Catálogo de publicaciones - revistas

<jats:p>The hypervolume indicator is one of the most used set-quality indicators for the assessment of stochastic multiobjective optimizers, as well as for selection in evolutionary multiobjective optimization algorithms. Its theoretical properties justify its wide acceptance, particularly the strict monotonicity with respect to set dominance, which is still unique of hypervolume-based indicators. This article discusses the computation of hypervolume-related problems, highlighting the relations between them, providing an overview of the paradigms and techniques used, a description of the main algorithms for each problem, and a rundown of the fastest algorithms regarding asymptotic complexity and runtime. By providing a complete overview of the computational problems associated to the hypervolume indicator, this article serves as the starting point for the development of new algorithms and supports users in the identification of the most appropriate implementations available for each problem.</jats:p>

<jats:p>Data association is a key step within the multi-object tracking pipeline that is notoriously challenging due to its combinatorial nature. A popular and general way to formulate data association is as the NP-hard multi-dimensional assignment problem. Over the past few years, data-driven approaches to assignment have become increasingly prevalent as these techniques have started to mature. We focus this survey solely on learning algorithms for the assignment step of multi-object tracking, and we attempt to unify various methods by highlighting their connections to linear assignment and to the multi-dimensional assignment problem. First, we review probabilistic and end-to-end optimization approaches to data association, followed by methods that learn association affinities from data. We then compare the performance of the methods presented in this survey and conclude by discussing future research directions.</jats:p>

<jats:p> Multitier programming deals with developing the components that pertain to different <jats:italic>tiers</jats:italic> in the system (e.g., client and server), mixing them in the same compilation unit. In this paradigm, the code for different tiers is then either generated at run time or it results from the compiler splitting the codebase into components that belong to different tiers based on user annotations, static analysis, types, or a combination of these. In the Web context, multitier languages aim at reducing the distinction between client and server code, by translating the code that is to be executed on the clients to JavaScript or by executing JavaScript on the server, too. Ultimately, the goal of the multitier approach is to improve program comprehension, simplify maintenance and enable formal reasoning about the properties of the <jats:italic>whole</jats:italic> distributed application. </jats:p> <jats:p>A number of multitier research languages have been proposed over the last decade, which support various degrees of multitier programming and explore different design tradeoffs. In this article, we provide an overview of the existing solutions, discuss their positioning in the design space, and outline open research problems.</jats:p>

<jats:p>For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords.</jats:p>

<jats:p>Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and Naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies.</jats:p> <jats:p>This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced, and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain.</jats:p>

<jats:p>The rapid evolution of mobile devices, their applications, and the amount of data generated by them causes a significant increase in bandwidth consumption and congestions in the network core. Edge Computing offers a solution to these performance drawbacks by extending the cloud paradigm to the edge of the network using capable nodes of processing compute-intensive tasks. In the recent years, vehicular edge computing has emerged for supporting mobile applications. Such paradigm relies on vehicles as edge node devices for providing storage, computation, and bandwidth resources for resource-constrained mobile applications. In this article, we study the challenges of computation offloading for vehicular edge computing. We propose a new classification for the better understanding of the literature designing vehicular edge computing. We propose a taxonomy to classify partitioning solutions in filter-based and automatic techniques; scheduling is separated in adaptive, social-based, and deadline-sensitive methods, and finally data retrieval is organized in secure, distance, mobility prediction, and social-based procedures. By reviewing and analyzing literature, we found that vehicular edge computing is feasible and a viable option to address the increasing volume of data traffic. Moreover, we discuss the open challenges and future directions that must be addressed towards efficient and effective computation offloading and retrieval from mobile users to vehicular edge computing.</jats:p>

<jats:p> The appropriate selection of the arguments of functions that <jats:italic>can be evaluated</jats:italic> in function calls is often useful to improve efficiency, speed, termination behavior, and so on. This is essential, e.g., in the conditional if - then - else operator. We can specify this by associating a set <jats:italic>μ(f)</jats:italic> of indices of <jats:italic>evaluable arguments</jats:italic> to each function symbol <jats:italic>f</jats:italic> . With <jats:italic>μ</jats:italic> (if - then - else)={1}, only the Boolean argument <jats:italic>b</jats:italic> in calls if <jats:italic>b</jats:italic> , then <jats:italic>e</jats:italic> else <jats:italic>e'</jats:italic> is evaluated. In the realm of term rewriting, this is called <jats:italic>context-sensitive rewriting</jats:italic> . It has been proven useful to improve the termination behavior of rewriting computations while it is still able to compute (or approximate) canonical forms like head-normal forms, (infinite) values, and (infinite) normal forms by requiring a few reasonable conditions. This article provides an overview of basic results to use context-sensitive rewriting in practice. </jats:p>

<jats:p>The massive spread of false information on social media has become a global risk, implicitly influencing public opinion and threatening social/political development. False information detection (FID) has thus become a surging research topic in recent years. As a promising and rapidly developing research field, we find that much effort has been paid to new research problems and approaches of FID. Therefore, it is necessary to give a comprehensive review of the new research trends of FID. We first give a brief review of the literature history of FID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection. We further investigate the extraction and usage of various crowd intelligence in FID, which paves a promising way to tackle FID challenges. Finally, we give our views on the open issues and future research directions of FID, such as model adaptivity/generality to new events, embracing of novel machine learning models, aggregation of crowd wisdom, adversarial attack and defense in detection models, and so on.</jats:p>

<jats:p>Recent years have witnessed an exponential increase in the use of mobile and embedded devices. With the great success of deep learning in many fields, there is an emerging trend to deploy deep learning on mobile and embedded devices to better meet the requirement of real-time applications and user privacy protection. However, the limited resources of mobile and embedded devices make it challenging to fulfill the intensive computation and storage demand of deep learning models. In this survey, we conduct a comprehensive review on the related issues for deep learning on mobile and embedded devices. We start with a brief introduction of deep learning and discuss major challenges of implementing deep learning models on mobile and embedded devices. We then conduct an in-depth survey on important compression and acceleration techniques that help adapt deep learning models to mobile and embedded devices, which we specifically classify as pruning, quantization, model distillation, network design strategies, and low-rank factorization. We elaborate on the hardware-based solutions, including mobile GPU, FPGA, and ASIC, and describe software frameworks for mobile deep learning models, especially the development of frameworks based on OpenCL and RenderScript. After that, we present the application of mobile deep learning in a variety of areas, such as navigation, health, speech recognition, and information security. Finally, we discuss some future directions for deep learning on mobile and embedded devices to inspire further research in this area.</jats:p>

<jats:p>This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from—or the same as—the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.</jats:p>