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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

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A novel decision tree for depression recognition in speech

arXiv

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arXiv 2020年

作者： Liu, Zhenyu Wang, Dongyu Zhang, Lan Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Beijing China Lanzhou University Second Hospital Lanzhou China

Depression is a common mental disorder worldwide which causes a range of serious outcomes. The diagnosis of depression relies on patient-reported scales and psychiatrist’s interview which may lead to subjective bias. In recent years, more and more researchers are devoted to depression recognition in speech, which may be an effective and objective indicator. This study proposes a new speech segment fusion method based on decision tree to improve the depression recognition accuracy and conducts a validation on a sample of 52 subjects (23 depressed patients and 29 healthy controls). The recognition accuracy are 75.8% and 68.5% for male and female respectively on gender-dependent models. It can be concluded from the data that the proposed decision tree model can improve the depression classification performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

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A dual-channel deep learning approach to continuous prediction of acute kidney injury in the intensive care unit

A dual-channel deep learning approach to continuous predicti...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuetong Kong Peng Zhang Yunchao Ling Daqing Lv Jun Xu Guoqing Zhang Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China CAS Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai China Shanghai Southgene Technology Co. Ltd Shanghai China Shanghai Sixth People’s Hospital Shanghai China CAS Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences. Shanghai Sixth People’s Hospital Shanghai China

Acute Kidney Injury (AKI) often occurs in the intensive care units (ICU), where it is associated with high morbidity and mortality. Early and continuous prediction of AKI plays a crucial role in preventing AKI events, providing timely treatment, and reducing mortality. Continuously monitoring the high dimensional vital signs and lab measurements of the patients has always been a challenging task. In this study, we proposed a novel dual-channel deep learning approach named DC-AKI to address this challenge. DC-AKI extracts feature of temporal variables at different granularity, comprehensively and accurately obtains effective information, and finally continuous predicting risks of the patients. One channel of the model calculates corresponding local interactions of the variables with convolution kernels. The other channel uses the gated recurrent units (GRU) network and attention mechanism to obtains global interactions information among the variables. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Sigmoid classifier for prediction. We trained and tested our model on the MIMIC IV dataset, and the AUC ROC value of DC-AKI for predicting the risk of AKI in ICU patients 48 hours in advance was 0.9527, outperforming other methods. And our model was externally validated on the eICU dataset with an AUC ROC of 0.9054. These results demonstrate the robustness and accuracy of the model on different datasets. This finding provides clinicians with an opportunity to identify and manage patients with AKI in a timely manner, thereby improving patient prognosis. The implemented code is available online at https://***/BioMedBigdataCenter/DC-AKI.

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TRUST: A Toolkit for TEE-Assisted Secure Outsourced Computation over Integers

arXiv

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arXiv 2024年

作者： Zhao, Bowen Li, Jiuhui Xu, Peiming Li, Xiaoguo Pei, Qingqi Shen, Yulong Guangzhou Institute of Technology Xidian University Guangzhou510555 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Electric Power Research Institute CSG Guangdong Guangzhou510663 China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou510663 China The College of Computer Science Chongqing University Chongqing400044 China The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710126 China

Secure outsourced computation (SOC) provides secure computing services by taking advantage of the computation power of cloud computing and the technology of privacy computing (e.g., homomorphic encryption). Expanding computational operations on encrypted data (e.g., enabling complex calculations directly over ciphertexts) and broadening the applicability of SOC across diverse use cases remain critical yet challenging research topics in the field. Nevertheless, previous SOC solutions frequently lack the computational efficiency and adaptability required to fully meet evolving demands. To this end, in this paper, we propose a toolkit for TEE-assisted (Trusted Execution Environment) SOC over integers, named TRUST. In terms of system architecture, TRUST falls in a single TEE-equipped cloud server only through seamlessly integrating the computation of REE (Rich Execution Environment) and TEE. In consideration of TEE being difficult to permanently store data and being vulnerable to attacks, we introduce a (2, 2)-threshold homomorphic cryptosystem to fit the hybrid computation between REE and TEE. Additionally, we carefully design a suite of SOC protocols supporting unary, binary and ternary operations. To achieve applications, we present SEAT, secure data trading based on TRUST. Security analysis demonstrates that TRUST enables SOC, avoids collusion attacks among multiple cloud servers, and mitigates potential secret leakage risks within TEE (e.g., from side-channel attacks). Experimental evaluations indicate that TRUST outperforms the state-of-the-art and requires no alignment of data as well as any network communications. Furthermore, SEAT is as effective as the Baseline without any data protection. Copyright © 2024, The Authors. All rights reserved.

关键词： Side channel attack

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Content-Noise Feature Fusion Neural network for Image Denoising in Magnetic Particle Imaging∗

Content-Noise Feature Fusion Neural Network for Image Denois...

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

作者： Tan Wang Liwen Zhang Zechen Wei Yusong Shen Jie Tian Hui Hui CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Molecular Imaging Beijing China School of Computer Science and Engineering Southeast University Nanjing China Ministry of Industry and Information Technology of the People’s Republic of China Key Laboratory of Big Data-Based Precision Medicine (Beihang University) Beijing People’s Republic of China

Magnetic particle imaging (MPI) is a tomographic imaging method that quantitatively determines the distribution of magnetic nanoparticles (MNPs). However, the performance of MPI is primarily limited by the noise in the receive coil and electronic devices, which causes quantification errors for MPI images. Existing methods cannot efficiently eliminate noise while preserve structural details in MPI images. To address this problem, we propose a Content-Noise Feature Fusion Neural network equipped with tailored modules of noise learning and content learning. It can simultaneously learn content and noise features of raw MPI images. Experimental results show that the proposed method outperforms the state-of-the-art methods on structural details preservation and image noise reduction of different levels.

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution

arXiv

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arXiv 2024年

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code will be publicly released. © 2024, CC BY.

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Risk Modeling and Traceability of Boundary Attacks on Internet of Power Systems Based on Complex networks 9

Risk Modeling and Traceability of Boundary Attacks on Intern...

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

作者： Li, Xin Qu, Zhaoyang Yu, Tong Xie, Ming Yu, Fu Ding, Wei Northeast Electric Power University School of Electrical Engineering Jilin132012 China People's Bank of China Qinghai Provincial Branch Xining810000 China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning530023 China China Guizhou Power Grid Co. Ltd. Bijie Power Supply Bureau Bijie551700 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 National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350391367

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines. © 2024 IEEE.

关键词： Internet of things

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An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

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science Bulletin 2022年第7期67卷 665-669页

作者： Yunchao Ling Ruifang Cao Jiaqiang Qian Jiefu Li Haokui Zhou Liyun Yuan Zhen Wang Liangxiao Ma Guangyong Zheng Guoping Zhao Zefeng Wang Guoqing Zhang Yixue Li CAS Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Institute of Synthetic Biology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China Collaborative Innovation Center for Genetics and Development Fudan UniversityShanghai 200433China CAS Center for Excellence in Molecular Cell Science Chinese Academy of SciencesShanghai 200031China School of Life Sciences and Biotechnology Shanghai Jiao Tong UniversityShanghai 200240China Guangzhou Laboratory Guangzhou 510005China

The comprehensive analyses of the SARS-CoV-2 genomes could provide a global picture of how the virus was transmitted among different populations,which may help predict the oncoming trends of the *** main approach for ... 详细信息

关键词：快速分析方法 thoroughly among

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Efficient Federated Learning with Spike Neural networks for Traffic Sign Recognition

arXiv

引用

arXiv 2022年

作者： Xie, Kan Zhang, Zhe Li, Bo Kang, Jiawen Niyato, Dusit Xie, Shengli Wu, Yi The School of Automation Guangdong University of Technology Guangzhou510006 China The Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China The Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China The School of Data Science and Technology Heilongjiang University Harbin150080 China The Institute for Cryptology & Network Security Heilongjiang University Harbin150080 China School of Computer Science and Engineering Nanyang Technological University Singapore

With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information. To address this issue, we first exploit privacy-preserving federated learning to perform collaborative training for accurate recognition models without sharing raw traffic sign data. Nevertheless, due to the limited computing and energy resources of most devices, it is hard for vehicles to continuously undertake complex artificial intelligence tasks. Therefore, we introduce powerful Spike Neural networks (SNNs) into traffic sign recognition for energy-efficient and fast model training, which is the next generation of neural networks and is practical and well-fitted to IoV scenarios. Furthermore, we design a novel encoding scheme for SNNs based on neuron receptive fields to extract information from the pixel and spatial dimensions of traffic signs to achieve high-accuracy training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Traffic signs

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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