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Ying Bi, Bing Xue, Mengjie Zhang: Genetic programming for image classification—an automated approach to feature learning

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Genetic Programming and Evolvable Machines 2022年第4期23卷 589-590页

作者： Zafra, Amelia Department of Computer Science and Numerical Analysis Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Cordoba Cordoba Spain

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Optimizing Version Innovation Age for Monitoring Markovian Source in Energy-Harvesting Systems

Optimizing Version Innovation Age for Monitoring Markovian S...

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IEEE conference on Wireless communications and Networking

作者： Mehrdad Salimnejad Anthony Ephremides Marios Kountouris Nikolaos Pappas Department of Computer and Information Science Linköping University Sweden Electrical and Computer Engineering University of Maryland College Park MD USA Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

关键词： Wireless communication Technological innovation Wireless sensor networks Energy consumption Markov decision processes Receivers Real-time systems Batteries Energy harvesting Monitoring

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Analyzing α-divergence in Gaussian Rate-Distortion-Perception Theory

Analyzing α-divergence in Gaussian Rate-Distortion-Percepti...

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IEEE Workshop on Signal Processing Advances in Wireless communications (SPAWc)

作者： Martha V. Sourla Giuseppe Serra Photios A. Stavrou Marios Kountouris School of Electrical & Computer Engineering Technical University of Crete Chania Greece Communication Systems Department EURECOM France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The problem of the estimation of the information rate-distortion-perception function (RDPF), which is a relevant information-theoretic quantity in goal-oriented lossy compression and semantic information reconstruction, is investigated here. Specifically, we study the RDPF tradeoff for Gaussian sources subject to a mean-squared error (MSE) distortion and a perception measure that belongs to the family of α-divergences. Assuming a jointly Gaussian RDPF, which forms a convex optimization problem, we characterize an upper bound for which we find a parametric solution. We show that evaluating the optimal parameters of this parametric solution is equivalent to finding the roots of a reduced exponential polynomial of degree α. Additionally, we determine which disjoint sets contain each root, which enables us to evaluate them numerically using the well-known bisection method. Finally, we validate our analytical findings with numerical results and establish connections with existing results.

关键词： Wireless communication Upper bound Semantics Measurement uncertainty Estimation Distortion Polynomials convex functions Distortion measurement Information theory

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A Group Decision-Making Approach Leveraging Preference Relations Derived from Large Language Model

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Procedia computer science 2024年 242卷 452-459页

作者： José Ramón Trillo María Ángeles Martínez Sławomir Zadrożny Janusz Kacprzyk Enrique Herrera-Viedma Francisco Javier cabrerizo Department of Computer Science and Artificial Intelligence University of Granada Granada 18071 Spain Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada 18071 Spain Department of Social Work and Social Services University of Granada 18001 Granada Spain Systems Research Institute Polish Academy of Sciences Newelska 6 01-447 Warsaw Poland

Group decision-making involves selecting among limited options. Experts share their perspectives in a comparative debate but then evaluate alternatives using preference relations, which can result in inconsistencies between their expressions and assessments. To address this, a method is proposed that automates the generation of these relationships from the debate comments, classifying them into positive and negative using sentiment analysis, namely with the Large Language Model. A new operator is introduced that weights these comments to calculate preference relations. Furthermore, modification of the relationships is allowed if the experts so wish. Moreover, another operator is incorporated that adjusts the weight of each expert according to his or her active participation in the discussion, assigning more weight to those who contribute more comments. Finally, this innovative method promotes coherence and equal participation in group decision-making by employing an innovative sentiment analysis detection system.

关键词： Sentiment Analysis Large Language Model Group Decision-Making Detection System consensus Method

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Balancing Performance, Efficiency and Robustness in Open-World Machine Learning via Evolutionary Multi-objective Model compression

Balancing Performance, Efficiency and Robustness in Open-Wor...

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International Joint conference on Neural Networks (IJcNN)

作者： Javier Del Ser Aitor Martinez-Seras Miren Nekane Bilbao Jesus L. Lobo Ibai Laña Francisco Herrera TECNALIA Basque Research and Technology Alliance (BRTA) Derio Spain University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

When deploying machine learning models on resource-constrained hardware, reducing the memory footprint required by the model without compromising its performance is critical. Moreover, in open-world scenarios models often operate in dynamic and unpredictable environments where the data distribution evolves over time. Robust models can generalize well to unforeseen circumstances, including out-of-distribution inputs that may not have been encountered during the training phase. This adaptability is essential to handle the inherent variability of real-world data. This work formulates a multi-objective optimization problem that aims at optimizing the quantization resolution of the parameters of an already trained machine learning model based on three conflicting goals: maximizing the performance of the model on its designated learning task, minimizing the memory footprint of the compressed model, and enhancing its robustness against out-of-distribution data. Given the complexity of the resulting combinatorial optimization problem, we employ multi-objective evolutionary algorithms to efficiently obtain an approximation of the Pareto front balancing among the aforementioned objectives. Experiments with a randomized neural network compressed under the proposed formulation are run over several benchmark classification datasets. Different multi-objective solvers are employed to compare their effectiveness in terms of the convergence and diversity of their produced Pareto estimations. Additionally, we assess the achieved equilibrium between the three objectives against a floating-point implementation of the same model. Our experiments reveal that both the computational resources and the robustness of the model can be optimized via evolutionary quantization without significantly sacrificing its performance for the task at hand.

关键词： Training Adaptation models Quantization (signal) computational modeling Neural networks Machine learning Evolutionary computation

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An Evolutionary Fuzzy System for Multiclustering in data Streaming

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Procedia computer science 2023年 230卷 33-43页

作者： Luis Alfonso Pérez Martos Ángel Miguel García-Vico Pedro González cristóbal J. carmona Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Jaen E-23071 Jaen Spain Leicester School of Pharmacy De Montfort University LEI 9BH Leicester United Kingdom

The rapid advance of technology has led to exponential growth in the generation of data in various fields, revolutionising how information is captured, processed and analysed. The continuous flow of data has proven to be a valuable resource for agile decision-making. The complexity of being able to make decisions from different sources has led to the emergence of new techniques that aim to obtain as much information as possible to relate data efficiently, taking into account multiple perspectives. A new approach to clustering streaming data from different perspectives is introduced in this paper. It is based on evolutionary algorithms (cHc), fuzzy rules and consensus function. The proposed approach is called FuzzyMulticHcclust-DS. This approach addresses the clustering of streaming data by optimising the clustering process taking into account traditional clustering perspectives such as k-means, clara, Pam, Fanny and MiniBatchKMeans. It has been tested using different sets of linguistic labels, achieving a high degree of compactness and separation of the cl ustered data .

关键词： Evolutionary Fuzzy clustering Multiclustering data Streaming data mining

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Managing Group Decision Making criteria values using Fuzzy Ontologies

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Procedia computer science 2022年 199卷 166-173页

作者： J.A. Morente-Molinera F.J. cabrerizo J.R. Trillo I.J. Pérez E. Herrera-Viedma Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada 18071 Granada Spain Department of Computer Sciences and Engineering University of Cádiz 11519 Puerto Real Spain

Most of the available Multi-criteria Group Decision Making methods that deal with a high number of elements usually focus on managing scenarios that have high number of alternatives and/or experts. Nevertheless, there are also cases in which the number of criteria values is difficult for the experts to tackle. In this paper, a novel Group Decision Making method that employs Fuzzy Ontologies in order to deal with a high number of criteria values is presented. Our method allows the criteria values to be combined in order to generate a reduced set of criteria values that the experts can comfortably deal with.

关键词： Fuzzy Ontologies Multi-criteria Group Decision Making linguistic modelling clustering

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A Large Scale Group Decision Making System Based on Sentiment Analysis cluster

SSRN

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SSRN 2022年

作者： Trillo, José Ramón Herrera-Viedma, Enrique Morente-Molinera, Juan Antonio cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Nowadays, Group Decision Making is an everyday occurrence in different scenarios, such as marketing or social networks. These social networks have facilitated communication between experts because they do not need to meet in person. However, communication between experts through the Internet generates three problems: the management of large amounts of information, the fact that experts often provide their information using natural language, and the lack of analysis of experts’ intentions. In this paper, we propose a novel Large Scale Group Decision Making method to manage the information generated by a large number of experts, using a sentiment analysis with bag-of-words approach. This approach makes it possible to detect the degree of positivity and aggressiveness of each expert. Once the behaviours have been detected, the experts are grouped according to them and, for each group, a weight and a unique preference relation are obtained. Moreover, we propose an optimised consensus analysis process, where it is not necessary to compare all experts with each other, but only groups of experts. Thanks to our method, we can conduct studies of experts’ behaviour in large scale environments. © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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Version Innovation Age and Age of Incorrect Version for Monitoring Markovian Sources

Version Innovation Age and Age of Incorrect Version for Moni...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Mehrdad Salimnejad Marios Kountouris Anthony Ephremides Nikolaos Pappas Department of Computer and Information Science Linköping University Linköping Sweden Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

In this paper, we study the problem of real-time remote tracking of a two-state Markov process. We introduce a modified randomized stationary sampling and transmission policy where the decision to perform sampling occurs probabilistically depending on the current state of the source and whether the system was in a sync state during the previous time slot or not. We then propose two new performance metrics, coined the Version Innovation Age (VIA) and the Age of Incorrect Version (AoIV), and analyze their performance under the modified randomized stationary and other state-of-the-art sampling and transmission policies. Specifically, we derive closed-form expressions for the distribution and the average of VIA and AoIV under these policies. Furthermore, we formulate and solve two constrained optimization problems. The first optimization problem aims to minimize the average VIA subject to constraints on the time-averaged sampling cost and time-averaged reconstruction error. In the second problem, the objective is to minimize the average AoIV while considering a constraint on the time-averaged sampling cost. Finally, we compare the performance of various sampling and transmission policies and identify the conditions under which each policy outperforms the others in optimizing the proposed metrics.

关键词： Measurement Technological innovation costs Transmitters Wireless networks System performance Receivers Synchronization Optimization Monitoring

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Adaptive Payoff-driven Interaction in Networked Snowdrift Games

arXiv

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arXiv 2024年

作者： Xiong, Xiaojin Yao, Yichao Feng, Minyu chica, Manuel College of Artificial Intelligence Southwest University Chongqing400715 China Andalusian Research Institute DaSCI "Data Science and Computational Intelligence" University of Granada Granada18071 Spain School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

In social dilemmas, most interactions are transient and susceptible to restructuring, leading to continuous changes in social networks over time. Typically, agents assess the rewards of their current interactions and adjust their connections to optimize outcomes. In this paper, we introduce an adaptive network model in the snowdrift game to examine dynamic levels of cooperation and network topology, involving the potential for both the termination of existing connections and the establishment of new ones. In particular, we define the agent's asymmetric disassociation tendency toward their neighbors, which fundamentally determines the probability of edge dismantlement. The mechanism allows agents to selectively sever and rewire their connections to alternative individuals to refine partnerships. Our findings reveal that adaptive networks are particularly effective in promoting a robust evolution toward states of either pure cooperation or complete defection, especially under conditions of extreme cost-benefit ratios, as compared to static network models. Moreover, the dynamic restructuring of connections and the distribution of network degrees among agents are closely linked to the levels of cooperation in stationary states. Specifically, cooperators tend to seek broader neighborhoods when confronted with the invasion of multiple defectors. copyright © 2024, The Authors. All rights reserved.

关键词： Defects

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