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conceptual structure of federated learning research field

Procedia computer science 2022年 214卷 1374-1381页

作者： A. Velez-Estevez P. Ducange I.J. Perez M.J. cobo Department of Computer Science and Engineering University of Cadiz Cádiz Spain Department of Information Engineering University of Pisa Italy Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain

Nowadays there are a great amount of data that can be used to train artificial intelligent systems for classification, or prediction purposes. Although there are tons of publicly available data, there are also very valuable data that is private, and therefore, it can not be shared without breaking the data protections laws. For example, hospital data has great value, but it involves persons, so we must try to preserve their privacy rights. Furthermore, although it could be interesting to train a model with the data of only one entity (i.e. a hospital), it could have more value to train the model with the data of several entities. But, since the data of each entity might not be shared, it is not possible to train a global model. In that sense, Federated Learning has emerged as a research field that deals with the training of complex models, without the necessity to share data, and therefore, keeping the data private. In this contribution, we present a global conceptual analysis based on co-words networks of the Federated Learning research field. To do that, the field was delimited using an advance query in Web of science. The corpus contain a total of 2444 documents. As the main result, it should be highlighted that the Federated Learning research field is focused on six main global areas: telecommunications, privacy and security, computer architecture and data modeling, machine learning, and applications.

关键词： Federated Learning bibliometric analysis science mapping analysis co-words SciMAT

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Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations

arXiv

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arXiv 2024年

作者： Germán-Morales, M. Rivera-Rivas, A.J. del Jesus Díaz, M.J. carmona, c.J. Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science University of Jaen JaenE-23071 Spain Leicester School of Pharmacy DeMontfort University LeicesterLE1 7RH United Kingdom

High computational power and the availability of large datasets have supported the development of Foundational Models. They are a new emerging technique widely used in Generative Artificial intelligence, characterized by their scalability and their use in Transfer Learning. The enormous and heterogeneous amounts of data used in their initial training phase, known as pre-training, give them a higher generalization capacity than any other specific model, constituting a solid base that can be adapted or adjusted to a wide range of tasks, increasing their applicability. This study proposes LLIAM, the Llama Lora-Integrated Autorregresive Model. Low-Rank Adaptations are used to enhance the knowledge of the model with diverse time series datasets, known as the fine-tuning phase. To illustrate the capabilities of our proposal, two sets of experiments have been carried out that obtained favorable and promising results with lower training times than other Deep Learning approaches. With this work, we also encourage the use of available resources (such as these pre-trained models) to avoid unnecessary and costly training, narrowing the gap between the goals of traditional Artificial intelligence and those specified by the definition of Green Artificial intelligence. © 2024, cc BY-Nc-ND.

关键词： contrastive Learning

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Ensemble Learning Using Weighted convolutional Neural Networks for Parkinson's Disease

Ensemble Learning Using Weighted Convolutional Neural Networ...

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2022 IEEE Nuclear science Symposium, Medical Imaging conference, and Room Temperature Semiconductor Detector conference, IEEE NSS MIc RTSD 2022

作者： castillo-Barnes, D. Garcia-Diaz, R. Merino-chica, J. Segovia, F. Jimenez-Mesa, c. Arco, J.E. Ramírez, J. Górriz, J.M. University of Granada Andalusian Research Institute on Data Science and Computational Intelligence Dept. of Signal Theory Networking and Communications C. Periodista Daniel Saucedo Aranda S/N Granada18071 Spain University of Malaga Dept. of Communications Engineering Blvr. Louis Pasteur 35 Malaga29004 Spain

ISBN: (纸本)9781665488723

In recent years, there has been a growing interest on computer-Aided-Diagnosis (cAD) systems based on Deep Learning to disentangle how neurodegenerative disorders such as Parkinson's Disease (PD) behave. Although these models are quite accurate, they are often criticized for their lack of interpretability, high complexity and tendency to overfitting (as a result of the amount of input features -intensity voxels from brain scans- in comparison with the reduced number of input samples). Though there are some proposals that are trying to solve these limitations, such as data augmentation and/or interpretable models, it would be interesting, from the point of view of neuroimaging, to develop a cAD model able to 1) split the problem of analyzing the full brain scans and 2) combine the contribution of smaller Regions of Interest (ROIs). This could help us not only to reduce the complexity of the neural networks focused on the analysis of each ROI, but also as a way to quantify the contribution of each region to cAD decisions. In this context, we have proposed an ensemble learning methodology with penalization of non-reliable input sources focused on the analysis of 7 ROIs (including: accumbens, amygdala, anterior cingulate cortex, cingulate, striatum, substantia nigra and thalamus). This method is able to combine the resulting predictions obtained for each ROI by means of a Weighted Majority Voting decision schema using the scores derived from a two-sample Welch's test. As shown by our results, this proposal achieves classification rates of 97.5% and is an easily adaptable solution for the analysis of other pathologies. © 2022 IEEE.

关键词： convolutional neural networks deep learning ensemble learning neuroimaging Parkinson's Disease signal processing

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Learning Agents' Behavioral Patterns in Agent-Based Modeling by Means of Evolutionary Algorithms

Learning Agents' Behavioral Patterns in Agent-Based Modeling...

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congress on Evolutionary computation

作者： Péricles B.c. Miranda Jesús Giráldez-cru Moésio W. Silva-Filho carmen Zarco Oscar cordón Departamento de Computação Universidade Federal Rural de Pernambuco Brazil Dept. of Computer Science and Artificial Intelligence (DECSAI) University of Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain Department of Marketing and Market Research University of Granada Spain

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Agent-based models (ABM) stand as a well-established paradigm in developing computational models. ABM enable the simulation of complex systems by consolidating individual-level interactions through an underlying artificial social network. Upon the accurate construction of a model, experts can employ it as a decision support system for assessing policies in hypothetical what-if scenarios and gaining insights into the operational dynamics of the target system. However, ABM still face diverse challenges such as learning realistic agents' behavioral patterns to model real-world conducts and habits. Existing research shows that machine learning techniques, when used in ABM, can address such challenges. This work focuses on using evolutionary algorithms (EAs) to learn agents' behavior through the definition of their micro-rules, aiming to accurately model them, and thus improving the global performance of the system. To do so, we model the machine learning problem and undertake a comparative analysis of seven EAs, encompassing both classical and recent approaches, to learn agents' micro-rules with a specific focus on consumers' behavior for brand selection. Our investigation involves the assessment of the performance of each EA across problem instances derived from an ABM applied to real-world marketing domains such as dairy products and automakers. The findings of our study reveal a distinct dominance of SHADE-ILS and DEcc-g over the remaining algorithms considered. Furthermore, we underline the advantages of these methods to assist modelers in selecting among the best solutions.

关键词： Decision support systems Analytical models Dairy products consumer behavior Social networking (online) Evolutionary computation Machine learning

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Multicalib4DEB: A toolbox exploiting multimodal optimisation in Dynamic Energy Budget parameters calibration

arXiv

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arXiv 2023年

作者： Robles, Juan Francisco chica, Manuel Filgueira, Ramón Agüera, Antonio Damas, Sergio Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Benthic resources Norwegian Institute of Marine Research BergenNO-5817 Norway Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Marine Affairs Program Life Sciences Centre Dalhousie University 1459 Oxford Street HalifaxNSB3H 4R2 Canada

1. calibration is a crucial step for the validation of computational models and a challenging task to accomplish. 2. Dynamic Energy Budget (DEB) theory has experienced an exponential rise in the number of published papers, which in large part has been made possible by the DEBtool toolbox. Multimodal evolutionary optimisation could provide DEBtool with new capabilities, particularly relevant on the provisioning of equally optimal and diverse solutions. 3. In this paper we present Multicalib4DEB, a MATLAB toolbox directly integrated into the existing DEBtool toolbox, which uses multimodal evolutionary optimisation algorithms to find multiple global and local optimal and diverse calibration solutions for DEB models. 4. Multicalib4DEB adds powerful calibration mechanisms, statistical analysis, and visualisation methods to the DEBtool toolbox and provides a wide range of outputs, different calibration alternatives, and specific tools to strengthen the DEBtool calibration module and to aid DEBtool users to evaluate the performance of the calibration results. © 2023, cc BY-Nc-ND.

关键词： calibration

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On children's Exploration, Aha! Moments and Explanations in Model Building for Self-Regulated Problem-Solving

On Children's Exploration, Aha! Moments and Explanations in ...

引用

2022 Workshop on AI Evaluation Beyond Metrics, EBeM 2022

作者： charisi, Vicky Díaz-Rodríguez, Natalia Mawhin, Barbara Merino, Luis Joint Research Centre European Commission Seville Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence Univers. of Granada Spain Human Factors Department EBT-Salient Aero Foundation Spain Service Robotics Laboratory University Pablo de Olavide Seville Spain

In certain problem-solving tasks that require Human-AI interactions, a mutual understanding of the reasoning behind the performed actions can benefit both humans and artificial agents. However, identifying and predicting the cognitive strategies involved in such a hybrid setting, especially in novel, self-regulated exploratory tasks, is a challenging endeavour. Our aim is to identify behavioural properties relevant to young children's cognitive strategies that are present in problem-solving, with an emphasis on the Aha! moment as an intermediate step between exploratory actions, that typically relate to the development of tacit knowledge, and the generation of explanations that requires explicit knowledge. We use data from existing, previously published, behavioural studies with children 5 to 7 years old to explore these mechanisms in two self-regulated problem-solving tasks. In addition, we reflect on our observations of an Artificial Agent (Q-learning algorithm) that learns to solve the same task. Our findings indicate that while in current reinforcement learning practice, detecting the moment of the cognitive transformation of the problem representation normally translates into observing convergence curves of the objective functions being optimized, in young children this involves more complex behavioural properties, such as verbal metacognition. These behavioural processes can be used as a proxy for the identification of the Aha! moment. Finally, we propose a conceptual map which integrates the observed behaviours that are used to detect, communicate and corroborate learning both in humans and machines and we discuss the association of children's exploratory behaviours, the Aha! moments and ultimately their explanation generation. © 2022 copyright for this paper by its authors. Use permitted under creative commons License Attribution 4.0 International (cc BY 4.0).

关键词： curve fitting

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Explaining Agent-Based Modeling Outputs Using Subgroup Discovery: A case Study in Marketing

SSRN

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SSRN 2024年

作者： Romero, Elena del Jesús, cristóbal José carmona cordón, Oscar del Jesus, María José Damas, Sergio chica, Manuel Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Dept. of Computer Science and Artificial Intelligence DECSAI University of Granada Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Jaen Jaen23071 Spain Leicester School of Pharmacy De Montfort University LeicesterLEI 9BH United Kingdom Dept. of Computer Science University of Jaen Jaen23071 Spain Dept. of Software Engineering University of Granada Granada18071 Spain School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia

Agent-based modeling (ABM) is a popular tool for simulating complex systems. During the simulation, ABM generates valuable information about the agents' roadmap, but the large volume of generated data is difficult to analyze and understand. Improving the understanding of ABM outputs is a key challenge, which can also lead to increase the model understanding. However, most studies analyzing model outputs have mainly focused on model validation or identifying relationships between input and output, with limited emphasis on extracting insights about agent behavior from simulated data. We propose the application of Explainable Artificial intelligence techniques, specifically Subgroup Discovery (SD), to improve model transparency and interpretability. We provide a methodology for applying SD to ABM for extracting useful knowledge of agents' behavior with respect to a variable of interest. We compare the performance of different SD algorithms and show the effectiveness of our methodology through a case study in the marketing area. We analyze the obtained set of rules from the point of view of a marketing expert. The results show that our methodology can provide a deeper understanding of consumer behavior and market dynamics, providing potential areas of opportunity for marketers. © 2024, The Authors. All rights reserved.

关键词： consumer behavior

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Multilevel Modelling of Parkinson’s Disease Symptom Progression in 123I FP-cIT SPEcT

Multilevel Modelling of Parkinson’s Disease Symptom Progres...

引用

IEEE Symposium on Nuclear science (NSS/MIc)

作者： Jose Antonio Simón Rodríguez F. J. Martinez-Murcia J. Ramirez D castillo-Barnes J.M. Gorriz Dept. of Signal Theory Networking and Communications Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder. It is due to the reduction in the neurotransmitter dopamine by the loss of dopamine neurons. Thanks to new high-precision markers, it is possible to study PD before the development of symptoms and thus to model the progression of the disease. In this study, we apply a non-linear decomposition of 123I FP-cIT SPEcT imaging in order to unveil the sources of variability, and relate them to symptomatology and disease progression. Isomap was compared to PcA, and multi-level modelling was used to assess the relation of the resulting composite imaging features and different symptomatology biomarkers and neuropsychological scales. The main source of variability was found to be the intensity at the striata, significantly linked to progression scales such as UPDRS or ScOPA. On the other hand, the modelling found additional sources of variance related to rigidity, tremor or other relevant symptoms.

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The evolution of cooperation in spatial public goods game with tolerant punishment based on reputation threshold

arXiv

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arXiv 2024年

作者： Zhang, Gui Yao, Yichao Zeng, Ziyan Feng, Minyu chica, Manuel The College of Artificial Intelligence Southwest University No.2 Tiansheng Road Beibei Chongqing400715 China Department of Computer Science A.I. 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

Reputation and punishment are significant guidelines for regulating individual behavior in human society, and those with a good reputation are more likely to be imitated by others. In addition, society imposes varying degrees of punishment for behaviors that harm the interests of groups with different reputations. However, conventional pairwise interaction rules and the punishment mechanism overlook this aspect. Building on this observation, this paper enhances a spatial public goods game in two key ways: 1) We set a reputation threshold and use punishment to regulate the defection behavior of players in low-reputation groups while allowing defection behavior in high-reputation game groups. 2) Differently from pairwise interaction rules, we combine reputation and payoff as the fitness of individuals to ensure that players with both high payoff and reputation have a higher chance of being imitated. Through simulations, we find that a higher reputation threshold, combined with a stringent punishment environment, can substantially enhance the level of cooperation within the population. This mechanism provides deeper insight into the widespread phenomenon of cooperation that emerges among individuals. copyright © 2024, The Authors. All rights reserved.

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A consensus model for linguistic group decision making considering emotion analysis

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Procedia computer science 2023年 221卷 1235-1241页

作者： Mengru Xu Fuying Jing Xiangrui chao Enrique Herrera-viedma School of Management Science and Engineering Chongqing Technology and Business University Chongqing 400067 China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing 400067 China School of Business Sichuan University Chengdu 610065 China Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada 18071 Spain

In a problem of linguistic group decision-making (GDM), various emotional characteristics of decision-makers’ (DMs) behind their preference series, which is defined as emotion factor, can impact consensus reaching process. As words mean different things to different people, this study comprehensively proposes a novel consensus model with emotion factors based on personalized individual semantics. An improved grey clustering method facilitates to obtain emotion factors. Finally, the proposal model can provide more accurate judgments for moderator's management and further promote consensus.

关键词： Emotion factors linguistic group decision-making personalized individual semantics grey clustering

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