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

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Blockchain-Based Decentralized Authentication Model for IoT-Based E-Learning and Educational Environments

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Computers, Materials & Continua 2023年第5期75卷 3133-3158页

作者： Osama A.Khashan Sultan Alamri Waleed Alomoush Mutasem K.Alsmadi Samer Atawneh Usama Mir Research and Innovation Centers Rabdan AcademyP.O.Box 114646Abu DhabiUnited Arab Emirates College of Computing and Informatics Saudi Electronic UniversityRiyadh13316Saudi Arabia School of Information Technology Skyline University CollegeP.O.Box 1797SharjahUnited Arab Emirates Department of MIS College of Applied Studies and Community ServicesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982DammamSaudi Arabia Department of Computer Science University of WindsorWindsorN9J3Y1Canada

In recent times,technology has advanced significantly and is currently being integrated into educational environments to facilitate distance learning and interaction between *** the Internet of Things(IoT)into education can facilitate the teaching and learning process and expand the context in which students ***,learning data is very sensitive and must be protected when transmitted over the network or stored in data ***,the identity and the authenticity of interacting students,instructors,and staff need to be verified to mitigate the impact of ***,most of the current security and authentication schemes are centralized,relying on trusted third-party cloud servers,to facilitate continuous secure *** addition,most of these schemes are resourceintensive;thus,security and efficiency issues arise when heterogeneous and resource-limited IoT devices are being *** this paper,we propose a blockchain-based architecture that accurately identifies and authenticates learners and their IoT devices in a decentralized manner and prevents the unauthorized modification of stored learning records in a distributed university *** allows students and instructors to easily migrate to and join multiple universities within the network using their identity without the need for user *** proposed architecture was tested using a simulation tool,and measured to evaluate its *** simulation results demonstrate the ability of the proposed architecture to significantly increase the throughput of learning transactions(40%),reduce the communication overhead and response time(26%),improve authentication efficiency(27%),and reduce the IoT power consumption(35%)compared to the centralized authentication *** addition,the security analysis proves the effectiveness of the proposed architecture in resisting various attacks and ensuring the security requirements of learning data in the unive

关键词： Blockchain decentralized authentication Internet of Things(IoT) E-learning IoT security

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Building Knowledge-Grounded Dialogue Systems with Graph-Based Semantic Modeling

arXiv

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arXiv 2022年

作者： Yang, Yizhe Huang, Heyan Gao, Yang Li, Jiawei School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Southeast Academy of Information Technology Beijing Institute of Technology Fujian Putian351100 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

The knowledge-grounded dialogue task aims to generate responses that convey information from given knowledge documents. However, it is a challenge for the current sequence-based model to acquire knowledge from complex documents and integrate it to perform correct responses without the aid of an explicit semantic structure. To address these issues, we propose a novel graph structure, Grounded Graph (G2), that models the semantic structure of both dialogue and knowledge to facilitate knowledge selection and integration for knowledge-grounded dialogue generation. We also propose a Grounded Graph Aware Transformer (G2AT) model that fuses multi-forms knowledge (both sequential and graphic) to enhance knowledge-grounded response generation. Our experiments results show that our proposed model outperforms the previous state-of-the-art methods with more than 10% gains in response generation and nearly 20% improvement in factual consistency. Further, our model reveals good generalization ability and robustness. By incorporating semantic structures as prior knowledge in deep neural networks, our model provides an effective way to aid language generation. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Slice Imputation: Intermediate Slice Interpolation for Anisotropic 3D Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Wu, Zhaotao Wei, Jia Wang, Jiabing Li, Rui The School of Computer Science and Engineer- ing South China University of Technology Guangzhou China The School of Computer Science and Engineering South China University of Technology Guangzhou China The Golisano College of Computing and Information Sciences Rochester Institute of Technology RochesterNY14623 United States

We introduce a novel frame-interpolation-based method for slice imputation to improve segmentation accuracy for anisotropic 3D medical images, in which the number of slices and their corresponding segmentation labels can be increased between two consecutive slices in anisotropic 3D medical volumes. Unlike previous inter-slice imputation methods, which only focus on the smoothness in the axial direction, this study aims to improve the smoothness of the interpolated 3D medical volumes in all three directions: axial, sagittal, and coronal. The proposed multitask inter-slice imputation method, in particular, incorporates a smoothness loss function to evaluate the smoothness of the interpolated 3D medical volumes in the through-plane direction (sagittal and coronal). It not only improves the resolution of the interpolated 3D medical volumes in the through-plane direction but also transforms them into isotropic representations, which leads to better segmentation performances. Experiments on whole tumor segmentation in the brain, liver tumor segmentation, and prostate segmentation indicate that our method outperforms the competing slice imputation methods on both computed tomography and magnetic resonance images volumes in most cases. © 2022, CC BY.

关键词： Magnetic resonance imaging

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Evolutionary Multitasking Based on Team Learning Strategy

Evolutionary Multitasking Based on Team Learning Strategy

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Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Wei Li Xinyu Gao Lei Wang Qingzheng Xu School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China College of Information and Communication National University of Defense Technology Wuhan China

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm.

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Concatenation of the Gottesman-Kitaev-Preskill code with the XZZX surface code

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Physical Review A 2023年第6期107卷 062408-062408页

作者： Jiaxuan Zhang Yu-Chun Wu Guo-Ping Guo Key Laboratory of Quantum Information Chinese Academy of Sciences School of Physics University of Science and Technology of China Hefei Anhui 230026 People's Republic of China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 People's Republic of China Hefei National Laboratory University of Science and Technology of China Hefei 230088 People's Republic of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei Anhui 230088 People's Republic of China Origin Quantum Computing Hefei Anhui 230026 People's Republic of China

Bosonic codes provide an alternative option for quantum error correction. An important category of bosonic codes called the Gottesman-Kitaev-Preskill (GKP) code has aroused much interest recently. Theoretically, the error correction ability of the GKP code is limited since it can only correct small shift errors in position and momentum quadratures. A natural approach to promote the GKP error correction for large-scale, fault-tolerant quantum computation is concatenating encoded GKP states with a stabilizer code. The performance of the XZZX surface-GKP code, i.e., the single-mode GKP code concatenated with the XZZX surface code is investigated in this paper under two different noise models. Firstly, in the code-capacity noise model, the asymmetric rectangular GKP code with parameter λ is introduced. Using the minimum weight perfect matching decoder combined with the continuous-variable GKP information, the optimal threshold of the XZZX-surface GKP code reaches σ≈0.67 when λ=2.1, compared with the threshold σ≈0.60 of the standard surface-GKP code. Secondly, we analyze the shift errors of two-qubit gates in the actual implementation and build the full circuit-level noise model. By setting the appropriate bias parameters, the logical error rate is reduced by several times in some cases. These results indicate the XZZX surface-GKP codes are more suitable for asymmetric concatenation under the general noise models. We also estimate the overhead of the XZZX-surface GKP code which uses about 291 GKP states with the noise parameter 18.5 dB (κ/g≈0.71%) to encode a logical qubit with the error rate 2.53×10−7, compared with the qubit-based surface code using 3041 qubits to achieve almost the same logical error rate.

关键词： Quantum error correction

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Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap Visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

引用

Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： L. Velmurugan R. K Maheswari K. Janani K. Agalya M. Poornima K.C. Gayathri School of Computing Science and Engineering (SCOPE VIT Bhopal University Sehore Madhya Pradesh India Artificial Intelligence and Data Science M.A.M College of Enginnering and Technology Trichy Tamilnadu India Department of Data science and computer applications Manipal institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Information Technology OASYS Institute of Technology Trichy Tamil Nadu India Chettinad School of physiotherapy (CSP Chettinad Hospital and Research Institute (CHRI) Chettinad Academy of Research and Education (CARE) Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet’ s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Medical services Predictive models Brain modeling Robustness Stress Biomedical imaging

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Texture-Guided Saliency Distilling for Unsupervised Salient Object Detection

Texture-Guided Saliency Distilling for Unsupervised Salient ...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Huajun Zhou Bo Qiao Lingxiao Yang Jianhuang Lai Xiaohua Xie School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China

Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundaries. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance. Code is available at ***/moothes/A2S-v2.

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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LHAASO-KM2A detector simulation using Geant4

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Radiation Detection technology and Methods 2024年第3期8卷 1437-1447页

作者： Zhen Cao F.Aharonian Q.An Axikegu Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi J.T.Cai Q.Cao W.Y.Cao Zhe Cao J.Chang J.F.Chang A.M.Chen E.S.Chen Liang Chen Lin Chen Long Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen Y.Chen N.Cheng Y.D.Cheng M.Y.Cui S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu X.Q.Dong K.K.Duan J.H.Fan Y.Z.Fan J.Fang K.Fang C.F.Feng L.Feng S.H.Feng X.T.Feng Y.L.Feng S.Gabici B.Gao C.D.Gao L.Q.Gao Q.Gao W.Gao W.K.Gao M.M.Ge L.S.Geng G.Giacinti G.H.Gong Q.B.Gou M.H.Gu F.L.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.Y.He X.B.He Y.He Y.K.Hor B.W.Hou C.Hou X.Hou H.B.Hu Q.Hu S.C.Hu D.H.Huang T.Q.Huang W.J.Huang X.T.Huang X.Y.Huang Y.Huang Z.C.Huang X.L.Ji H.Y.Jia K.Jia K.Jiang X.W.Jiang Z.J.Jiang M.Jin M.M.Kang T.Ke D.Kuleshov K.Kurinov B.B.Li Cheng Li Cong Li D.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li Jian Li Jie Li K.Li W.L.Li W.L.Li X.R.Li Xin Li Y.Z.Li Zhe Li Zhuo Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.Liu Y.N.Liu R.Lu Q.Luo H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao Z.Min W.Mitthumsiri H.J.Mu Y.C.Nan A.Neronov Z.W.Ou B.Y.Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi Y.Q.Qi B.Q.Qiao J.J.Qin D.Ruffolo A.Sáiz D.Semikoz C.Y.Shao L.Shao O.Shchegolev X.D.Sheng F.W.Shu H.C.Song Yu.V.Stenkin V.Stepanov Y.Su Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Q.W.Tang Z.B.Tang W.W.Tian C.Wang C.B.Wang G.W.Wang H.G.Wang H.H.Wang J.C.Wang K.Wang L.P.Wang L.Y.Wang P.H.Wang R.Wang W.Wang X.G.Wang X.Y.Wang Y.Wang Y.D.Wang Y.J.Wang Z.H.Wang Z.X.Wang Zhen Wang Zheng Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu X.F.Wu Y.S.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang D.X.Xiao G.Xiao G.G.Xin Y.L.Xin Y.Xing Z.Xiong D.L.Xu R.F.Xu R.X.Xu W.L.Xu L.Xue D.H.Yan J.Z.Yan T.Yan C.W.Yang F.Yang F.F.Yang H.W.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.Yue H.D.Zeng T.X.Zeng W.Zeng M.Zha B.B.Zhang F.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang L.X.Zhang Li Zhang P.F.Zhang P.P.Zhang R.Zhang S.B.Zh Key Laboratory of Particle Astrophysics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing100049China University of Chinese Academy of Sciences Beijing100049China TIANFU Cosmic Ray Research Center ChengduSichuanChina Dublin Institute for Advanced Studies 31 Fitzwilliam Place2 DublinIreland Max-Planck-Institute for Nuclear Physics 69029Heidelberg103980Germany State Key Laboratory of Particle Detection and Electronics BeijingChina University of Science and Technology of China Hefei230026AnhuiChina School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong UniversityChengdu610031SichuanChina School of Astronomy and Space Science Nanjing UniversityNanjing210023JiangsuChina Center for Astrophysics Guangzhou UniversityGuangzhou510006GuangdongChina Hebei Normal University Shijiazhuang050024HebeiChina Key Laboratory of Dark Matter and Space Astronomy&Key Laboratory of Radio Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023JiangsuChina Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000TibetChina National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Physics and Astronomy(Zhuhai)&School of Physics(Guangzhou)&Sino-French Institute of Nuclear Engineering and Technology(Zhuhai) Sun Yat-sen UniversityGuangzhouZhuhai 510275519000GuangdongChina School of Physics and Astronomy Yunnan UniversityKunming 650091YunnanChina Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 266237ShandongChina APC UniversitéParis CitéCNRS/IN2P3CEA/IRFUObservatoire de ParisParis 11975205France Department of Engineering Phys

KM2A is one of the main sub-arrays of LHAASO,working on gamma ray astronomy and cosmic ray physics at energies above 10 *** simulation is the important foundation for estimating detector performance and data *** is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units(>6000)with large altitude difference(30)and huge coverage(1.3).In this paper,the design of the KM2A simulation code G4KM2A based on Geant4 is *** process of G4KM2A is optimized mainly in memory consumption to avoid memory *** simplifications are used to significantly speed up the execution of *** running time is reduced by at least 30 times compared to full detector *** particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented,which show good agreement.

关键词： LHAASO KM2A Simulation GEANT4

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