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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Binyan Ren, Ao Zhang, Zihao Duan, Moming Liu, Duo Tan, Yujuan Zhong, Kan College of Computer Science Chongqing University Chongqing China School of Big Data Software & Engineering Chongqing University Chongqing China Institute of Data Science National University of Singapore Singapore

ISBN: (纸本)9798350368741

With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to reduce the size of unimodal models, specific challenges remain for Multimodal Sentiment Analysis (MSA) models. First, existing techniques prune fixed original models into sparse models, while our findings indicate that different model architectures of identical size yield varying performance outcomes. Second, prior studies fail to explore the unique characteristics of MSA models, resulting in suboptimal pruning performance. To address these challenges, we propose MPNAS, a unified pruning framework via Neural Architecture Search (NAS) for MSA models. Specifically, we formulate pruning as a NAS problem and analyze MSA model characteristics to guide the subnet search. We conduct an initial coarse-grained NAS on the original model, expanding the search space slightly to identify suitable subnets that enhance pruning rates and accuracy. Subsequently, we refine coarse-grained subnets in a fine-grained NAS stage, where MSA model characteristics guide the search process. Extensive experiments on three representative datasets demonstrate the superiority of our approach over existing methods. © 2025 IEEE.

关键词： Multimodal Compression NAS

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Belief Kullback-Leibler Divergence-based Dynamical Complexity Analysis for Biological Systems 10

Belief Kullback-Leibler Divergence-based Dynamical Complexit...

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10th International Conference on Information Systems and Computing Technology, ISCTech 2022

作者： Zhang, Lang Xiao, Fuyuan School of Big Data and Software Engineering Chongqing University Chongqing401331 China

ISBN: (纸本)9798350332933

Physical states can be fed back by the physiological signal as it contains the information of healthy systems. Specifically, dynamical time series is valuable data to reflect the pathological states by means of measuring the complexity of time series. Nevertheless, how to measure the complexity of physical signal is still an open issue. In this paper, a novel complexity measurement algorithm based on belief Kullback-Leibler (KL) divergence, called BKLDC, is proposed to calculate the complexity of biological systems time series. BKLDC algorithm firstly truncates biological systems signal into several slices with boundaries. In this case, the interval and border values are taken into account, which can differentiate the time series data. Then, based on the Dempster-Shafer (D-S) evidence theory, the format of time series data is converted to basic probability assignments (BPA). So, the feature of time series is obtained effectively as BKLDC algorithm considers the uncertainty of data signal. Hence, the complexity of physiological signal can be obtained by figuring out the divergence of BPAs. The divergence reflects discrepancy of BPAs, which presents the inherent complexity of data to some extent. In addition, an implementation in cardiac interbeat interval time series demonstrates the out-performance of BKLDC algorithm for pathological states analysis. © 2022 IEEE.

关键词： Biological systems

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A Large-Capacity Information Hiding Algorithm Based on the Fractal Carrier 4

A Large-Capacity Information Hiding Algorithm Based on the F...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Tao, Xuejiao Leng, Yahong Yang, Qian Bai, Sen Chongqing Institute of Engineering School of Software Chongqing China Chongqing Institute of Engineering Office of Teaching Affairs Chongqing China Chongqing Institute of Engineering School of Big Data and Artificial Intelligence Chongqing China

ISBN: (纸本)9798350361643

Information security is a pressing concern in the era of the Internet. Within the realm of information security, significant research focus has been placed on information hiding technology. Researchers strive to enhance both the information hiding capacity and the anti-attack capability. This paper explores the concept of concealing information within fractal images. Fractal images possess a notable attribute of being affine transformed through simple IFS code parameters, thereby generating unique fractal representations. The simplicity of the transmission parameters facilitates an effortless restoration of the carrier. By employing straightforward affine or iterative parameters, the recipient can successfully extract the originally embedded carrier, subsequently unveiling the concealed information. To further augment the security of the information hiding process, an encryption strategy may be implemented to transmit sensitive data. In this scenario, the secret carrier serves as the public key, while the fractal parameter information functions as the private key, thereby intensifying the complexity of steganography analysis. Consequently, this paper introduces a high-capacity fractal carrier information hiding algorithm, which effectively embeds information within fractal images, amplifies and adapts fractal parameters based on requirements, and enhances the overall capacity of the fractal carrier. Exploiting fractal images for information hiding purposes conveys advantages such as minimized reliance on key information and exceptional concealment abilities, thus bolstering the secrecy of hidden data, expanding the storage capacity, and fortifying the resistance against steganalysis. © 2024 IEEE.

关键词： Fractals

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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Intelligent Blocking and Prevention of SARS-CoV-2 Based on Evolutionary Reinforcement Learning 13

Intelligent Blocking and Prevention of SARS-CoV-2 Based on E...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： He, Chengwei Hong, Huaping Ma, Lijia Zhu, Qingling Bai, Yuan College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

In recent years, the global repercussions of SARS-CoV-2 and its variants have posed significant challenges to various areas, including the economic order, transportation, healthcare, and education, and the mitigation and prevention of SARS-CoV-2 and other infectious diseases have received much attention. This paper tries to develop a rational and effective intervention strategy in response to the dynamic evolution of the SARS-CoV-2 epidemic. To address this issue, we first use the SEIR model to analyze the population distribution under the SARS-CoV-2 epidemic, and then integrate a deep Q-neural network (DQN) to reflect an economic quantification model contextualized within the SARS-CoV-2 scenario. Next, we propose an evolutionary deep reinforcement learning to generate an optimal and adaptive intervention strategy, by optimizing the weight sequence of the DQN. The experimental results show the effectiveness of the proposed method in maximizing economic benefits, while ensuring the uninterrupted functionality of medical institutions. Importantly, the learned intervention strategy exhibits the applicability to other infectious diseases. © 2024 IEEE.

关键词： Deep reinforcement learning

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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big data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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An Automatic Segmentation Fitting Method for Freehand Sketches Based on Greedy Strategy 7th

An Automatic Segmentation Fitting Method for Freehand Sketch...

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7th International Conference on Computing, Control and Industrial engineering, CCIE 2023

作者： Zhang, Xiuli Chen, Lei Wan, Zhong College of Information and Intelligence Engineering Zhejiang Wanli University Ningbo China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9789819927296

In this paper, we propose an automatic segmentation and fitting method for hand-drawn sketches based on greedy strategy. This method first resamples and smoothly filter the input sketch stroke sequence, and then automatically segments the hand-drawn sketches based on the greedy strategy. The maximum Euclidean distance is found as the segmentation point in the sketch strokes before each iteration starts, the proximity error of sub-strokes between adjacent segmentation points is calculated, the parameters of sub-strokes whose proximity error less than the threshold are stored, the sub-strokes whose proximity error great than the threshold are recorded to be repartitioned in next iteration processing until proximity error of all the sub-strokes less than the threshold. The least squares method is used to fit the sub-strokes one by one, and removes the redundant segmentation points by relying on error control to maximize the retention of the original information of the hand-drawn sketches while obtaining the canonical geometric This provides the basis for recognition, semantic understanding and retrieval from sketches to normative design drawings. The experiments show that the method has good segmentation fitting effect for all kinds of figures, and the fitting residuals are controllable. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Errors

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Improving Adaptive Runoff Forecasts in data-Scarce Watersheds Through Personalized Federated Learning 27th

Improving Adaptive Runoff Forecasts in Data-Scarce Watershe...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xie, Zaipeng Zhang, Xiangqin Wang, Yunfei Jie, Xuanyao Fang, Wenhao Cai, Yanping Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031781827

Runoff forecasting plays a crucial role in water resource management and flood mitigation, but it often faces significant challenges due to data deficiency and decentralized datasets. Inadequate hydrological data in many watersheds hinders the development of accurate prediction models, while organizational barriers and concerns about data privacy lead to information silos, preventing effective collaborative modeling. To address these issues, we propose FedHydroDSW, a novel personalized federated learning framework for runoff forecasting in data-scarce watersheds. Our approach enables decentralized model training across multiple organizations without requiring direct data sharing, thus preserving data privacy. FedHydroDSW incorporates unique model similarity metrics and parameter adaptation methods, utilizing insights from data-rich areas to refine forecasts in data-scarce regions. Experiments on the CAMELS dataset show that FedHydroDSW significantly outperforms standalone and conventional federated learning methods, with an average 30.1% increase in NSE, and reductions of 28% in RAE and 26.6% in RMSE across multiple data-scarce basins. By enabling secure cross-silo learning, our FedHydroDSW strategy paves the way toward more equitable runoff forecasting globally. The customized training and pattern recognition techniques represent major progress in deploying federated intelligence for impactful hydrological predictions. Our code is publicly available at https://***/xyjie37/FedHydroDSW. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Research on Automation Operation and Maintenance Service Platform of Information System

Research on Automation Operation and Maintenance Service Pla...

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2023 International Conference on Computer Science and Automation Technology, CSAT 2023

作者： Chen, Yunqi Xu, Jin China Coal Technology Engineering Group Chongqing Research Institute Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350371437

In view of the problems exists in the operation and maintenance work of information system, such as variety of hardware, complexity of business system structure, lack of automatic monitor and troubleshooting measures. In order to improve efficiency of operation and maintenance, and to keep healthy operation of the information system, a automation operation and maintenance service platform of coal mine information systems was designed. The platform uses automated operation and maintenance tools Zabbix, real-time monitor running status and resource consumption of the servers, network devices, and applications, and uses variety of alarm methods to provide fault alert. The fault diagnosis and repair strategy is designed to achieve active operation and maintenance. The knowledge base of fault disposal methods based on Elasticsearch was designed, intelligent matching and push fault diagnosis and handle method to the maintainers, which improves the efficiency of fault repair. © 2023 IEEE.

关键词： Coal mines

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Personalized Federated Learning with Feature Alignment via Knowledge Distillation 21st

Personalized Federated Learning with Feature Alignment via...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Qi, Guangfei Qu, Zhihao Lyu, Shen-Huan Jia, Ninghui Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9789819601189

Personalized Federated Learning (PFL) has gained significant attention for its ability to handle heterogeneous data effectively. Parameter decoupling is a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature information and ignores global information, the generalization ability of the feature extractor is limited. To improve the performance of the local model, a feasible approach is to make the local feature extractor more generalized. However, prior work requires the transmission of additional feature data beyond the transmission of model parameters, which leads to privacy leakage and higher communication overhead. To address these shortcomings, we propose a PFL algorithm with feature alignment via knowledge distillation, named PFAKD. PFAKD enhances the training of local feature extractors by explicitly aligning each sample’s local features with global features, providing more detailed guidance. Meanwhile, it avoids additional communication overhead and the risk of privacy leakage. We conduct extensive experiments in heterogeneous data scenarios. PFAKD outperforms other state-of-the-art methods by up to 4.35% in terms of model accuracy. Our code is available at https://***/fei0829/PFAKD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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