dimension to the former, hence resulting in a three-dimensional relationship among users, locations, and activities. In this article, we summarize LBSN recommender systems from the perspective of such a relationship. User, activity, and location are called objects, and recommender objectives are formed and achieved by mining and using such 3D relationships. From the perspective of the 3D relationship among these objects, we summarize the state-of-the-art of LBSN recommender systems to fulfill the related objectives. We finally indicate some future research directions in this area.</jats:p>

<jats:p>High performance computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show that hybrid environments are the natural path to get the best of the on-premise and cloud resources—steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This article brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.</jats:p>

<jats:p>Organizations are exposed to threats that increase the risk factor of their ICT systems. The assurance of their protection is crucial, as their reliance on information technology is a continuing challenge for both security experts and chief executives. As risk assessment could be a necessary process in an organization, one of its deliverables could be utilized in addressing threats and thus facilitate the development of a security strategy. Given the large number of heterogeneous methods and risk assessment tools that exist, comparison criteria can provide better understanding of their options and characteristics and facilitate the selection of a method that best fits an organization's needs. This article aims to address the problem of selecting an appropriate risk assessment method to assess and manage information security risks, by proposing a set of comparison criteria, grouped into four categories. Based upon them, it provides a comparison of the 10 popular risk assessment methods that could be utilized by organizations to determine the method that is more suitable for their needs. Finally, a case study is presented to demonstrate the selection of a method based on the proposed criteria.</jats:p>

<jats:p>Approximate computing has gained research attention recently as a way to increase energy efficiency and/or performance by exploiting some applications’ intrinsic error resiliency. However, little attention has been given to its potential for tackling the communication bottleneck that remains one of the looming challenges to be tackled for efficient parallelism. This article explores the potential benefits of approximate computing for communication reduction by surveying three promising techniques for approximate communication: compression, relaxed synchronization, and value prediction. The techniques are compared based on an evaluation framework composed of communication cost reduction, performance, energy reduction, applicability, overheads, and output degradation. Comparison results demonstrate that lossy link compression and approximate value prediction show great promise for reducing the communication bottleneck in bandwidth-constrained applications. Meanwhile, relaxed synchronization is found to provide large speedups for select error-tolerant applications, but suffers from limited general applicability and unreliable output degradation guarantees. Finally, this article concludes with several suggestions for future research on approximate communication techniques.</jats:p>

<jats:p>Smartphone applications to support healthcare are proliferating. A growing and important subset of these apps supports emergency medical intervention to address a wide range of illness-related emergencies to speed the arrival of relevant treatment. The emergency response characteristics and strategies employed by these apps are the focus of this study, resulting in an mHealth Emergency Strategy Index. While a growing body of knowledge focuses on usability, safety, and privacy aspects that characterize such apps, studies that map the various emergency intervention strategies and suggest assessment indicators to evaluate their role as emergency agents are limited. We survey an extensive range of mHealth apps designed for emergency response along with the related assessment literature and present an index for mobile-based medical emergency intervention apps that can address future assessment needs of mHealth apps.</jats:p>

<jats:p>We present a survey of multi-robot assembly applications and methods and describe trends and general insights into the multi-robot assembly problem for industrial applications. We focus on fixtureless assembly strategies featuring two or more robotic systems. Such robotic systems include industrial robot arms, dexterous robotic hands, and autonomous mobile platforms, such as automated guided vehicles. In this survey, we identify the types of assemblies that are enabled by utilizing multiple robots, the algorithms that synchronize the motions of the robots to complete the assembly operations, and the metrics used to assess the quality and performance of the assemblies.</jats:p>

<jats:p>With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.</jats:p>