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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huo, Yejing Huang, Guoheng Cheng, Lianglun He, Jianbin Chen, Xuhang Yuan, Xiaochen Zhong, Guo Pun, Chi-Man Guangdong University of Technology School of Computer Science and Technology China Huizhou University School of Computer Science and Engineering China University of Macau Department of Computer and Information Science China Macao Polytechnic University Faculty of Applied Sciences China Guangdong University of Foreign Studies School of Information Science and Technology China

ISBN: (纸本)9798350386226

Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data;the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration;and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN's robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with incom

关键词： Patient treatment

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A Survey on Low-Dose CT Image Denoising Algorithms in IoT 4th

A Survey on Low-Dose CT Image Denoising Algorithms in IoT

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4th International Conference on IoT and Big Data Technologies for Health Care, IotCare 2023

作者： Wang, Mengke School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China

ISBN: (纸本)9783031900839

In recent years, with the development of medical imaging technology, low-dose computed tomography (CT) has become popular in clinical medicine. However, low-dose CT is often disturbed by noise, and the image becomes blurred, which is not conducive to diagnosis. Therefore, low-dose CT image denoising becomes an urgent problem. With the development and maturity of the Internet of Things (IoT), IoT is applied to more and more fields. It is worth mentioning that the combination of IoT technology and low-dose CT image denoising has made a significant contribution to the area of image denoising. For example, IoT provides more selectable datasets for low-dose CT image denoising. It makes real-time and automated processing of the denoising process possible, improving the efficiency of diagnosis and advancing the field of medical imaging. This paper focuses on the development of low-dose CT image denoising based on IoT technology. It describes the application of IoT in medical imaging and discusses the noise characteristics and classification of CT images. Different methods of IoT-based denoising of low-dose CT images are further discussed, as well as some current problems. Finally, new directions for future research are proposed in the hope that by combining different algorithms and techniques, the quality of low-dose CT images can be further improved to enhance a more solid guarantee for medical diagnosis. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Image denoising

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Multi-modal Negative Sampling for Recommendation with User Interest 25

Multi-modal Negative Sampling for Recommendation with User I...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Jianfang Wang Menghao Liang Anunobi Victor Chibueze School of Computer Science and Technology Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9798400713316

Multi-modal Recommender Systems (MRSs) integrate various types of data, such as images, text, and videos, to deliver more accurate and personalized recommendations to users. However, traditional Multi-modal recommendation methods typically rely on random negative sampling strategies, overlooking the latent semantic relationships between items. Furthermore, these methods often suffer from the loss of user interest information during the denoising process. To address these challenges, this paper proposes a novel approach called Multi-modal Negative Sampling for Recommendation with User Interest (MNS-UI). To correct negative sample selection, MNS-UI introduces a dynamic negative sampler that incorporates Multi-modal information and allow for more precise capture of item semantic relationships. Additionally, we propose a reliable graph construction technique that preserves trusted edges in the user-item interaction graph, thereby mitigating the loss of user interest during the denoising process. Furthermore, we design a loss function to align Multi-modal features with item IDs, which significantly enhances the system's recommendation performance. Experimental results demonstrate that MNS-UI significantly outperforms state-of-the-art methods across three real-world datasets, with an average improvement of 2.4% in Recall@20, 6.7% in NDCG@20, and a reduction of 87.3% in the number of training epochs required for convergence.

关键词： multi-modal

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Research progresses on cathode materials of aqueous zinc-ion batteries

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Journal of Energy Chemistry 2024年第10期97卷 237-264,I0005页

作者： Zengyuan Fan Jiawei Wang Yunpeng Wu Xuedong Yan Dongmei Dai Xing-Long Wu Jilin Provincial Science and Technology Innovation Centre of Optical Materials and Chemistry Jilin Provincial International Joint Research Center of Photo-functional Materials and ChemistrySchool of Chemistry and Environmental EngineeringChangchun University of Science and TechnologyChangchun 130022JilinChina PMOE Key Laboratory for UV LightEmitting Materials and Technology School of PhysicsNortheast Normal UniversityChangchun 130024JilinChina Chongqing Research Institute Changchun University of Science and TechnologyChongqing 401135China Institute of Energy Storage and Conversion Technology College of Chemical EngineeringNingbo PolytechnicNingbo 315800ZhejiangChina School of Chemistry and Chemical Engineering Henan Normal UniversityXinxiang 453007HenanChina

Electrochemical energy storage and conversion techniques that exhibit the merits such as high energy density,rapid response kinetics,economical maintenance requirements and expedient installation procedures will hold a pivotal role in the forthcoming energy storage technologies *** recent years,aqueous zinc-ion batteries(AZIBs)have garnered substantial attention as a compelling candidate for large-scale energy storage systems,primarily attributable to their advantageous featu res encompassing cost-effectiveness,environmental sustainability,and robust safety ***,one of the primary factors hindering the further development of AZIBs originates from the challenge of cathode ***,the three mainstream types of mainstream cathode materials,in terms of manganese-based compounds,vanadium-based compounds and Prussian blue analogues,surfer from the dissolution of Mn~(2+),in the low discharge voltage,and the low specific capacity,*** strategies have been developed to compensation the above intrinsic defects for these cathode materials,including the ionic doping,defect engineering,and materials ***,this review first provides a systematic summarization of the zinc storage mechanism in AZIBs,following by the inherent merit and demerit of three kind of cathode materials during zinc storage analyzed from their structure characteristic,and then the recent development of critical strategies towards the intrinsic insufficiency of these cathode *** this review,the methodologies aimed at enhancing the efficacy of manganese-based and vanadium-based compounds are emphasis ***,the article outlines the future prospective directions as well as strategic proposal for cathode materials in AZIBs.

关键词： Aqueous zinc-ion batteries Cathode materials Optimization strategies

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Joint Covert and Secure Communication for SWIPT Assisted CNOMA Systems

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IEEE Internet of Things Journal 2025年第12期12卷 20406-20419页

作者： Huang, Gaojian Lei, Yuxin Li, Xingwang Khan, Wali Ullah Wang, Gongpu Nallanathan, Arumugam Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Luxembourg City1855 Luxembourg Beijing Jiaotong University Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing100044 China Institute of Internet of Things Wuxi214111 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

With the rapid advancement of physical-layer security technology, the covert and secure communication has become crucial in safeguarding wireless communication systems. In this paper, we propose a joint covert and secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) assisted cooperative non-orthogonal multiple access (CNOMA) systems. In the CNOMA system, a greedy relay transmits the confidential information to the far user (Carol), with the assistance of the near user (Bob). Meanwhile, as a SWIPT node, Bob is self-sustained by harvesting energy from relay. What's more, a warden (Alice) and non-colluding eavesdroppers (Eves) always attempt to detect and capture the confidential information, respectively. To counteract the attacks from Alice and Eves, a jamming-assisted scheme is employed. For the proposed system model, we derive closed-form expressions for the detection error probability (DEP) and the average minimum detection error probability (AMDEP) of Alice. Additionally, closed-form expressions for the outage probability (OP) of users and the intercept probability (IP) of Eves are obtained. Furthermore, to maximize the effective covert rate (ECR) of Carol, an optimization problem is formulated, subject to covertness and security constraints. Numerical results are provided to demonstrate the impact of the system parameters on covert and secure performance, with the results showing perfect agreement with the theoretical analysis. © 2014 IEEE.

关键词： Inductive power transmission

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Two-Stream Reverse Distillation for Anomaly Detection 10

Two-Stream Reverse Distillation for Anomaly Detection

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Ding, Yunkang Tang, Hao Huang, Guoheng Cheng, Lianglun Pun, Chi-Man Li, Yan School of Computer Science and Technology Guangdong University of Technology Guangzhou China University of Macau Faculty of Science and Technology China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

ISBN: (纸本)9798331510138

Traditional industrial anomaly detection methods based on reverse distillation have been extensively studied due to their simplicity and efficiency. However, the intermediate embedding features generated by pre-trained teacher models often fail to provide precise reconstruction information to the student model, resulting in poor performance when detecting small defects or handling anomalies with significant errors. To address these limitations, we propose a Two-Stream Reverse Distillation (TSRD) framework that integrates self-similarity and edge information into the distillation process. In TSRD, the intermediate embedding features are divided into two streams: one employs a Regional Self-Similarity Encoder Module(RSSE) to capture intrinsic geometric features, while the other leverages an Shape Feature Enhancement Module (SFE) to enhance boundary perception. This design effectively harnesses local relational features and critical detail enhancements, significantly improving the accuracy and robustness of anomaly detection. Experimental results on the MVTec AD dataset demonstrate the effectiveness of the model and highlight its competitive performance. Source code will be available at https://***/neuraaal/TSRD. © 2024 IEEE.

关键词： Unsupervised learning

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Prediction of economic benefits of market digital transformation based on federal learning algorithm

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International Journal of Sustainable Development 2023年第3-4期26卷 190-205页

作者： Zhang, Lu Chen, Wanqing Nie, Hengzhi Department of Economics and Trade Henan Polytechnic Institute Hennan Nanyang473001 China Department of Computer Science and Technology Huazhong University of Science and Technology Wuhan430073 China Zhengzhou Fengyang Foreign Language School Hennan Zhengzhou450000 China Fuzhou350003 China

In order to solve the problems of low prediction accuracy and long prediction time existing in traditional methods for predicting the economic benefits of market digital transformation, a method for predicting the economic benefits of market digital transformation based on federal learning algorithm is proposed. Obtain the prediction indicators and build a prediction indicator system for the economic benefits of market digital transformation. Pre-process the indicator data using the principal component analysis method, build a sample dataset, build a federal optimisation algorithm using the random gradient descent method, establish a minimum loss function, build a prediction model for the economic benefits of market digital transformation, and output the prediction results. The experimental results show that the prediction accuracy of the proposed method can reach more than 95%, and the prediction time is always kept within 3 s, with good prediction effect and efficiency. © 2023 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Learning algorithms

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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YOLOT: Multi-scale and diverse tire sidewall text region detection based on You-Only-Look-Once(YOLOv5)

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Cognitive Robotics 2024年第1期4卷 74-87页

作者： Liu, Dehua Tian, Yongqin Xu, Yibo Zhao, Wenyi Pan, Xipeng Ji, Xu Yang, Mu Yang, Huihua School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China School of Information Engineering Henan Institute of Science and Technology Xinxiang453003 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Industrial Technology Co. Ltd Beijing102600 China

Driving safety is significant to building a people-oriented and harmonious society, Tires are one of the key components of a vehicle and the character information on the tire sidewall is critical to their storage and usage. However, due to the diverse and differentiated features of typographic fonts, simultaneously extracting comprehensive characteristics is an extremely challenging task. To effectively break through these performance degradation issues, a multi-scale tire sidewall text region detection algorithm based on YOLOv5 is introduced, called YOLOT, which fuses comprehensive feature information in both width and depth directions. In this study, we firstly propose the Width and Depth Awareness (WDA) module in the text region detection field and successfully integrated it with the FPN structure to form the WDA-FPN. The purpose of WDA-FPN is to empower the network to capture multi-scale and multi-shape features in images, thereby augmenting the algorithm's abstraction and representation of image features and concurrently boosting its robustness and generalization performance. Experimental findings indicate that, compared to the primary algorithm, YOLOT achieves significant improvement in accuracy, providing a higher detection reliability. The dataset and code for the paper are available at: https://***/Cloude-dehua/YOLOT. © 2024 The Authors

关键词： Tire sidewall Multi-scale Diverse characters YOLO Character detection

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Local symmetry distortion induced anomalous thermal conduction in thermoelectric layered oxyselenides

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npj Computational Materials 2023年第1期9卷 658-665页

作者： Bin Wei Jiale Li Yueyang Yang Wang Li Zhifang Zhou Yunpeng Zheng Wenyu Zhang Yifan Chai Zheng Chang Ce-Wen Nan Yuan-Hua Lin Henan Key Laboratory of Materials on Deep-Earth Engineering School of Materials Science and EngineeringHenan Polytechnic UniversityJiaozuo 454000PR China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua UniversityBeijing 100084PR China School of Materials and Metallurgy Inner Mongolia University of Science and TechnologyBaotou 014010PR China School of Naval Architecture and Maritime Zhejiang Ocean UniversityZhoushan 316022PR China

Polyhedral distortion,associated closely with the atomic arrangement and interatomic interactions,drives many unique behaviors in solids,such as phase transition and negative thermal *** thermoelectric heteroanionic oxides,the anionic polyhedra are widely present,but their effect on thermal transport is rarely ***,we report an anomalous thermal conduction induced by local symmetry distortion in layered oxyselenides via solving the Boltzmann transport equation based on first-principles *** found interestingly that lighter BiCuSeO exhibits lower thermal conductivity than heavier *** to the different distorted degrees of CuSe4 and AgSe4 tetrahedrons,Cu prefers the in-plane vibration,while Ag has more tendency of out-of-plane ***,the heat-carrying phonons dominated by the rattling-like vibration of Cu are significantly suppressed,resulting in lower thermal conductivity of *** study highlights the importance of polyhedral distortion in regulating thermal conduction in layered heteroanionic materials.

关键词： distortion thermal conduction

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