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JGC-IAGCL: Fusing joint graph convolution and intent-aware graph contrastive learning for explainable recommendation

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Information Fusion 2025年 123卷

作者： Yang, Zhi Lin, Chuan Qin, Yongbin Huang, Ruizhang Chen, Yanping Qin, Jiwei State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang Guizhou550025 China College of Information Science and Engineering Xinjiang University Xinjiang Uygur Autonomous Region Urumqi830046 China

Graph contrastive learning (GCL) enhances recommendation accuracy by leveraging self-supervised features to refine node representations from large-scale unlabeled data. Traditional GCL-based recommendation models typically construct contrastive views via graph augmentation (e.g., stochastic node/edge dropout) or embedding-space perturbation, aiming to maximize representation consistency. However, these methods struggle to effectively model and interpret user preferences and consumption intents, limiting explainability and recommendation performance. To address these challenges, we propose JGC-IAGCL (Joint Graph Convolution and Intent-Aware Graph Contrastive Learning), an explainable recommendation framework. JGC-IAGCL integrates joint graph convolution to capture implicit user preferences and employs intent-aware graph contrastive learning to extract explicit user intents from user–item interactions. By fusing these features, our method generates evenly distributed, intent-propensity-aware user/item representations. Theoretical analysis shows that JGC-IAGCL mitigates popularity bias while enhancing the exposure of long-tail items. Extensive experiments on four highly sparse public datasets validate its effectiveness, demonstrating superior recommendation accuracy and improved interpretability. © 2025

关键词： Self-supervised learning

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big data driven product design: A survey

arXiv

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arXiv 2021年

作者： Quan, Huafeng Li, Shaobo Zeng, Changchang Wei, Hongjing Hu, Jianjun College of Big Data and Statistics Guizhou Universify of Finance and Economics Guiyang550050 China State Key Laboratory of Public Big Data Guizhou University Guiyang550050 China Chengdu Institute of Computer Application Chinese Academy of Sciences Chengdu610041 China School of Mechanical Engineering Guizhou Institute of Technology Guiyang550050 China Department of Computer Science and Engineering University of South Carolina ColumbiaSC29201 United States

With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements;product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design. © 2021, CC BY-NC-SA.

关键词： Product design

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data privacy quantification and de-identification model based on information theory

Data privacy quantification and de-identification model base...

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2019 International Conference on Networking and Network Applications, NaNA 2019

作者： Zhang, Zeyu Lu, Zhiyang Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China College of Electrical Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9781728126296

De-identification enables to protect privacy of data from different attacks. At present, the specific de-identification standards and privacy quantification methods are given in many models, such as K-anonymity model and differential privacy model. But, the K-anonymity model does not provide an effective method to prove its degree of privacy protection, and when the model parameters change, the degree of privacy protection cannot be quantified. And due to rigorous calculation method of the differential privacy model, which quantifies the degree of privacy protection based on a mathematical basis, it is difficult to be used widely in organizations and institutions. So, this paper proposes a de-identification model, which includes quantification solution of identifying the sensitivity of the personal information, de-identification approach for adaptively matching different standard de-identification methods to personal information with different sensitivities and de-identification effect detection function. The objective is to provide an automatic, efficient and widely used de-identification model, which is built by quantitatively analyzing degree of privacy protection based on conditional entropy. Finally, performance analysis results show that the model makes the trade-off between privacy and secure of data and the widespread use of data. © 2019 IEEE.

关键词： data privacy

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Discrete-time quantum walk approach to high-dimensional quantum state transfer and quantum routing

arXiv

引用

arXiv 2021年

作者： Li, Heng-Ji Li, Jian Chen, Xiu-Bo Information Security Center State Key Laboratory Networking and Switching Technology Beijing University of Posts Telecommunications Beijing100876 China School of Computer Science Beijing University of Posts Telecommunications Beijing100876 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data Guizhou Guiyang550025 China

High-dimensional quantum systems can offer extended possibilities and multiple advantages while developing advanced quantum technologies. In this paper, we propose a class of quantum-walk ar-chitecture networks that admit the efficient routing of high-dimensional quantum states. Perfect state transfer of an arbitrary unknown qudit state can be achieved between two arbitrary nodes via a one-dimensional lackadaisical discrete-time quantum walk. In addition, this method can be gener-alized to the high-dimensional lattices, where it allows distillable entanglement to be shared between arbitrary input and output ports. Implementation of our scheme is more feasible through exploiting the coin degrees of freedom and the settings of the coin ipping operators are simple. These results provide a direct application in a high-dimensional computational architecture to process much more information. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum optics

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Chronic Disease Detection Via Non-negative Latent Feature Analysis

Chronic Disease Detection Via Non-negative Latent Feature An...

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IEEE International Conference on Networking, Sensing and Control

作者： Leming Zhou Qing Li Mingsheng Shang College of Computer Science and Technology Chongqing University of Posts and Telecommunications Academy of Sciences Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781665440493

Chronic diseases such as coronary heart disease (CHD) are of significant harm to human health. However, chronic disease detection commonly relies on many examinations to implement reliable diagnosis, resulting in a fatal delay in therapy. On the other hand, obtained examination data are often incomplete with numerous missing data, which further increases the difficulty in detection. To address this issue, this paper develops an inherently non-negative latent feature analysis-incorporating into a chronic disease detection (INC) model. The main idea are as follows: 1) adopting an inherently non-negative latent feature analysis model with a non-linear function to extract the non-negative latent features from incomplete examination data for accurate prediction of the unknown missing ones; 2) detecting the chronic disease based on the high-dimensional and different types of inspection features via classifiers. Experiments on actual CHD datasets demonstrate that the proposed INC model outperforms state-of-the-art models in detection accuracy, which well fits the desire of preliminary screening on coronary heart disease (CHD).

关键词： Learning systems Heart Representation learning Analytical models Predictive models Feature extraction data models

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ computer science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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Masked latent transformer with random masking ratio to advance the diagnosis of dental fluorosis

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Journal of Visual Communication and Image Representation 2025年 111卷

作者： Xu, Hao Wu, Yun Wu, Junpeng Xie, Rui Gu, Maohua Wang, Rongpin State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China College of Computer Science and Technology Guizhou University Guiyang550025 China Zhijin County People's Hospital Bijie552102 China Department of Medical Imaging Guizhou Provincial People's Hospital Guiyang550002 China

Dental fluorosis is a chronic condition caused by long-term overconsumption of fluoride, which leads to changes in the appearance of tooth enamel. Diagnosing its severity can be challenging for dental professionals, and limited research on deep learning applications in this field. Therefore, we propose a novel deep learning model, masked latent transformer with random masking ratio (MLTrMR), to advance the diagnosis of dental fluorosis. MLTrMR enhances contextual learning by using a masked latent modeling scheme based on Vision Transformer. It extracts latent tokens from the original image with a latent embedder, processes unmasked tokens with a latent transformer (LT) block, and predicts masked tokens. To improve model performance, we incorporate an auxiliary loss function. MLTrMR achieves state-of-the-art results, with 80.19% accuracy, 75.79% F1 score, and 81.28% quadratic weighted kappa on the first open-source dental fluorosis image dataset (DFID) we constructed. The dataset and code are available at https://***/uxhao-o/MLTrMR. © 2025 Elsevier Inc.

关键词： Deep learning

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Q-learning-based exponential distribution optimizer with multi-strategy guidance for solving engineering design problems and robot path planning

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Results in Engineering 2025年

作者： Fengbin Wu Shaobo Li Junxing Zhang Liya Yu Xuan Xiong Libang Wu State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 China Guizhou Institute of Technology Guiyang 550025 China Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang 550025 China School of Mechanical Engineering Guizhou University Guiyang 550025 China School of Computer Science and Technology Guizhou University Guiyang 550025 China

Exponential distribution optimizer (EDO) is an effective mathematics-based optimization technique with widespread application across numerous domains. However, it still suffers from insufficient exploitation capability, poor exploration capability, and weak adaptability of switching parameter methods. To tackle these problems, we present a Q-learning and multi-strategy guidance-enhanced EDO (QMEDO), integrating three essential innovations into the EDO framework. Firstly, the algorithm uses two local search strategies to enrich its exploitation ability and accuracy. Secondly, it employs a modified search operator with two equations to identify promising search regions effectively. Finally, a Q-learning-based switching parameter adaptive mechanism improves the adaptability of the algorithm under different conditions. The QMEDO’s performance was verified on the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2020 and 2022 test functions. The results underscore that QMEDO demonstrates significant overall effectiveness, robustness, and convergence superiority. Then, we employed QMEDO to handle 17 engineering problems. The results indicate that QMEDO ranks first overall on these design problems and is more robust in finding the optimal solution. We further employed the QMEDO to address the mobile robot path planning problem with different initial conditions and obstacles, and the findings exhibit that our QMEDO ranks first in overall performance, has higher path planning accuracy, and better stability. The above-mentioned results reveal that in QMEDO, the collaborative effect of multiple strategies makes it reliable and applicable for solving different problems.

关键词： Math-based optimization algorithm Exponential distribution optimizer Multi-strategy search strategy Reinforcement learning Engineering design problems

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DE-RSTC: A rational secure two-party computation protocol based on direction entropy

arXiv

引用

arXiv 2021年

作者： Chen, Yuling Ma, Juan Wang, Xianmin Zhang, Xinyu Zhou, Huiyu State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University Of Electronic Technology Guilin541000 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510000 China University of Leicester LeicesterLe1 7RH United Kingdom

Rational secure multi-party computation (RSMC) means two or more rational parties to complete a function on private inputs. In the process, the rational parties choose strategies to maximize utility, which will cause players to maliciously execute the protocol and undermine the fairness and correctness of the protocol. To solve this problem, we leverage game theory to propose the direction entropy-based solution. First, we utilize the direction vector of the direction entropy to examine the player's strategy uncertainty and quantify its strategy from different dimensions. Specifically, when parties choose a cooperation strategy, the direction vector is positive, and the information transmitted is positive, conversely, it is negative information. Then, we provide mutual information to construct new utility functions for the players. What's more, we measure the mutual information of players to appraise their strategies. Finally, we prove in detail the protocol we gave, and the result show that the fairness problem in rational secure two-party computation. We also prove that the proposed protocol reaches the Nash equilibrium. Furthermore, we conduct experiments using mutual information to construct utility, and the results show that the utility obtained when the player is honest will be higher. Copyright © 2021, The Authors. All rights reserved.

关键词： Entropy

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An Asymmetric Controlled Bidirectional Quantum state Transmission Protocol

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computers, Materials & Continua 2019年第4期59卷 215-227页

作者： Yiru Sun Yuling Chen Haseeb Ahmad Zhanhong Wei GuiZhou University Sate Key Laboratory of Public Big DataGuizhou550025China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile University Faisalabad37610Pakistan College of Information Engineering Beijing Institute of Petrochemical TechnologyBeijing102617China

In this paper,we propose an asymmetric controlled bidirectional transmission *** the protocol,by using the thirteen-qubit entangled state as the quantum channel,Alice can realize the transmission of a two-qubit equatorial state for Bob and Bob can transmit a four-qubit equatorial state for Alice under the control of ***,we give the construction of the quantum channel,which can be done by performing several H and CNOT ***,through implementing the appropriate measurements and the corresponding recovery operations,the desired states can be transmitted simultaneously,securely and ***,we analyze the performance of the protocol,including the efficiency,the necessary operations and the classical communication *** then,we describe some comparisons with other *** our protocol does not require auxiliary particles and additional operations,the classic communication costs less while achieving the multi-particle bidirectional transmission,so the overall performance of the protocol is better.

关键词： Controlled asymmetric bidirectional quantum state transmission remote state preparation

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