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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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Predicting Cervical Cancer Using Deep Learning Mutation-based Atom Search Optimization (MASO) Algorithm

Predicting Cervical Cancer Using Deep Learning Mutation-base...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： A V R Mayuri Selvekumar K Subash Anurag Aeron School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Ethiopia Department of Data Science St. Joseph College (Autonomous) Trichy India Department of Computer Science and Engineering MIET Meerut Uttar Pradesh India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Worldwide, women are compressed by cervical cancer, which is a prevalent malignancy. This disease, which is currently the fourth leading cause of death for women, shows no symptoms when it first arises. Cells that cause cervical cancer multiply gradually at the cervix. If this cancer is detected early enough, treatment can be effective. Presently, it is difficult for medical workers to detect this kind of cancer before it spreads rapidly. This study used a number of machine learning classification algorithms with risk markers to predict cervical cancer. These study suggestions mutation-based Atom Search Optimization (MASO) with a Deep Convolutional Neural Network (DCNN) to offer a original method for automated diagnosis of uterine cervical cancer. Though DCNN is used to extract features from medical imaging data, MASO is used to increase the optimization of the diagnostic model. Composed, these technologies have the potential to rise cervical cancer diagnosis accuracy and efficiency, offering an automated approach for early detection and prompt intervention. Our proposed model established a strong 95% accuracy rate for generalization. This study explores the difficulties caused by missing values and class imbalance in the specific dataset with the goal of assisting medical professionals in the early detection of cervical cancer and improving treatment for people that are impacted by the illness.

关键词： Deep learning Ethics Statistical analysis Medical services Prediction algorithms Feature extraction Convolutional neural networks

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Adversarial Discriminative Domain Adaptation and Transformers for EEG-based Cross-Subject Emotion Recognition

Adversarial Discriminative Domain Adaptation and Transformer...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Shadi Sartipi Mujdat Cetin Department of Electrical and Computer Engineering University of Rochester Rochester USA Department of Electrical and Computer Engineering Goergen Institute for Data Science University of Rochester Rochester USA

Decoding the human emotional states based on electroencephalography (EEG) in affective brain-computer interfaces (BCI) is a great challenge due to inter-subject variability. Existing methods mostly use large amounts of EEG data of each new subject to calibrate the algorithm, which could be time-consuming and not user-oriented. To address this issue, we propose a combination of using transformers (TF) and adversarial discriminative domain adaptation (ADDA) to perform the emotion recognition task in a cross-subject manner. TF principally relies on the attention mechanism. Our proposed approach performs scaledot product attention on the feature-channel aspect of EEG data to improve the spatial features. Then, the temporal transforming is applied to get the global discriminative representations from the time component. Moreover, ADDA aims to minimize the discrepancy of EEG data from various subjects. We evaluate the proposed ADDA-TF on the publicly available DEAP dataset and demonstrate the improvements it provides on low versus high valence and arousal classification.

关键词： Emotion recognition Neural engineering Transformers Electroencephalography Brain-computer interfaces Decoding Task analysis

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An efficient multi-attribute combinatorial auction for 4PL transportation service procurement

An efficient multi-attribute combinatorial auction for 4PL t...

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第35届中国控制与决策会议

作者： Na Yuan Haiming Liang Min Huang Yanfeng Zhang College of Information Science and Engineering Northeastern University Center for Network Big Data and Decision-Making Business School Sichuan University College of Computer Science and Engineering Northeastern University

In this paper,we study the mechanism design problem of combinatorial reverse auction for 4 PL transportation service procurement(TSP) considering price and multiple nonprice *** order to achieve efficient winner determination and transportation request allocation,we develop a 0-1 programing model to minimize social cost,where social cost includes real transportation cost of 3 PLs and the costs incurred by non-price attributes.A multi-attribute VCG(MA-VCG) reverse auction mechanism is proposed,which satisfies efficiency,incentive compatibility,individual rationality,and budget ***,a numerical example is designed to demonstrate the effectiveness and applicability of the proposed ***,sensitivity analysis is implemented to illustrate the influences of attribute preference,cost coefficient and other parameters on auction results.

关键词： Transportation service procurement Mechanism design Combinatorial reverse auction Multi-attribute auction

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MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation 39

MTVHunter: Smart Contracts Vulnerability Detection Based on ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Guokai Zhuang, Yuan Zhang, Shuo Feng, Xiaoyu Liu, Zhenguang Zhang, Liguo College of Computer Science and Technology Harbin Engineering University Heilongjiang China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)157735897X

Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions;(ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Teaching

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COMET: NFT Price Prediction with Wallet Profiling

arXiv

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arXiv 2024年

作者： Wang, Tianfu Deng, Liwei Wang, Chao Lian, Jianxun Yan, Yue Yuan, Nicholas Jing Zhang, Qi Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Hefei China Microsoft Inc Suzhou China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China Microsoft Research Asia Beijing China Microsoft Inc Beijing China Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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TWO-STAGE PREDICT+OPTIMIZE FOR MIXED INTEGER LINEAR PROGRAMS WITH UNKNOWN PARAMETERS IN CONSTRAINTS

arXiv

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arXiv 2023年

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Sciences Institute for Foundations of Data Science University of Wisconsin–Madison WI United States

Consider the setting of constrained optimization, with some parameters unknown at solving time and requiring prediction from relevant features. Predict+Optimize is a recent framework for end-to-end training supervised learning models for such predictions, incorporating information about the optimization problem in the training process in order to yield better predictions in terms of the quality of the predicted solution under the true parameters. Almost all prior works have focused on the special case where the unknowns appear only in the optimization objective and not the constraints. Hu et al. proposed the first adaptation of Predict+Optimize to handle unknowns appearing in constraints, but the framework has somewhat ad-hoc elements, and they provided a training algorithm only for covering and packing linear programs. In this work, we give a new simpler and more powerful framework called Two-Stage Predict+Optimize, which we believe should be the canonical framework for the Predict+Optimize setting. We also give a training algorithm usable for all mixed integer linear programs, vastly generalizing the applicability of the framework. Experimental results demonstrate the superior prediction performance of our training framework over all classical and state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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FullTransNet: Full Transformer with Local-Global Attention for Video Summarization

arXiv

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arXiv 2025年

作者： Lan, Libin Jiang, Lu Yu, Tianshu Liu, Xiaojuan He, Zhongshi The College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China The School of Data Science Chinese University of Hong Kong Shenzhen518172 China The College of Artificial Intelligence Chongqing University of Technology Chongqing401135 China The College of Computer Science Chongqing University Chongqing400044 China

Video summarization mainly aims to produce a compact, short, informative, and representative synopsis of raw videos, which is of great importance for browsing, analyzing, and understanding video content. Dominant video summarization approaches are generally based on recurrent or convolutional neural networks, even recent encoder-only transformers. We propose using full transformer as an alternative architecture to perform video summarization. The full transformer with an encoder-decoder structure, specifically designed for handling sequence transduction problems, is naturally suitable for video summarization tasks. This work considers supervised video summarization and casts it as a sequence-to-sequence learning problem. Our key idea is to directly apply the full transformer to the video summarization task, which is intuitively sound and effective. Also, considering the efficiency problem, we replace full attention with the combination of local and global sparse attention, which enables modeling long-range dependencies while reducing computational costs. Based on this, we propose a transformer-like architecture, named FullTransNet, which has a full encoder-decoder structure with local-global sparse attention for video summarization. Specifically, both the encoder and decoder in FullTransNet are stacked the same way as ones in the vanilla transformer, and the local-global sparse attention is used only at the encoder side. Extensive experiments on two public multimedia benchmark datasets SumMe and TVSum demonstrate that our proposed model can outperform other video summarization approaches, achieving F-Measures of 54.4% on SumMe and 63.9% on TVSum with relatively lower compute and memory requirements, verifying its effectiveness and efficiency. The code and models are publicly available on GitHub. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yiyuan Liang Hong Jia Licai Wang Yangchen Huang Zhe Yang Big data research and development center North China Institute of Computing Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering The Chinese University of Hong Kong China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions.

关键词： Seminars Accuracy Correlation Focusing Feature extraction data mining Task analysis

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Blockchain-Based Identity Management: Enhancing Privacy and Security in Digital Identity System

Blockchain-Based Identity Management: Enhancing Privacy and ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： S Saranya Monika K Manikandan Jangam Nagaraju S. Nagendiran B. T. Geetha Computer Science and Engineering Dr. N.G.P. Institute of Technology Coimbatore Mangalmay Institute of Management and Technology Greater Noida India School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Data Science MLR Institute of Technology Dundigal Hyderabad Department of Artificial Intelligence and Data Science R. M. K. Engineering College Department of ECE Saveetha School of Engineering (SIMATS) Saveetha University Tamil Nadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Privacy and security are major concerns when it comes to digital identities, particularly in centralized systems where data can be easily hacked or where people engage in identity fraud. These issues can be addressed using blockchain technology since it allows for the creation of a decentralized digital identity that is both secure and tamper-proof. This study employs Kaggle dataset to document the transactions and the devices involved to make the distinctions on how blockchain can boost privacy and security. Predominantly, various machine learning algorithms are used to analyze blockchain applications, and the results emphasize the fact blockchain systems greatly enhance transaction viability and diminish fraudulent actions. Therefore, the study argues that blockchain-based identity management systems offset the shortcomings of the centralized model by enhancing data ownership and combating fraud. Subsequent analysis expanded on the discussion of elaborating the scalability, interoperability, and application of algorithms to blockchains as well as how it can fundamentally change the way that certain industries, particularly identity management, would work with consumers at the center of reliable and secure blockchain solutions.

关键词： Machine learning algorithms Protocols Scalability Instruments Machine learning Blockchains Fraud Security Reliability Interoperability

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