Catálogo de publicaciones - revistas

<jats:p>In this article, we present literature reviews on fire prevention methods, especially in mining industries, using thermal image processing techniques. Fire protection systems are crucial because of the increased loss of human lives due to coal fires and fatal explosions in coal mines across the world in the past few decades. And with the growth in the demand for energy and the mining of coal expected up to the year 2050, determining conditions leading up to a breakout of fire is paramount. To detect uncertain fire breakout conditions, thermal imaging is considered the most significant among several early warning methods to recognize spontaneous combustion of coal piles (e.g., temperature recordings by sensors, compaction testing of ore seam, gas tests). The evolution of thermographic imaging applied in various industrial sectors (e.g., coal furnaces, oil tankers, building inspections, security) with numerous applications of mathematical models will be presented in the light of safety dimensions in the mining industry. The missing links or unattended areas of mathematics in the application of thermal image processing in mining, especially in the coal industry, will be evolved as the gap in knowledge suggested in our concluding statements.</jats:p>

<jats:p>Regression test case selection techniques attempt to increase the testing effectiveness based on the measurement capabilities, such as cost, coverage, and fault detection. This systematic literature review presents state-of-the-art research in effective regression test case selection techniques. We examined 47 empirical studies published between 2007 and 2015. The selected studies are categorized according to the selection procedure, empirical study design, and adequacy criteria with respect to their effectiveness measurement capability and methods used to measure the validity of these results.</jats:p> <jats:p>The results showed that mining and learning-based regression test case selection was reported in 39% of the studies, unit level testing was reported in 18% of the studies, and object-oriented environment (Java) was used in 26% of the studies. Structural faults, the most common target, was used in 55% of the studies. Overall, only 39% of the studies conducted followed experimental guidelines and are reproducible.</jats:p> <jats:p>There are 7 different cost measures, 13 different coverage types, and 5 fault-detection metrics reported in these studies. It is also observed that 70% of the studies being analyzed used cost as the effectiveness measure compared to 31% that used fault-detection capability and 16% that used coverage.</jats:p>

<jats:p>As the trends of process scaling make memory systems an even more crucial bottleneck, the importance of latency hiding techniques such as prefetching grows further. However, naively using prefetching can harm performance and energy efficiency and, hence, several factors and parameters need to be taken into account to fully realize its potential. In this article, we survey several recent techniques that aim to improve the implementation and effectiveness of prefetching. We characterize the techniques on several parameters to highlight their similarities and differences. The aim of this survey is to provide insights to researchers into working of prefetching techniques and spark interesting future work for improving the performance advantages of prefetching even further.</jats:p>

<jats:p>One of the main characteristics of Wireless Sensor Networks (WSNs) is the constrained energy resources of their wireless sensor nodes. Although this issue has been addressed in several works and received much attention over the years, the most recent advances pointed out that the energy harvesting and wireless charging techniques may offer means to overcome such a limitation. Consequently, an issue that had been put in second place now emerges: the low availability of spectrum resources. Because of it, the incorporation of the WSNs into the Internet of Things and the exponential growth of the latter may be hindered if no control over the data generation is taken. Alternatively, part of the sensed data can be predicted without triggering transmissions that could congest the wireless medium. In this work, we analyze and categorize existing prediction-based data reduction mechanisms that have been designed for WSNs. Our main contribution is a systematic procedure for selecting a scheme to make predictions in WSNs, based on WSNs’ constraints, characteristics of prediction methods, and monitored data. Finally, we conclude the article with a discussion about future challenges and open research directions in the use of prediction methods to support the WSNs’ growth.</jats:p>

<jats:p>Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide range of problems. Their robustness, however, may be affected by several adjustable parameters, such as mutation rate, crossover rate, and population size. Algorithm parameters are usually problem-specific, and often have to be tuned not only to the problem but even the problem instance at hand to achieve ideal performance. In addition, research has shown that different parameter values may be optimal at different stages of the optimisation process. To address these issues, researchers have shifted their focus to adaptive parameter control, in which parameter values are adjusted during the optimisation process based on the performance of the algorithm. These methods redefine parameter values repeatedly based on implicit or explicit rules that decide how to make the best use of feedback from the optimisation algorithm.</jats:p> <jats:p>In this survey, we systematically investigate the state of the art in adaptive parameter control. The approaches are classified using a new conceptual model that subdivides the process of adapting parameter values into four steps that are present explicitly or implicitly in all existing approaches that tune parameters dynamically during the optimisation process. The analysis reveals the major focus areas of adaptive parameter control research as well as gaps and potential directions for further development in this area.</jats:p>

<jats:p>One of the main challenges in security today is defending against malware attacks. As trends and anecdotal evidence show, preventing these attacks, regardless of their indiscriminate or targeted nature, has proven difficult: intrusions happen and devices get compromised, even at security-conscious organizations. As a consequence, an alternative line of work has focused on detecting and disrupting the individual steps that follow an initial compromise and are essential for the successful progression of the attack. In particular, several approaches and techniques have been proposed to identify the command and control (C8C) channel that a compromised system establishes to communicate with its controller.</jats:p> <jats:p>A major oversight of many of these detection techniques is the design’s resilience to evasion attempts by the well-motivated attacker. C8C detection techniques make widespread use of a machine learning (ML) component. Therefore, to analyze the evasion resilience of these detection techniques, we first systematize works in the field of C8C detection and then, using existing models from the literature, go on to systematize attacks against the ML components used in these approaches.</jats:p>

<jats:p>Today, soft errors are one of the major design technology challenges at and beyond the 22nm technology nodes. This article introduces the soft error problem from the perspective of processor design. This article also provides a survey of the existing soft error mitigation methods across different levels of design abstraction involved in processor design, including the device level, the circuit level, the architectural level, and the program level.</jats:p>

<jats:p>This article comprehensively surveys Arabic Online Handwriting Recognition (AOHR). We address the challenges posed by online handwriting recognition, including ligatures, dots and diacritic problems, online/offline touching of text, and geometric variations. Then we present a general model of an AOHR system that incorporates the different phases of an AOHR system. We summarize the main AOHR databases and identify their uses and limitations. Preprocessing techniques that are used in AOHR, viz. normalization, smoothing, de-hooking, baseline identification, and delayed stroke processing, are presented with illustrative examples. We discuss different techniques for Arabic online handwriting segmentation at the character and morpheme levels and identify their limitations. Feature extraction techniques that are used in AOHR are discussed and their challenges identified. We address the classification techniques of non-cursive (characters and digits) and cursive Arabic online handwriting and analyze their applications. We discuss different classification techniques, viz. structural approaches, Support Vector Machine (SVM), Fuzzy SVM, Neural Networks, Hidden Markov Model, Genetic algorithms, decision trees, and rule-based systems, and analyze their performance. Post-processing techniques are also discussed. Several tables that summarize the surveyed publications are provided for ease of reference and comparison. We summarize the current limitations and difficulties of AOHR and future directions of research.</jats:p>

<jats:p>The integration of graphics processing units (GPUs) on high-end compute nodes has established a new accelerator-based heterogeneous computing model, which now permeates high-performance computing. The same paradigm nevertheless has limited adoption in cloud computing or other large-scale distributed computing paradigms. Heterogeneous computing with GPUs can benefit the Cloud by reducing operational costs and improving resource and energy efficiency. However, such a paradigm shift would require effective methods for virtualizing GPUs, as well as other accelerators. In this survey article, we present an extensive and in-depth survey of GPU virtualization techniques and their scheduling methods. We review a wide range of virtualization techniques implemented at the GPU library, driver, and hardware levels. Furthermore, we review GPU scheduling methods that address performance and fairness issues between multiple virtual machines sharing GPUs. We believe that our survey delivers a perspective on the challenges and opportunities for virtualization of heterogeneous computing environments.</jats:p>

<jats:p>Searchable Symmetric Encryption (SSE) when deployed in the cloud allows one to query encrypted data without the risk of data leakage. Despite the widespread interest, existing surveys do not examine in detail how SSE’s underlying structures are designed and how these result in the many properties of a SSE scheme. This is the gap we seek to address, as well as presenting recent state-of-the-art advances on SSE. Specifically, we present a general framework and believe the discussions may lead to insights for potential new designs. We draw a few observations. First, most schemes use index table, where optimal index size and sublinear search can be achieved using an inverted index. Straightforward updating can only be achieved using direct index, but search time would be linear. A recent trend is the combinations of index table, and tree, deployed for efficient updating and storage. Secondly, mechanisms from related fields such as Oblivious RAM (ORAM) have been integrated to reduce leakages. However, using these mechanisms to minimise leakages in schemes with richer functionalities (e.g., ranked, range) is relatively unexplored. Thirdly, a new approach (e.g., multiple servers) is required to mitigate new and emerging attacks on leakage. Lastly, we observe that a proposed index may not be practically efficient when implemented, where I/O access must be taken into consideration.</jats:p>