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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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A Reinforcement Learning-Based Image Mapping-Topology Optimization for Antenna Design

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Dou, Jiangling Gong, Hao Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming Yunnan650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College China

A Reinforcement Learning (RL)-based Image Mapping Topology (IMT) optimization approach is proposed. To enable RL to optimize antenna topology and increase the efficiency of the optimization process. Two crucial components, Stratified Sampling Balance (SSB) and IMT are introduced. SSB is utilized to balance different types of data samples. IMT is utilized to facilitate the interaction between RL and simulation software, it also pretrains the actor network within the RL algorithm. This methodology allows the RL agent to acquire knowledge from the interaction of the antenna topology with the environment. Subsequently, the agent outputs antenna topology with the maximum reward signal. Compared with other machine learning-based optimization methods, the proposed method significantly alleviates the issue of learning inefficiency and reduces the exploration time for feasible solutions. To validate the effectiveness of the optimization method, a microstrip antenna is optimized. The measurement results show that the impedance bandwidth is doubled while maintaining the realized gain with fewer simulation samples and less optimization time. © 2025 IEEE.

关键词： Reinforcement learning

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Indoor visible light localization method based on embedded artificial intelligence 7

Indoor visible light localization method based on embedded a...

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7th International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services, UPINLBS 2022

作者： Zhang, F. Ke, W. Ouyang, H. Qiu, S. Jiangsu Key Laboratory on Opto-Electronic Technology School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing210023 China Jiangsu Ctr. for Collab. Innovation in Geographical Information Resource Development and Application Nanjing210023 China

This paper proposes an indoor visible light location method based on embedded platform and optical frequency image recognition technology with artificial intelligence*which can effectively improve the location effect in complex indoor environment. By transplanting the artificial intelligence (AI) based image classification algorithm into the embedded platform, this method uses a forward neural network to analyse the position information coming from the coded optical frequency image received by a camera, and then the positioning results can be obtained. In view of the "motion state"and "occlusion state"that are most likely to fail in traditional visible image localization, we specially supplement the training set of relevant characteristic optical frequency images to enhance the robustness of the method. According to the track and positioning results of the moving platform receiver, the proposed method can provide accurate and reliable navigation and positioning and has stronger anti-interference ability compared with the traditional light intensity or light image positioning methods. © 2022 Copernicus. All rights reserved.

关键词： Natural frequencies

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Knowledge-Infused Spatiotemporal Graph Convolution Network for Megacity Origin-Destination Taxi Demand Forecasting

SSRN

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SSRN 2024年

作者： Tong, Ning Yu, Xuehui Li, Xiaorong Li, Fengqi Dalian Key Laboratory of Blockchain Technology and Application Liaoning Dalian116028 China School of Software Dalian Jiaotong University Dalian116028 China School of Computer and Communication Engineering Dalian Jiaotong University Dalian116028 China

In the management of urban transportation within major cities, the precise prediction of traffic demand is fundamental to the implementation of intelligent transport systems. Although existing studies have endeavoured to describe the spatiotemporal characteristics of taxi demand and the influence of external factors, megacities such as Beijing still face two primary challenges: 1. The pronounced spatiotemporal variations in metropolitan areas can lead to severe imbalances between taxi demand and supply, exacerbating the difficulty passengers face in successfully hailing cabs;2. Taxi demand is influenced by dynamically changing, complex interactions of multidimensional external factors. Without accurate identification and integration of these factors, prediction models may fail to capture demand patterns accurately, leading to imprecise demand forecasts and subsequently affecting the efficiency of taxi dispatch and management. To address these issues, this paper proposes a knowledge-Infused spatiotemporal graph convolution network for megacity origin-destination taxi demand forecasting (ODT-GCN). Initially, the TransR knowledge graph model is employed to map the complex relationships between external factors in the form of entity triplets. Subsequently, a dynamic fusion unit integrates the semantic information between traffic demand and external factors and it is also capable of capturing dynamic changes in the external factors and updating the traffic demand information. Finally, to capture the spatiotemporal characteristics of Beijing’s traffic demand, the updated traffic demand information, combined with a regional adjacency matrix, is input into a spatiotemporal prediction model, thereby forecasting taxi demand. Tests on the Shenzhou Premium Taxi order dataset in Beijing demonstrate that the ODT-GCN outperforms other baseline models in terms of prediction accuracy, interpretability and robustness. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Consistency-aware Multi-modal Network for Hierarchical Multi-label Classification in Online Education System 12

Consistency-aware Multi-modal Network for Hierarchical Multi...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Lei, Siqi Huang, Wei Tong, Shiwei Liu, Qi Huang, Zhenya Chen, Enhong Su, Yu University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology China IFLYTEK Research IFLYTEK Co. Ltd China

ISBN: (纸本)9781665438582

In the online education system, predicting the knowledge of exercises is a fundamental task of many applications, such as cognitive diagnosis. Usually, experts consider this problem as Hierarchical Multi-label Classification (HMC), since the knowledge concepts exhibit a multi-level structure. However, existing methods either sacrificed knowledge consistency for classification accuracy or sacrificed classification accuracy for knowledge consistency. Maintaining the balance is difficult. To forgo this dilemma, in this paper, we develop a novel frame-work called Consistency-Aware Multi-modal Network (Cam-Net). Specifically, we develop a multi-modal embedding module to learn the representation of the multi-modal exercise. Then, we adopt a hybrid prediction method consisting of the flat prediction module and the local prediction module. The local prediction module deals with the relation between the knowledge hierarchy and the input exercise. The flat prediction module focuses on maintaining knowledge consistency. Finally, to balance classification accuracy and knowledge consistency, we combine the outputs of two modules to make a final prediction. Extensive experimental results on two real-world datasets demonstrate the high performance and the ability to reduce knowledge inconsistency of CamNet. © 2021 IEEE.

关键词： Forecasting

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Epidemic Prevention System Based on Voice Recognition Combined with Intelligent Recognition of Mask and Helmet 2021

Epidemic Prevention System Based on Voice Recognition Combin...

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2021 3rd International Conference on Video, Signal and Image Processing

作者： Huaming Zhou Aili Wang Meixin Li Yinghui Zhao Yuji Iwahori Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology China Computer Science Chubu University Japan

ISBN: (纸本)9781450385886

At present, COVID-19 cross-infection is easy to occur in dense places such as elevators. There are no epidemic prevention measures for construction site elevators on the market, and most of them require manual temperature measurement and reminders to wear masks and helmets to avoid the spread of the epidemic. This paper designs an intelligent epidemic prevention system for the elevator ride process in a modern construction site environment, which can achieve non-contact human temperature measurement, mask and helmet recognition and voice call elevator function. The system uses Arduino UNO as the control core, Kendryte K210 as machine vision processing module, non-contact infrared temperature sensor MLX90614, and voice recognition sensor LD3320. The system has the functions of non-contact temperature detection, mask/helmet recognition(YOLOv3) and voice call elevator. Experimental results showed that the recognition accuracy rate of helmet, mask, voice call elevator is 91.5%, 92.0% and 93.0% respectively. The temperature measurement accuracy rate is 0.2℃, which can effectively prevent the spread of the epidemic caused by contact and breathing, and has the advantages of stable, intelligent, and safe work.

关键词： mask recognition infrared temperature measurement YOLOv3 helmet recognition voice call elevator

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A Cognitive Solver with Autonomously Knowledge Learning for Reasoning Mathematical Answers

A Cognitive Solver with Autonomously Knowledge Learning for ...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Jiayu Liu Zhenya Huang Xin Lin Qi Liu Jianhui Ma Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence

Reasoning answers to mathematical problems requires machines to think and operate like a human to learn knowledge from mathematical data, which is one of the fundamental tasks for exploring general artificial intelligence. Most solutions focus on mimicking how humans understand problems, which generate the necessary expressions for answers. However, they are still far from enough since they ignore the core ability of humans to acquire knowledge from experience. In this paper, we propose a Cognitive Solver (CogSolver) that is capable of autonomously learning knowledge from scratch to solve mathematical problems, inspired by two cognitive science theories. Specifically, we draw one insight from the dual process theory to establish an intelligent BRAIN-ARM framework, and refer to another information processing theory to summarize the knowledge learning process into Store-Apply-Update steps. In CogSolver, the BRAIN system stores three types of mathematical knowledge, including semantics knowledge, relation knowledge, and mathematic rule knowledge. Then, the ARM system applies the knowledge in BRAIN to answer the problems. Specifically, we design a knowledge-aware module and a commutative module in ARM to improve its reasoning ability, where the knowledge is organically integrated into answer reasoning process. After solving the problems, BRAIN updates the stored knowledge according to the feedback of ARM, where we develop knowledge filters to eliminate the redundant ones and further form a more reasonable knowledge base. Our CogSolver carries out the above three steps iteratively, which behaves more like a human. We conduct extensive experiments on real-world math word problem datasets. The experimental results demonstrate the improvement in answer reasoning and clearly show how CogSolver gains knowledge from the problems, leading to superior interpretability. Our codes are available at https://***/bigdata-ustc/CogSolver.

关键词： Commutation Semantics Knowledge based systems Information processing Cognition Mathematics Filtering theory

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End-to-End Tibetan Speech Recognition Study Based on Byte Pair Coding 23

引用

23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Cai, Yuqing Wang, Chao Duojie, Renzeng Zhu, Yulei Zhang, Jin Tashi, Nyima Tibet University School of Information Science and Technology Lhasa850000 China State Key Laboratory of Tibetan Intelligent Information Processing and Application Lhasa850000 China Qinghai Normal University College of Computer Xining810016 China

ISBN: (纸本)9780000000002

Aiming at the problems of inconsistent modeling units and unsatisfactory recognition results in Tibetan end-to-end speech recognition research, this paper proposes a BPE-Conformer-CTC/Attention end-to-end Tibetan speech recognition method. Firstly, the method adopts the byte-pair encoding algorithm for speech modeling, which solves the problem of inconsistent modeling units in Tibetan speech recognition by repeatedly merging the most frequent character pairs, segmenting the text into manageable and meaningful units, and balancing the granularity of modeling units. Secondly, the Con-former encoder is utilized to effectively fuse the global and local dependencies of audio sequences, thus enhancing the model's representational capability. Finally, the alignment and decoding process is accelerated by a joint CTC/Attention decoding strategy, which in turn improves the accuracy and efficiency of the recognition results. Experimental results on the open-source datasets XBMU-AMD031 and TIBMD@MUC show that the proposed BPE-Conformer-CTC/Attention model achieves a word error rate of 9.0% and 4.6%, respectively, and compared to the baseline Transformer-CTC/Attention model, the word error rate is reduced by 14.2% and 30.3%, respectively. This proposed approach provides an effective solution for the Tibetan end-to-end speech recognition task. © 2024 China National Conference on Computational Linguistics.

关键词： Decoding

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Rethinking Centered Kernel Alignment in Knowledge Distillation

arXiv

引用

arXiv 2024年

作者： Zhou, Zikai Shen, Yunhang Shao, Shitong Gong, Linrui Lin, Shaohui School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE China Youtu Lab Tencent Shanghai China Hunan University China

Knowledge distillation has emerged as a highly effective method for bridging the representation discrepancy between large-scale models and lightweight models. Prevalent approaches involve leveraging appropriate metrics to minimize the divergence or distance between the knowledge extracted from the teacher model and the knowledge learned by the student model. Centered Kernel Alignment (CKA) is widely used to measure representation similarity and has been applied in several knowledge distillation methods. However, these methods are complex and fail to uncover the essence of CKA, thus not answering the question of how to use CKA to achieve simple and effective distillation properly. This paper first provides a theoretical perspective to illustrate the effectiveness of CKA, which decouples CKA to the upper bound of Maximum Mean Discrepancy (MMD) and a constant term. Drawing from this, we propose a novel Relation-Centered Kernel Alignment (RCKA) framework, which practically establishes a connection between CKA and MMD. Furthermore, we dynamically customize the application of CKA based on the characteristics of each task, with less computational source yet comparable performance than the previous methods. The extensive experiments on the CIFAR-100, ImageNet-1k, and MS-COCO demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs for image classification and object detection, validating the effectiveness of our approaches. Our code is available in https://***/Klayand/PCKA. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Multi-Temporal Relationship Inference in Urban Areas

arXiv

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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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