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检索条件"机构=Big Data and Software Engineering"

共 1709 条记录，以下是161-170 订阅

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Adaptive Embedding Using Contrastive Learning with Median Absolute Deviation and Variance for Recommendation

Adaptive Embedding Using Contrastive Learning with Median Ab...

引用

2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Xu, Qinxin Li, Haofeng Zhou, Wei Yang, Zhiyong School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing China Tongji University Shanghai China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9798331506797

Recommendation systems have increasingly relied on embedding techniques to capture user preferences and item features. Adaptive temperature techniques have rapidly developed to provide more accurate recommendations. However, these methods often struggle with popularity bias and a lack of adaptability in dynamic environments, which will end in a decline of the robustness and the bias in temperature prediction. To address these challenges, we propose integrating noise-based data augmentation and InfoNCE loss from the XSimGCL framework into the adaptively modulating embedding magnitude model. Our method enhances traditional adaptive temperature adjustment by incorporating statistical measures like median absolute deviation and variance of embedding norms, aiming to create more robust and adaptive embeddings. This approach eliminates the necessity for adapting temperature by loss, instead focusing on incorporating statistical measures and adding noise to enhance robustness. Extensive evaluations on several real-world datasets demonstrate that our approach effectively mitigates popularity bias and significantly improves overall recommendation quality. This suggests that the nuanced integration of contrastive learning with adaptive embedding techniques can lead to more personalized and equitable recommendations. © 2025 IEEE.

关键词： Contrastive Learning data Augmentation Graph Convolutional Networks Recommendation Systems Self-Supervised Learning

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Research on the Relationship Between New Energy Vehicles and Dual Carbon in Yangtze River Delta Based on GRA-LSTM Model

Research on the Relationship Between New Energy Vehicles and...

引用

IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Jinxuan Zhou Liang Deng Yi Tong Yan Gao Department of Information Engineering Wuhan Business University Hubei China Major in Data Science and Big Data Technology Major in Internet of Things Engineering Major in Software Engineering the main research direction is Big Data Technology

New energy automobile industry plays an important role in building a green, low-carbon and recycling industrial system. In this paper, the prediction simulation training and prediction accuracy comparison study are carried out with the help of the newly constructed GRA-LSTM model, Biological Neural Network model and the first-order one-variable gray GM (1, 1) model. The LSTM model is created and trained by Learning Rate Decay function. The Learning Rate Decay callback function is set, and the learning rate is gradually reduced during the training process to carry out simulation training, and finally the trained model is used to make predictions, and the time for the Yangtze River Delta region to reach carbon peak is 2028, and the time to reach carbon neutrality is 2060, and at the same time this paper analyzes and finds out that the higher the new energy automobile market ownership is, the shorter the time for carbon peak and carbon neutrality will be.2.

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DT-MUSA: Dual Transfer Driven Multi-source Domain Adaptation for WEEE Reverse Logistics Return Prediction 19th

DT-MUSA: Dual Transfer Driven Multi-source Domain Adaptation...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Liu, Ruiqi Gao, Min Wu, Yujiang Zeng, Jie Zhang, Jia Gao, Jinyong Ministry of Education Chongqing401331 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Aibo Green Reverse Supply Chain Co. Ltd. Shenzhen518000 China

ISBN: (纸本)9783031545306

Reverse logistics (RL) return prediction for Waste Electrical and Electronic Equipment (WEEE) has gained attention due to its potential to improve operational efficiency in the recycling industry. However, in data-scarce regions, commonly used deep learning models perform poorly. Existing multi-source cross-domain transfer learning models can partially overcome data scarcity by using historical data from multiple sources. However, these models aggregate multi-source domain data into a single-source domain in transfer, ignoring the differences in time series features among source domains. Additionally, the lack of historical data in the target domain makes fine-tuning the prediction model inoperative. To address these issues, we propose Dual Transfer Driven Multi-Source domain Adaptation (DT-MUSA) for WEEE RL return prediction. DT-MUSA includes a dual transfer model that combines sample transfer and model transfer and a basic prediction model MUCAN (Multi-time Scale CNN-Attention Network). It employs a multi-task learning to aggregate predictors from multiple regions and avoids negative transfer learning. The dual transfer model enables fine-tuning of the base model MUCAN by generating long-term time series data through sample transfer. We applied DT-MUSA to real cases of an RL recycling company and conducted extensive experiments. The results show that DT-MUSA outperforms baseline prediction models significantly. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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Assessing the Risk of Currency Digitalization in Kazakhstan

Assessing the Risk of Currency Digitalization in Kazakhstan

引用

2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Baimaganbetov, Talgat Sodnomova, Mariia Aitmukhanbetova, Elvira Research and Innovation Center 'Big Data and Blockchain Technologies' Astana It University Astana Kazakhstan Astana It University Astana Kazakhstan Astana It University Department of Software Engineering Astana Kazakhstan

ISBN: (纸本)9798350335040

Every year, new technologies in every industry change the model and structure of processes in human life. Including the economy, which interacts so closely with us. After the advent of blockchain technology, many years have passed to test the viability of digital currency and digital wallets. Many countries are already introducing digital currencies at the state level. Since 2021, a study has begun on the need to introduce a national digital currency. A pilot project with basic functions was also developed and test launches of the digital tenge were carried out. © 2023 IEEE.

关键词： Electronic money

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Weakness of Enhanced DNA and ElGamal Cryptosystem for Secure data Storage and Retrieval in Cloud 13

Weakness of Enhanced DNA and ElGamal Cryptosystem for Secure...

引用

13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Fan, Yibiao Liu, Chenglian Yang, Yuxia Fang, Jie Software Engineering Institute of Guangzhou Department of Networking Technology Guangzhou510990 China School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuqing250300 China

ISBN: (纸本)9798350319156

DNA cryptography has been developed for nearly thirty years, and there is a lot of research on DNA based encryption and decryption. In 2018, Thangavel and Varlakshmi proposed an article 'Enhanced DNA and ElGamal cryptosystem for secure data storage and retrieval in cloud', they provide two cryptosystem schemes DNA and ElGamal. In this paper the authors would like to analyze security to DNA scheme, this is our research goal. We also prove it may occur insecure when the XOR operation using in special environment. The mathematical Proof and experimental evidence be demonstrated in this article, this is our method. The results point out it is not secure to Thangavel and Varlakshmi's DNA protocol. © 2023 IEEE.

关键词： DNA

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CHA-Sens: An End-to-End Comprehensive Residual Convolution Framework for CSI-based Human Activity Sensing 26

CHA-Sens: An End-to-End Comprehensive Residual Convolution F...

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhou, Fujia Zhang, Wei Zhu, Guangxu Li, Hang Shi, Qingjiang Tongji University School of Software Engineering Shanghai China Science and Technology on Electronic Information Control Laboratory Chengdu China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350331684

Channel state information (CSI)-based human activity recognition (HAR) receives increasing research interests due to its broad applications such as human-computer interaction, health care, and security surveillance. Deep learning (DL) methods have been widely adopted on CSI-based HAR tasks to extract features automatically, overcoming the complexity and unstableness of manual feature extraction process. However, many DL approaches fail to customize the designed model structure with the input CSI tensor shape, applying DL models recklessly. In addition, some researchers utilize attention mechanism yet independently along temporal, spatial, or frequency dimension. To address these issues, we propose an end-to-end comprehensive residual convolution framework, namely CHA-Sens, for general CSI-based human activity sensing. CHA-Sens consists of several comprehensive residual convolution modules (CRCM) that feature adaptive kernel size and stride, regional parameter-free attention mechanism and shortcut of identity mapping. Extensive experiments are conducted on three public CSI datasets for recognizing both single human activity (SHA) and human-to-human interaction (HHI) to show the superiority of the proposed design over other state-of-the-art benchmarks. © 2023 IEEE.

关键词： Channel state information

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Intelligent Deposit Product Recommendation for Flexible Employees of Housing Provident Fund Based on Random Forest 2

Intelligent Deposit Product Recommendation for Flexible Empl...

引用

2nd International Conference on Frontiers of Artificial Intelligence and Machine Learning, FAIML 2023

作者： Chen, Xue Hu, Yuxing Hong, Xiaoxia Zhu, Zhaohan Song, Yang Liu, Juan Song, Peng Xiong, Wei Yan, Meng Chongqing Housing Provident Fund Management Center Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Eletrical Engineering Southwest Jiaotong University Sichuan China

ISBN: (纸本)9798400707544

With the rapid development of big data and cloud computing, digital technology has been widely used to develop provident funds. We innovatively apply data mining and machine learning methods to analyze the impact factors of provident fund deposits and the recommendation model of the deposit products for flexible employees. After experiments, our model performs well in the performance evaluation index (such as Accuracy, Recall, and F1-score). Through our model and research analysis, the housing provident fund center can comprehensively analyze and mine the real data, promote more popular customer policies, and provide better customer service. © 2023 Copyright held by the owner/author(s).

关键词： Machine learning

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A Feature Distribution Smoothing Network Based on Gaussian Distribution for QoS Prediction

A Feature Distribution Smoothing Network Based on Gaussian D...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Lu, Tongxin Zhang, Xiaohong Wang, Ziliang Yan, Meng Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China Chongqing University School of Big Data and Software Engineering Chongqing China

ISBN: (纸本)9798350304855

With the increasing number of services and their homogenization, the use of Quality of Service (QoS) for recommendations has become necessary. However, existing QoS prediction solutions have limitations in solving the noise and label imbalance problems of dataset, which greatly limit the improvement of QoS prediction accuracy. In this paper, we propose FSNet that contains a feature distribution smoothing module and an improved W-Huber loss function. The feature distribution smoothing module mitigates the effect of noise problem by fitting potential Gaussian distribution of known features with a supervised feedforward neural network. W-Huber loss function mitigates the impact of label imbalance problem on QoS prediction by reweighting the two components of Huber loss function. We conduct extensive experiments on real large-scale QoS dataset, and the results demonstrate that the proposed FSNet method outperforms existing QoS prediction methods. © 2023 IEEE.

关键词： Forecasting

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An Improved Semantic Place Retrieval Method by Learning-based Clustering 7

An Improved Semantic Place Retrieval Method by Learning-base...

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7th International Conference on data Science and Information Technology, DSIT 2024

作者： Lu, Jiamin Liu, Jiahao Fan, Zhendong Zhou, Zhenyu Kong, Xu Feng, Jun Hohai University Key Laboratory of Water Big Data Technology Ministry of Water Resources Nanjing China Hohai University College of Computer and Software Nanjing China PowerChina Huadong Engineering Corporation Limited Hangzhou China

ISBN: (纸本)9798350384093

Traditional sorting-based semantic place retrieval methods may face the problem of a large retrieval space. An improved method is proposed in this paper, based on Learning-based Clustering. It has three components: encoding, clustering and retrieval. Specifically, we craft a spatial entity encoder that integrates both spatial and semantic dimensions to embed spatial entities. Then, we utilize a cascaded Siamese network architecture for deep clustering, thereby minimizing the retrieval space for downstream tasks. Finally, we employ the SSPR method to assess the impact of our approach on retrieval efficiency. © 2024 IEEE.

关键词： Content based retrieval

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Image denoising based on residual network 3

Image denoising based on residual network

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3rd International Conference on Electronic Information engineering, big data, and Computer Technology, EIBDCT 2024

作者： Ma, Lihe Wang, Yan Sha, Ning Ma, Lin School of Information Engineering Harbin University Harbin China Heilongjiang Government Affairs Big Data Center3 Harbin China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin China

ISBN: (纸本)9781510680449

Image denoising has always been a hot research topic in the field of computer vision, playing an important role in improving image quality and accuracy. In this paper, a method for image denoising based on residual network is proposed, which uses deep learning technology to improve the effect of image processing. This method can automatically learn useful features in the image and effectively remove noise in the image, achieving automatic removal of image noise. Finally, experiments were conducted on standard image datasets for evaluation. The experimental results show that the image denoising method based on residual network has better performance compared to traditional methods. It can effectively remove various types of noise, improve image quality and clarity, and has broad application prospects in image processing. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image denoising

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