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5th International Conference on computer Vision and data Mining, ICCVDM 2024

作者： Huang, Shan Liu, Yanan Wu, Yufan Wang, Jinlan Duan, Xiaodong College of Computer Science and Engineering Dalian Minzu University Dalian116600 China State Ethnic Affairs Commission Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781510682863

In social recommendation, user's interest preference is influenced by multiple factors. It is a challenging problem to mine data information effectively in multiple types of interaction behaviors between users and items to obtain more accurate embedding of user items. Existing social recommendation models tend to take a homogeneous approach to the interaction effects of neighbourhood nodes on target nodes. We propose a social recommendation model based on deep representation learning of hypergraphs. The model aims to learn embedded representations of users and items from multi-type user-item interaction data efficiently. An attention mechanism that integrating time interval information is introduced for computing the preference weights of each item to the user. Messages are generated with time-interval attention weights the inter-user and inter-item hypergraph information is combined to update the messages and aggregate the information from multiple layers. Extensive experiments on two real datasets show that the proposed models are superior to other advanced recommendation methods in improving the recommendation performance. © 2024 SPIE.

关键词： Federated learning

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A Brief Overview of Recent Energy-Saving Researches in Smart Homes 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Sung, Tien-Wen Liang, Qiaoxin Huang, Zeming Hong, Chuanbo Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

This paper gives a brief overview on the topic of energy saving in smart homes. It provides introduction of some recent researches and developments of energy-saving schemes for smart homes, so that helps readers to know information about the potential issues and to make improvements in the future. A brief summary given in the discussion indicates that not only considering the problem of energy consumption but also paying attention to the comfort and safety for users in the energy-saving solutions is also important. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conservation

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An Overview of Recent Research on IoT-Based Energy Management System in Smart Homes 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Increasing load demand and energy crisis issues are becoming more and more concerned. Nowadays, the demand for electricity in household life is rising sharply and the structure of electricity consumption is gradually becoming complex. With the development of the Internet of Things technology, it provides a better platform for the improvement of the energy management system. The home energy management system (HEMS) based on the Internet of Things comes into being, which can integrate the management of all home power loads and distributed energy, realize the optimal configuration of energy, and help users save electricity. This paper firstly describes the basic concept of IoT smart home energy management system, then describes the framework of HEMS, and finally reviews the current research status in this field from three modules, namely, data collection module, communication module, and management and optimization module, and discusses its technical challenges and future research directions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy management systems

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FLAP: Federated Learning Aggregation Scheme Based on Privileged Secret Sharing

FLAP: Federated Learning Aggregation Scheme Based on Privile...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Mu, Xianyu Tian, Youliang Xiong, Jinbo Wang, Shuai Gong, Boxiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Guizhou Xishan Technology Co. Ltd China

ISBN: (纸本)9798350327380

Federated Learning (FL) is a privacy-preserving machine learning technique that trains models on client devices and only uploads new model gradients to servers for aggregation. However, transmitting the true gradients introduces the risk of reverse inference attack, which can compromise client privacy. To address this issue, we present the federated learning aggregation scheme based on privileged secret sharing (FLAP). Firstly, we present privileged secret sharing by combining the one-time pad with shamir secret sharing. Secondly, we introduce game theory to generate games between servers to resist collusion attacks. Finally, the analysis demonstrates that FLAP enables the server to successfully update the global gradient while ensuring the utmost protection of client privacy and FLAP achiev the effect of resisting collusion attack and reverse inference attack. We have proved through experiments that FLAP is 26.42% more efficient than the current scheme, and has strong robustness to drop out. © 2023 IEEE.

关键词： Game theory

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Multifactor prediction of carbon price based on principal component analysis and long short-term memory 3

Multifactor prediction of carbon price based on principal co...

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3rd International Conference on Electronic Information Engineering, big data, and computer technology, EIBDCT 2024

作者： Wang, Yuanling Wen, Hao Deng, Shuyun Xia, Xin Chongqing Key Laboratory of Public Big Data Security Technology Chongqing College of Mobile Communication Chongqing401527 China School of Management Science and Engineering Chongqing Technology and Business University Chongqing400067 China

ISBN: (纸本)9781510680449

In recent years, the development of the carbon trading market has become an important policy for China to achieve its "dual carbon"goals at an early date and to take on the role of a major international player in reducing emissions. Since the opening of the carbon market in 2013, there are now eight mature carbon trading pilot cities in China. Considering that carbon prices are susceptible to influences from factors such as the natural environment and financial policies, this paper introduces eight influencing factors from the energy, economic, financial, and natural environment levels for analysis. This paper selects the carbon trading market in Hubei, with the highest carbon trading volume, and the carbon trading market in Fujian, with the lowest carbon trading volume, as research objects. Firstly, the PCA model is applied to extract the main factors that influence carbon prices from the eight influential factors. Then, the LSTM model is used to establish a multifactor prediction model, and the GRU, RNN, and CNN models are used as comparative models. Finally, the predicted fitting graph is drawn, and the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are calculated. The research results show that the multi-factor prediction model based on LSTM performs the best in terms of price prediction accuracy. Through an in-depth exploration of various models, this study has made a significant contribution to the progress of carbon market research. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Carbon

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A novel feature alignment-based cell nucleus identification scheme for medical decision-making systems

A novel feature alignment-based cell nucleus identification ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gou, Fangfang Dai, Tingyi Wu, Jia Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Hunan University of Medicine General Hospital Huaihua418000 China Monash University Research Center for Artificial Intelligence MelbourneVIC 3800 Australia

ISBN: (纸本)9798350386226

AI medical imaging diagnosis enables physicians to rapidly acquire and analyze image information using deep learning techniques. Consequently, it holds a significant position in medical decision-making assistance. Specifically, the complexity and large scale of digital pathology images necessitate accurate recognition of cell arrangement, size, and shape, which aids physicians in identifying cancerous regions. However, existing medical image segmentation techniques often fail to deliver sufficiently detailed results in unknown target domains, resulting in the blurring of cell nucleus boundaries. In order to introduce more display-like information to solve the problem of obscure cell nucleus boundaries, this study proposes an accurate recognition method (UNW) for cell nucleus recognition in medical decision-making systems. It solves the problem of the distribution difference between the source domain and the unknown target domain by inserting feature center alignment (FCA) and feature distribution alignment (FDA) modules into the encoder and decoder. UNW performs instance normalization (IN) and instance whitening (IW) on the feature images to better identify the feature detail information, which results in more distinct boundaries between the cell nucleus and other tissues. Experimental results on various backbone networks show that our method can obtain more accurate decision results. Among them, the IOU reaches 0.704, which outperforms existing models, while the FCA and FDA maintain the segmentation ability of the model in the face of test data that is inconsistent with the distribution of the training data. © 2024 IEEE.

关键词： Image segmentation

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Kernel Fence GAN: Unsupervised Anomaly Detection Model Based on Kernel Function 1

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Niu, Lu Li, Shaobo Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610041 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

data imbalance and difficult construction of high-dimensional data cause the low accuracy of anomaly detection. This paper proposes an unsupervised anomaly detection model Kernel Fence GAN to increase the accuracy. In this adversarial generative network, the generator's loss function consists of three items: the encirclement loss limits the generated samples to lie at the decision boundary of normal samples, the central dispersion loss maximizes the range of the decision boundary, and the enhanced dispersion loss uses kernel function to nonlinearly map the generated samples to RKHS space, which can maximize sample differences and avoid outliers. The purpose of training the discriminator is to accurately identify whether the input sample is a normal sample in the original dataset or an abnormal sample generated by the generator. In this paper, validation experiments are performed on two datasets: on two-dimensional synthetic dataset, the experiment result shows that the model has faster convergence rate than Fence GAN. On the KDD99 dataset, compared with traditional and advanced deep learning methods, both precision and recall have been correspondingly improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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A Differential Privacy Protection Protocol Based on Location Entropy

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Tsinghua science and technology 2023年第3期28卷 452-463页

作者： Ping Guo Baopeng Ye Yuling Chen Tao Li Yixian Yang Xiaobin Qian Xiaomei Yu State Key Laboratory of Public Big Data School of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Information Technology Innovation Service Center of Guizhou Province Guiyang 550025China School of Cyberspace Security Beijing University of Posts and TelecommnuicationsBeijing 100000China Guizhou CoVision Science&Technology Co. Ltd.Guiyang 550025China School of Information Science and Engineering Shandong Normal UniversityJinan 250000China

A Location-Based Service(LBS)refers to geolocation-based services that bring both convenience and *** an increase in the scale and value of data,most existing location privacy protection protocols cannot balance privacy and *** solve the revealing problems in LBS,we propose a differential privacy protection protocol based on location ***,we design an algorithm of the best-assisted user selection for constructing anonymity ***,we employ smart contracts to evaluate the credibility of participants,which ensures the honesty of ***,we provide a comprehensive experiment;the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge ***,our protocol improves data ***,it realizes user-controllable privacy protection,which improves privacy protection and strengthens security.

关键词： Location-Based Services(LBS) smart contract location entropy differential privacy privacy protection

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Optimization of cloud computing task scheduling based on whale optimization algorithm with quasi-opposition-based and nonlinear factor

Optimization of cloud computing task scheduling based on wha...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Xiong Xuan Shaobo Li Liya Yu Fengbin Wu Xingxing Zhang Xue An State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Mechanical Engineering Guizhou University Guiyang China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Task scheduling has a significant impact on the resource availability and operation cost of the system in cloud computing. In order to improve the efficiency of task execution in the cloud for a given computational resource, the improved whale optimization algorithm is applied to the multi-objective optimization model for cloud task scheduling. Specifically, the quasi-opposition-based learning strategy is added in the initiation phase to increase the diversity of the population. Meanwhile, a novel nonlinear convergence factor was introduced to augment the local search capability. Experimental results indicate that the improved whale optimization algorithm converges faster and searches with higher precision in finding the optimal task scheduling solution compared to existing meta-heuristic algorithms. In addition, the modified algorithm achieves superior system resource usage when dealing with both small and large tasks.

关键词： Cloud computing Costs Processor scheduling System performance Optimization models Metaheuristics Scheduling Whale optimization algorithms Internet of Things Convergence

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An Optimal Wind Turbine Control Based on Improved Chaotic Sparrow Search Algorithm with Normal Cloud Model

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 108-125页

作者： Yang, Qi-Peng Wang, Chia-Hung Dao, Thi-Kien Nguyen, Trong-The Zhao, Qi-Gen Chen, Shu-Meng College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam

This study suggests a Chaotic Sparrow Search Algorithm (CSSA) based on the Sparrow Search Algorithm (SSA) for a wind turbine generator unit’s pitch controller optimal parameters. The optimal parameters set of a wind turbine pitch controller is a complex problem that needs to be tuned to archive a smooth quality performance of the outcome power operations. The CSSA is carried out with Tent chaotic mapping updating equations and a standard cloud model to avoid the original SSA algorithm drawbacks and increase quality performance to solve the optimal controller parameters. The experiments compare the CSSA against other algorithms on testing function, and the optimal pitch controller parameters demonstrate that the CSSA can solve smoothing the output power of the wind turbine unit, reducing the effect of wind speed fluctuations on the power grid. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Wind Turbine Generators

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