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International Conference on Big data Computing and Communications (BIGCOM)

作者： Yuan Ji Tian Ye Xiang-Yang Li LINKE Lab University of Science and Technology of China School of Data Science University of Science and Technology of China CAS Key Laboratory of Wireless-optical Communications School of Computer Science and Technology University of Science and Technology of China

With the gradual development of the Internet of Things, people rely more and more on the Internet of Things devices to assist their daily lives. The permissions of IoT devices make it easy for them to rescue or destroy our lives. This paper proposes a new scalable IoT security framework (ISDF), which uses the unforgeable physical layer characteristics of IoT devices as the verification basis to identify illegal devices. This framework combined with the identifier generation algorithm to generate a unique trusted identifier. Under the verification of more than a dozen devices such as bluetooth, wifi, and voice, the identifiers we generate have high robustness and stability, the accuracy of verification is more than 93%. Compared with existing solutions, our framework has the advantages of supporting multimodality and high verification success rate.

关键词： Bluetooth Systems architecture Speech recognition Fingerprint recognition Physical layer Robustness Internet of Things

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques

arXiv

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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at this url. © 2024, CC BY.

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Information and Sensing Beamforming Optimization for Multi-User Multi-Target MIMO ISAC Systems

Information and Sensing Beamforming Optimization for Multi-U...

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International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Minghe Zhu Lei Li Shuqiang Xia Tsung-Hui Chang School of Science & Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology

In this paper, we consider the joint beamforming design for simultaneous sensing and communication in a wireless multi-user system. Different from the existing works that mostly are for single target, we consider sensing the channel parameters of multiple targets while communicating with multiple users. The design goal is to minimize a weighted sum of the Cramer-Rao bounds (CRB) of target parameters subject to the communication sum rate and transmission power constraints. While the classical weighted minimum mean square error (WMMSE) and semidefinite relaxation (SDR) can be used to handle the problem, we propose to reformulate the problem into a max-min form, by leveraging the tightness of SDR, and solve it by a low-complex first-order method. Numerical results not only demonstrate the computation efficiency of the proposed algorithm but also its effectiveness in enhancing the sensing performance in practice.

关键词： Wireless communication Wireless sensor networks Array signal processing Signal processing algorithms Mean square error methods Minimax techniques Sensors

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Adversarial Training Via Multi-Guidance and Historical Memory Enhancement

SSRN

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SSRN 2024年

作者： Zhao, Chenyu Qian, Yaguan Wang, Bin Gu, Zhaoquan Ji, Shouling Wang, Wei Zhang, Yanchun School of Science Zhejiang University of Science and Technology China Network and Data Security China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Zhejiang University China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University China Institute for Sustainable Industries and Liveable Cities Victoria University Australia Peng Cheng Laboratory China

DNNs are often susceptible to the influence of adversarial examples, potentially leading to severe security issues. Adversarial training stands out as one of the most effective defenses. In this paper, we empirically demonstrate that weakly robust models maintain resilience to adversarial examples generated on a fully trained robust model. We propose a novel weight ensemble training method called Guided Adversarial Training (GAT). This method enhances both clean and adversarial accuracy through two guiding approaches (Self-Guidance and Expert-Guidance), as well as by improving the historical memory of member models' weights. The experiments prove that our method is effective in defending against most adversarial attacks. It emphasizes the need to maintain the diversity of decision boundaries of member models while improving the alignment with natural decision boundaries when ensemble the model weights. The code will be released after acceptance. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Back to science in Searching for SARS-CoV-2 Origins

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China CDC weekly 2023年第14期5卷 315-317页

作者： William J Liu Wenwen Lei Xiaozhou He Peipei Liu Qihui Wang Zhiqiang Wu Yun Tan Shuhui Song Gary Wong Jian Lu Jingkun Jiang Qiang Wei Mingkun Li Juncai Ma Xiaozhong Peng Yixue Li Baoxu Huang Yigang Tong Jun Han Guizhen Wu NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational MedicineRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina Beijing Institute of Genomics Chinese Academy of Sciencesand China National Center for BioinformationBeijingChina Laboratory of Molecular Virology and Immunology Institut Pasteur of ShanghaiChinese Academy of SciencesShanghaiChina State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina School of Environment Tsinghua UniversityBeijingChina National Pathogen Resource Center Chinese Center for Disease Control and Prevention(China CDC)BeijingChina Microbial Resource and Big Data Center Chinese National Microbiology Data Center(NMDC)Institute of MicrobiologyChinese Academy of SciencesBeijingChina State Key Laboratory of Medical Molecular Biology Department of Molecular Biology and BiochemistryInstitute of Basic Medical SciencesMedical Primate Research CenterNeuroscience CenterChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijingChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina Bio-Med Big Data Center Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghaiChina China Animal Health and Epidemiology Center Qingdao CityShandong ProvinceChi

In recent decades,emerging and re-emerging human-infecting pathogens have been represented as huge threats to public health and have become a global concern(1).After outbreaks of two coronaviruses(CoVs),severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV),severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)became the first-known pandemic hastening CoV with tremendous wrecking to the world(2).The origin tracing of these emerging pathogens is of great significance in infectious disease prevention and control(3–4).The origin of SARS-CoV-2 remains elusive after the more than 3-year pandemic,though scientists around the world are making great *** the experience of studying many other infectious pathogens,origin tracing is systematic and time-consuming *** supposed origins of many infectious pathogens are still in debate,including SARS-CoV and human immunodeficiency virus,etc(5).

关键词： prevention respiratory acute

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An Efficient Deep Transfer Learning network for Characterization of Stroke Patients’ Motor Execution from Multi-Channel EEG-Recordings

An Efficient Deep Transfer Learning Network for Characteriza...

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

作者： Oluwarotimi Williams Samuel Mojisola Grace Asogbon Frank Kulwa Alistair A. McEwan Sunday Timothy Aboyeji Rami Khushaba Peng Fang Guanglin Li School of Computing and Data Science Research Centre University of Derby Derby United Kingdom Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS Shenzhen China Australian Centre for Field Robotics Sydney University Chippendale NSW Australia

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Recent advances in stroke rehabilitation technology have been focused on developing Intelligent Rehabilitation Robots (IRR) that can effectively engage post-stroke patients (PSP) in intuitive motor training for full function recovery. Most existing rehabilitation robots incorporate functionalities that are passive in nature, constraining PSP to predetermined trajectories that often deviate from patients’ limb movement intentions, consequently hindering recovery. To resolve this issue, a robust deep-transfer learning driven network (DTLN) is developed to adequately characterize PSP’s motion intention signatures from neural oscillations towards achieving intuitive and active training. Thus, we investigated and proposed the utilization of mu-frequency spectrum (muFS) based CWT approach for Scalograms construction, which serves as inputs to the DTLN model that characterizes multiple classes of PSP’s motor execution signatures from multi-channel electroencephalography (EEG) recordings. Then, we evaluated the proposed method using EEG data from six PSP and compared the decoding results to those of related approaches under similar experimental settings. The proposed method resulted in a significant increment of 10.84 % - 13.19% decoding accuracy across stroke patients and better convergence in comparison to other methods. Additionally, the method exhibited distinct task separability for individual motor execution signature across patients. In conclusion, our method offers a consistently accurate decoding of motor tasks that could enable intuitively active robotic training in PSPs with impaired motor function.

关键词： Training Accuracy Transfer learning Stroke (medical condition) Motors Assistive robots Electroencephalography Decoding Trajectory Robots

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Pan-cancer network disorders revealed by overall and local signaling entropy

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Journal of Molecular Cell Biology 2021年第9期13卷 622-635页

作者： Li Feng Yi-Di Sun Chen Li Yi-Xue Li Luo-Nan Chen Rong Zeng CAS Key Laboratory of Systems Biology CAS Center for Excellence in Molecular Cell ScienceInstitute of Biochemistry and Cell BiologyChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Shanghai 200031China Institute of Neuroscience CAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai 200031China Center for Single-Celt Omics School of Public HealthShanghai Jiao Tong University School of MedicineShanghai 200025China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China CAS Key Laboratory of Systems Biology Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics,which is driven by intracellular rewiring of signaling *** measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of ***,we proposed signaling entropy(SR)to assess the degree of tumor network *** calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcrip-tomic and proteomic *** SR of tumors was significantly higher than that of normal samples and was highly correlated with cell sternness,cancer type,tumor grade,and *** further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response ***,SR could reveal cancer network disorders related to tumor malignant potency,clinical prognosis,and drug response.

关键词： pan-cancer network entropy

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Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin

arXiv

引用

arXiv 2024年

作者： Guo, Tianlin Zhang, Lingling Wang, Jiaxin Lei, Yunkuo Li, Yifei Wang, Haofen Liu, Jun School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China School of Computer Science and Technology Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi’an710049 China College of Design and Innovation Tongji University Shanghai200092 China

Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than the conventional few-shot relation extraction task, since the boundaries of unknown classes are complex and difficult to learn. Meta-learning based methods, especially prototype-based methods, are the mainstream solutions to this task. They obtain the classification boundary by learning the sample distribution of each class. However, their performance is limited because few-shot overfitting and NOTA boundary confusion lead to misclassification between known and unknown classes. To this end, we propose a novel framework based on Gaussian prototype and adaptive margin named GPAM for FsRE with NOTA, which includes three modules, semi-factual representation, GMM-prototype metric learning and decision boundary learning. The first two modules obtain better representations to solve the few-shot problem through debiased information enhancement and Gaussian space distance measurement. The third module learns more accurate classification boundaries and prototypes through adaptive margin and negative sampling. In the training procedure of GPAM, we use contrastive learning loss to comprehensively consider the effects of range and margin on the classification of known and unknown classes to ensure the model's stability and robustness. Sufficient experiments and ablations on the FewRel dataset show that GPAM surpasses previous prototype methods and achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Joint Optimization Task Offloading Strategy for Mobile Edge Computing 2

Joint Optimization Task Offloading Strategy for Mobile Edge ...

引用

2nd IEEE International Conference on Information technology, Big data and Artificial Intelligence, ICIBA 2021

作者： Tang, Xue Wen, Zhan Chen, Jiali Li, Yiquan Li, Wenzao College of Communication Engineer Chengdu University of Information Technology Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu611730 China

ISBN: (纸本)9781665428767

With increase of Internet of Things (IoT) devices, the burden of cloud computing servers is gradually increasing. Mobile Edge Computing (MEC), as an emerging solution, can relieve the pressure of computing and improve the quality of user experience. One of the critical challenges in MEC is to make effective use of communication and computing resources and select the task processing mode reasonably. In this paper, based on the scenario of a single mobile device (MD) to one mobile edge server (MES), we propose a Joint optimization method base on Discrete Binary Particle Swarm Optimization Algorithm (JOBPSO) for system load. The JOBPSO can optimize MD's task offloading, reduce delay and energy consumption. Finally, the simulation results indicate that the performance of JOBPSO in reducing system load is better than the other two traditional offloading methods. © 2021 IEEE.

关键词： Mobile edge computing

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Learning Hybrid Negative Probability Model for Weakly-Supervised Whole Slide Image Recognition

Learning Hybrid Negative Probability Model for Weakly-Superv...

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International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Yining Qiu Yuxi Li Jiafu Wu Zhenye Gan Mingmin Chi Yabiao Wang Chengjie Wang Pei Wang School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Tencent Youtu Lab China Zhongshan PoolNet Technology Co. Ltd Zhongshan Fudan Joint Innovation Center China Zhejiang University China NAOC CAS China

Classifying an entire Whole Slide Image (WSI) in a single forward pass is challenging due to its vast resolution. Consequently, current effort on WSI classification resorts to multiple instance learning (MIL), using patch-wise instances to predict categories under image-wise supervision. However, recent MIL methods usually follow implicit instance selection strategy and ignore the effect from inherent patch category imbalances. In a statistical sense, negative patches dominate in WSIs and provide sufficient samples for accurate density estimation. Therefore, in this paper, we learn from anomaly detection and propose a deep MIL framework which learns a hybrid negative probability model to bootstrap discovery of potential positive lesion. We associate attention-based MIL approach with a regularization loss function to explicitly improve patch selection process in positive images. Experiments conducted on benchmarks of WSI recognition demonstrate that our method brings significant improvement to classic attention-based MIL baseline and achieves state-of-the-art performance.

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