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Information technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Changhua Zhao Yimei Yang Yujun Yang Bo Tang Chunqiao Mi College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

This study examines ways to create personalized learning pathways that are influenced by large collaborative models and multi-modal knowledge graphs. By integrating multi-modal data to construct a comprehensive knowledge network, this study utilizes the generative and comprehension capabilities of large models to achieve personalized customization of learning resources. The focus of the research is on accurately depicting learner characteristics through multi-modal profiling techniques, ensuring the transparency and credibility of the content generated by large models. The personalized learning route suggestion model proposed in this paper includes two parts: recommendations for learning paths within courses and recommendations for learning paths within learning tasks, aiming to enhance learning efficiency and outcomes through the collaborative effects of multi-modal knowledge graphs and large models. Experimental results indicate that the learning path recommendation model driven by multi-modal knowledge graphs and large collaborative models performs excellently in improving learning outcomes and recommendation performance.

关键词： Knowledge engineering Analytical models Accuracy Biological system modeling Education Ecosystems Collaboration Knowledge graphs Data models Information technology

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Power Analysis Attack on a Lightweight Block Cipher GIFT 9th

Power Analysis Attack on a Lightweight Block Cipher GIFT

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9th International Conference on computer Engineering and Networks, CENet2019

作者： Zhang, Jian Li, Lang Li, Qiuping Zhao, Junxia Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

ISBN: (纸本)9789811537523

GIFT is a new lightweight block cipher with smaller area and higher efficiency, which is very suitable for the Internet of Things (IoT) devices with constrained resources. The power analysis attack is an efficient method to extract the key from the cryptographic equipment. However, it is not easy to reveal the key by means of power analysis attack, when the cipher is implemented by hardware. In this article, we present the method of power analysis attack against GIFT. Firstly, we implemented GIFT on FPGA using the SAKURA-G board. Then, we explored the impact of point of interest (POI) on power analysis attack. We proposed the method of power analysis attack against the diffusion layer of GIFT. The experimental results show that the result of power analysis attack is affected by POI, and the key can be recovered when POI is registered. We can reveal the key using correlation power analysis, when targeting the diffusion layer of GIFT. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Side channel attack

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Multi-Domains Personalized Local Differential Privacy Frequency Estimation Mechanism for Utility Optimization

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SSRN 2024年

作者： Li, Yunfei Fu, Xiaodong Liu, Li Ding, Jiaman Peng, Wei Jia, Lianyin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Graduate School Yunnan University of Finance and Economics Kunming650221 China Yunnan Provincial Key Laboratory of Computer Technology Application Kunming University of Science and Technology Kunming650500 China

Local Differential Privacy (LDP) has garnered considerable attention in recent years because it does not rely on trusted third parties and has low interactivity and high operational efficiency. However, current LDP frequency estimation mechanisms aggregate data using different privacy budgets within the same data domain, overlooking the aggregation requirements across different data domains. This limits the potential for enhancing the data utility under fixed privacy budgets and meeting user preferences in multiple data domains and privacy budgets. To address this issue, we define a Multi-Domains Personalized Local Differential Privacy (MDPLDP) model that allows users to freely choose data domains and privacy budgets according to their privacy preferences. Furthermore, based on the MDPLDP model, two new frequency estimation mechanisms are proposed: MDPLDP-Generalized Randomized Response and MDPLDP-basic Randomized Aggregatable Privacy-Preserving Ordinal Response. These mechanisms support cross-domains data aggregation and optimize data utility by adjusting the data domains and increasing privacy budgets. Theoretical analysis reveals that these new mechanisms have lower estimation errors than the traditional LDP mechanisms. Experiments on real and synthetic datasets demonstrate that the proposed mechanisms effectively reduce estimation errors and enhance the utility of data-frequency estimation. © 2024, The Authors. All rights reserved.

关键词： Frequency estimation

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Research on Crop Disease and Pest Surveillance based on Big Data and Image Processing Technologies

Research on Crop Disease and Pest Surveillance based on Big ...

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Information technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Yimei Yang Yujun Yang Huixia Shu Changhua Zhao Shaowei Deng College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua China Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

Recently, with the increasing complexity of agricultural systems and climate change, agricultural pest and disease surveillance and early detection have become critical tasks for guaranteeing food stability and promoting sustainable farming growth. Agricultural pest and disease surveillance is a key component of agricultural production, directly affecting crop yield and quality. As big data advances and image processing technologies rapidly, their application in crop pest and disease surveillance has become increasingly widespread. By integrating multi-source data, such as remote sensing data, meteorological data, ecological data, and pest and disease survey data, researchers can build more accurate and efficient pest and disease surveillance models. This paper reviews the research progress of big data and image processing technologies in crop pest and disease surveillance, analyzes the advantages and challenges of current technologies, and proposes future research directions to enhance the automation and intelligence of systems. Additionally, it discusses the integration of deep learning and other techniques to achieve efficient information mining and accurate retrieval of disaster-related data, aiming to provide scientific guidance and technical support for agricultural production.

关键词： Crops Plant diseases Climate change Pest control Agriculture Big Data Surveillance Image processing

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

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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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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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A Cognitive Solver with Autonomously Knowledge Learning for Reasoning Mathematical Answers

A Cognitive Solver with Autonomously Knowledge Learning for ...

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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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