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arXiv 2021年

作者： Xu, Jing Pan, Yu Pan, Xinglin Hoi, Steven Yi, Zhang Xu, Zenglin School of Science and Technology Harbin Institute of Technology Shenzhen Shenzhen Guangdong510085 China Department of SMILE Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610031 China Singapore Management University Singapore Machine Intelligence Laboratory College of Computer Science Sichuan University Chengdu610065 China

he ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple shortcut connection mechanism limits the ability of re-exploring new potentially complementary features due to the additive function. To address this issue, in this paper, we propose to introduce a regulator module as a memory mechanism to extract complementary features, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional RNNs (e.g., Convolutional LSTMs or Convolutional GRUs), which are shown to be good at extracting spatio-temporal information. We named the new regulated networks as RegNet. The regulator module can be easily implemented and appended to any ResNet architectures. We also apply the regulator module for improving the Squeeze-and-Excitation ResNet to show the generalization ability of our method. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, SE-ResNet, and other state-of-the-art architectures. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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Low-Speed Injection Attack Detection on CAN Bus 3rd

Low-Speed Injection Attack Detection on CAN Bus

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3rd International Conference on Frontiers in Cyber Security, FCS 2020

作者： Wang, Chundong Li, Chuang An, Tongle Cheng, Xiaochun Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin300384 China Department of Computer Science Middlesex University LondonNW4 4BT United Kingdom

ISBN: (纸本)9789811597381

The car CAN (Controller Area Network) bus message injection attacks seriously affects various functions of the safety of cars, life and property. However, low-speed injection attack is detection inconspicuous in a majority of existing researches. This paper proposes a self-contained low-speed injection attacks detection system including whole detection process and principle. This paper first analyzes the feasibility of low-speed injection attacks;then we propose to use LOF (Local Outlier Factor) to detect the injection attack, and compare with the previous detection algorithms. Experimental results show that our algorithm has obvious advantages in detection rate over the previous algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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Defending Against Poisoning Attacks in Federated Learning with Blockchain

arXiv

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arXiv 2023年

作者： Dong, Nanqing Wang, Zhipeng Sun, Jiahao Kampffmeyer, Michael Knottenbelt, William Xing, Eric The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Computing Imperial College London LondonSW7 2AZ United Kingdom FLock.io LondonWC2H 9JQ United Kingdom The Department of Physics and Technology UiT The Arctic University of Norway Tromsø9019 Norway The Machine Learning Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most existing FL approaches rely on a centralized server for global model aggregation, leading to a single point of failure. This makes the system vulnerable to malicious attacks when dealing with dishonest clients. In this work, we address this problem by proposing a secure and reliable FL system based on blockchain and distributed ledger technology. Our system incorporates a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are powered by on-chain smart contracts, to detect and deter malicious behaviors. Both theoretical and empirical analyses are presented to demonstrate the effectiveness of the proposed approach, showing that our framework is robust against malicious client-side behaviors. © 2023, CC BY.

关键词： Blockchain

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SpEx+: A complete time domain speaker extraction network 21

SpEx+: A complete time domain speaker extraction network

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21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

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Nikkei Stock Market Price Index Prediction Using machine Learning 4

Nikkei Stock Market Price Index Prediction Using Machine Lea...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Harahap, Lukman A. Lipikorn, Rajalida Kitamoto, Asanobu Department of Agricultural Engineering Faculty of Agriculture University of Sumatera Utara Medan Indonesia Machine Intelligence and Multimedia Information Technology Laboratory Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Research Organization of Information and Systems Department of Informatics National Institute of Informatics Tokyo Japan

Stock market prediction has always been a difficult process, most of the prediction rely solely on the data of the corresponding stock market. Relationship of gold and oil price with stock market performance has been proven significant in some major world stock index. Prediction of stock market price index using machine learning methods is expected to perform well, with the ability of machine learning method to predict using nonlinear inputs. The methods were commonly able to predict relatively well in predicting the values of Japanese Nikkei 225 and Japanese Nikkei 400 indexes. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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EFaR 2023: Efficient Face Recognition Competition

EFaR 2023: Efficient Face Recognition Competition

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Jan Niklas Kolf Fadi Boutros Jurek Elliesen Markus Theuerkauf Naser Damer Mohamad Alansari Oussama Abdul Hay Sara Alansari Sajid Javed Naoufel Werghi Klemen Grm Vitomir Štruc Fernando Alonso-Fernandez Kevin Hernandez Diaz Josef Bigun Anjith George Christophe Ecabert Hatef Otroshi Shahreza Ketan Kotwal Sébastien Marcel Iurii Medvedev Bo Jin Diogo Nunes Ahmad Hassanpour Pankaj Khatiwada Aafan Ahmad Toor Bian Yang Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Université de Lausanne (UNIL) Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Information Security and Communication Technology eHealth and Welfare Security Group Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well.

关键词：

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines Brain Accuracy Stroke (medical condition) Feature extraction Motors Brain-computer interfaces Neurorehabilitation Decoding Paralysis

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Accurately Identifying Coronary Atherosclerotic Heart Disease through Merged Beats of Electrocardiogram

Accurately Identifying Coronary Atherosclerotic Heart Diseas...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinfeng Wang Mengling Qi Chengzhi Dong Haoyang Zhang Yuedong Yang Huiying Zhao Key Laboratory of Machine Intelligence and Advanced Computing of MOE School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Medical Research Center Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Sun Yat-sen Memorial Hospital Guangzhou China School of Life Sciences Sun Yat-sen University Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Coronary Atherosclerotic Heart Disease (CAHD) is one kind of severe heart disease that is the dominating cause of death from non-communicable diseases worldwide. CAHD can be early detected through pre-symptomatic health check-ups, and the electrocardiogram (ECG) is common for non-invasive health check diagnoses. Traditionally, ECG signals are utilized to extract clinical features that are then input into machine learning methods for training and prediction. While these extracted features are interpretable, they are difficult to break through known features. On the other hand, ECG can be directly input to deep learning techniques, but such methods are usually limited by small sample sizes. Here, we propose to merge multiple beats of raw signal into one beat, which greatly reduces the complexity while maintaining the raw information. Moreover, we have constructed the largest benchmark dataset for 1113 CAHD patients of 12-lead ECG signals from the UK Biobank database and used the data to train a deep learning model. The results indicated that merged beat signals could achieve the best performance corresponding to an AUC of 0.71 and accuracy of 0.7, which is 4% higher than models using the raw signals and 6% higher than those using the clinical features. Further intuitive interpretation revealed that ST waves in lead II and V3 are the most closely associated with CAHD, consistent with clinical observations.

关键词： Heart Deep learning Training Databases Biological system modeling Atherosclerosis Electrocardiography

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Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy

arXiv

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arXiv 2022年

作者： Guo, Binjie Zheng, Hanyu Jiang, Haohan Li, Xiaodan Guan, Naiyu Zuo, Yanming Zhang, Yicheng Yang, Hengfu Wang, Xuhua Department of Neurobiology Department of Rehabilitation Medicine First Affiliated Hospital Zhejiang University School of Medicine Zhejiang Province Hangzhou310058 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou310058 China Co-innovation Center of Neuroregeneration Nantong University Jiangsu Nantong226001 China School of Computer Science Hunan First Normal University Hunan Changsha410205 China

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug screening tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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B2e-Cdg: Artifact Removal for Enhanced Optical Coherence Tomography Angiography Using Conditional Guidance Diffusion

SSRN

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SSRN 2024年

作者： Xu, Jing Fu, Suzhong Xing, Jiwei Xue, Linyan Wang, Cheng Tuchin, Valery V. Zhao, Qingliang State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics Organ Transplantation Institute China Institute of Artificial Intelligence Xiamen University Xiamen361102 China State Key Laboratory of Vaccines for Infectious Diseases Xiang An Biomedicine Laboratory School of Public Health Xiamen University Xiamen361102 China Shenzhen518172 China College of Quality and Technical Supervision Hebei University Baoding071002 China School of Informatics Computer Science and Technology Department Xiamen University Xiamen361005 China Institute of Physics and Science Medical Center Saratov State University Saratov410012 Russia Laboratory of Laser Molecular Imaging and Machine Learning Tomsk State University Tomsk634050 Russia Institute of Precision Mechanics and Control FRS "Saratov Scientific Centre of the RAS" Saratov410028 Russia China

Optical Coherence Tomography Angiography (OCTA) is a revolutionary technology widely used to diagnose and manage fundus, skin and cardiovascular diseases. However, unavoidable movements, such as breathing, cause motion artifacts in images, which can significantly degrade image quality, obscure critical vascular details, and reduce the diagnostic reliability of the modality. Although recent advances in learning-based image inpainting methods for OCTA enface images have made notable progress in artifact removal, these methods still require the collection of large amounts of accurately labeled data and the generation of pseudo stripes to create paired training sets. Furthermore, the abundant structural information and flow intensity signals present in OCTA B-scans are nonnegligible. Hence we proposed B-scans to Enface Conditional Diffusion Guidance (B2E-CDG) for translation from signal-void B-scans to available B-scans. We introduce the normal B-scans in a connection manner and the specified reference B-scans in a gradient-based manner as style feature guidance into the diffusion model. Conditional guidance facilitates a more controllable and precise generation process for B-scans flow signal recovery. The requirement for labeled image collection and pseudo stripes is obviated, as the repetitive scanning nature of OCTA naturally results in paired datasets comprising signal-void and normal B-scans. The experimental results demonstrated that B2E-CDG effectively removes artifacts and restores vascular and structural information obscured by artifacts. This work shows superior vascular recovery and artifact removal capabilities in metrics and downstream tasks, enhancing the usability of OCTA in diagnostics. © 2024, The Authors. All rights reserved.

关键词： Optical coherence tomography

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