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Low-rank matrix recovery with total generalized variation for defending adversarial examples

Frontiers of Information Technology & electronic engineering 2024年第3期25卷 432-445页

作者： Wen LI Hengyou WANG Lianzhi HUO Qiang HE Linlin CHEN Zhiquan HE Wing W.Y.Ng School of Science Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060China Institute of Big Data Modeling and Technology Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Low-rank matrix decomposition with first-order total variation(TV)regularization exhibits excellent performance in exploration of image *** advantage of its excellent performance in image denoising,we apply it to improve the robustness of deep neural ***,although TV regularization can improve the robustness of the model,it reduces the accuracy of normal samples due to its *** our work,we develop a new low-rank matrix recovery model,called LRTGV,which incorporates total generalized variation(TGV)regularization into the reweighted low-rank matrix recovery *** the proposed model,TGV is used to better reconstruct texture information without *** reweighted nuclear norm and Li-norm can enhance the global structure ***,the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the *** solve the challenging optimal model issue,we propose an algorithm based on the alternating direction method of *** results show that the proposed algorithm has a certain defense capability against black-box attacks,and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.

关键词： Total generalized variation Low-rank matrix Alternating direction method of multipliers Adversarial example

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The Weisfeiler-Leman Dimension of Conjunctive Queries

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Proceedings of the ACM on Management of data 2024年第2期2卷 1-17页

作者： Andreas Göbel Leslie A. Goldberg Marc Roth Hasso Plattner Institute University of Potsdam Potsdam Germany Department of Computer Science University of Oxford Oxford United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

A graph parameter is a function f on graphs with the property that, for any pair of isomorphic graphs G1 and G2, f(G1)=f(G2). The Weisfeiler--Leman (WL) dimension of f is the minimum k such that, if G1 and G2 are indistinguishable by the k-dimensional WL-algorithm then f(G1)=f(G2). The WL-dimension of f is ∞ if no such k exists. We study the WL-dimension of graph parameters characterised by the number of answers from a fixed conjunctive query to the graph. Given a conjunctive query φ, we quantify the WL-dimension of the function that maps every graph G to the number of answers of φ in *** works of Dvorak (J. Graph Theory 2010), Dell, Grohe, and Rattan (ICALP 2018), and Neuen (ArXiv 2023) have answered this question for full conjunctive queries, which are conjunctive queries without existentially quantified variables. For such queries φ, the WL-dimension is equal to the treewidth of the Gaifman graph of φ.In this work, we give a characterisation that applies to all conjunctive queries. Given any conjunctive query φ, we prove that its WL-dimension is equal to the semantic extension width sew(φ), a novel width measure that can be thought of as a combination of the treewidth of φ and its quantified star size, an invariant introduced by Durand and Mengel (ICDT 2013) describing how the existentially quantified variables of φ are connected with the free variables. Using the recently established equivalence between the WL-algorithm and higher-order Graph Neural Networks (GNNs) due to Morris et al. (AAAI 2019), we obtain as a consequence that the function counting answers to a conjunctive query φ cannot be computed by GNNs of order smaller than sew(φ).The majority of the paper is concerned with establishing a lower bound of the WL-dimension of a query. Given any conjunctive query φ with semantic extension width k, we consider a graph F of treewidth k obtained from the Gaifman graph of φ by repeatedly cloning the vertices corresponding to existentially quantified variables.

关键词： CFI graphs Weisfeiler Leman algorithm conjunctive queries counting problems graph homomorphisms

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment

arXiv

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arXiv 2024年

作者： Ma, Danqing Wang, Meng Xiang, Ao Qi, Zongqing Yang, Qin Computer Science Stevens Institute of Technology HobokenNJ United States Department of Computer Science University of Sofia Palo AltoCA United States School of Computer Science & Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Integrated Circult Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects. Copyright © 2024, The Authors. All rights reserved.

关键词： computerized tomography

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Exploratory data Analysis and Predicting Indian Crime Against Women Using Machine Learning Approaches

Exploratory Data Analysis and Predicting Indian Crime Agains...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： N. Srinivasa Gupta B. Valarmathi K. Santhi T.Chellatamilan Sameeksha Nanda Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Crime forecasting is a critical endeavor aimed at preventing future criminal actions, specifically the rising rate of violence against women in India. This study proposes the model to predict crimes committed against women using data from the National Crime Records Bureau's (NCRB) monthly crime report. The dataset encompasses district and state-wise crime activities from 2001 to 2014. The proposed work objectives are identifying factors influencing these crimes and developing predictive models using machine learning (ML). The study employs Exploratory data Analysis (EDA), to enhance the predictive power of the models. The suggested study uses XGBoost, Random Forest (RF), and Neural Network (NN). The neural network algorithm outperforms the Random Forest and XGBoost techniques among these three. The Random Forest algorithm has the worst performance. After comparing the accuracies of various methods, such as XGBoost (92%), and Random Forest (89.84%) classifiers using imbalanced datasets (without employing Synthetic Minority Oversampling Technique (SMOTE) technique), it was found that the neural network algorithm (95.81%) delivers the highest accuracy. This study looks at model accuracies and finds that, after applying the SMOTE algorithm, neural network algorithms performed best, with an accuracy of 96.76%, outperforming the accuracies of the remaining models, which included the Random Forest (93.08%) and XG-Boost (95%).

关键词： Radio frequency Technological innovation Accuracy Machine learning algorithms data analysis Predictive models Prediction algorithms data models Classification algorithms Random forests

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Federated Learning for 6G:Applications,Challenges,and Opportunities

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engineering 2022年第1期8卷 33-41页

作者： Zhaohui Yang Mingzhe Chen Kai-Kit Wong H.Vincent Poor Shuguang Cui Department of Electronic and Electrical Engineering University College LondonLondon WC1E 6BTUK Department of Electrical and Computer Engineering Princeton UniversityPrincetonNJ 08544USA Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and Future Network of Intelligence Institute The Chinese University of Hong KongShenzhen 518172China

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and ***,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter *** approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and *** paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless *** particular,the essential requirements for applying FL to wireless communications are first *** potential FL applications in wireless communications are *** main problems and challenges associated with such applications are ***,a comprehensive FL implementation for wireless communications is described.

关键词： Federated learning 6G Reconfigurable intelligent surface Semantic communication Sensing Communication and computing

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Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-Network DRL Inference

Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Irfan, Muhammad Hu, Hang Lee, Myung J. Qadeer, Arslan Kim, Yang G. Ahmed, Kazi Nobayashi, Daiki City College of New York Department of Electrical Engineering New YorkNY10031 United States School of Computing & Data Science Wentworth Institute of Technology BostonMA02115 United States NYCCT Department of Computer Engineering Technology BrooklynNY11201 United States NYIT Department of Electrical & Computer Engineering Technology New YorkNY10023 United States Kyushu Institute of Technology Department of Electrical and Electronic Engineering Fukuoka804-8550 Japan

ISBN: (纸本)9798331507022

The Software-Defined Networking (SDN) paradigm has significantly improved network efficiency by integrating machine learning (ML) capabilities into the control plane (CP). This integration allows adaptive management of network resources in response to dynamic traffic conditions. However, the significant geographical distance between the CP and data plane (DP) causes considerable round-trip latency, measured in milliseconds, which poses a major challenge to time-sensitive traffic. To address this challenge, our research proposes a novel in-network Reinforcement Learning (RL) inference framework. This framework extends programmability from the CP to the DP, enabling timely and precise control of network resources to meet the stringent Quality of Service (QoS) requirements of mission-critical applications. The in-network RL inference is implemented using match-action tables within the Protocol Independent Switch Architecture (PISA) framework in the DP and validated through hardware deployment of protocol-independent packet processors (P4). To allocate bandwidth precisely based on QoS requirements, a P4 meter extern is used to differentiate the unique demands of individual traffic flows. Our enhanced deep-deterministic policy gradient (eDDPG)-based RL inference achieves superior performance with minimal processing overhead, reducing latency by 88.7% and jitter by 89.1 % compared to systems without RL inference. © 2025 IEEE.

关键词： Quality of service

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Accurate measurement for the orbital angular momentum spectrum in scattering media based on a Faraday atomic filter

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Optics Letters 2025年第13期50卷 4162-4165页

作者： Xiaoqian Liang Yanfeng Bai Lei Chen Xiaohui Zhu Weijun Zhou Liyu Zhou Qin Fu Qi Zhou Xuanpengfan Zou Wei Tan Xianwei Huang Longfei Yin Xiquan Fu College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China yfbai@*** School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing 100876 China School of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science Hubei University of Technology Wuhan 430068 China Hunan Police Academy Changsha 410138 China School of Electrical and Information Engineering Hunan Institute of Engineering Xiangtan 411104 China fuxq@***

The orbital angular momentum (OAM) of beams has important applications in various fields such as large-capacity data transmission, imaging, and quantum information. So the precise measurement of the OAM spectrum is critical. Here, we present a direct and efficient method for measuring the OAM spectrum in a scattering medium using a Faraday atomic filter. Experimental results show that the Faraday atomic filter offers significant advantages in accurately identifying OAM under scattering media when compared to the results in the filterless device and the polarizer. Our work presents a novel, to the best of our knowledge, approach to precisely measure the OAM spectrum of vortex beams in complex scattering environments and will contribute to the advancement of various applications, including optical communications, lidar, and others.

关键词： Optical elements Optical filters Orbital angular momentum multiplexing Quantum information Scattering media Spiral phase

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Revisiting the Efficacy of Signal Decomposition in AI-based Time Series Prediction

Revisiting the Efficacy of Signal Decomposition in AI-based ...

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2024 International Conference Optoelectronic Information and Optical engineering, OIOE 2024

作者： Zhang, Dan Jiang, Kexin Wu, Chuhan Peng, Yan Han, Zhaolong Chen, Yaoran Institute of Artificial Intelligence Shanghai University Shanghai200444 China Department of Computer Engineering and Science Shanghai University Shanghai200444 China Department of Electronic Engineering Tsinghua University Beijing100084 China School of Naval Architecture Ocean & Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

Time series prediction is a fundamental problem in scientific exploration and artificial intelligence (AI) technologies have substantially bolstered its efficiency and accuracy. A well-established paradigm in AI-driven time series prediction is injecting physical knowledge into neural networks through signal decomposition methods, and sustaining progress in numerous scenarios has been reported. However, we uncover non-negligible evidence that challenges the effectiveness of signal decomposition in AI-based time series prediction. We confirm that improper dataset processing with subtle future label leakage is unfortunately widely adopted, possibly yielding abnormally superior but misleading results. By processing data in a strictly causal way without any future information, the effectiveness of additional decomposed signals diminishes. Our work probably identifies an ingrained and universal error in time series modeling, and the de facto progress in relevant areas is expected to be revisited and calibrated to prevent future scientific detours and minimize practical losses. © 2025 SPIE.

关键词： Information leakage

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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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