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arXiv 2022年

作者： Zhang, Jianping Wu, Weibin Huang, Jen-Tse Huang, Yizhan Wang, Wenxuan Su, Yuxin Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University China

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus imperative to devise effective attack algorithms to identify the deficiencies of DNNs beforehand in security-sensitive applications. To efficiently tackle the black-box setting where the target model’s particulars are unknown, feature-level transfer-based attacks propose to contaminate the intermediate feature outputs of local models, and then directly employ the crafted adversarial samples to attack the target model. Due to the transferability of features, feature-level attacks have shown promise in synthesizing more transferable adversarial samples. However, existing feature-level attacks generally employ inaccurate neuron importance estimations, which deteriorates their transferability. To overcome such pitfalls, in this paper, we propose the Neuron Attribution-based Attack (NAA), which conducts feature-level attacks with more accurate neuron importance estimations. Specifically, we first completely attribute a model’s output to each neuron in a middle layer. We then derive an approximation scheme of neuron attribution to tremendously reduce the computation overhead. Finally, we weight neurons based on their attribution results and launch feature-level attacks. Extensive experiments confirm the superiority of our approach to the state-of-the-art benchmarks. Our code is available at: https://***/jpzhang1810/NAA . Copyright © 2022, The Authors. All rights reserved.

关键词： Neurons

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An efficient and secure arbitrary N-party quantum key agreement protocol using Bell states

arXiv

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arXiv 2023年

作者： Liu, Wen-Jie Xu, Yong Yang, Ching-Nung Gao, Pei-Pei Yu, Wen-Bin Nanjing210044 China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Department of Computer Science and Information Engineering National Dong Hwa University Hualien974 Taiwan

Two quantum key agreement protocols using Bell states and Bell measurement were recently proposed by Shukla et al. [Quantum Inf. Process. 13, 2391(2014)]. However, Zhu et al. pointed out that there are some security flaws and proposed an improved version[Quantum Inf. Process. 14, 4245(2015)]. In this study, we will show Zhu et al.’s improvement still exists some security problems, and its efficiency is not high enough. For solving these problems, we utilize four Pauli operations {I, Z, X, Y } to encode two bits instead of the original two operations {I, X} to encode one bit, and then propose an efficient and secure arbitrary N-party quantum key agreement protocol. In the protocol, the channel checking with decoy single photons is introduced to avoid the eavesdropper’s flip attack, and a post-measurement mechanism is used to prevent against the collusion attack. The security analysis shows the present protocol can guarantee the correctness, security, privacy and fairness of quantum key agreement. © 2023, CC BY.

关键词： Photons

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Flexible Capacitive Pressure Sensors and Their Applications in Human-Machine Interactions

Flexible Capacitive Pressure Sensors and Their Applications ...

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Sensor Technology and Control (ISSTC), International Symposium on

作者： Chenyu Fang Tianli Fu Yao Shi Changzhou No.1 High School Changzhou China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan China College of Computer and Information Science College of Software Southwest University Chongqing China

Flexible capacitive pressure sensors have shown excellent application potential in human-machine interactions, wearable electronic devices, biological and medical, and electronic skin. Compared with piezoresistive and piezoelectric pressure sensors, capacitive pressure sensors with advantages such as high flexibility, high stability, low consumption, and simple construction. Because of these advantages, flexible capacitive pressure sensors have become the key research direction of pressure sensors in recent years. This paper introduces the working principle of flexible capacitive pressure sensors and discusses numerous research on improving the performance of flexible capacitive pressure sensors. Then two methods of improving the dielectric layer structures to improve the sensitivity of the sensor and some related works are reviewed. Finally, the flexible capacitive pressure sensors' huge potential for advances in human-machine interactions areas such as health care and motion monitoring is also discussed. The paper concludes with an overview of future research directions of improving the performance of flexible capacitive pressure sensors.

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Handwritten Signature Recognition using Parallel CNNs and Transfer Learning for Forensics

Handwritten Signature Recognition using Parallel CNNs and Tr...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Jaouhar Fattahi Feriel Sghaier Mohamed Mejri Ridha Ghayoula Emil Pricop Baha Eddine Lakdher Department of Computer Science and Software Engineering Laval University Quebec Canada Carthage National Engineering School Carthage University Tunis Tunisia Faculty of Engineering University of Moncton Canada Computers & Electronics Department Automatic Control Petroleum-Gas University of Ploieşti Ploieşti Romania Faculty of Science University of Monastir Monastir Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Handwritten signatures hold paramount importance in legal, financial, and administrative domains, necessitating the development of robust signature recognition tools for forensic applications. This paper introduces a handwritten signature recognition (HSR) model employing Parallel Convolutional Neural Networks (CNN) tailored for forensic endeavors. Utilizing the parallel processing capabilities of CNN, our proposed approach adeptly analyzes and extracts discriminative features from handwritten signature images to facilitate precise recognition. In addition, we leverage several transfer learning techniques by parallelizing proven pre-trained CNNs. Extensive experimentation validates the efficacy of our approach on a standard dataset, demonstrating high accuracy and resilience in signature recognition tasks. The proposed approach exhibits substantial promise in augmenting forensic investigations by automating signature verification processes, thereby bolstering fraud detection efforts and upholding the integrity of legal documentation.

关键词： Deep learning Handwriting recognition Forensics Face recognition Transfer learning Feature extraction Fraud Convolutional neural networks Standards Resilience

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Fusing Dynamics Equation: A Social Opinions Prediction Algorithm with LLM-based Agents

arXiv

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arXiv 2024年

作者： Yao, Junchi Zhang, Hongjie Ou, Jie Zuo, Dingyi Yang, Zheng Dong, Zhicheng College of Computer Science Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou China College of Information Science Technology Tibet University Lhasa China

In the context where social media is increasingly becoming a significant platform for social movements and the formation of public opinion, accurately simulating and predicting the dynamics of user opinions is of great importance for understanding social phenomena, policy making, and guiding public opinion. However, existing simulation methods face challenges in capturing the complexity and dynamics of user behavior. Addressing this issue, this paper proposes an innovative simulation method for the dynamics of social media user opinions, the FDE-LLM algorithm, which incorporates opinion dynamics and epidemic model. This effectively constrains the actions and opinion evolution process of large language models (LLM), making them more aligned with the real cyber world. In particular, the FDE-LLM categorizes users into opinion leaders and followers. Opinion leaders are based on LLM role-playing and are constrained by the CA model, while opinion followers are integrated into a dynamic system that combines the CA model with the SIR model. This innovative design significantly improves the accuracy and efficiency of the simulation. Experiments were conducted on four real Weibo datasets and validated using the open-source model ChatGLM. The results show that, compared to traditional agent-based modeling (ABM) opinion dynamics algorithms and LLM-based opinion diffusion algorithms, our FDE-LLM algorithm demonstrates higher accuracy and interpretability. Copyright © 2024, The Authors. All rights reserved.

关键词： Chatbots

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Solving Math Word Problems Following Logically Consistent Template

Solving Math Word Problems Following Logically Consistent Te...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zeyu Huang Xiaofeng Zhang Jun Bai Wenge Rong Yuanxin Ouyang Zhang Xiong State Key Laboratory of Software Development Environment Beihang University Beijing China Sino-French Engineer School Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Solving math word problems (MWPs) is a challenging task. Some existing solvers retrieve textually similar problems and draw on their solutions to solve the given problem. However, textually similar questions are not guaranteed to have similar solutions, and vice versa. Therefore, this work investigates the logical consistency among different problems and proposes a novel framework to solve math word problems following logically consistent templates. Experimental results show that our method outperforms many strong baselines, including some pre-trained language model-based methods. Further analysis shows that our retrieval method does learn the logical similarity between Questions and plays a crucial role in our model's nerformance.

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Towards Time-Series Key Points Detection Through Self-supervised Learning and Probability Compensation 28th

Towards Time-Series Key Points Detection Through Self-superv...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yuan, Mingxu Bi, Xin Huang, Xuechun Zhang, Wei Hu, Lei Yuan, George Y. Zhao, Xiangguo Sun, Yongjiao Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University Shenyang110819 China Key Laboratory of Liaoning Province on Deep Engineering and Intelligent Technology Northeastern University Shenyang110819 China School of Computer Science and Engineering Northeastern University Shenyang China Thinvent Digital Technology Co. Ltd. Nanchang India College of Software Northeastern University Shenyang China

ISBN: (纸本)9783031306365

Key points detection is crucial for signal analysis by marking the identification points of specific events. Deep learning methods have been introduced into key points detection tasks due to their significant representation learning ability. However, in contrast to common time series classification and prediction tasks, the target key points correspond to significantly different time-series patterns and account for an extremely small proportion in a whole sample. Consequently, existing end-to-end methods for key points detection encounter two major problems: specificity and sparsity. Thus, in this work, we address these issues by proposing a probability compensated self-supervised learning framework named ProCSS. Our ProCSS consists of two major components: 1) a pretext task module pretraining an encoder based on self-supervised learning to capture effective time-series representations with a higher generalization ability;2) a joint loss function providing both dynamic focal adaptation and probability compensation by extreme value theory. Extensive experiments using both real-world and benchmark datasets are conducted. The results indicate that our method outperforms our rival methods for time-series key points detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supervised learning

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Deep Learning-Based Approach for Identification of Potato Leaf Diseases Using Wrapper Feature Selection and Feature Concatenation

arXiv

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arXiv 2025年

作者： Naeem, Muhammad Ahtsam Saleem, Muhammad Asim Sharif, Muhammad Imran Akber, Shahzad Saleem, Sajjad Akhtar, Zahid Siddique, Kamran School of Information and Software Engineering University of Electronic Science and Technology of China China Faculty of Computing Riphah International University Faisalabad Campus Pakistan Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Department of Information and Technology Washington University of Science and Technology AlexandriaVA United States Department of Network and Computer Security State University of New York Polytechnic Institute United States Department of Computer Science and Engineering University of Alaska Anchorage Anchorage United States

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image;(2) Feature extraction is performed using a Deep CNN model, then these extracted features are concatenated;(3) Feature selection is performed using wrapper-based feature selection;(4) Classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features. © 2025, CC BY.

关键词： Deep learning

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Subgraph-Oriented Heterogeneous Drug-Target Interaction Identification

Subgraph-Oriented Heterogeneous Drug-Target Interaction Iden...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xiaofeng Zhang Zeyu Huang Jun Bai Wenge Rong Yuanxin Ouyang Zhang Xiong State Key Laboratory of Software Development Environment Beihang University Beijing China Sino-French Engineer School Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Drug-target interaction (DTI) is an important task in drug discovery and drug repurposing. Currently, most methods utilizing drug-based and protein-based similarity values to predict DTIs achieve promising results. However, calculating similarities for each pair of nodes is time-consuming, especially for relatively large datasets. In this research, we propose a novel subgraph-oriented heterogeneous DTI identification method that transforms the DTI task from a link prediction task to a subgraph classification task. For each link, a local subgraph around this link is extracted. Then, a subgraph labeling process distinguishes different topologies of subgraphs. A random walk-based node representation generation is also integrated with the model. Finally, we apply a graph neural network for the subgraph classification. Our method avoids incorporating human-made similarity values by extracting more meaningful local subgraph topological information. Experimental studies for known DTI predictions on two DTI datasets show promising results for DTI prediction. Empirical results for new DTI predictions on two external public databases show the generalization ability of the proposed method.

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