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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

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Leveraging Deep Learning for COVID-19 Detection: A Comparative Study Using ResNet Models on X-Ray Images

Leveraging Deep Learning for COVID-19 Detection: A Comparati...

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

作者： Disha Tuteja Anurag Sinha Deepak Chandra Uprety Dyuti Banerjee Ayushi Dewan Chetna Sharma Department of ECE College Guru Tegh Bahadur Institute of Technology Delhi India Department of Computer Science IGNOU New Delhi India Department of Computer Science and Engineering (CS) School of Computer Science in Emerging Technologies Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh India Rawal Institutions Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The COVID-19 pandemic has rapidly spread across the globe, infecting millions of people, with no signs of slowing down. This continuous surge in cases presents a major challenge for healthcare systems worldwide. To combat the exponential rise in infections, medical researchers have primarily relied on RT-PCR testing to distinguish between COVID-19 positive and negative cases. However, this approach faces significant hurdles, particularly in low-income countries where these tests are often too expensive and inaccessible in rural areas. Another critical challenge is the shortage of skilled healthcare professionals, especially doctors and radiologists, whose expertise is in high demand for diagnosing and treating COVID-19 patients. As a result, healthcare resources have been stretched to their limits. To address these issues, innovative solutions are essential, and artificial intelligence (AI) and machine learning (ML) offer promising possibilities. In medical diagnostics, AI, ML, and deep learning (DL) have demonstrated remarkable accuracy. This study explores the application of DL models, specifically ResNet-34 and ResNet-50, for COVID-19 detection using X-ray images. The results indicate an impressive accuracy of 96.4% with ResNet-34. To comprehensively evaluate the performance of these models, the study employs various quantitative metrics, including Matthews Correlation Coefficient (MCC), F1 Score, Precision, and Recall. By contributing to both the effective detection of COVID-19 and addressing the shortage of healthcare professionals, this research provides a hopeful step forward in the ongoing fight against the pandemic.

关键词： COVID-19 Deep learning Measurement Technological innovation Accuracy Pandemics Medical services Public healthcare X-ray imaging Testing

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Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model

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computers, Materials & Continua 2020年第10期65卷 529-541页

作者： Yatian Shen Yubo Mai Xiajiong Shen Wenke Ding Mengjiao Guo School of Computer Science and Engineering Southeast UniversityNanjing210000China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng475000China Swinburne Data Science Research Institute Swinburne University of TechnologyVictoria3122Australia

Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic *** the same time,the predicate-argument structure in a sentence is important information for semantic role labeling *** this work,we introduce the auxiliary deep neural network model,which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role *** on the framework of joint learning,part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role *** addition,we introduce the argument recognition layer in the training process of the main task-semantic role labeling,so the argument-related structural information selected by the predicate through the attention mechanism is used to assist the main *** the model makes full use of the semantic dependency between part-of-speech and semantic roles and the structural information of predicate-argument,our model achieved the F1 value of 89.0%on the WSJ test set of CoNLL2005,which is superior to existing state-of-the-art model about 0.8%.

关键词： Part-of-speech tagging semantic role labeling multi-task learning

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HVIS: A Human-like Vision and Inference System for Human Motion Prediction

arXiv

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arXiv 2025年

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. © 2025, CC BY.

关键词： Motion estimation

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Research Progress of Network Public Opinion Based on Fuzzy Set from the Perspective of Big data 2

Research Progress of Network Public Opinion Based on Fuzzy S...

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2nd International Conference on Artificial Intelligence and computer Science, AICS 2020

作者： Tang, Jianguo Wang, Jianghua Li, Fang School of Artificial Intelligence and Big Data Chongqing College of Electronic Engineering Chongqing China School of Computer Engineering Changji University Changji China

Granular computing is a new method of intelligent information processing. It describes knowledge and simulates human thinking mode in a granular way. It can realize the rapid transformation between different granular knowledge and is suitable for the analysis and modeling of large-scale and complex data. Fuzzy set is an important model in granular computing, which describes uncertain problems through fuzzy membership function and has been widely used in many fields such as network public opinion analysis. This paper combs the application research of fuzzy sets in network public opinion analysis from the perspective of big data, and discusses the different roles of fuzzy sets in network public opinion analysis from four aspects: fuzzy comprehensive evaluation, fuzzy reasoning, fuzzy granularity and generalized fuzzy set. The characteristics of different methods are compared and the unique features of fuzzy sets in these four aspects are pointed out. The unique advantages and some problems are discussed as well, and the future trend of this field is discussed. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy sets

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Intelligent Reflecting Surface-Aided Electromagnetic Stealth Against Radar Detection

arXiv

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arXiv 2023年

作者： Zheng, Beixiong Xiong, Xue Tang, Jie Zhang, Rui School of Microelectronics South China University of Technology Guangzhou511442 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

While traditional electromagnetic stealth materials/metasurfaces can render a target virtually invisible to some extent, they lack flexibility and adaptability, and can only operate within a limited frequency and angle/direction range, making it challenging to ensure the expected stealth performance. In view of this, we propose in this paper a new intelligent reflecting surface (IRS)-aided electromagnetic stealth system mounted on targets to evade radar detection, by utilizing the tunable passive reflecting elements of IRS to achieve flexible and adaptive electromagnetic stealth in a cost-effective manner. Specifically, we optimize the IRS’s reflection at the target to minimize the sum received signal power of all adversary radars. We first address the IRS’s reflection optimization problem using the Lagrange multiplier method and derive a semi-closed-form optimal solution for the single-radar setup, which is then generalized to the multi-radar case. To meet real-time processing requirements, we further propose low-complexity closed-form solutions based on the reverse alignment/cancellation and minimum mean-square error (MMSE) criteria for the single-radar and multi-radar cases, respectively. Additionally, we propose practical low-complexity estimation schemes at the target to acquire angle-of-arrival (AoA) and/or path gain information via a small number of receive sensing devices. Simulation results validate the performance advantages of our proposed IRS-aided electromagnetic stealth system with the proposed IRS reflection designs. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost effectiveness

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Inductive Graph-based Knowledge Tracing

Inductive Graph-based Knowledge Tracing

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International Conference on Big data and Smart Computing (BIGCOMP)

作者： Donghee Han Daehee Kim Keejun Han Mun Yong Yi Graduate School of Data Science Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Division of Computer Engineering Hansung University Seoul Republic of Korea Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

The rise of virtual education and increase in distance, partly owing to the spread of COVID-19 pandemic, has made it more difficult for teachers to determine each student’s learning status. In this situation, knowledge tracing (KT), which tracks a student’s mastery of specific knowledge concepts, is receiving increasing attention. KT utilizes a sequence of studentexercise interactive activities to predict the mastery of concepts corresponding to a target problem, recommending appropriate learning resources to students and optimizing learning sequences for adaptive learning. With the development of deep learning, various studies have been proposed, such as sequential models using recurrent neural networks, attention models influenced by transformers, and graph-based models that depict the relationships between knowledge concepts. However, they all have common limitations in that they cannot utilize the learning activities of students other than the target student and can only use a limited form of exercise information. In this study, we have applied the concept of rating prediction to the studentexercise knowledge tracing problem and solved the limitations of the existing models. Our proposed Inductive Graph-based Knowledge Tracing (IGKT) designed to integrate structural information and various unrestricted types of additional information into the model through subgraph sampling, has been found superior over the existing models across two different datasets in predicting student performances.

关键词： Knowledge engineering Deep learning Adaptation models Target tracking Recurrent neural networks Pandemics Predictive models

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

JSRevealer: A Robust Malicious JavaScript Detector against O...

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International Conference on Dependable Systems and Networks (DSN)

作者： Kunlun Ren Weizhong Qiang Yueming Wu Yi Zhou Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the convenience and popularity of Web applications, they have become a prime target for attackers. As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role because of their high effectiveness and efficiency. However, obfuscation techniques are commonly used in JavaScript, which makes the features extracted by static analysis contain many useless and disguised features, leading to many false positives and false negatives in detection results. In this paper, we propose a novel method to find out the essential features related to the semantics of JavaScript code. Specifically, we develop JS-Revealer, a robust, effective, scalable, and interpretable detector for malicious JavaScript. To test the capabilities of JSRevealer, we conduct comparative experiments with four other state-of-the-art malicious JavaScript detection tools. The experimental results show that JSRevealer has an average F1 of 84.8% on the data obfuscated by different obfuscators, which is 21.6%, 22.3%, 18.7%, and 22.9% higher than the tools CUJO, ZOZZLE, JAST, and JSTAP, respectively. Moreover, the detection results of JSRevealer can be interpreted, which can provide meaningful insights for further security research.

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CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction

arXiv

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arXiv 2023年

作者： Ekanayake, Mevan Chen, Zhifeng Harandi, Mehrtash Egan, Gary Chen, Zhaolin Monash Biomedical Imaging Monash University Australia Department of Electrical and Computer Systems Engineering Monash University Australia Department of Data Science and AI Monash University Australia School of Psychological Sciences Monash University Australia

In Magnetic Resonance Imaging (MRI), image acquisitions are often undersampled in the measurement domain to accelerate the scanning process, at the expense of image quality. However, image quality is a crucial factor that influences the accuracy of clinical diagnosis;hence, high-quality image reconstruction from undersampled measurements has been a key area of research. Recently, deep learning (DL) methods have emerged as the state-of-the-art for MRI reconstruction, typically involving deep neural networks to transform undersampled MRI images into high-quality MRI images through data-driven processes. Nevertheless, there is clear and significant room for improvement in undersampled DL MRI reconstruction to meet the high standards required for clinical diagnosis, in terms of eliminating aliasing artifacts and reducing image noise. In this paper, we introduce a self-supervised pretraining procedure using contrastive learning to improve the accuracy of undersampled DL MRI reconstruction. We use contrastive learning to transform the MRI image representations into a latent space that maximizes mutual information among different undersampled representations and optimizes the information content at the input of the downstream DL reconstruction models. Our experiments demonstrate improved reconstruction accuracy across a range of acceleration factors and datasets, both quantitatively and qualitatively. Furthermore, our extended experiments validate the proposed framework's robustness under adversarial conditions, such as measurement noise, different k-space sampling patterns, and pathological abnormalities, and also prove the transfer learning capabilities on MRI datasets with completely different anatomy. Additionally, we conducted experiments to visualize and analyze the properties of the proposed MRI contrastive learning latent space. Code available here. © 2023, CC BY.

关键词： Magnetic resonance imaging

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