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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Wang, Ping Tang, Fei Xiao, Yunpeng Zhu, Huihui Yu, Jiang School of Computer Science and Technology School of Artificial Intelligence Chongqing University of Posts and Telecommunications Chongqing China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China Institute of Electronic Information and Network Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350358780

The quality of empirical statistical analysis is closely related to the quality and quantity of available data. However, due to privacy concerns, it is often difficult to collect data from different data owners, thus limiting the usefulness of the collected data. Existing solutions mostly focus on evaluating federated statistical analysis using homomorphic encryption (HE) or secure multiparty computation (SMPC). Our solution incorporates trusted execution environment (TEE) for federated statistical tests. Initially, we propose federated statistical tests framework based on single-party TEE. Considering the security of the proposed framework, we then extend single-party TEE into a distributed TEE cluster and combine it with secure resharing protocols to realize model security and confidential computation while preserving data privacy in federated setting. We provide a comprehensive security analysis for the proposed federated statistical tests framework and evaluate the common Welch's t-test on diabetic dataset. security analysis and experimental evaluation prove that our framework is end-to-end secure and efficient. © 2023 IEEE.

关键词： Statistical tests

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Laryngeal Leukoplakia Classification Via Dense Multiscale Feature Extraction in White Light Endoscopy Images 48

Laryngeal Leukoplakia Classification Via Dense Multiscale Fe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： You, Zhenzhen Yan, Yan Shi, Zhenghao Zhao, Minghua Yan, Jing Liu, Haiqin Hei, Xinhong Ren, Xiaoyong Xi'An University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an China Xi'An Jiaotong University Second Affiliated Hospital of Medical College Department of Otorhinolaryngology Xi'an China

ISBN: (纸本)9781728163277

Laryngeal leukoplakia classification is challenging using white light endoscopy images. Relevant research focus on normal tissues versus non normal tissues, cancer versus non cancer classification. The objective of this paper is to classify laryngeal leukoplakia in white light endoscopy images into six classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia and squamous cell carcinoma. We proposed a dense multiscale convolutional neural network including parallel multiscale convolution, dense convolution and recurrent convolution in favor of extracting dense multiscale features of laryngeal leukoplakia for fine classification. The proposed network achieved an overall accuracy of 0.8958 for the six-class classification. It has high sensitivity and specificity for each class which are, respectively, 1.0000 and 0.9394 for normal tissues, 0.6667 and 1.0000 for inflammatory keratosis, 0.8889 and 0.9744 for mild dysplasia and moderate dysplasia, 0.7500 and 1.0000 for severe dysplasia, 1.0000 and 0.9767 for squamous cell carcinoma. The experimental results show that our proposed model is superior to the state-of-the-art deep learning-based models. © 2023 IEEE.

关键词： Endoscopy

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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Revisit the AWGN-Goodness of Polar-Like Lattices

arXiv

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arXiv 2025年

作者： Liu, Ling Yu, Junjiang Lyu, Shanxiang Bai, Baoming Guangzhou Institute of Technology Xidian University Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Cyber Security Jinan University Guangzhou China

This paper aims to provide a comprehensive introduction to lattices constructed based on polar-like codes and demonstrate some of their key properties, such as AWGN-goodness. We first present polar lattices directly from the perspective of their generator matrix. Next, we discuss their connection with the recently proposed PAC (polarization adjusted convolutional) lattices and analyze the structural advantages of PAC lattices, through which the AWGN-goodness of PAC lattices can be conveniently demonstrated. Copyright © 2025, The Authors. All rights reserved.

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Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

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2nd International Conference on Business Analytics for technology and security, ICBATS 2023

作者： Ghazal, Taher M. Ibrahim, Amer Akram, Ali Sheraz Qaisar, Zahid Hussain Munir, Sundus Islam, Shanza Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Skyline University College University City School of Information Technology Sharjah1797 United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NFC Institute of Engineering and Technology Department of Computer Science Multan Pakistan Lahore Garrison University Lahore Department of Computer Science Lahore Pakistan Lahore Garrion University Lahore Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc. © 2023 IEEE.

关键词： Support vector machines

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Network Intrusion Detection Based on Hybrid Network Model and Federated Learning 7th

Network Intrusion Detection Based on Hybrid Network Model an...

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7th International Conference on Information Science, Communication and Computing, ISCC 2023

作者： Kou, Yuqing Cheng, Jieren Yang, Yue Wu, Hao Li, Yajing Sheng, Victor S. Hainan Blockchain Engineering Research Center Hainan China School of Computer Science and Technology Hainan University Haikou570228 China School of Cyberspace Security Hainan University Haikou570228 China Department of Computer Science Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9789819971602

Data is a valuable strategic resource for the development of modern society. However, with the increasingly complex network environment, privacy leaks and malicious attacks emerge in endlessly. For example, blockchain has also begun to become a new outlet for network black production, which poses a huge security threat to cryptocurrency. In this paper, we propose a hybrid network model (Cb Net), which uses Convolutional Neural Networks (CNN) and Bidirectional recurrent neural networks (BiGRU) to fully extract the space-time characteristics of network data traffic. Then, we propose an intrusion detection method (FLD), which introduces federated learning to collect traffic data from different network institutions, analyze network traffic and identify network attacks. We have fully evaluated the performance of the proposed model and method on the public dataset NSL-KDD. Experiments show that the proposed hybrid network model can achieve high detection accuracy, and the FLD method can effectively identify network attacks on the premise of ensuring the privacy of the local data of the users involved, and its performance is better than other methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding

Tracking Objects and Activities with Attention for Temporal ...

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

作者： Zeyu Xiong Daizong Liu Pan Zhou Jiahao Zhu 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 Wangxuan Institute of Computer Technology Peking University

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.

关键词： Solid modeling Target tracking Three-dimensional displays Grounding Benchmark testing Signal processing Feature extraction

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Advancements in sensors and actuators technologies for smart cities: a comprehensive review

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Smart Construction and Sustainable Cities 2023年第1期1卷 1-19页

作者： Arshi, Oroos Mondal, Surajit Department of Cyber Security and Forensics School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Electrical and Electronics Engineering School of Advanced Engineering University of Petroleum and Energy Studies Dehradun India

The creation of smart cities has benefited greatly from the quick advancement of sensor and actuator technology. The basis of data-driven urban environments is supported by these technologies, which seamlessly connect with the Internet of Things (IoT). This in-depth review paper explores the crucial part that sensors and actuators play in the development of smart cities, covering important topics such as technological kinds, data security, regulatory frameworks, and future possibilities. The review begins by explaining the importance of sensors and actuators in the Internet of Things (IoT) connections that serve as the framework for smart cities. Additionally, it sheds light on the wide range of sensors designed for different IoT applications as well as the variables affecting their service life, highlighting how crucial precision and durability are. Actuators are examined in detail to clarify how it might be used to create smarter cities. Actuators are the dynamic counterparts of sensors. This review discusses data security in big data exchange among actuators, legal foundations for smart city development, and key elements for creating a smart city. It highlights the benefits of advanced actuator technology and sensor integration, and emerging trends like AI-driven urban management and blockchain-enhanced data security. The paper serves as a guide for researchers, policymakers, and urban planners. The graphical abstract below illustrates the multifaceted advancements in sensor and actuator technologies, showcasing their pivotal role in shaping smarter, more sustainable cities.

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Extracting Biomedical Entities from Noisy Audio Transcripts 30

Extracting Biomedical Entities from Noisy Audio Transcripts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ebadi, Nima Morgan, Kellen Tan, Adrian Linares, Billy Osborn, Sheri Majors, Emma Davis, Jeremy Rios, Anthony Department of Electrical and Computer Engineering The University of Texas San Antonio United States Department of Management Science and Statistics The University of Texas San Antonio United States Data Analytics The University of Texas San Antonio United States Department of Information Systems and Cyber Security The University of Texas San Antonio United States Department of Neurology Division of Neuropsychology UT Health San Antonio United States

ISBN: (纸本)9782493814104

Automatic Speech Recognition (ASR) technology is fundamental in transcribing spoken language into text, with considerable applications in the clinical realm, including streamlining medical transcription and integrating with Electronic Health Record (EHR) systems. Nevertheless, challenges persist, especially when transcriptions contain noise, leading to significant drops in performance when Natural Language Processing (NLP) models are applied. Named Entity Recognition (NER), an essential clinical task, is particularly affected by such noise, often termed the ASR-NLP gap. Prior works have primarily studied ASR's efficiency in clean recordings, leaving a research gap concerning the performance in noisy environments. This paper introduces a novel dataset, BioASR-NER, designed to bridge the ASR-NLP gap in the biomedical domain, focusing on extracting adverse drug reactions and mentions of entities from the Brief Test of Adult Cognition by Telephone (BTACT) exam. Our dataset offers a comprehensive collection of almost 2,000 clean and noisy recordings. In addressing the noise challenge, we present an innovative transcript-cleaning method using GPT4, investigating both zero-shot and few-shot methodologies. Our study further delves into an error analysis, shedding light on the types of errors in transcription software, corrections by GPT4, and the challenges GPT4 faces. This paper aims to foster improved understanding and potential solutions for the ASR-NLP gap, ultimately supporting enhanced healthcare documentation practices. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Error analysis

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