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8th International Conference on Multimedia and Image Processing, ICMIP 2023

作者： Liu, Jing Zhang, Feng Guo Zhao, Hao Ping Lu, Qi De Feng, Bin Feng, Lichang Shenyang Institute of Computing Technology University of Chinese Academy of Sciences China School of Information Science and Technology Taishan University Shenyang110000 China

ISBN: (纸本)9781450399586

With the increasing application scenarios of UAVs in industry, agriculture, military and other fields, the potential threats to national security and public security cannot be ignored. In addition, effective UAV detection and/or tracking is becoming an increasingly important security service. This paper integrates deep learning and image processing technology to conduct research in this context. In this paper, a transfer learning based UAV detection model (YOLOV5-UAV) is proposed. In order to reduce the influence of the amount of supervised data and the imbalance of target distribution on the performance of the model, the dataset is constructed based on self-shot videos and Internet downloaded videos in different natural scenes, combined with Mosaic data enhancement and adaptive scaling techniques. Therefore, the problem of data security is also effectively solved. Furthermore, real-time tests were carried out in two different time periods, namely day and night, from multiple scales, multiple perspectives and multiple natural scenes, for purpose of verifying the validity of the model. The applicability of different detection models is compared and analyzed for small target, moving background and weak contrast between UAV and background. The results show that YOLOV5-UAV model has a good performance in both detection accuracy and detection speed. © 2023 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Application of Artificial Intelligence technology in Text Recognition and Detection Algorithms 1

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13th International Conference on Frontier computing, FC 2023

作者： Liang, Junxia Qi, Yongjun Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China School of Information and Communication Technology Mongolian University of Science and Technology Bayanzurkh district 13341 Ulaanbaatar Mongolia

ISBN: (数字)9789819992997

ISBN: (纸本)9789819992980

With the development of the times, words play an indispensable role in daily life. Therefore, text recognition detection is very important. In recent years, although there have been many research achievements and progress in text recognition detection, there are still situations of ambiguity and distortion in text recognition. Therefore, it is necessary to further upgrade the text recognition detection algorithm. This paper studied the application of artificial intelligence (AI) technology in text recognition and detection algorithms, aiming to further improve the accuracy of text recognition and detection algorithms through AI technology. This article tested the improvement of text recognition detection accuracy using AI technology through experiments. The experimental data showed an improvement of at least 11% and at most 19%, indicating that AI technology can achieve good results in text recognition and detection algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Character recognition

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Learning Contrastive Representations for Dense Passage Retrieval in Open-Domain Conversational Question Answering 25th

Learning Contrastive Representations for Dense Passage Retr...

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25th International Conference on Web information Systems Engineering, WISE 2024

作者： Zaib, Munazza Sheng, Quan Z. Zhang, Wei Emma Alhazmi, Elaf Mahmood, Adnan Department of Human-Centred Computing Faculty of Information Technology Monash University MelbourneVIC Australia School of Computing Macquarie University SydneyNSW Australia School of Computer and Mathematical Science The University of Adelaide Adelaide Australia

ISBN: (纸本)9789819605781

Recent research on the task of Conversational Question Answering (ConvQA) emphasizes the role of open-retrieval in a multi-turn interaction setting consisting of a retriever-reader pipeline, wherein the former focuses on selecting relevant passages from a large collection, and the latter is required to resolve the contextual dependency to understand the question and predict the accurate answer. This open-domain ConvQA (OD-ConvQA) setting relies heavily on the correct retrieval of the passages, otherwise, the error propagated from the retriever module can make the reader vulnerable, thereby, resulting in the model’s performance degradation. The existing approaches based on the retriever-reader pipeline in OD-ConvQA utilize the entire conversational context to retrieve the passages. This retrieval, however, results in the selection of irrelevant passages, which subsequently reduces the model’s overall performance. To address the limitation, this work proposes an approach, called Dense Passage Retrieval in Conversational Question Answering (DPR-ConvQA), that utilizes carefully curated history turns to improve the dense passage retrieval, helping the selection of more accurate answers. Our approach solves two key challenges. First, it allows the filtration of irrelevant context from the input that limits the retrieval of entirely unrelated passages from the huge collection. Second, the model utilizes dense passage retrieval, based on contrastive representation learning, which minimizes the distance between positive samples and maximizes the distance between negative ones, providing better passage representation. We validate our proposed model on two popular OD-ConvQA datasets, called OR-QuAC and TopiOCQA. The experimental result shows that our proposed method outperforms the traditional baselines methods and complements the reader-retriever setup. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Question answering

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Adaptive Control Strategy for PMSM Current Loop Based on Parameter Identification 2

Adaptive Control Strategy for PMSM Current Loop Based on Par...

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2nd International Conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Wei, Yisu Chen, Meng Zhang, Jiarong Shenyang Ligong University School of Information Science and Engineering Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798350368888

This study explores the importance and impact of current loop controller parameters in permanent magnet synchronous motor control systems. Parameter variations during motor operation, such as changes in temperature and operating current, can directly affect the control performance of the current loop and the overall stability of the system. This underscores the critical role of real-time and accurate motor parameter acquisition in current loop control to enhance controller dynamic performance and system stability. On the other hand, adaptive control methods offer a robust strategy to effectively handle variations in system parameters and external disturbances, demonstrating their value and extensive application potential in practical scenarios. Therefore, conducting research on adaptive control strategies for permanent magnet synchronous motors based on precise system parameters is of practical significance. © 2024 IEEE.

关键词： Permanent magnets

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Based on Chaos Neural Network Vector Control for Permanent Magnet Synchronous Motors 2

Based on Chaos Neural Network Vector Control for Permanent M...

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2nd International Conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Zhang, Jiarong Chen, Meng Wei, Yisu Shenyang Ligong University School of Information Science and Engineering Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798350368888

between current, voltage, and rotor position on the stationary coordinate axis of a permanent magnet synchronous motor is a common method for vector control of the motor. The online training algorithm of the network is simple, the system response is fast, and it exhibits good adaptability. Building upon this, a hybrid approach combining chaos optimization and gradient descent is employed for the offline training of the BP neural network, which is subsequently utilized for vector control of the permanent magnet synchronous motor. © 2024 IEEE.

关键词： Vector control (Electric machinery)

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A Data-Driven Study of Prediction Methods for Coronary Heart Disease 16th

A Data-Driven Study of Prediction Methods for Coronary Hea...

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16th International Conference on Service science, ICSS 2023

作者： He, Xu Fan, Xindi Zheng, Wanxi Ti, Ziming Li, Chunshan Zhang, Hua Zhou, Xuequan School of Computer Science and Technology Harbin Institute of Technology Weihai China School of Science Harbin Institute of Technology Weihai China School of Information Science and Engineering Harbin Institute of Technology Weihai China Research Center of Intelligent Computing for Enterprises & Services Harbin Institute of Technology Harbin China

ISBN: (纸本)9789819944019

Coronary heart disease (CHD) is a globally recognised, highly prevalent disease with a high risk of death and a low cure rate. The World Health Organization estimates that deaths from heart disease will reach 23 million by 2030. Therefore, it is imperative to find a fast and effective method for early diagnosis in order to provide patients with early intervention and improve the effectiveness of treatment. With the in-depth development of machine learning, the function of data analysis and prediction will efficiently help doctors to make a preliminary cluster for a large number of people and detect those who have a dangerous rate of developing coronary heart disease. In this paper, three data pre-processing methods, Smote, Borderline Smote and K-means Smote, were used to construct a risk prediction model for coronary heart disease (CHD) based on an unbalanced data set, combined with four algorithms, Logistic Regression, Random Forest, KNN and SVM. After analysing the data characteristics and adjusting the parameters, different combinations of these methods were compared and a better classification method was selected to predict CHD, achieving higher accuracy, precision, AUC and f1 score. Overall, through experiments, the random oversampling and SMOTE methods can effectively solve the data imbalance problem in most *** final training accuracy could be up to 99%, and the testing accuracy could reach 93%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Patient treatment

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Research on Mobile Device Path Planning Based on Improved A∗ Algorithm 2

Research on Mobile Device Path Planning Based on Improved A...

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2nd International Conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Wei, Xianxin Yu, Bo Wu, Fangshi Shenyang Ligong University School of Information Science and Engineering Shenyang China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798350368888

Path planning for mobile devices is currently a hot research topic. To address the issue of excessive path turning points in the A∗ algorithm, we expand the search neighborhood. To improve the low search efficiency of the traditional A∗ algorithm, we enhance the cost function. Through experimental simulations and data comparisons, it is concluded that compared to the traditional A∗ algorithm, the optimized and improved A∗ algorithm results in fewer path turning points, shorter path lengths, and higher planning efficiency. The optimized and improved A∗ algorithm outperforms the traditional A∗ algorithm in terms of path planning time, path nodes, and turning points. © 2024 IEEE.

关键词： Smart devices

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NLP-Enhanced Long Document Summarization: A Comprehensive Approach for information Condensation 2

NLP-Enhanced Long Document Summarization: A Comprehensive Ap...

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2nd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2024

作者： Maurya, Karan Guru Prasad, M.S. Gupta, Shubham Ameta, Hardik Negi, Abhijeet Singh Amrutha, P.V. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Reva University School of computing & Information Technology Bengaluru India

ISBN: (纸本)9798350371314

The present era is characterized by information overload, making the ability to distil multitude of data into concise and meaningful summaries very important. The response to ever-increasing challenge is this revolutionary NLP based model called text Summarizer that will redefine the interactions with information. Text Summarizer aims at providing users with quick, accurate and coherent summaries of lengthy texts so to promote their understanding. The cutting-edge methods harnessed by the Text Summarizer enable it to assimilate given text context, identify major themes and provide brief summaries that capture the essence of source content. To build this text summarizer advanced NLP algorithms are used. Modern deep learning models are mainly based on transformer architectures like BERT (Bidirectional Encoder Representations from Transformers) or GPT (Generative Pre-trained Transformer);they are used because they have the capacity to capture complicated language patterns and nuances. Transformer is a sequence-to-sequence model which uses attention technique to process text sequentially. It is made up of encoders and decoders interconnection. This transformer takes in the text input and provide the summary as output. Transformer discussed in this paper has an accuracy of around 98% which is trained over cnn-dailymail dataset. © 2024 IEEE.

关键词： Natural language processing systems

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua science and technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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