Catálogo de publicaciones - revistas

<jats:p> Many proofs in discrete mathematics and theoretical computer science are based on the probabilistic method. To prove the existence of a good object, we pick a random object and show that it is bad with low probability. This method is effective, but the underlying probabilistic machinery can be daunting. “Encoding arguments” provide an alternative presentation in which probabilistic reasoning is encapsulated in a “uniform encoding lemma.” This lemma provides an upper bound on the probability of an event using the fact that a uniformly random choice from a set of size <jats:italic>n</jats:italic> cannot be encoded with fewer than log <jats:sub>2</jats:sub> <jats:italic>n</jats:italic> bits on average. With the lemma, the argument reduces to devising an encoding where bad objects have short codewords. </jats:p> <jats:p>In this expository article, we describe the basic method and provide a simple tutorial on how to use it. After that, we survey many applications to classic problems from discrete mathematics and computer science. We also give a generalization for the case of non-uniform distributions, as well as a rigorous justification for the use of non-integer codeword lengths in encoding arguments. These latter two results allow encoding arguments to be applied more widely and to produce tighter results.</jats:p>

<jats:p>Traffic congestion has become a vexing and complex issue in many urban areas. Of particular interest are the intersections where traffic bottlenecks are known to occur despite being traditionally signalized. Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted in traffic signal control for monitoring and ameliorating traffic congestion. RL enables autonomous decision makers (e.g., traffic signal controllers) to observe, learn, and select the optimal action (e.g., determining the appropriate traffic phase and its timing) to manage traffic such that system performance is improved. This article reviews various RL models and algorithms applied to traffic signal control in the aspects of the representations of the RL model (i.e., state, action, and reward), performance measures, and complexity to establish a foundation for further investigation in this research field. Open issues are presented toward the end of this article to discover new research areas with the objective to spark new interest in this research field.</jats:p>

<jats:p>Software testing activities account for a considerable portion of systems development cost and, for this reason, many studies have sought to automate these activities. Test data generation has a high cost reduction potential (especially for complex domain systems), since it can decrease human effort. Although several studies have been published about this subject, articles of reviews covering this topic usually focus only on specific domains. This article presents a systematic mapping aiming at providing a broad, albeit critical, overview of the literature in the topic of test data generation using genetic algorithms. The selected studies were categorized by software testing technique (structural, functional, or mutation testing) for which test data were generated and according to the most significantly adapted genetic algorithms aspects. The most used evaluation metrics and software testing techniques were identified. The results showed that genetic algorithms have been successfully applied to simple test data generation, but are rarely used to generate complex test data such as images, videos, sounds, and 3D (three-dimensional) models. From these results, we discuss some challenges and opportunities for research in this area.</jats:p>

<jats:p>While cloud computing has brought paradigm shifts to computing services, researchers and developers have also found some problems inherent to its nature such as bandwidth bottleneck, communication overhead, and location blindness. The concept of fog/edge computing is therefore coined to extend the services from the core in cloud data centers to the edge of the network. In recent years, many systems are proposed to better serve ubiquitous smart devices closer to the user. This article provides a complete and up-to-date review of edge-oriented computing systems by encapsulating relevant proposals on their architecture features, management approaches, and design objectives.</jats:p>

<jats:p>Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them.</jats:p> <jats:p>This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.</jats:p>

<jats:p>Networks represent relationships between entities in many complex systems, spanning from online social interactions to biological cell development and brain connectivity. In many cases, relationships between entities are unambiguously known: are two users “friends” in a social network? Do two researchers collaborate on a published article? Do two road segments in a transportation system intersect? These are directly observable in the system in question. In most cases, relationships between nodes are not directly observable and must be inferred: Does one gene regulate the expression of another? Do two animals who physically co-locate have a social bond? Who infected whom in a disease outbreak in a population?</jats:p> <jats:p>Existing approaches for inferring networks from data are found across many application domains and use specialized knowledge to infer and measure the quality of inferred network for a specific task or hypothesis. However, current research lacks a rigorous methodology that employs standard statistical validation on inferred models. In this survey, we examine (1) how network representations are constructed from underlying data, (2) the variety of questions and tasks on these representations over several domains, and (3) validation strategies for measuring the inferred network’s capability of answering questions on the system of interest.</jats:p>

<jats:p>A huge increase in the number of digital music tracks has created the necessity to develop an automated tool to extract the useful information from these tracks. As this information has to be extracted from the contents of the music, it is known as content-based music information retrieval (CB-MIR). In the past two decades, several research outcomes have been observed in the area of CB-MIR. There is a need to consolidate and critically analyze these research findings to evolve future research directions. In this survey article, various tasks of CB-MIR and their applications are critically reviewed. In particular, the article focuses on eight MIR-related tasks such as vocal/non-vocal segmentation, artist identification, genre classification, raga identification, query-by-humming, emotion recognition, instrument recognition, and music clip annotation. The fundamental concepts of Indian classical music are detailed to attract future research on this topic. The article elaborates on the signal-processing techniques to extract useful features for performing specific tasks mentioned above and discusses their strengths as well as weaknesses. This article also points to some general research issues in CB-MIR and probable approaches toward their solutions so as to improve the efficiency of the existing CB-MIR systems.</jats:p>

<jats:p>During a processor development cycle, post-silicon validation is performed on the first fabricated chip to detect and fix design errors. Design errors occur due to functional issues when a unit in a design does not meet its specification. The chances of occurrence of such errors are high when new features are added in a processor. Thus, in multicore architectures, with new features being added in core and uncore components, the task of verifying the functionality independently and in coordination with other units gains significance. Several new techniques are being proposed in the field of functional validation. In this article, we undertake a survey of these techniques to identify areas that need to be addressed for multicore designs. We start with an analysis of design errors in multicore architectures. We then survey different functional validation techniques based on hardware, software, and formal methods and propose a comprehensive taxonomy for each of these approaches. We also perform a critical analysis to identify gaps in existing research and propose new research directions for validation of multicore architectures.</jats:p>

<jats:p>The use of software is pervasive in all fields of science. Associated software development efforts may be very large, long lived, and complex, requiring the commitment of significant resources. However, several authors have argued that the “gap” or “chasm” between software engineering and scientific programming is a serious risk to the production of reliable scientific results, as demonstrated in a number of case studies. This article reviews the research that addresses the gap, exploring how both software engineering and research practice may need to evolve to accommodate the use of software in science.</jats:p>

<jats:p>Business process models play an important role in today’s enterprises, hence, model repositories may contain hundreds of models. These models are, for example, reused during process modeling activities or utilized to check the conformance of processes with legal regulations. With respect to the amount of models, such applications benefit from or even require detailed insights into the correspondences between process models or between process models’ nodes. Therefore, various process similarity and matching measures have been proposed during the past few years. This article provides an overview of the state-of-the-art regarding business process model similarity measures and aims at analyzing which similarity measures exist, how they are characterized, and what kind of calculations are typically applied to determine similarity values. Finally, the analysis of 123 similarity measures results in the suggestions to conduct further comparative analyses of similarity measures, to investigate the integration of human input into similarity measurement, and to further analyze the requirements of similarity measurement usage scenarios as future research opportunities.</jats:p>