Artículos (Ciencias de la Computación e Inteligencia Artificial)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11302

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Mostrando 1 - 20 de 436

Uniform solution to Subset Sum by means of virus machines
(Springer Nature, 2025-06-25) Ramírez de Arellano, Antonio; Orellana Martín, David; Cabarle, Francis George C.; Pérez Jiménez, Mario de Jesús; Ciencias de la Computación e Inteligencia Artificial
Unconventional computing plays an important role in computational complexity theory, providing unconventional computing paradigms to tighten the gap between tractable and presumable intractable problems; however, most unconventional paradigms reach similar gaps and their perspective may become stagnant. In this work, we develop a new outlook by a young natural computing paradigm called virus machines (VMs) which takes inspiration from the biological virus life cycle. A new computational complexity theory through VM is developed by attacking a classical NP-complete problem, the Subset Sum problem. It has been uniformly solved by means of deterministic VM. The uniform construction consists of three different modules: one module B for selecting the possible subset; another module that encodes the selection, adds it or not to the final sum, and compares the result; and one last module END to reach the halting configuration and make the output consistent. This design provides a new perspective for solving presumably hard problems by means of families of VMs, opening new research lines in this framework.
Impact of face swapping and data augmentation on sign language recognition
(Springer Nature, 2024-07-24) Perea Trigo, Marina; López Ortiz, Enrique José; Soria Morillo, Luis Miguel; Álvarez García, Juan Antonio; Vegas-Olmos, J. J.; Lenguajes y Sistemas Informáticos; Ciencias de la Computación e Inteligencia Artificial; Universidad de Sevilla/CBUA; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
This study addresses the challenge of improving communication between the deaf and hearing community by exploring different sign language recognition (SLR) techniques. Due to privacy issues and the need for validation by interpreters, creating large-scale sign language (SL) datasets can be difficult. The authors address this by presenting a new Spanish isolated sign language recognition dataset, CALSE-1000, consisting of 5000 videos representing 1000 glosses, with various signers and scenarios. The study also proposes using different computer vision techniques, such as face swapping and affine transformations, to augment the SL dataset and improve the accuracy of the model I3D trained using them. The results show that the inclusion of these augmentations during training leads to an improvement in accuracy in top-1 metrics by up to 11.7 points, top-5 by up to 8.8 points and top-10 by up to 9 points. This has great potential to improve the state of the art in other datasets and other models. Furthermore, the analysis confirms the importance of facial expressions in the model by testing with a facial omission dataset and shows how face swapping can be used to include new anonymous signers without the costly and time-consuming process of recording.
Energy-efficient edge and cloud image classification with multi-reservoir echo state network and data processing units
(MDPI, 2024-06-04) López Ortiz, Enrique José; Perea Trigo, Marina; Soria Morillo, Luis Miguel; Álvarez García, Juan Antonio; Vegas-Olmos, J. J.; Lenguajes y Sistemas Informáticos; Ciencias de la Computación e Inteligencia Artificial; Ministerio de Ciencia e Innovación (MICIN). España
In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational complexity and shorter training times. This study explores the applicability of ESN-based architectures in image classification and weather forecasting tasks, using benchmarks such as the MNIST, FashionMnist, and CloudCast datasets. Through comprehensive evaluations, the Multi-Reservoir ESN (MRESN) architecture emerges as a standout performer, demonstrating its potential for deployment on DPUs or home stations. In exploiting the dynamic adaptability of MRESN to changing input signals, such as weather forecasts, continuous on-device training becomes feasible, eliminating the need for static pre-trained models. Our results highlight the importance of lightweight models such as MRESN in cloud and edge computing applications where efficiency and sustainability are paramount. This study contributes to the advancement of efficient computing practices by providing novel insights into the performance and versatility of MRESN architectures. By facilitating the adoption of lightweight models in resource-constrained environments, our research provides a viable alternative for improved efficiency and scalability in modern computing paradigms.
Dissecting OLMS membrane algorithms: understanding the role of communication and evolutionary operators in optimization strategies
(Springer Nature, 2025-06-24) Andreu Guzmán, José A.; Orellana Martín, David; Valencia Cabrera, Luis; Ciencias de la Computación e Inteligencia Artificial; Lenguajes y Sistemas Informáticos
Metaheuristics are general-purpose optimization techniques designed to explore the solution space of complex problems, balancing between exploration and exploitation, trying to escape local optima. Some techniques are inspired by natural processes, such as simulated annealing, particle swarm optimization, or genetic algorithms. Membrane computing, a computational paradigm based on the behavior and the structure of living cells, has proved capable in solving computationally hard problems in an efficient way. From the intersection of both fields, the framework of membrane algorithms embeds metaheuristics as a way to evolve objects in a membrane system. A thorough study of this framework is presented in this work, deeply analyzing the mutual influence of a variety of strategies of membrane and genetic algorithms, enhancing their synergy in searching for optimal solutions. Specifically, this paper assesses the impact of aspects, such as communication rules, genetic operators or number of membranes, among others. All strategies are compared using well-known problems as Traveling Salesman Problem and Graph Coloring Problem, taken as a benchmark. The results show the best solutions are dependent on the specific problem addressed and the genetic algorithm used but, overall, a distributive send-in strategy is ideal for specializing membranes, allowing some to focus on exploration of the state space and others on exploitation of good solutions.
An Integrative Decision-Making Mechanism for Consumers' Brand Selection using 2-Tuple Fuzzy Linguistic Perceptions and Decision Heuristics
(Springer Heidelberg, 2023) Giraldez Cru, Jesus; Chica, M.; Cordón, O.; Ciencias de la Computación e Inteligencia Artificial; Ministerio de Ciencia e Innovación. España
Consumers perform decision-making (DM) processes to select their preferred brands during their entire consumer journeys. These DM processes are based on the multiple perceptions they have about the products available in the market they are aware of. These consumers usually perform different DM strategies and employ diverse heuristics depending on the nature of the purchase, ranging from more pure optimal choices to faster decisions. Therefore, the design of realistic DM approaches for modeling these consumer behaviors requires a good representation of consumer perceptions and a reliable process for integrating their corresponding heuristics. In this work, we use fuzzy linguistic information to represent consumer perceptions and propose four consumer DM heuristics to model the qualitative linguistic information for the consumer buying decision. In particular, we use 2-tuple fuzzy linguistic variables, which is a substantially more natural and realistic representation without falling in a loss of information. The set of selected heuristics differ in the degree of involvement the consumers give to their decisions. Additionally, we propose a heuristic selection mechanism to integrate the four heuristics in a single DM procedure by using a regulation parameter. Our experimental analysis shows that the combination of these heuristics in a portfolio manner improves the performance of our model with a realistic representation of consumer perceptions. The model’s outcome matches the expected behavior of the consumers in several real market scenarios.
Modeling the opinion dynamics of superstars in the film industry
(Elsevier, 2024-09) Giraldez Cru, Jesus; Suárez-Vázquez, Ana; Zarco, Carmen; Cordón, Oscar; Ciencias de la Computación e Inteligencia Artificial
One of the most challenging questions in the film industry is to rank superstars, which ultimately affects some performance indicators like movie success. In this work, we address this question by means of opinion dynamics models, where the evolution of opinions in a population is analyzed. We apply a model of this kind to study the evolution of opinions about a set of well-known movie superstars in a real-world population. Also, we use real-world data from a specialized cinema website to model mass communication processes (representing film releases and their related news and marketing campaigns), and to measure the performance of our model. Our results show that the proposed model is able to accurately represent this complex system, where the opinion dynamics of superstars are mostly driven by emotional mechanisms, and reveal that film releases and their corresponding marketing campaigns only have a short term effect on those opinions. To the best of our knowledge, this is the first work that applies opinion dynamics models to the study of opinions about superstars in the film industry.
A gene regulatory network critical for axillary bud dormancy directly controlled by Arabidopsis BRANCHED1
(Wiley, 2024) van Es, Sam W.; Muñoz-Gasca, Aitor; Romero Campero, Francisco José; González-Grandío, Eduardo; de Los Reyes, Pedro; Tarancón, Carlos; van Dijk, Aalt D. J.; van Esse, Wilma; Pascual-Garcia, Alberto; Angenent, Gerco C.; Immink, Richard G. H.; Cubas, Pilar; Ciencias de la Computación e Inteligencia Artificial
The Arabidopsis thaliana transcription factor BRANCHED1 (BRC1) plays a pivotal role in the control of shoot branching as it integrates environmental and endogenous signals that influence axillary bud growth. Despite its remarkable activity as a growth inhibitor, the mechanisms by which BRC1 promotes bud dormancy are largely unknown. We determined the genome-wide BRC1 binding sites in vivo and combined these with transcriptomic data and gene co-expression analyses to identify bona fide BRC1 direct targets. Next, we integrated multi-omics data to infer the BRC1 gene regulatory network (GRN) and used graph theory techniques to find network motifs that control the GRN dynamics. We generated an open online tool to interrogate this network. A group of BRC1 target genes encoding transcription factors (BTFs) orchestrate this intricate transcriptional network enriched in abscisic acid-related components. Promoter::b-GLUCURONIDASE transgenic lines confirmed that BTFs are expressed in axillary buds. Transient co-expression assays and studies in planta using mutant lines validated the role of BTFs in modulating the GRN and promoting bud dormancy. This knowledge provides access to the developmental mechanisms that regulate shootbranching and helps identify candidate genes to use as tools to adapt plant architecture and crop production to ever-changing environmental conditions.
Wireless spiking neural P systems
(Springer, 2025-06) Orellana Martín, David; George Cabarle, Francis; Paul, Prithwineel; Zeng, Xiangxiang; Freund, Rudolf; Ciencias de la Computación e Inteligencia Artificial
Spiking neural P systems (SN P systems) are computing models based on the third generation of neuron models known as spiking neurons. Recent results in neuroscience highlight the importance of extrasynaptic activities of neurons, that is, features and functioning of neurons outside their synapses. Previously it was thought that signals such as neuropeptides only assist neurons, but recently such signals have been given additional importance. Inspired by recent results, we define wireless SN P systems (WSN P systems). In WSN P systems, no synapses exist: regular expressions associated with each neuron are used to decide which spikes it receives. We provide two semantics of how to “interpret” the spikes released by neurons. A specific register machine is simulated to show the different style of programming WSN P systems compared to programming standard SN P systems and other variants. This style emphasizes a trade-off: WSN P systems can be more “flexible” since they are not limited by their synapses for sending spikes; however, losing the useful directed graph structure requires careful design of rules and expressions associated with each neuron. We use linear prime number encodings in constructing the expressions and rules of the neurons to prove that WSN P systems are Turing-complete in both spike semantics.
Bike sharing systems data interoperability by a unified station status concept and big data solutions
(Keai Publishing ltd, 2024-04) Márquez-Saldaña, Francisco; Aranda-Corral, Gonzalo A.; Borrego Díaz, Joaquín; Ciencias de la Computación e Inteligencia Artificial; Ministerio de Ciencia e Innovación. España; Junta de Andalucía
The impact of bike sharing systems (BSS) on urban mobility, and their study as part of the overall transport system in smart cities, has attracted significant academic interest in recent years. However, the lack of historical and standardized data in current service tools hinders the analysis and improvement of these platforms, i.e. by reusing technical databased solutions. Big data nature (in volume, variety and velocity) of collecting BSS historical information must be also addressed, in order to take an integrated perspective. This paper describes an integrated solution to this challenge by (1) proposing a unified station status concept for recording historical information, based on the identification, study and unification of common relevant fields found in almost all BSS data warehouses, and (2) implementing a big data-inspired ETL infrastructure together with a storage optimization, methodology which not only allows to access and collect previous defined concepts but also overcomes existing big data challenge when storing BSS information. The system also consumes other external relevant information, such as weather factors, which have been aggregated, enhancing stored knowledge, with KPIs and statistics. The developed solution illustrates how it can manage over seven years of data from twentyseven BSS, serving not only machine-to-machine communication but also human-computer communication and enabling data-driven solutions.
Refining satellite trajectories with celestial body features using neural networks
(Elsevier, 2025-04) Calderón, J.; Ayala Hernández, Daniel; Ayala, R.; Valencia Cabrera, Luis; Hernández Salmerón, Inmaculada Concepción; Ruiz Cortés, David; Lenguajes y Sistemas Informáticos; Ciencias de la Computación e Inteligencia Artificial; Ministerio de Innovación y Ciencia. España
Satellite orbit propagation involves predicting a satellite’s future position and velocity based on initial conditions. Traditional physical models, such as SGDP4, simplify the forces that act on the satellite to achieve high computational efficiency at the cost of reduced prediction accuracy, especially over longer time intervals where error accumulates. More sophisticated models like HPOP offer improved accuracy at the cost of high prediction times, rendering them unusable for realtime long-term predictions. Recent advancements have introduced machine learning techniques to refine these predictions and reduce errors. However, they often lack an analysis of model design choices, such as input feature selection and architectural configurations. Existing models do not incorporate features related to the state of celestial bodies, such as the positions of the Moon or Sun, which can influence the satellite’s trajectory. This paper proposes a novel model that integrates such features at both the initial time and throughout the prediction interval, leveraging their potential impact on the orbit of the satellite. The model is based on a neural network architecture employing GRU layers for encoding sequential data about the celestial conditions. Our results demonstrate that the inclusion of these sequential features significantly reduces prediction errors. Additionally, we have evaluated a variety of design choices such as independent sub-models for specific spatial coordinates and time intervals, further enhancing performance. These innovations lead to substantial improvements in both short- and long-term orbit predictions, providing a more robust and accurate alternative for satellite orbit propagation.
Text-Conditioned Diffusion-Based Synthetic Data Generation for Turbine Engine Sensor Analysis and RUL Estimation
(MDPI, 2025-04-30) Mora-de-León, Luis Pablo; Solís Martín, David; Galán Páez, Juan; Borrego Díaz, Joaquín; Ciencias de la Computación e Inteligencia Artificial; Ministerio de Educación y Ciencia. España
This paper introduces a novel framework for generating synthetic time-series data from turbine engine sensor readings using a text-conditioned diffusion model. The ap proach begins with dataset preprocessing, including correlation analysis, feature selection, and normalization. Principal Component Analysis (PCA) transforms the normalized sig nals into three components, mapped to the RGB channels of an image. These components, combined with engine identifiers and cycle information, form compact 19 × 19 × 3 pixel images, later scaled to 512 × 512 × 3pixels. Avariational autoencoder (VAE)-based diffusion model, fine-tuned on these images, leverages text prompts describing engine characteristics to generate high-quality synthetic samples. A reverse transformation pipeline reconstructs synthetic images back into time-series signals, preserving the original engine-specific at tributes while removing padding artifacts. The quality of the synthetic data is assessed by training Remaining Useful Life (RUL) estimation models and comparing performance across original, synthetic, and combined datasets. Results demonstrate that synthetic data can be beneficial for model training, particularly in the early epochs when working with limited datasets. Compared to existing approaches, which rely on generative adversarial networks (GANs) or deterministic transformations, the proposed framework offers en hanceddatafidelity andadaptability.
DiffLIME: Enhancing Explainability with a Diffusion-Based LIME Algorithm for Fault Diagnosis
(PHM Society, 2025) Solís Martín, David; Galán Páez, Juan; Borrego Díaz, Joaquín; Ciencias de la Computación e Inteligencia Artificial; Ministerio de Ciencia e Innovación. España
The aim of predictive maintenance within the field of Prog nostics and Health Management (PHM) is to identify and anticipate potential issues in equipment before they become serious. Deep learning models, such as deep convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers, have been widely adopted for this task, achieving significant success. However, these mod els are often considered “black boxes” due to their opaque decision-making processes, making it challenging to explain their outputs to stakeholders, such as industrial equipment ex perts. The complexity and large number of parameters in these models further complicate the understanding of their predictions. This paper presents a novel explainable AI algorithm that ex tends the well-known Local Interpretable Model-agnostic Ex planations (LIME). Our approach utilizes a conditioned prob abilistic diffusion model to generate altered samples in the neighborhood of the source sample. We validate our method using various rotating machinery diagnosis datasets. Addi tionally, we compare our method against LIME, employing a set of metrics to quantify desirable properties of any ex plainable AI approach. The results highlight that DiffLIME consistently outperforms LIME in terms of coherence and stability while maintaining comparable performance in the selectivity metric. Moreover, the ability of DiffLIME to in corporate domain-specific meta-attributes, such as frequency components and signal envelopes, significantly enhances its explainability in the context of fault diagnosis. This approach provides more precise and meaningful insights into the pre dictions made by the model.
Exploring deep echo state networks for image classification: a multi-reservoir approach
(Springer, 2024-04-18) López Ortiz, Enrique José; Perea Trigo, María; Soria Morillo, Luis Miguel; Sancho Caparrini, Fernando; Vegas-Olmos, J. J.; Lenguajes y Sistemas Informáticos; Ciencias de la Computación e Inteligencia Artificial; Ministerio de Ciencia e Innovación. España
Echo state networks (ESNs) belong to the class of recurrent neural networks and have demonstrated robust performance in time series prediction tasks. In this study, we investigate the capability of different ESN architectures to capture spatial relationships in images without transforming them into temporal sequences. We begin with three pre-existing ESN-based architectures and enhance their design by incorporating multiple output layers, customising them for a classification task. Our investigation involves an examination of the behaviour of these modified networks, coupled with a comprehensive performance comparison against the baseline vanilla ESN architecture. Our experiments on the MNIST data set reveal that a network with multiple independent reservoirs working in parallel outperforms other ESN-based architectures for this task, achieving a classification accuracy of 98.43%. This improvement on the classical ESN architecture is accompanied by reduced training times. While the accuracy of ESN-based architectures lags behind that of convolutional neural network based architectures, the significantly lower training times of ESNs with multiple reservoirs operating in parallel make them a compelling choice for learning spatial relationships in scenarios prioritising energy efficiency and rapid training. This multi-reservoir ESN architecture overcomes standard ESN limitations regarding memory requirements and training times for large networks, providing more accurate predictions than other ESN-based models. These findings contribute to a deeper understanding of the potential of ESNs as a tool for image classification.
Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program
(Nature Publishing Group, 2025-02-27) Moraza, Javier; Esteban-Aizpiri, Cristobal; Aramburu, Amaia; Garcia, Pedro; Sancho Caparrini, Fernando; Resino, Sergio; Chasco, Leyre; Conde, Francisco Jose; Gutierrez, Jose Antonio; Santano, Dabi; Esteban, Cristobal; Ciencias de la Computación e Inteligencia Artificial
COPD exacerbations have a profound clinical impact on patients. Accurately predicting these events could help healthcare professionals take proactive measures to mitigate their impact. For over a decade, telEPOC, a telehealthcare program, has collected data that can be utilized to train machine learning models to anticipate COPD exacerbations. The objective of this study is to develop a machine learning model that, based on a patient’s history, predicts the probability of an exacerbation event within the next 3 days. After cleaning and harmonizing the different subsets of data, we split the data along the temporal axis: one subset for model training, another for model selection, and another for model evaluation. We then trained a gradient tree boosting approach as well as neural network-based approaches. After conducting our analysis, we found that the CatBoost algorithm yielded the best results, with an area under the precision-recall curve of 0.53 and an area under the ROC curve of 0.91. Additionally, we assessed the significance of the input variables and discovered that breathing rate, heart rate, and SpO2 were the most informative. The resulting model can operate in a 50% recall and 50% precision regime, which we consider has the potential to be useful in daily practice.
Analysis of selection noise in genetic algorithms
(Springer, 2025-05) Gulayeva, Nataliya M.; Borrego Díaz, Joaquín; Sancho Caparrini, Fernando; Ciencias de la Computación e Inteligencia Artificial; Universidad de Sevilla
Selection is often considered as a fundamental force in the evolutionary process. Genetic drift, or selection noise, is an important characteristic of selection methods. It has a direct effect on the performance of genetic algorithms. In this paper, a brief review of methods to analyze genetic drift is given, and known estimations of selection noise of various selection schemes used in genetic algorithms are presented. After that, genetic drift ofwidely used proportional, ranking, and tournament selection schemes is thoroughly studied. To this end, two new measures for selection noise analysis are proposed, namely the noise takeover time and pure reproduction rate. Using these measures, the effect of population size, chromosome length, and selection scheme parameters on genetic drift is analyzed. Also, selection schemes known as being selection pressure equivalent are tested for selection noise equivalence. Both theoretical and experimental approaches are used for the analysis. The results obtained are presented in tabular form. Wherever possible, it is indicated whether the obtained results are identical or different from the results of previous studies. Since no comprehensive study of selection noise has been conducted previously, this indication concerns only some of the results. Although our results differ at some points from those presented earlier, they are consistent on both measures.
Review of spoken dialogue systems
(Consejo Superior de Investigaciones Científicas CSIC, 2014-07) López-Cózar Delgado, Ramón; Callejas, Zaraida; Griol, David; Quesada Moreno, José Francisco; Ciencias de la Computación e Inteligencia Artificial
Spoken dialogue systems are computer programs developed to interact with users employing speech in order to provide them with specific automated services. The interaction is carried out by means of dialogue turns, which in many studies available in the literature, researchers aim to make as similar as possible to those between humans in terms of naturalness, intelligence and affective content. In this paper we describe the fundaments of these systems including the main technologies employed for their development. We also present an evolution of this technology and discuss some current applications. Moreover, we discuss development paradigms, including scripting languages and the development of conversational interfaces for mobile apps. The correct modelling of the user is a key aspect of this technology. This is why we also describe affective, personality and contextual models. Finally, we address some current research trends in terms of verbal communication, multimodal interaction and dialogue management.
Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
(MDPI, 2024-10-19) Pérez-Zarate, Sergio A.; Corzo-García, Daniel; Pro Martín, José Luis; Álvarez García, Juan Antonio; Martínez del Amor, Miguel Ángel; Fernández-Cabrera, David; Ciencias de la Computación e Inteligencia Artificial; Lenguajes y Sistemas Informáticos; MCIN/AEI/ 10.13039/501100011033
Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the company analyzes the customer’s images, the models used for the inference, and the results. We perform extensive experiments to show that our proposal, an ensemble of 10 deep learning detectors based on YOLOv5, improves the state-of-the-art not only in terms of typical metrics but also in terms of inference speed, allowing scalability to thousands of instances per minute. A comparison with YOLOv8 is carried out, showing the differences between both ensembles. Furthermore, a dataset called TartesiaDS, labeled under the supervision of professional appraisers from insurance companies, is available to the community for evaluation of future proposals.
Infinite Spike Trains in Spiking Neural P Systems
(EDITURA ACAD ROMANE, 2023) Păun, Gheorghe; Pérez Jiménez, Mario de Jesús; Rozenberg, Grzegorz; Ciencias de la Computación e Inteligencia Artificial
We initiate the study of spiking neural P systems associated with infinite sequences, by considering them as computability devices which generate infinite sequences of bits (1 indicates a step when a spike exits the system, and 0 indicates a step when the system does not send a spike to the environment), and as devices which process infinite sequence of bits (for instance, computing Boolean operations or other operations on two input sequences). For both the generating and the transduction case we introduce some basic notions illustrated by numerous examples, establish some basic properties, and formulate a number of research topics.
A New Methodology for Software-Simulation of Membrane Systems Using a Multi-Thread Programming Model
(2024-01) Cascado Caballero, Daniel; Díaz del Río, Fernando; Cagigas Muñiz, Daniel; Orellana Martín, David; Pérez Hurtado de Mendoza, Ignacio; Ciencias de la Computación e Inteligencia Artificial; Arquitectura y Tecnología de Computadores
The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a methodology for simulating P systems using a multi-threaded methodological approach in a multi-core processor. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a multiprocessor. The results obtained suggest that the methodology is valid and that it is worth testing it with more complex systems to find the limits of the methodology
Using virus machines to compute pairing functions
(World Scientific Publishing, 2023-03) Ramírez de Arellano Marrero, Antonio; Orellana Martín, David; Pérez Jiménez, Mario de Jesús; Ciencias de la Computación e Inteligencia Artificial; FEDER/Junta de Andalucía — Paidi 2020/Proyecto
Virus machines are computational devices inspired by the movement of viruses between hosts and their capacity to replicate using the resources of the hosts. This behavior is controlled by an external graph of instructions that opens different channels of the system to make viruses capable of moving. This model of computation has been demonstrated to be as powerful as turing machines by different methods: by generating Diophantine sets, by computing partial recursive functions and by simulating register machines. It is interesting to investigate the practical use cases of this model in terms of possibilities and efficiency. In this work, we give the basic modules to create an arithmetic calculator. As a practical application, two pairing functions are calculated by means of two different virus machines. Pairing functions are important resources in the field of cryptography. The functions calculated are the Cantor pairing function and the G¨odel pairing function.

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