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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Ding, Haorun Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server and Storage Technology Jinan China

ISBN: (纸本)9798350330717

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models. © 2023 IEEE.

关键词： counterfactual explanation machine learning parallel computing

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RSVP for VPSA: A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis

arXiv

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arXiv 2024年

作者： Klaffenboeck, Manfred Gleicher, Michael Sorger, Johannes Wimmer, Michael Möller, Torsten The Institute of Visual Computing and Human Centered Technology TU Wien Austria The Complexity Science Hub Vienna Austria The Department of Computer Sciences The University of Wisconsin - Madison United States The Faculty of Computer Science The University of Vienna The Research Network Data Science Uni Wien Austria

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights. © 2024, CC BY.

关键词： Visualization

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines 6

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

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6th International Conference on Power, Energy and Innovations, ICPEI 2024

作者： Phanomupatum, Pratya Phromloungsri, Ravee Tama, Rachata Pongthavornkamol, Tiwat Wongpatsa, Decha Sonasang, Somchat Nakhon Phanom University Faculty Industrial Technology Department of Electronics Nakhon Phanom Thailand Udon Thani Rajabhat University Computer and Communication Engineering Faculty of Technology Udon Thani Thailand National Electronic and Computer Technology Center National Science and Technology Development Agency Location and Automatic Identification System Research Team Communication and Network Research Group Pathumthani Thailand Faculty Industrial Technology Nakhon Phanom Thailand

ISBN: (纸本)9798350356779

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency. © 2024 IEEE.

关键词： Radio astronomy

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Field-tunable Weyl points and large anomalous Hall effect in the degenerate magnetic semiconductor EuMg2Bi2

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Physical Review B 2023年第12期107卷 L121112-L121112页

作者： M. Kondo M. Ochi R. Kurihara A. Miyake Y. Yamasaki M. Tokunaga H. Nakao K. Kuroki T. Kida M. Hagiwara H. Murakawa N. Hanasaki H. Sakai Department of Physics Osaka University Toyonaka Osaka 560-0043 Japan Forefront Research Center Osaka University Toyonaka Osaka 560-0043 Japan The Institute for Solid State Physics The University of Tokyo Kashiwa Chiba 277-8581 Japan Department of Physics Faculty of Science and Technology Tokyo University of Science Noda Chiba 278-8510 Japan Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Science(NIMS) Tsukuba Ibaraki 305-0047 Japan Center for Emergent Matter Science (CEMS) RIKEN Wako Saitama 351-0198 Japan Photon Factory Institute of Materials Structure Science KEK Tsukuba Ibaraki 305-0801 Japan Center for Advanced High Magnetic Field Science (AHMF) Graduate School of Science Osaka University Toyonaka Osaka 560-0043 Japan Spintronics Research Network Division Institute for Open and Transdisciplinary Reserch Initiatives Osaka University Suita Osaka 565-0871 Japan

Magnets, with topologically nontrivial Dirac/Weyl points, have recently attracted significant attention owing to their unconventional physical properties, such as a large anomalous Hall effect. However, they typically have a high carrier density and a complicated band structure near the Fermi energy. In this Letter, we report a degenerate magnetic semiconductor EuMg2Bi2, which exhibits a single valley at the Γ point, where field-tunable Weyl points form via a magnetic exchange interaction with the local Eu spins. By the high-field measurements on high-quality single crystals, we observed quantum oscillations in the resistivity, elastic constant, and surface impedance, which enabled us to determine the position of the Fermi energy EF. In combination with a first-principles calculation, we revealed that the Weyl points are located in the vicinity of EF when the Eu spins are fully polarized, leading to a peak of energy-dependent anomalous Hall conductivity due to the Berry curvature. Accordingly, in the forced ferromagnetic phase, we observed a large anomalous Hall effect (Hall angle ΘAH∼0.07) qualitatively consistent with the calculation, which demonstrates a marked impact of the Weyl points in the simple band structure.

关键词： Anomalous Hall effect Electrical conductivity Fermi surface First-principles calculations Hall effect Magnetic interactions Magnetic order Magnetoresistance Magnetotransport Shubnikov-de Haas effect Spin-orbit coupling Topological Hall effect Topological materials Transport phenomena de Haas-van Alphen effect

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GPU-based Parallel Technique for Solving the N-Similarity Problem in Textual data Mining 3

GPU-based Parallel Technique for Solving the N-Similarity Pr...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Fazlali, Mahmood Mirhosseini, Mina Shahsavari, Mahyar Shafarenko, Alex Mashinchi, Mashallah University of Hertfordshire Cyber Security and Computing Research Group Department of Computer Science Hertfordshire United Kingdom Shahid Beheshti University Faculty of Mathematical Sciences Department of Computer and Data Sciences Tehran Iran Radboud University Donders Institute for Brain Cognition and Behaviour Ai Department Nijmegen Netherlands Shahid Bahonar University of Kerman Faculty of Mathematics and Computer Department of Statistics Kerman Iran

ISBN: (纸本)9798350381580

An important issue in data mining and information retrieval is the problem of multiple similarity or n-similarity. This problem entails finding a group of n data points with the highest similarity within a large dataset. Exact methods to solve this problem exist but come with high time and space complexities. Additionally, various metaheuristic algorithms have been proposed, including genetic algorithms, gravitational search algorithms, particle swarm optimization, imperialist competitive algorithms, and fuzzy imperialist competitive algorithms. These metaheuristics are capable of finding near-optimal solutions within a reasonable timeframe, although there is no guarantee of achieving exact results. In this paper, we employ a parallelization technique using CUDA to expedite the exact method. We conduct experiments on textual datasets to identify a group of n textual documents with the highest similarity to each other. The experimental results demonstrate that the proposed parallel exact method significantly reduces execution time compared to the best sequential approach and CPU multi-core implementation. Furthermore, it is evident that the proposed method requires less memory space than the exact method. © 2024 IEEE.

关键词： Large datasets

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An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

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AGNN: Alternating Graph-Regularized Neural networks to Alleviate Over-Smoothing

arXiv

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arXiv 2023年

作者： Chen, Zhaoliang Wu, Zhihao Lin, Zhenghong Wang, Shiping Plant, Claudia Guo, Wenzhong The College of Computer and Data Science Fuzhou University Fuzhou350116 China The Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China The Faculty of Computer Science Austria The research network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

Graph Convolutional network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic for complex graph datasets because a deeper GCN should be beneficial to propagating information across remote neighbors. Recent works have devoted effort to addressing over-smoothing problems, including establishing residual connection structure or fusing predictions from multi-layer models. Because of the indistinguishable embeddings from deep layers, it is reasonable to generate more reliable predictions before conducting the combination of outputs from various layers. In light of this, we propose an Alternating Graph-regularized Neural network (AGNN) composed of Graph Convolutional Layer (GCL) and Graph Embedding Layer (GEL). GEL is derived from the graph-regularized optimization containing Laplacian embedding term, which can alleviate the over-smoothing problem by periodic projection from the low-order feature space onto the high-order space. With more distinguishable features of distinct layers, an improved Adaboost strategy is utilized to aggregate outputs from each layer, which explores integrated embeddings of multi-hop neighbors. The proposed model is evaluated via a large number of experiments including performance comparison with some multi-layer or multi-order graph neural networks, which reveals the superior performance improvement of AGNN compared with state-of-the-art models. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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