Catálogo de publicaciones - revistas

<jats:p>The privacy of individuals does not only depend on their own actions and data but may also be affected by the privacy decisions and by the data shared by other individuals. This interdependence is an essential aspect of privacy and ignoring it can lead to serious privacy violations. In this survey, we summarize and analyze research on interdependent privacy risks and on the associated (cooperative and non-cooperative) solutions. We also demonstrate that interdependent privacy has been studied in isolation in different research communities. By doing so, we systematize knowledge on interdependent privacy research and provide insights on how this research should be conducted and which challenges it should address.</jats:p>

<jats:p>This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.</jats:p>

<jats:p>Computer architecture simulation tools are essential for implementing and evaluating new ideas in the domain and can be useful for understanding the behavior of programs and finding microarchitectural bottlenecks. One particularly important part of almost any processor is the cache hierarchy. While some simulators support simulating a whole processor, including the cache hierarchy, cores, and on-chip interconnect, others may only support simulating the cache hierarchy. This survey provides a detailed discussion on 28 CPU cache simulators, including popular or recent simulators. We compare between all of these simulators in four different ways: major design characteristics, support for specific cache design features, support for specific cache-related metrics, and validation methods and efforts. The strengths and shortcomings of each simulator and major issues that are common to all simulators are highlighted. The information presented in this survey was collected from many different sources, including research papers, documentations, source code bases, and others. This survey is potentially useful for both users and developers of cache simulators. To the best of our knowledge, this is the first comprehensive survey on cache simulation tools.</jats:p>

<jats:p>Ateniese et al. proposed the Provable Data Possession (PDP) model in 2007. Following that, Erway et al. adapted the model for dynamically updatable data and called it the Dynamic Provable Data Possession (DPDP) model. The idea is that a client outsources her files to a cloud server and later challenges the server to obtain a proof of the integrity of her data. Many schemes have later been proposed for this purpose, all following a similar framework.</jats:p> <jats:p>We analyze dynamic data outsourcing schemes for the cloud regarding security and efficiency and show a general framework for constructing such schemes that encompasses existing DPDP-like schemes as different instantiations. This generalization shows that a dynamic outsourced data integrity verification scheme can be constructed given black-box access to an implicitly-ordered authenticated data structure. Moreover, for blockless verification efficiency, a homomorphic verifiable tag scheme is also needed. We investigate the requirements and conditions these building blocks should satisfy, using which one may easily check the applicability of a given building block for dynamic data outsourcing. Our framework serves as a guideline/tutorial/survey and enables us to provide a comparison among different building blocks that existing schemes employ.</jats:p>

<jats:p>Blockchain technology can be extensively applied in diverse services, including online micro-payments, supply chain tracking, digital forensics, health-care record sharing, and insurance payments. Extending the technology to the Internet of things (IoT), we can obtain a verifiable and traceable IoT network. Emerging research in IoT applications exploits blockchain technology to record transaction data, optimize current system performance, or construct next-generation systems, which can provide additional security, automatic transaction management, decentralized platforms, offline-to-online data verification, and so on. In this article, we conduct a systematic survey of the key components of IoT blockchain and examine a number of popular blockchain applications.</jats:p> <jats:p>In particular, we first give an architecture overview of popular IoT-blockchain systems by analyzing their network structures and protocols. Then, we discuss variant consensus protocols for IoT blockchains, and make comparisons among different consensus algorithms. Finally, we analyze the traffic model for P2P and blockchain systems and provide several metrics. We also provide a suitable traffic model for IoT-blockchain systems to illustrate network traffic distribution.</jats:p>

<jats:p>Today, GPUs are widely used as coprocessors/accelerators in High-Performance Heterogeneous Computing due to their many advantages. However, many researches emphasize that GPUs are not as reliable as desired yet. Despite the fact that GPUs are more vulnerable to hardware errors than CPUs, the use of GPUs in HPCs is increasing more and more. Moreover, due to native reliability problems of GPUs, combining a great number of GPUs with CPUs can significantly increase HPCs’ failure rates. For this reason, analyzing the reliability characteristics of GPU-based HPCs has become a very important issue. Therefore, in this study we evaluate the reliability of GPU-based HPCs. For this purpose, we first examined field data analysis studies for GPU-based and CPU-based HPCs and identified factors that could increase systems failure/error rates. We then compared GPU-based HPCs with CPU-based HPCs in terms of reliability with the help of these factors in order to point out reliability challenges of GPU-based HPCs. Our primary goal is to present a study that can guide the researchers in this field by indicating the current state of GPU-based heterogeneous HPCs and requirements for the future, in terms of reliability. Our second goal is to offer a methodology to compare the reliability of GPU-based HPCs and CPU-based HPCs. To the best of our knowledge, this is the first survey study to compare the reliability of GPU-based and CPU-based HPCs in a systematic manner.</jats:p>

<jats:p>Decentralized trust management is used as a referral benchmark for assisting decision making by human or intelligence machines in open collaborative systems. During any given period of time, each participant may only interact with a few other participants. Simply relying on direct trust may frequently resort to random team formation. Thus, trust aggregation becomes critical. It can leverage decentralized trust management to learn about indirect trust of every participant based on past transaction experiences. This article presents alternative designs of decentralized trust management and their efficiency and robustness from three perspectives. First, we study the risk factors and adverse effects of six common threat models. Second, we review the representative trust aggregation models and trust metrics. Third, we present an in-depth analysis and comparison of these reference trust aggregation methods with respect to effectiveness and robustness. We show our comparative study results through formal analysis and experimental evaluation. This comprehensive study advances the understanding of adverse effects of present and future threats and the robustness of different trust metrics. It may also serve as a guideline for research and development of next-generation trust aggregation algorithms and services in the anticipation of risk factors and mischievous threats.</jats:p>

<jats:p>Autonomous mobile robots are required to move throughout map the environment, locate themselves, and plan paths between positions. Vision stands out among the other senses for its richness and practicality. Even though there are well-established autonomous navigation solutions, as far as we can tell, no complete autonomous navigation system that is solely based on vision and that is suitable for dynamic indoor environments has fully succeeded. This article presents a systematic literature review of methods and techniques used to solve the complete autonomous navigation problem or its parts: localization, mapping, path planning, and locomotion. The focus of this review lays on vision-based methods for indoor environments and ground robots. A total of 121 studies were considered, comprising methods, conceptual models, and other literature reviews published between 2000 and 2017. To the best of our knowledge, this is the first systematic review about vision-based autonomous navigation suitable for dynamic indoor environments. It addresses navigation methods, autonomous navigation requirements, vision benefits, methods testing, and implementations validation. The results of this review show a deficiency in testing and validation of presented methods, poor requirements specification, and a lack of complete navigation systems in the literature. These results should encourage new works on computer vision techniques, requirements specification, development, integration, and systematic testing and validation of general navigation systems. In addition to these findings, we also present the complete methodology used for the systematic review, which provides a documentation of the process (allowing quality assessment and repeatability), the criteria for selecting and evaluating the studies, and a framework that can be used for future reviews in this research area.</jats:p>

<jats:p> Recent advancements in artificial intelligence (AI) technologies have induced tremendous growth in innovation and automation. Although these AI technologies offer significant benefits, they can be used maliciously. Highly targeted and evasive attacks in benign carrier applications, such as DeepLocker, have demonstrated the intentional use of AI for harmful purposes. Threat actors are constantly changing and improving their attack strategy with particular emphasis on the application of AI-driven techniques in the attack process, called <jats:italic>AI-based cyber attack</jats:italic> , which can be used in conjunction with conventional attack techniques to cause greater damage. Despite several studies on AI and security, researchers have not summarized AI-based cyber attacks enough to be able to understand the adversary’s actions and to develop proper defenses against such attacks. This study aims to explore existing studies of AI-based cyber attacks and to map them onto a proposed framework, providing insight into new threats. Our framework includes the classification of several aspects of malicious uses of AI during the cyber attack life cycle and provides a basis for their detection to predict future threats. We also explain how to apply this framework to analyze AI-based cyber attacks in a hypothetical scenario of a critical smart grid infrastructure. </jats:p>

<jats:p>The past decade has witnessed the great success of deep learning in many disciplines, especially in computer vision and image processing. However, deep learning-based video coding remains in its infancy. We review the representative works about using deep learning for image/video coding, an actively developing research area since 2015. We divide the related works into two categories: new coding schemes that are built primarily upon deep networks, and deep network-based coding tools that shall be used within traditional coding schemes. For deep schemes, pixel probability modeling and auto-encoder are the two approaches, that can be viewed as predictive coding and transform coding, respectively. For deep tools, there have been several techniques using deep learning to perform intra-picture prediction, inter-picture prediction, cross-channel prediction, probability distribution prediction, transform, post- or in-loop filtering, down- and up-sampling, as well as encoding optimizations. In the hope of advocating the research of deep learning-based video coding, we present a case study of our developed prototype video codec, Deep Learning Video Coding (DLVC). DLVC features two deep tools that are both based on convolutional neural network (CNN), namely CNN-based in-loop filter and CNN-based block adaptive resolution coding. The source code of DLVC has been released for future research.</jats:p>