Persona: Heradio Gil, Rubén
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Heradio Gil
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Rubén
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Publicación A bibliometric analysis of off-line handwritten document analysis literature (1990–2020)(Elsevier, 2022-05) Ruiz Parrado, Victoria; Vélez, José F.; Heradio Gil, Rubén; Aranda Escolástico, Ernesto; Sánchez Ávila, ÁngelProviding computers with the ability to process handwriting is both important and challenging, since many difficulties (e.g., different writing styles, alphabets, languages, etc.) need to be overcome for addressing a variety of problems (text recognition, signature verification, writer identification, word spotting, etc.). This paper reviews the growing literature on off-line handwritten document analysis over the last thirty years. A sample of 5389 articles is examined using bibliometric techniques. Using bibliometric techniques, this paper identifies (i) the most influential articles in the area, (ii) the most productive authors and their collaboration networks, (iii) the countries and institutions that have led research on the topic, (iv) the journals and conferences that have published most papers, and (v) the most relevant research topics (and their related tasks and methodologies) and their evolution over the years.Publicación Using IoT-Type Metadata and Smart Web Design to Create User Interfaces Automatically(Institute of Electrical and Electronics Engineers (IEEE), 2023-02) Chacón, Jesús; Chandramouli, Rajarathnam; Torre Cubillo, Luis de la; Chaos García, Dictino; Heradio Gil, RubénThe advent of the Internet of Things has generated loads of data from the devices that are now connected to the Internet. While the majority of the data corresponds to measurements done by these devices, there is a second type of information (the metadata) that provides information about the devices themselves. Most of this metadata is still underused when used at all. On the other hand, the graphical user interfaces that allow operating and/or monitoring the connected devices from a computer or smartphone, are usually programmed from zero. However, the metadata that describes the main properties of the devices (i.e., inputs, outputs, precision, range, etc.) can be used along with smart web design techniques to automatically create these interfaces. This article proposes a framework to achieve this, and presents an application example consisting of an online lab of a servo-motor.Publicación Finding Near-optimal Configurations in Colossal Spaces with Statistical Guarantees(Association for Computing Machinery (ACM), 2023-11-23) Oh, Jeho; Batory, Don; Heradio Gil, RubénA Software Product Line (SPL) is a family of similar programs. Each program is defined by a unique set of features, called a configuration, that satisfies all feature constraints. “What configuration achieves the best performance for a given workload?” is the SPLOptimization (SPLO) challenge. SPLO is daunting: just 80 unconstrained features yield 1024 unique configurations, which equals the estimated number of stars in the universe. We explain (a) how uniform random sampling and random search algorithms solve SPLO more efficiently and accurately than current machine-learned performance models and (b) how to compute statistical guarantees on the quality of a returned configuration; i.e., it is within x% of optimal with y% confidence.Publicación A Monte Carlo tree search conceptual framework for feature model analyses(Elsevier, 2023-01) Horcas, José Miguel; Galindo, José A.; Benavides, David; Heradio Gil, Rubén; Fernández Amoros, David JoséChallenging domains of the future such as Smart Cities, Cloud Computing, or Industry 4.0 expose highly variable systems with colossal configuration spaces. The automated analysis of those systems’ variability has often relied on SAT solving and constraint programming. However, many of the analyses have to deal with the uncertainty introduced by the fact that undertaking an exhaustive exploration of the whole configuration space is usually intractable. In addition, not all analyses need to deal with the configuration space of the feature models, but with different search spaces where analyses are performed over the structure of the feature models, the constraints, or the implementation artifacts, instead of configurations. This paper proposes a conceptual framework that tackles various of those analyses using Monte Carlo tree search methods, which have proven to succeed in vast search spaces (e.g., game theory, scheduling tasks, security, program synthesis, etc.). Our general framework is formally described, and its flexibility to cope with a diversity of analysis problems is discussed. We provide a Python implementation of the framework that shows the feasibility of our proposal, identifying up to 11 lessons learned, and open challenges about the usage of the Monte Carlo methods in the software product line context. With this contribution, we envision that different problems can be addressed using Monte Carlo simulations and that our framework can be used to advance the state-of-the-art one step forward.Publicación Uniform and scalable sampling of highly configurable systems(Springer, 2022-01-21) Galindo, José A.; Benavides, David; Batory, Don; Heradio Gil, Rubén; Fernández Amoros, David JoséMany analyses on configurable software systems are intractable when confronted with colossal and highly-constrained configuration spaces. These analyses could instead use statistical inference, where a tractable sample accurately predicts results for the entire space. To do so, the laws of statistical inference requires each member of the population to be equally likely to be included in the sample, i.e., the sampling process needs to be “uniform”. SAT-samplers have been developed to generate uniform random samples at a reasonable computational cost. However, there is a lack of experimental validation over colossal spaces to show whether the samplers indeed produce uniform samples or not. This paper (i) proposes a new sampler named BDDSampler, (ii) presents a new statistical test to verify sampler uniformity, and (iii) reports the evaluation of BDDSampler and five other state-of-the-art samplers: KUS, QuickSampler, Smarch, Spur, and Unigen2. Our experimental results show only BDDSampler satisfies both scalability and uniformity.Publicación Scalable Sampling of Highly-Configurable Systems: Generating Random Instances of the Linux Kernel(Association for Computing Machinery (ACM), 2023-01-05) Mayr Dorn, Christoph; Egyed, Alexander; Fernández Amoros, David José; Heradio Gil, RubénSoftware systems are becoming increasingly configurable. A paradigmatic example is the Linux kernel, which can be adjusted for a tremendous variety of hardware devices, from mobile phones to supercomputers, thanks to the thousands of configurable features it supports. In principle, many relevant problems on configurable systems, such as completing a partial configuration to get the system instance that consumes the least energy or optimizes any other quality attribute, could be solved through exhaustive analysis of all configurations. However, configuration spaces are typically colossal and cannot be entirely computed in practice. Alternatively, configuration samples can be analyzed to approximate the answers. Generating those samples is not trivial since features usually have inter-dependencies that constrain the configuration space. Therefore, getting a single valid configuration by chance is extremely unlikely. As a result, advanced samplers are being proposed to generate random samples at a reasonable computational cost. However, to date, no sampler can deal with highly configurable complex systems, such as the Linux kernel. This paper proposes a new sampler that does scale for those systems, based on an original theoretical approach called extensible logic groups. The sampler is compared against five other approaches. Results show our tool to be the fastest and most scalable one.