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3rd International Symposium on Computer Applications and Information Systems, ISCAIS 2024

作者： Liang, Jian Quan, Fan Yang, Suyi Tao, Ping Ge, Fei Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China School of Information and Communication Studies University College Dublin Dublin Ireland Department of Statistics and Data Science University of California Los Angeles United States

ISBN: (纸本)9781510681316

Invoice data is critical and highly sensitive for enterprises, yet it stands as one of the most extensively used data categories in the financial credit sector. Participants in the credit market, engaged in corporate lending activities, face the dual challenge of ensuring the security of enterprise invoice data and safeguarding the integrity of institutional rules. Traditional data computation and encryption present a dilemma, as two encrypted datasets cannot directly engage in calculations, and two datasets participating in calculations must be in plaintext. This necessitates the convergence of enterprise data and institutional rules for computation. Consequently, two sets of data must be exposed to one of the parties or a mutually trusted third party. In practice, identifying a genuinely trusted third party proves to be a formidable task. This paper introduces a federated privacy computing method based on an oblivious transfer algorithm in the context of risk control admission rules within the credit risk management field. This approach enables both parties holding data to determine admission rules without disclosing their own data. © 2024 SPIE.

关键词： Risk management

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HiPoser: 3D Human Pose Estimation with Hierarchical Shared Learning at Parts-Level Using Inertial Measurement Units 39

HiPoser: 3D Human Pose Estimation with Hierarchical Shared L...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liao, Guorui Zheng, Chunyuan Cheng, Li Xie, Haoyu Huang, Shanshan Liao, Jun Li, Haoxuan Liu, Li School of Big Data & Software Engineering Chongqing University China School of Mathematical Sciences Peking University China Department of Electrical and Computer Engineering University of Alberta Canada Center for Data Science Peking University China

ISBN: (纸本)157735897X

This paper considers the challenging problem of 3D Human Pose Estimation (HPE) from a sparse set of Inertial Measurement Units (IMUs). Existing efforts typically reconstruct a pose sequence by either directly tackling whole-body motions or focusing on distinctive spatio-temporal features of local body parts. Unfortunately, these methods ignore existing interdependent motor synergies amongst body parts, which may lead to pose estimation with ambiguous local parts. This observation motivates us to propose a hierarchical learning-based approach, HiPoser, which utilizes a hierarchical shared structure using Mamba blocks as the backbone to focus on the following estimation tasks, involving: 1) torso pose, 2) lower limbs pose, 3) upper limbs pose, and finally 4) global translation. These tasks selectively incorporate body motion states and are to be carried out sequentially in reconstructing part-based poses, which are amalgamated to estimate the final full-body pose with the global translation that satisfies inter-part consistencies. Our hierarchical structure allows HiPoser the flexibility in prioritizing different aspects of pose estimation, to emphasize more on detail or stability. Empirical evaluations over three benchmark datasets demonstrate the superiority of HiPoser over existing state-of-the-art models, suggesting that analyzing the synergistic movement of body parts is indeed important for advancing IMU-based 3D HPE. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Metric system

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Xinquan Xie, Jinheng Li, Xuechen Li, Xuguang Shen, Linlin Deng, Yongqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China National University of Singapore Show Lab Singapore Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University General Hospital Department of Stomatology Shenzhen China

ISBN: (纸本)9798350337488

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods. © 2023 IEEE.

关键词： Dental Implant Multi-module Detection Multi-slice Learning Text Guided Detection Transformer

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HGDRec:Next POI Recommendation Based on Hypergraph Neural Network and Diffusion Model

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IEEE Transactions on Services Computing 2025年第3期18卷 1445-1458页

作者： Yinchen Pan Jun Zeng Ziwei Wang Haoran Tang Junhao Wen Min Gao School of Big Data and software engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong

In recent years, next Point-of-Interest (POI) recommendation is essential for many location-based services, aiming to predict the most likely POI a user will visit next. Current research employs graph-based and sequential methods, which have significantly improved performance. However, there are still limitations: numerous methods overlook the fact that user intent is constantly changing and complex. Furthermore, prior studies have seldom addressed spatiotemporal correlations while considering differences in user behavior patterns. Additionally, implicit feedback contains noise. To address these issues, we propose a recommender model named HGDRec for the next POI recommendation. Specifically, we introduce an approach for extracting trajectory intent by integrating multi-dimensional trajectory representations to achieve a multi-level understanding of user trajectories. Then, by analyzing users’ long trajectories, we construct global hypergraph structures across spatiotemporal regions to comprehensively capture user behavior patterns. Additionally, to further optimize trajectory intent representation, we employ a feature optimization method based on the improved diffusion model. Extensive experiments on three real-world datasets validate the superiority of HGDRec over the state-of-the-art methods.

关键词： Trajectory Diffusion models Spatiotemporal phenomena Feature extraction Optimization Transformers data mining Noise measurement data models Correlation

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A Novel Design of Fatigue Driving Detector with mmWave Sensor AWR1642boost 23

A Novel Design of Fatigue Driving Detector with mmWave Senso...

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2023 International Conference on Intelligent Computing and Its Emerging Applications, ICEA 2023

作者： Tseng, Shih-Pang Hsiao, I-Hui Wang, Jhing-Fa School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400709050

According to statistics from the Ministry of Transportation, distracted driving is one of the main causes of traffic accidents. In Taiwan, approximately 20% of total accidents each year are attributed to distracted or fatigued driving, ranking it as the second leading cause of accidents. Typically, drivers start to feel fatigued and experience decreased reaction and decision-making abilities after driving continuously for 2-3 hours. Post-meal and midnight driving also tend to induce drowsiness more than other times. Additionally, alcohol consumption, drug use, and the use of hypnotic medication can lead to loss of consciousness. Fatigued driving poses a serious public safety issue, endangering both drivers and other road users. Therefore, developing effective methods for detecting driver fatigue is crucial. Based on existing research, employing facial monitoring systems can reduce accident rates by 10% to 20%. However, these systems are limited by lighting conditions. Thus, this paper proposes the use of millimeter-wave technology to realize a fatigue detection system, effectively addressing the high reliance of facial monitoring systems on lighting conditions. In this study, data collection was performed using the AWR1642boost, a highly integrated millimeter-wave radar development board developed by Texas Instruments. Subsequently, a Multi-experts system was developed to integrate and analyze four features: nodding, yawning, breathing, and heart rate. The system employed a Support Vector Machine (SVM) model to improve the accuracy of fatigue detection in driving scenarios. Experimental results demonstrated that the system achieved an accuracy rate of 90%, effectively discerning whether the driver was experiencing fatigue. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Highway accidents

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The Investigation and Implementation of Automated Pricing and Replenishment Decisions for Vegetable Commodities Relying on Time Series Forecasting and Linear Programming Models

The Investigation and Implementation of Automated Pricing an...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Yi Tong Jinxuan Zhou Liang Deng Congao Zhang Tao Guo Xueling Long Department of Information Engineering Wuhan Business University Hubei China Major in Software Engineering Major in Data Science and Big DataTechnology Major in Internet of Things Engineering Major in Financial Management

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

In modern supermarkets, given the limited shelf life and the propensity for rapid declines in the sales of perishable goods, such as vegetables, it becomes imperative to conduct daily restocking based on historical sales and consumer demand to preclude the occurrence of undersold items the following day. Consequently, conducting restocking and pricing strategies based on sales and demand holds significant research importance for supermarkets. This study initially employs a multiple linear regression model to investigate the impact on profit by adjusting total sales (Q), sales price (P), and each component of cost-plus pricing (C, T, L, A), thereby determining the relationship between total sales and cost-plus pricing for each vegetable category, as well as the functional relationship between total sales and cost-plus pricing for each vegetable category. Based on the derived functional relationship, time series prediction and linear programming models are utilized to forecast the market demand for each vegetable category over a one-week period, subsequently formulating the daily restocking plan and pricing strategy for each vegetable category in the subsequent week to maximize supermarket profit while satisfying market demand. Finally, an analysis of saleable vegetable items reveals that 29 vegetable items can be displayed, with a total profit of 846.35 yuan. Upon testing the sales data of 300,000 vegetables, compared to the conventional display method sans the use of this model, the overall profit margin surged by 269.75 yuan, an increment of 47% relative to the initial profit, effectively enhancing the operational efficiency and profitability of the supermarket.

关键词： Profitability Time series analysis Linear regression Pricing Predictive models Linear programming data models Information technology Forecasting Testing

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DMSDRec: Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation

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IEEE Transactions on Services Computing 2025年

作者： Li, Yue Zeng, Jun Tang, Haoran Wen, Junhao Gao, Min Zhou, Wei Chongqing University School of big data and software engineering Chongqing40044 China Hong Kong Polytechnic University Department of Computing Hung Hom Kowloon Hong Kong

The rise of smart devices has accelerated location-based services, creating vast trajectory data for Point-of-Interest (POI) recommendations. However, user omissions or privacy concerns often result in incomplete trajectory data, compromising sequential patterns and spatiotemporal relationships. Existing solutions using graph structures, self-supervised learning (SSL), or spatiotemporal contexts still face two limitations: (1) graph-based and SSL methods produce suboptimal trajectory representations due to respective inherent constraints;(2) noise interference persists when modeling distorted spatiotemporal signals. To mitigate these issues, we propose a Dynamic Structure-aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation (DMSDRec). Specifically, we introduce a dynamic structure-aware improved graph masked autoencoder that adaptively and dynamically distills global transitional information for self-supervised augmentation. It naturally avoids the noise introduced by existing SSL methods' dependency on manual views augmentation. Meanwhile, the masked reconstruction task synergistically enhances trajectory representations by capturing deeper cross-sequence dependencies. Additionally, we propose an effective latent-space spatiotemporal diffusion denoising method. First, we employ graph structures to model spatiotemporal relationships, utilizing higher-order structural information to alleviate the linear spatiotemporal relationship deviations caused by incomplete trajectories. Building on this, we implement diffusion models in the latent space to systematically identify and remove noise from spatiotemporal representations. Through experimental results on two real-world datasets, we demonstrate the superiority of our proposed DMSDRec in terms of recommendation accuracy and robustness. © 2008-2012 IEEE.

关键词： Dynamic Structure-awareness Graph Masked Autoencoder Next POI Recommendation Spatiotemporal Diffusion

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Tensor Nuclear Norm Based Matrix Regression Based Projections for Feature Extraction of Hyperspectral Images 26

Tensor Nuclear Norm Based Matrix Regression Based Projection...

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

作者： Qiu, Hong Jin, Heng Wang, Renfang Liu, Xiufeng Gao, Guang Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Shanghai Ocean University College of Information Shanghai200120 China Technical University of Denmark Department of Technology Management and Economics Denmark Co. Ltd. Ningbo China

ISBN: (纸本)9798350331684

With high spectral resolution, hyperspectral image(HSI) data will result in the Hughes phenomenon, which brings a huge challenge to hyperspectral image classification(HIC). Feature extraction can be applied to address this problem. But several traditional methods often ignore the spatial structure information of HSI data. In this paper, we propose a tensor nuclear norm based matrix regression based projections(TNMRP) for feature extraction of hyperspectral images. Firstly, TNMRP preprocesses the data by a filling method. Then, it automatically builds the graph of block-tensor samples and uses the optimal sparse coding coefficients to obtain the weight matrix. Finally, based on tensor representation, TNMRP calculates the optimal projection matrix. Experiments of classification on Indian Pines and Pavia University databases demonstrate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Matrix algebra

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A Novel Certificateless Authentication and Key Agreement Protocol for Smart Grid

A Novel Certificateless Authentication and Key Agreement Pro...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zewei Liu Chunqiang Hu Conghao Ruan Ruinian Li School of Big Data and Software Engineering Chongqing University China Computer Science Department Bowling Green State University USA

With the rapid development of the smart grid (SG), the security and reliability of communication for SG has become an urgent and critical issue. The authentication and key agreement (AKA) protocol has emerged as important means to address the security challenges in smart grid. This paper proposes a novel certificateless authentication and key agreement protocol (NCL-AKA) for the smart grid, which overcomes the limitations of existing AKA protocol such as complex certificate management, untimely certificate revocation, and man-in-the-middle attacks. Firstly, the system architecture and threat model of the proposed protocol is presented. Then, the correctness and security of the proposed protocol are analyzed. Finally, comparative experiments and analysis reveal that the NCL-AKA has lower computational cost and communication overhead.

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Dual Space Multiple Instance Representative Learning for Medical Image Classification 33

Dual Space Multiple Instance Representative Learning for Med...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Zhang, Xiaoxian Huang, Sheng Zhang, Yi Gao, Mingchen Liu, Chen Zhang, Xiaohong School of Big Data & Software Engineering Chongqing University Chongqing China Department of Computer Science and Engineering University at Buffalo SUNY New York United States Department of Radiology First Affiliated Hospital of Army Medical University Chongqing China

Medical image classification plays a vital role in AI-aided medical diagnosis and is often addressed as a Multiple Instance Learning (MIL) issue (i.e., each sample is a bag of instances). For medical images, the disease area or the discriminative area is usually smaller than the whole tissue. In other words, most of the instances in a bag are irrelevant and could interfere with the bag label inference. To address this issue, we add an instance representative selection process before MIL and propose a novel MIL approach named Dual Space Multiple Instance Representative Learning (DSMIRL). DSMIRL consists of two core steps, Adaptive Instance Representative Selection (AIRS) and Multiple Instance Representative Learning (MIRL). In AIRS, the instances in the same bag are grouped into different sub-bags via clustering, and only one sub-bag is selected as the final collection of instance representatives by ranking the maximum instance predictions of sub-bags, thus adaptively filtering out the irrelevant instances. In MIRL, we perform aggregations on the selected instance representatives in label and feature spaces to further exploit the complementary information of the two spaces. Finally, these two steps are iteratively conducted in each iteration to optimize all modules of DSMIRL progressively. Extensive experiments on five standard MIL benchmarks and two medical image datasets demonstrate the promising performance of DSMIRL over the state-of-the-art MIL approaches. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Benchmarking

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