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Exploring implicit influence for social recommendation based on GNN

Soft Computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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Ultrasensitive electrospinning fibrous strain sensor with synergistic conductive network for human motion monitoring and human-computer interaction

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材料科学技术（英文版） 2025年第10期213卷 213-222页

作者： Jingwen Wang Shun Liu Zhaoyang Chen Taoyu Shen Yalong Wang Rui Yin Hu Liu Chuntai Liu Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China China Astronaut Research and Training Center Beijing 100094China

With the rapid development of wearable electronic skin technology,flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection,medical diagnostics,and human-computer interaction owing to their outstanding sensing *** pa-per reports a strain sensor with synergistic conductive network,consisting of stable carbon nanotube dispersion(CNT)layer and brittle MXene layer by dip-coating and electrostatic self-assembly method,and breathable three-dimensional(3D)flexible substrate of thermoplastic polyurethane(TPU)fibrous membrane prepared through electrospinning *** MXene/CNT@PDA-TPU(MC@p-TPU)flexible strain sensor had excellent air permeability,wide operating range(0-450％),high sensitivity(Gauge Fac-tor,GF max=8089.7),ultra-low detection limit(0.05％),rapid response and recovery times(40 ms/60 ms),and excellent cycle stability and durability(10,000 cycles).Given its superior strain sensing capabilities,this sensor can be applied in physiological signals detection,human motion pattern recognition,and driv-ing exoskeleton *** addition,MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater,which has far-reaching application potential in the photothermal therapy of human joint diseases.

关键词： Flexible strain sensors Synergistic conductive network Electrospinning fibrous membrane Motion monitoring Human-machine interface

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Stock price prediction research in steel and food industries using random forest algorithm 2

Stock price prediction research in steel and food industries...

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Jiang, Yuxiao Yang, Lei Du, Shipei Sun, Yu Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Dongguan China

ISBN: (纸本)9781510689053

The application of computer technology in the financial field and the utilization of computer algorithms to enhance the accuracy of stock price prediction have drawn the attention of numerous researchers. Against this backdrop, this study aims to precisely predict the stock prices in the steel and food industries by leveraging the random forest algorithm in the computer domain, so as to provide valuable reference for investors. This study selected the historical trading data of six listed A-shares as the basic data source. Firstly, the dataset was divided into the training set and the testing set according to a scientific proportion. Subsequently, the parameters of the random forest model were reasonably set, and thus the random forest model was constructed for subsequent analysis. Through the practical application of this model to predict the daily closing prices of stocks, it was found that the model demonstrated excellent capabilities and adaptability, with relatively small deviations between the predicted values and the actual values. This indicates that the model has relatively high reliability and practical value in predicting the stock prices of the steel and food industries. In conclusion, the application of the random forest algorithm in predicting the stock prices of the steel and food industries is an effective method, which has a promising application prospect and is worthy of further in-depth research and promotion. © 2025 SPIE.

关键词： Food industry

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Research on stock prediction of the automobile manufacturing industry and agricultural product processing industry based on the Decision Tree Optimization Algorithm 2

Research on stock prediction of the automobile manufacturing...

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Jiang, Yuxiao Yang, Lei Du, Shipei Sun, Yu Liang, Yuhao Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Dongguan China

ISBN: (纸本)9781510689053

Machine learning algorithms provide new ways to solve the problem of stock prediction. Among them, the decision tree algorithm has been widely used in financial market prediction because of its strong interpretability, no need for complex data preprocessing, and the ability to handle various types of data. However, the decision tree algorithm is prone to overfitting during the training process, resulting in poor generalization ability of the model on unseen data and affecting prediction accuracy. In this research, the decision tree pruning technique is used to conduct predictive research on the closing prices of stocks in two representative industries. Through experimental analysis, it is verified that the pruned decision tree improves both the accuracy and stability of its prediction, providing more effective methods and bases for financial market prediction. © 2025 SPIE.

关键词： Prediction models

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Predicting financial and manufacturing stocks in China's stock market with Random Forest 2

Predicting financial and manufacturing stocks in China's sto...

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhang, Yanting Yang, Lei Du, Shipei Sun, Yu Hou, Yaxin Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Dongguan China

ISBN: (纸本)9781510689053

Machine learning, a key area of artificial intelligence, has proven to be an effective tool for making predictions based on large, complex datasets. In finance, stock market prediction is crucial for guiding investment decisions, as it directly affects investors' wealth management. This study applies the Random Forest algorithm to predict stock prices in the Chinese market, focusing on both financial and manufacturing sectors. Three stocks from each sector were selected for analysis. Historical trading data from the Shanghai Composite Index between the second half of 2020 and the first half of 2022 was collected, including features such as opening dates, prices, and trading volumes. A total of 200 decision trees were used as base classifiers in the Random Forest model. To prevent overfitting, a rule was set to stop node splitting when the sample size dropped below 5. The results showed no significant difference in prediction accuracy between the two sectors. This study provides insights for investors, helping them better understand the predictability of stocks in different sectors. It also highlights the potential of the Random Forest algorithm in financial forecasting, suggesting that it may be further refined for improved prediction models in future research. © 2025 SPIE.

关键词： Decentralized finance

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Anisotropic phonon thermal transport in two-dimensional layered materials

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Frontiers of physics 2023年第4期18卷 217-225页

作者： Yuxin Cai Muhammad Faizan Huimin Mu Yilin Zhang Hongshuai Zou Hong Jian Zhao Yuhao Fu Lijun Zhang State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of MOEInternational Center of Computational Method and SoftwareSchool of Materials Science and EngineeringJilin UniversityChangchun 130012China State Key Laboratory of Superhard Materials International Center of Computational Method and SoftwareCollege of PhysicsJilin UniversityChangchun 130012China

Two-dimensional layered materials(2DLMs)have attracted growing attention in optoelectronic devices due to their intriguing anisotropic physical *** members of 2DLMs exhibit unique anisotropic electrical,optical,and thermal properties,fundamentally related to their crystal *** them,directional heat transfer plays a vital role in the thermal management of electronic ***,we use density functional theory calculations to investigate the thermal transport properties of representative layered materials:β-InSe,γ-InSe,MoS2,and *** found that the lattice thermal conductivities ofβ-InSe,γ-InSe,MoS_(2),and h-BN display diverse anisotropic behaviors with anisotropy ratios of 10.4,9.4,64.9,and 107.7,*** analysis of the phonon modes further indicates that the phonon group velocity is responsible for the anisotropy of thermal ***,the low lattice thermal conductivity of the layered InSe mainly comes from low phonon group velocity and atomic *** findings provide a fundamental physical understanding of the anisotropic thermal transport in layered *** hope this study could inspire the advancement of 2DLMs thermal management applications in next-generation integrated electronic and optoelectronic devices.

关键词： thermal conductivity two-dimensional layered materials firstprinciples calculation Boltzmann transport theory

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Container-Aware Service Function Chains Placement and Optimization in Vehicular Edge Computing 22

Container-Aware Service Function Chains Placement and Optimi...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Kaixin Hu, Shihong Qu, Zhihao Ye, Baoliu Hohai University School of Computer Science and Software Engineering China Nanjing University National Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798331509712

In the domain of Vehicular Edge Computing (VEC), this paper addresses the complex problem of Service Function Chain (SFC) placement, which is crucial for the efficient deployment of cloud applications in vehicular environments. Our approach leverages Virtual Network Function (VNF) technology, transitioning network services from traditional hardware to more flexible, container-based edge computing frameworks. The primary objective is to organize these VNFs into functional SFCs, thereby minimizing service delays in VEC systems. SFC placement faces two significant challenges. The first is the sequential dependency among VNFs within an SFC, which adds substantial complexity to container deployment. The second challenge is the cold start delay of VNF containers, a critical issue in scenarios requiring swift response times, which negatively impacts service quality in VEC applications. To address these challenges, we propose a comprehensive model for SFC placement that considers the deployment status of VNF containers, acknowledging the complexity of this NP-hard problem. The core of our contribution is the development of the Single SFC Placement Algorithm (SSPA), a sophisticated, greedy-based approach designed for the effective placement of individual SFCs. We enhance this algorithm by incorporating a Particle Swarm Optimization (PSO) technique, making it capable of efficiently handling the placement of multiple SFCs. Our solution aims to improve edge resource utilization and mitigate startup delays associated with the initial activation of containers, thereby reducing service delays. Extensive experimental evaluations demonstrate that our algorithm achieves a 34.3% average reduction in service delay compared to four baselines and a 5.8% average reduction compared to other container-aware algorithms. © 2024 IEEE.

关键词： service function chain SFC placement vehicle edge computing virtual network function

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A Land-Based War-Gaming Simulation Method Based on Multi-Agent Proximal Policy Optimization 24

A Land-Based War-Gaming Simulation Method Based on Multi-Age...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Gao, Xule Wang, Rui Liu, Zhaohui Ren, Yi State Key Laboratory of Intelligent Game Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350365658

War-gaming simulation is an important means of assisting and simulating modern warfare, characterized by simulation, uncertainty, and asymmetric gameplay. With the development of deep reinforcement learning techniques, deep reinforcement learning methods can be used to generate combat strategies in war-gaming simulations. This paper ad-dresses the challenges of complex instructions, sparse re-wards, and collaborative multi-agent combat in land-based war-gaming simulations. We propose a situation extraction method based on feature concatenation, design task-specific reward functions, and utilize the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to train intelligent agents. The proposed method achieves a good win rate in land-based war-gaming simulation scenarios, which is more reliable than the baselines. © 2024 IEEE.

关键词： Deep reinforcement learning

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Alloy-induced reduction and anisotropy change of lattice thermal conductivity in Ruddlesden–Popper phase halide perovskites

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Frontiers of physics 2023年第6期18卷 183-191页

作者： Huimin Mu Kun Zhou Fuyu Tian Yansong Zhou Guoqi Zhao Yuhao Fu Lijun Zhang State Key Laboratory of Superhard Materials International Center of Computational Method and SoftwareCollege of PhysicsJilin UniversityChangchun 130012China State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of MOEInternational Center of Computational Method and SoftwareCollege of Materials Science and EngineeringJilin UniversityChangchun 130012China

The effective modulation of the thermal conductivity of halide perovskites is of great importance in optimizing their optoelectronic device *** on first-principles lattice dynamics calculations,we found that alloying at the B and X sites can significantly modulate the thermal transport properties of 2D Ruddlesden−Popper(RP)phase halide perovskites,achieving a range of lattice thermal conductivity values from the lowest(κ_(c)=0.05 W·m^(−1)·K^(−1)@Cs_(4)AgBiI_(8))to the highest(κ_(a/b)=0.95 W·m^(−1)·K^(−1)@Cs4NaBiCl_(4)I_(4)).Compared with the pure RP-phase halide perovskites and three-dimensional halide perovskite alloys,the two-dimensional halide perovskite introduces more phonon branches through alloying,resulting in stronger phonon branch coupling,which effectively scatters phonons and reduces thermal *** can also dramatically regulate the thermal transport anisotropy of RP-phase halide perovskites,with the anisotropy ratio ranging from 1.22 to ***,analysis of the phonon transport modes in these structures revealed that the lower phonon velocity and shorter phonon lifetime were the main reasons for their low thermal *** work further reduces the lattice thermal conductivity of 2D pure RP-phase halide perovskites by alloying methods and provides a strong support for theoretical guidance by gaining insight into the interesting phonon transport phenomena in these compounds.

关键词： first-principles lattice dynamics calculations Boltzmann transport all-inorganic RP-phase halide perovskite alloys thermal conductivity

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