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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Cao, Weihao Cheng, Yong Xue, Shengjun Dai, Fei School of Software Nanjing University of Information Science and Technology Nanjing210044 China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

ISBN: (纸本)9789819998951

In recent years, massive services that provide similar functions continue to emerge. Since services sensitive to latency and throughput are often expected to have high Quality of Service (QoS), how to accurately predict QoS has become a challenging issue. Some current deep learning (DL) based approaches usually simply concatenate the embedding vectors, without considering the correlation between embedding dimensions. Besides, the high-order feature interactions are not sufficiently learned. To this end, this paper proposes a Convolutional Neural Network based QoS prediction model with Dimensional Correlation, named QPCN. First, the two dimensional interaction features is explicitly obtained by modeling the embedding vectors. Then, the convolutional neural network is utilized to perform feature extraction and complete QoS prediction. Compared with the fully connected network, QPCN can build a deeper model and learn high-order features. In addition, the parameters of QPCN are significantly reduced, which will reduce the time and energy consumption of inference. The effectiveness of QPCN is validated by experiments on a real-world dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Quality of service

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Deep mining algorithm for traffic accident data on highways under severe weather conditions

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第Speical issue期1卷 47-58页

作者： Zhang, J.Y. Cao, Y. Jiangsu Youth Science and Technology Center Nanjing210000 China School of Computer Engineering Jiangsu Second Normal University Nanjing210000 China Jiangsu Province Engineering Research Center of Basic Education Big Data A PPlication Nanjing210013 China

Analyzing traffic accident data is crucial for pinpointing contributing factors’ forecasting accident patterns’ and informing effective safety measures. This insight leads to enhanced road safety’ decreased fatalities’ and better resource allocation in transportation planning. Severe weather conditions’ including rain’ fog’ and sandstorms’ can significantly reduce the visibility of highways and increase the risk of traffic accidents. The paper proposes a deep mining algorithm for road traffic accident data under adverse weather conditions. Firstly’ collect traffic accident data under adverse weather conditions and complete attribute reduction’ denoising’ and normalization processing. Then’ by improving the decision tree algorithm’ the attribute selection criteria of the decision tree algorithm were enhanced;Finally’ the improved decision tree algorithm is aPPlied to achieve deep mining of traffic accident data. The experimental results show that proposed algorithm has an accuracy rate of over 95.3%’ an accuracy rate of over 94.3%’ a recall rate of over 92.7%’ and a stable F1 value of around 0.8’ demonstrating excellent performance. The provided deep mining algorithm for traffic accident data can provide more scientific and accurate decision suPPort for traffic management departments. © 2024, Aracne Editrice. All rights reserved.

关键词： data mining

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Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

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Computers, Materials & Continua 2025年第3期82卷 5035-5055页

作者： Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang Henan Key Laboratory of Big Data Analysis and Processing Computer and Information EngineeringHenan UniversityKaifeng475004China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China Henan Branch China Life Insurance Co.Ltd.Zhengzhou450000China

Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关键词： Fingerprint recognition privacy protection federated learning adaptive weight adjustment

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Clustering Analysis of Water Usage Structures Based on Knowledge Graph 4

Clustering Analysis of Water Usage Structures Based on Knowl...

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4th International Conference on Computer, big data and Artificial Intelligence, ICCBD+AI 2023

作者： Yan, Jianzhuo Jiao, Han Zhang, Hang Yu, Yongchuan Chen, Jianhui Zhou, Shiwei Beijing University of Technology Faculty of Information Technology Beijing China Beijing Institute of Water Science and Technology Institute of Big Data Analysis and Applications Beijing China

ISBN: (纸本)9798350373233

A key issue in the management of water resources in China is the adoption of a water supply total quantity control framework at the regional level based on socioeconomic development. This necessitates more effective management of water resource demand to achieve a balance between supply and demand. This paper aims to conduct an in-depth analysis of the industrial water usage structure, identify the primary influencing factors, and thereby provide insights for the design of demand management policies. Using a typical application scenario of "water extraction and supply"in a subsidiary of a water supply company in Beijing, a water knowledge graph incorporating multiple data sources is constructed. The study proposes a knowledge graph embedding model based on graph convolutional neural networks. Over the 36 months from 2019 to 2021, the water units in the accommodation, office, urban resident, healthcare, retail, education, catering, and residential services industries are clustered, and the water usage is analyzed. The results of this study are expected to provide references for optimizing urban water resource utilization. © 2023 IEEE.

关键词： Convolutional neural networks

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Predictive Modeling of Traditional Korean Paper Characteristics Using Machine Learning Approaches (Part 1): Discriminating Manufacturing Origins with Artificial Neural Networks and Infrared Spectroscopy

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2023年第4期55卷 57-69页

作者： Hwang, Sung-Wook Park, Geunyong Kim, Jinho Jeong, Myung-Joon Human Resources Development Center for Big Data-based Glocal Forest Science Kyungpook National University Korea Republic of Department of Wood Science and Technology Kyungpook National University Korea Republic of Department of Wood Science & Technology College of Agricultural Life Science Jeonbuk National University Korea Republic of

This study focuses on machine learning-based approaches in combination with infrared spectroscopy to discriminate the manufacturing origin of Hanji, a traditional Korean paper. Infrared spectra provide useful information about the chemical composition and structural features of Hanji, while principal component analysis and hierarchical clustering extract meaningful patterns related to the manufacturing region. Score plots and hierarchical clustering of the principal components provide enhanced clustering patterns based on manufacturing region by focusing on the spectral region 1800-1200 cm -1 . The clustering patterns are driven by key absorption bands, such as those associated with carboxyl groups, crystalline cellulose, and aromatic rings. In addition, feed-forward neural network classification models that were developed using the spectral data exhibit significant accuracy when classifying the Hanji manufacturing regions. In particular, models utilizing the raw and second derivative spectra in the 1800-1200 cm-1 region exhibit excellent classification performance, indicating the effectiveness of this spectral region for classification purposes. This study demonstrates the effective application of artificial neural networks in conjunction with infrared spectroscopy to characterize and classify Hanji based on its manufacturing region. The results contribute to a better understanding of the unique properties of Hanji and the discovery of new insights from paper cultural artifacts. © 2023 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Infrared spectroscopy

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models

arXiv

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arXiv 2024年

作者： Chen, Tingting Si, Shijing Department of Data Science and Big Data Technology Shanghai International Studies University Shanghai China

Housing has emerged as a crucial concern among young individuals residing in major cities, including Shanghai. Given the unprecedented surge in property prices in this metropolis, young people have increasingly resorted to the rental market to address their housing needs. This study utilizes five traditional machine learning methods-multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), decision tree (DT), and random forest (RF)-along with a Large Language Model (LLM) approach using ChatGPT, for predicting the rental prices of lane houses in Shanghai. It applies these methods to examine a public data sample of about 2,609 lane house rental transactions in 2021 in Shanghai, and then compares the results of these methods. In terms of predictive power, RF has achieved the best performance among the traditional methods. However, the LLM approach, particularly in the 10-shot scenario, shows promising results that surpass traditional methods in terms of R-Squared value. The three performance metrics-mean squared error (MSE), mean absolute error (MAE), and R-Squared-are used to evaluate the models. Our conclusion is that while traditional machine learning models offer robust techniques for rental price prediction, the integration of LLM such as ChatGPT holds significant potential for enhancing predictive accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiple linear regression

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Rare earth element Ce enables high thermoelectric performance in n-type SnSe polycrystals

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Journal of Materials science & technology 2023年第12期143卷 234-241页

作者： Shan Li Li Yin Yijie Liu Xiaodong Wang Chen Chen Qian Zhang School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

SnSe exhibits excellent thermoelectric performance in both n-and p-type single crystals,but its n-type polycrystals are restricted because of the lower electrical ***,we dually introduced rare earth element Ce and PbTe to optimize the thermoelectric properties of n-type SnSe *** is demonstrated that Ce is an effective cationic dopant to convert SnSe from p-to n-type conductor,and an enhanced peak zT value of∼0.9 at 823 K was obtained in Sn 0.97 Ce 0.03 Se due to the improved power ***,PbTe alloying not only reduced the band gap to increase the carrier concentration,but also enhanced the density-of-states effective mass,and hence further increased the power factor in the whole measured temperature ***,the lattice thermal conductivity was significantly reduced owing to the enhanced phonon scattering by the mass and strain *** a result,the peak zT value was increased to∼1.3 for Sn 0.9 Pb 0.07 Ce 0.03 Se 0.93 Te 0.07 together with a high average zT value of∼0.52 in the temperature range of 300 to 823 K.

关键词： Thermoelectric N-type SnSe Ce doping PbTe alloying

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HEM: An Improved Parametric Link Prediction Algorithm Based on Hybrid Network Evolution Mechanism 9th

HEM: An Improved Parametric Link Prediction Algorithm Based ...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Ke, Dejing Pu, Jiansu University of Electronic Science and Technology of China Sichuan611731 China Big Data Visual Analysis Lab University of Electronic Science and Technology of China Sichuan610000 China

ISBN: (纸本)9783031466632

Link prediction plays an important role in the research of complex networks. Its task is to predict missing links or possible new links in the future via existing information in the network. In recent years, many powerful link prediction algorithms have emerged, which have good results in prediction accuracy and interpretability. However, the existing research still cannot clearly point out the relationship between the characteristics of the network and the mechanism of link generation, and the predictability of complex networks with different features remains to be further analyzed. In view of this, this article proposes the corresponding link prediction indices Reg, DFPA and LW on regular network, scale-free network and small-world network respectively, and studies their prediction properties on these three network models. At the same time, we propose a parametric hybrid index HEM and compare the prediction accuracy of HEM and many similarity-based indices on real-world networks. The experimental results show that HEM performs better than other indices. In addition, we study the factors that play a major role in the prediction of HEM and analyze their relationship with the characteristics of real-world networks. The results show that the predictive properties of factors are closely related to the features of networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Finite-Time Sampled-data Control for Networked Dynamic System 42

Finite-Time Sampled-Data Control for Networked Dynamic Syste...

引用

42nd Chinese Control Conference, CCC 2023

作者： Zhang, Dan Wang, Hui Chen, Xuefeng Xue, Xiaojuan School of Science Yanshan University Hebei Qinhuangdao066004 China Aliyun School of Big Data Changzhou University Jiangsu Changzhou213164 China College of Data Science Taiyuan University of Technology Shanxi Taiyuan030024 China

ISBN: (纸本)9789887581543

This paper studies the finite-time stability of the networked dynamic system under sampled-data control. By taking the interconnected structure into consideration and employing input-delay approach, tractable conditions that only depend on individual subsystem are proposed to ensure the finite-time stability of the networked dynamic system. By utilizing the Finsler's lemma, the existence conditions of the decentralized state feedback controller are derived. An example of interconnected seesaw-cart system is given to illustrate the effectiveness of the proposed method. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： State feedback

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