Catálogo de publicaciones - revistas

<jats:p>Digital video stabilization is a challenging task that aims to transform a potentially shaky video into a pleasant one by smoothing the camera trajectory. Despite the various works found in the literature addressing this task, their organization and analysis have not yet received much attention. In this work, we present a thorough review of the literature for video stabilization, organized according to a proposed taxonomy. A formal definition for the problem is introduced, along with a brief interpretation in physical terms. We also provide a comprehensive discussion on the main challenges and future trends for this active area.</jats:p>

<jats:p>Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.</jats:p>

<jats:p>AI in finance refers to the applications of AI techniques in financial businesses. This area has attracted attention for decades, with both classic and modern AI techniques applied to increasingly broader areas of finance, economy, and society. In contrast to reviews on discussing the problems, aspects, and opportunities of finance benefited from specific or some new-generation AI and data science (AIDS) techniques or the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense landscape of the overwhelming challenges, techniques, and opportunities of AIDS research in finance over the past decades. The challenges of financial businesses and data are first outlined, followed by a comprehensive categorization and a dense overview of the decades of AIDS research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. A comparison, criticism, and discussion of classic versus modern AIDS techniques for finance follows. Finally, the open issues and opportunities to address future AIDS-empowered finance and finance-motivated AIDS research are discussed.</jats:p>

<jats:p>With innovations in the field of gaze and eye tracking, a new concentration of research in the area of gaze-tracked systems and user interfaces has formed in the field of Extended Reality (XR). Eye trackers are being used to explore novel forms of spatial human–computer interaction, to understand human attention and behavior, and to test expectations and human responses. In this article, we review gaze interaction and eye tracking research related to XR that has been published since 1985, which includes a total of 215 publications. We outline efforts to apply eye gaze for direct interaction with virtual content and design of attentive interfaces that adapt the presented content based on eye gaze behavior and discuss how eye gaze has been utilized to improve collaboration in XR. We outline trends and novel directions and discuss representative high-impact papers in detail.</jats:p>

<jats:p>It has been a long time that computer architecture and systems are optimized for efficient execution of machine learning (ML) models. Now, it is time to reconsider the relationship between ML and systems and let ML transform the way that computer architecture and systems are designed. This embraces a twofold meaning: improvement of designers’ productivity and completion of the virtuous cycle. In this article, we present a comprehensive review of the work that applies ML for computer architecture and system design. First, we perform a high-level taxonomy by considering the typical role that ML techniques take in architecture/system design, i.e., either for fast predictive modeling or as the design methodology. Then, we summarize the common problems in computer architecture/system design that can be solved by ML techniques and the typical ML techniques employed to resolve each of them. In addition to emphasis on computer architecture in a narrow sense, we adopt the concept that data centers can be recognized as warehouse-scale computers; sketchy discussions are provided in adjacent computer systems, such as code generation and compiler; we also give attention to how ML techniques can aid and transform design automation. We further provide a future vision of opportunities and potential directions and envision that applying ML for computer architecture and systems would thrive in the community.</jats:p>

<jats:p>The proliferation of services and service interactions within microservices and cloud-native applications, makes it harder to detect failures and to identify their possible root causes, which is, on the other hand crucial to promptly recover and fix applications. Various techniques have been proposed to promptly detect failures based on their symptoms, viz., observing anomalous behaviour in one or more application services, as well as to analyse logs or monitored performance of such services to determine the possible root causes for observed anomalies. The objective of this survey is to provide a structured overview and qualitative analysis of currently available techniques for anomaly detection and root cause analysis in modern multi-service applications. Some open challenges and research directions stemming out from the analysis are also discussed.</jats:p>

<jats:p>Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the postprocessing approaches is, however, missing.</jats:p> <jats:p> In this article, we review 130 primary studies that propose postprocessing of alarms. The studies are collected by combining keywords-based database search and snowballing. We categorize approaches proposed by the collected studies into six main categories: <jats:italic>clustering, ranking, pruning, automated elimination of false positives, combination of static and dynamic analyses,</jats:italic> and <jats:italic>simplification of manual inspection.</jats:italic> We provide overview of the categories and sub-categories identified for them, their merits and shortcomings, and different techniques used to implement the approaches. Furthermore, we provide (1) guidelines for selection of the postprocessing techniques by the users/designers of static analysis tools; and (2) directions that can be explored by the researchers. </jats:p>

<jats:p>Given the need for efficient high-performance computing, computer architectures combining central processing units (CPUs), graphics processing units (GPUs), and field-programmable gate arrays (FPGAs) are currently prevalent. However, each of these components suffers from electrical-level security risks. Moving to heterogeneous systems, with the potential of multitenancy, it is essential to understand and investigate how the security vulnerabilities of individual components may affect the system as a whole. In this work, we provide a survey on the electrical-level attacks on CPUs, FPGAs, and GPUs. Additionally, we discuss whether these attacks can extend to heterogeneous systems and highlight open research directions for ensuring the security of heterogeneous computing systems in the future.</jats:p>

<jats:p> With the continued deployment of the <jats:bold>Internet of Things (IoT)</jats:bold> , increasing volumes of devices are being deployed that emit massive spatially referenced data. Due in part to the dynamic, decentralized, and heterogeneous architecture of the IoT, the varying and often low quality of <jats:bold>spatial IoT data (SID)</jats:bold> presents challenges to applications built on top of this data. This survey aims to provide unique insight to practitioners who intend to develop IoT-enabled applications and to researchers who wish to conduct research that relates to data quality in the IoT setting. The survey offers an inventory analysis of major data quality dimensions in SID and covers significant data characteristics and associated quality considerations. The survey summarizes data quality related technologies from both task and technique perspectives. Organizing the technologies from the task perspective, it covers recent progress in SID quality management, encompassing location refinement, uncertainty elimination, outlier removal, fault correction, data integration, and data reduction; and it covers low-quality SID exploitation, encompassing querying, analysis, and decision-making techniques. Finally, the survey covers emerging trends and open issues concerning the quality of SID. </jats:p>

<jats:p>How to transfer the semantic information in a sentence to a computable numerical embedding form is a fundamental problem in natural language processing. An informative universal sentence embedding can greatly promote subsequent natural language processing tasks. However, unlike universal word embeddings, a widely accepted general-purpose sentence embedding technique has not been developed. This survey summarizes the current universal sentence-embedding methods, categorizes them into four groups from a linguistic view, and ultimately analyzes their reported performance. Sentence embeddings trained from words in a bottom-up manner are observed to have different, nearly opposite, performance patterns in downstream tasks compared to those trained from logical relationships between sentences. By comparing differences of training schemes in and between groups, we analyze possible essential reasons for different performance patterns. We additionally collect incentive strategies handling sentences from other models and propose potentially inspiring future research directions.</jats:p>