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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Juárez, Julio Brizuela, Carlos A. Terashima-Marín, Hugo Coello Coello, Carlos A. School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Cicese Research Center Department of Computer Science Ensenada Mexico CINVESTAV-IPN Computer Science Department Mexico City Mexico

ISBN: (纸本)9798350308365

The Team Formation Problem in Social Networks (TFP-SN) describes the process of finding an effective group of people, drawn from a network of experts, to perform a particular task. For a team to be considered as effective, it requires to comply with a task-specific skills set while also showing a high degree of cohesiveness. Although team effectiveness is subject to multiple criteria, the study of the problem from a multi-objective (MO) perspective is still scarce. In this paper, we focus on an MO TFP-SN whose objective is to maximize the team's level of expertise and the team's density, simultaneously. To solve this problem, we introduce two novel recombination operators to be used within the framework of the well-known NSGA-II. Our proposed crossover operators act as heuristics that compute the parents' unique and shared information, which is then combined for generating potentially improved offspring. Our experiments show that each of the two proposed crossover operators lead to significantly better results when compared to a naive crossover operator taken from the specialized literature. Particularly, the results consistently show higher hypervolume values when compared to the use of an adaptation of a simple recombination operator commonly used for this problem. The good performance of our proposed operators may be attributed to the incorporation of knowledge that exploits the structure of the problem. © 2024 IEEE.

关键词： Combinatorial optimization Multi-objective optimization Recombination operators Team formation problem

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Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

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computers, Materials & Continua 2024年第1期78卷 899-914页

作者： Chengsheng Yuan Baojie Cui Zhili Zhou Xinting Li Qingming Jonathan Wu Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou510006China School of International Relations National University of Defense TechnologyNanjing210039China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable ***,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake *** of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial *** addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual *** response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for *** ridge texture area within the fingerprint image has been identified and designated as the region for perturbation ***,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient ***,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack *** results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

关键词： FLD adversarial attacks adversarial examples gradient optimization transferability

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Unknown Category Malicious Traffic Detection Based on Contrastive Learning 6th

Unknown Category Malicious Traffic Detection Based on Contra...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Yan, Leiming Zhou, Tao Chen, Xianyi Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Cyber Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

In response to the continuous emergence of novel unknown malicious traffic and the limitations of traditional detection methods, this paper presents an unknown-category malicious traffic detection approach based on contrastive learning. The proposed method utilizes contrastive learning to train an encoder for distinguishing normal traffic and multiple types of known malicious traffic, thereby enhancing inter-class separability. Subsequently, the model is fine-tuned to optimize the representations of malicious traffic categories with limited samples, amplifying the separation between different classes in the feature space. This ensures that the distance between unknown malicious traffic and other categories exceeds a predefined threshold, thereby achieving effective detection. Experimental results on the CICIoT2023 and UNSW-NB15 datasets demonstrate the superior performance of the proposed method, achieving multi-class F1-scores of 93.23 and 89.93, respectively, outperforming traditional approaches. Additionally, the method achieves an accuracy of over 83% in simulated scenarios involving unknown-category malicious traffic detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models

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Petroleum science 2025年第1期22卷 296-323页

作者： Shadfar Davoodi Hung Vo Thanh David A.Wood Mohammad Mehrad Sergey V.Muravyov Valeriy S.Rukavishnikov School of Earth Sciences&Engineering Tomsk Polytechnic UniversityLenin AvenueTomskRussia Laboratory for Computational Mechanics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityViet Nam Applied Science Research Center Applied Science Private UniversityAmmanJordan DWA Energy Limited LincolnUK Division for Automation&Robotics Tomsk Polytechnic UniversityLenin AvenueTomskRussia

To achieve carbon dioxide(CO_(2))storage through enhanced oil recovery,accurate forecasting of CO_(2) subsurface storage and cumulative oil production is *** study develops hybrid predictive models for the determination of CO_(2) storage mass and cumulative oil production in unconventional *** does so with two multi-layer perceptron neural networks(MLPNN)and a least-squares support vector machine(LSSVM),hybridized with grey wolf optimization(GWO)and/or particle swarm optimization(PSO).Large,simulated datasets were divided into training(70%)and testing(30%)groups,with normalization applied to both *** distance identifies/eliminates outliers in the training subset only.A non-dominated sorting genetic algorithm(NSGA-II)combined with LSSVM selected seven influential features from the nine available input parameters:reservoir depth,porosity,permeability,thickness,bottom-hole pressure,area,CO_(2) injection rate,residual oil saturation to gas flooding,and residual oil saturation to water *** models were developed and tested,with performance evaluated with an overfitting index(OFI),scoring analysis,and partial dependence plots(PDP),during training and independent testing to enhance model focus and *** LSSVM-GWO model generated the lowest root mean square error(RMSE)values(0.4052 MMT for CO_(2) storage and 9.7392 MMbbl for cumulative oil production)in the training *** trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group(RMSE of 0.6224 MMT for CO_(2) storage and 12.5143 MMbbl for cumulative oil production).PDP analysis revealed that the input features“area”and“porosity”had the most influence on the LSSVM-GWO model's pre-diction *** paper presents a new hybrid modeling approach that achieves accurate forecasting of CO_(2) subsurface storage and cumulative oil *** also establishes a new standard for such forecasting,which can

关键词： Hybrid machine learning Least-squares support vector machine Grey wolf optimization Feature selection Carbon dioxide storage Enhanced oil recovery

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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Enhancing Causal Discovery in Federated Settings with Limited Local Samples

Enhancing Causal Discovery in Federated Settings with Limi...

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International Workshop on Trustworthy Federated Learning, FL 2024

作者： Guo, Xianjie Yi, Liping Wu, Xiaohu Yu, Kui Wang, Gang School of Computer Science and Information Engineering Hefei University of Technology Hefei China College of Computer Science Nankai University Tianjin China National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031822391

Causal discovery from observational data is crucial for understanding complex systems, but traditional methods often require centralized data, conflicting with growing privacy concerns. Although federated causal discovery (FCD) has emerged as a solution, existing methods struggle when individual clients possess limited local samples. This paper introduces FedECD, a novel approach addressing causal discovery in federated settings with limited local samples. FedECD comprises two phases: 1) Federated Causal Skeleton Optimization and 2) Federated Causal Structure Refinement, both leveraging Bootstrapping techniques to enhance robustness and accuracy across distributed clients. Both phases employ a two-layer aggregation strategy: client-layer aggregates results from Bootstrapped sub-datasets within each client, while server-layer aggregates across all clients. The first phase uses weighted aggregation to iteratively remove false causal edges based on conditional independence tests. In contrast, the second phase utilizes majority voting to determine edge directions, ensuring robust estimation of the true causal structure. Extensive experiments on eight benchmark Bayesian network datasets demonstrate the superiority of FedECD over existing FCD methods, particularly with limited sample sizes. FedECD achieves an average improvement of 7.53% in the Ar_F1 score compared to the best baseline, addressing a critical challenge in FCD. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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Performance Analysis of VADER and RoBERTa Methods for Smart Retail Customer Sentiment on Amazon Go Store

Performance Analysis of VADER and RoBERTa Methods for Smart ...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Rahmania, Rissa Pohan, Husni Iskandar Arrahmah, Annisa Istiqomah Wibowo, Suryo Adhi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Electrical Engineering Telkom University Ai Center Bandung40257 Indonesia

ISBN: (纸本)9798350369359

The implementation of intelligent systems in the retail industry is still limited in various regions of the world. Amazon Go currently has four stores, located in the Seattle area, and has received customer reviews based on ratings that can be seen in the Google Maps feature. This paper aims to analyze Amazon Go customer sentiment through reviews and star ratings obtained from Google Maps reviews. First, we collect and prepare data from Google Maps Review. Second, we perform experiments and analysis of the VADER and Robustly Optimized BERT Pretraining Approach (RoBERTa) and compare both methods, which are represented by graphs. Lastly, both methods are evaluated in terms of precision, recall, F1-Score, and accuracy. Sentiment analysis from Amazon Go shows that most customers give it a rating of 5.0. Supported by text data from customer comments containing positive, neutral, and negative sentiments showing the relation between the rating given and the sentiment of the comment by the compound score from the results of the VADER method. Sentiment analysis on smart retail from the Amazon Go Store has provided insight and future challenges in the scope of smart retail implementation. © 2023 IEEE.

关键词： Sentiment analysis

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Automated heart disease prediction using improved explainable learning-based technique

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Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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The effect of LPSO phase on the high-temperature oxidation of a stainless Mg-Y-Al alloy

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Journal of Magnesium and Alloys 2024年第10期12卷 4045-4052页

作者： Zhipeng Wang Zhao Shen Yang Liu Yahuan Zhao Qingchun Zhu Yiwen Chen Jingya Wang Yangxin Li Sergio Lozano-Perez Xiaoqin Zeng National Engineering Research Center of Light Alloy Net Forming and State Key Laboratory of Metal Matrix Composites School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai 200240China Department of Materials Science University of OxfordParks RoadOxford OX13PHUK

In this study,we investigated the oxidation of the Mg-11Y-1Al alloy at 500℃in an Ar-20%*** analysis showed the network-like long-period stacking ordered(LPSO)phase transformed into needle-like LPSO and polygonal Mg24Y5 phases,leading to the formation of a high-dense network of needle-like oxides at the oxidation *** oxides grew laterally along the oxide/matrix interfaces,forming a thicker,continuous scale that effectively blocked elemental ***,the preferential oxidation along the needle-like LPSO is believed to accelerate the formation of a thicker and continuous oxide scale,further improving the oxidation resistance of the Mg-11Y-1Al alloy.

关键词： Mg alloy LPSO phase Oxidation TEM TKD.

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