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2nd International Conference on Computer Science and Educational Informatization, CSEI 2020

作者： Liu, Fudong Zhang, Ping Hou, Yifan Wang, Lixin Shan, Zheng Wang, Junchao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (数字)9781728170084

ISBN: (纸本)9781728170077

In recent years, a large number of malware has spread indiscriminately, which causes various significant cyberspace security incidents all over the world. Therefore, malware detection has become one of the important research topics in cyberspace security. At present, however, the practical teaching for malware detection mainly focuses on delivering the theory and skills, and actual combat training is relatively weak. As a novel method for malware recognition and analysis, software gene technology can quickly explore potential functional and behavioral information of malware, and detect unknown malware. Hence, building an experimental platform for malware gene analysis can effectively reduce the time required for students to quickly carry out the actual analysis process, support student needs for comprehensive data analysis, and provide a unified public service infrastructure for classroom teaching, practical training and scientific research. © 2020 IEEE.

关键词： Genes

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Thinking and practice of online teaching under COVID-19 Epidemic 2

Thinking and practice of online teaching under COVID-19 Epid...

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2nd International Conference on Computer Science and Educational Informatization, CSEI 2020

作者： Ping, Zhang Fudong, Liu Zheng, Shan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728170077

Response to the outbreak of COVID-19, classes in this semester are mainly replaced by online teaching to ensure continual learning. Teachers are actively exploring effective online teaching methods to provide a good learning experience for students at home. This paper presents an online teaching model based on the idea of Problem-Based Learning (PBL) and take 'Data Structure' as an example to discuss the design of teaching process and problems. © 2020 IEEE.

关键词： COVID-19

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Embedding Attribute and Relation Information for Person Entity Alignment 6

Embedding Attribute and Relation Information for Person Enti...

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6th International Conference on Big Data and Information Analytics, BigDIA 2020

作者： Wang, Can Liu, Xiaonan Yin, Meijuan State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Person entity alignment is to distinguish whether the person entity information extracted from different data sources refer to the same entity. Existing methods mainly use attribute and relation information of entity to solve entity alignment problem. Most of them are based on common information of entity, while ignore some non-common but semantically similar attribute information and they only consider the relation between first-order adjacent entity, which makes it difficult to accurately align entity in the case of scarcity of common information. so we propose a person entity alignment method based on joint embedding of attribute and relation information, which makes full use of non-common but related information of entity to align them and improves the Hit@k of alignment results when common information is sparse. In the method, the representation vector of attribute information and the representation vector of relation information are obtained by embedding. During embedding, we consider the identification of different attributes and the second-order relation between entities to improve the role of feature attributes and the second-order relation in entity alignment. When calculating the similarity of entities, binary linear regression model is used to fit the weight of attribute information and relation information similarity, so as to adapt to the different characteristics of the entity data under different circumstances. The experimental results show that, compared with the related methods, the Hit@1 value and Hit@10 value of the proposed method increased by more than 8.4% and more than 9.1% respectively. © 2020 IEEE.

关键词： Embeddings

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Attention-aligned mean-teacher learning for unsupervised domain adaptive person re-ID

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Science China Technological Sciences 2025年

作者： You LYU Zhen ZHANG Guoliang KANG Wei WEI Institute of Artificial Intelligence Beihang University Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education Beihang University State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University School of Automation Science and Electrical Engineering Beihang University AVICAS Generic Technology Co. Ltd Zhongguancun Laboratory School of Mathematics Sciences Beihang University

In recent years, artificial intelligence has fueled the development of numerous applications [1, 2]. Person re-identification (re-ID) is a typical artificial intelligence system designed to automatically retrieve imag...

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Over-the-RIS Modulation: A Reconfigurable Intelligent Surface Assisted Modulation Design 8

Over-the-RIS Modulation: A Reconfigurable Intelligent Surfac...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Huang, Yiqian Zammit, Saviour Yang, Ping Xiao, Yue Zhang, Bo Chen, Jienan Xiang, Wei University of Electronic Science and Technology of China National Key Laboratory of Sciencec and Technology on Communications Sichuan China University of Malta Department of Communications and Computer Engineering Msida Malta 32806 Troops Beijing China School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne Australia

ISBN: (纸本)9798350305838

This paper proposes an improved over the air modulation (OTAM) scheme by using the powerful reconfigurable intelligent surfaces (RISs), namely over-the-RIS modulation (ORM). In the design framework, we reconstruct multipath channels by employing several RISs to increase the attenuation difference between multiple paths. Compared with conventional OTAM, ORM can be used in more generic fading scenarios, e.g., channels with non-ideal attenuation properties, and thus extends the application of OTAM. Based on the Bayesian criterion, we propose an optimal likelihood detector for the ORM system and derive a closed-form expression of the bit error rate (BER). Furthermore, we investigate the phase optimization of the RISs to improve its BER and deduce its closed-form solutions. Simulation results are presented to show that the proposed ORM system can achieve a better BER performance and higher transmission rate than the conventional OTAM systems, especially for environments with severe shadow fading. © 2023 IEEE.

关键词： Bit error rate

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New advanced computing Architecture for Cryptography Design and Analysis by D-Wave Quantum Annealer

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Tsinghua Science and Technology 2022年第4期27卷 751-759页

作者： Xiangmin Ji Baonan Wang Feng Hu Chao Wang Huanguo Zhang College of Computer Information Science Fujian Agriculture and Forestry UniversityFuzhou 350002China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444 State Key Laboratory of Cryptology Beijing 100878China laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai UniversityShanghai 200444 State Key Laboratory of Cryptology Beijing 100878and also with Center for Quantum ComputingPeng Cheng LaboratoryShenzhen 518000China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072

Universal quantum computers are far from achieving practical *** D-Wave quantum computer is initially designed for combinatorial ***,exploring the potential applications of the D-Wave device in the field of cryptography is of great ***,although we optimize the general quantum Hamiltonian on the basis of the structure of the multiplication table(factor up to 1005973),this study attempts to explore the simplification of Hamiltonian derived from the binary structure of the integers to be factored.A simple factorization on 143 with four qubits is provided to verify the potential of further advancing the integer-factoring ability of the D-Wave ***,by using the quantum computing cryptography based on the D-Wave 2000 Q system,this research further constructs a simple version of quantum-classical computing architecture and a Quantum-Inspired Simulated Annealing(QISA)*** functions and a high-performance platform are introduced,and additional balanced Boolean functions with high nonlinearity and optimal algebraic immunity can be *** comparison between QISA and Quantum Annealing(QA)on six-variable bent functions not only shows the potential speedup of QA,but also suggests the potential of architecture to be a scalable way of D-Wave annealer toward a practical cryptography design.

关键词： Quantum Annealing(QA) factorization Boolean functions brain-inspired cognition

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An Adaptive Proximal Inexact Gradient Framework and Its Application to Per-Antenna Constrained Joint Beamforming and Compression Design

arXiv

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arXiv 2025年

作者： Fan, Xilai Jiang, Bo Liu, Ya-Feng State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Ministry of Education Key Laboratory of NSLSCS School of Mathematical Sciences Nanjing Normal University Nanjing210023 China

In this paper, we propose an adaptive proximal inexact gradient (APIG) framework for solving a class of nonsmooth composite optimization problems involving function and gradient errors. Unlike existing inexact proximal gradient methods, the proposed framework introduces a new line search condition that jointly adapts to function and gradient errors, enabling adaptive stepsize selection while maintaining theoretical guarantees. Specifically, we prove that the proposed framework achieves an ϵ-stationary point within O(ϵ−2) iterations for nonconvex objectives and an ϵ-optimal solution within O(ϵ−1) iterations for convex cases, matching the best-known complexity in this context. We then custom-apply the APIG framework to an important signal processing problem: the joint beamforming and compression problem (JBCP) with per-antenna power constraints (PAPCs) in cooperative cellular networks. This customized application requires careful exploitation of the problem’s special structure such as the tightness of the semidefinite relaxation (SDR) and the differentiability of the dual. Numerical experiments demonstrate the superior performance of our custom-application over state-of-the-art benchmarks for the JBCP. © 2025, CC BY.

关键词： Beamforming

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ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification

arXiv

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arXiv 2024年

作者： Qu, Zuomin Lu, Wei Luo, Xiangyang Wang, Qian Cao, Xiaochun The School of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Institute of Artificial Intelligence Sun Yat-sen University Guangzhou510006 China The State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China The School of Cyber Science and Engineering Wuhan University Wuhan430072 China The School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China

—The misuse of deep learning-based facial manipulation poses a potential threat to civil rights. To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process by adding invisible adversarial perturbations into images, making the forged output unconvincing to the observer. However, their non-directional disruption of the output may result in the retention of identity information of the person in the image, leading to stigmatization of the individual. In this paper, we propose a novel universal framework for combating facial manipulation, called ID-Guard. Specifically, this framework requires only a single forward pass of an encoder-decoder network to generate a cross-model universal adversarial perturbation corresponding to a specific facial image. To ensure anonymity in manipulated facial images, a novel Identity Destruction Module (IDM) is introduced to destroy the identifiable information in forged faces targetedly. Additionally, we optimize the perturbations produced by considering the disruption towards different facial manipulations as a multitask learning problem and design a dynamic weights strategy to improve cross-model performance. The proposed framework reports impressive results in defending against multiple widely used facial manipulations, effectively distorting the identifiable regions in the manipulated facial images. In addition, our experiments reveal the ID-Guard’s ability to enable disrupted images to avoid face inpaintings and open-source image recognition systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Positive-Unlabeled Learning with Field of View Consistency for Histology Image Segmentation 1

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13th International Conference on Frontier computing, FC 2023

作者： Jia, Xiaoqi Fu, Chong Hou, Jiaxin Qin, Wenjian School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (数字)9789819993420

ISBN: (纸本)9789819993413

Histology image annotation is costly and time-consuming. Utilizing Positive and Unlabeled (PU) data for model training offers a more resource-efficient alternative. However, previous methods for PU learning suffer from the noise arising from label assignment to unlabeled data. We observe that predictions on noisy data lack consistency under data augmentation. In this paper, we present Field of View (FoV) consistency regularization for PU segmentation in histology images, which effectively reduces the noise influence by promoting consistent predictions across varying FoVs. Using only 20% of positive labels on the Glas Dataset, our approach outperforms previous methods, achieving a Dice score of 90.69%-almost reaching the fully supervised result of 93.30%. Source code is available at: https://***/lzaya/PU_with_FoV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image segmentation

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