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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yunxiao He Chunqiang Hu Yuwen Pu Jiahao Chen Xingwang Li School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Zhejiang China College of Physics and Electronic Information Engineering Henan Polytechnic University Henan China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Split Learning is an emerging Distributed Machine Learning which allows clients and the server to collaborative train a model by splitting it successively. This approach not only addresses data scarcity, but also enhances data privacy while reducing communication and computation overhead. However, existing research on the security of Split Learning primarily focuses on privacy protection, neglecting model security. To fill this gap, we propose AdvUSL, a targeted adversarial attack method against U-shaped Split Learning. AdvUSL trains a surrogate model inexpensively with a small amount of labeled data; matches intermediate embedding and labels to infer clients' labels, and uses the matched embeddings as anchors to conduct targeted adversarial attacks. Comprehensive experiments demonstrate that AdvUSL outperforms traditional white-box adversarial attacks, validating the effectiveness of our method.

关键词： Training Privacy Computational modeling Machine learning Computer architecture Smart systems Security Servers Protection Glass box

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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups 18th

Visitors Vis: Interactive Mining of Suspected Medical Insura...

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18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Dong, Rixin Liu, Hanlin Guo, Xu Zhou, Jiantao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9789819996360

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health insurance

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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Congress on Evolutionary Computation

作者： Xunfeng Wu Songbai Liu Junkai Ji Lijia Ma Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

关键词： Training Heuristic algorithms Computational modeling Sociology Clustering algorithms Evolutionary computation Benchmark testing

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Automatic Telephone Interview Survey System 9

Automatic Telephone Interview Survey System

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9th International Conference on Orange Technology, ICOT 2021

作者： Chen, Che-Wen Cheng, Yao-Hsiang Lee, Tsung-Yen Chuang, Shu-Wei Hsu, Pin-Yang Tseng, Shih-Pang Wang, Jhing-Fa National Cheng Kung University Department of Electrical Engineering Tainan Taiwan Changzhou College of Information Technology School of Software and Big Data China

ISBN: (纸本)9781665478427

Nowadays, governments or enterprises often use questionnaires to investigate people's views on issues or products, and telephone interviews are one of the most commonly used methods for questionnaire surveys. The proposed system is mainly divided into four parts. The first part is pre-processing, according to the questionnaire to be interviewed to generate the data needed in the follow-up dialogue in advance. The second part is to build a VoIP soft phone, the purpose is to realize the function of making calls on the software side with multiple SIP lines at the same time through the SIP architecture. In addition, the system also connects the front-end softphone module with the back-end dialogue module through a dialogue interface to automatically conduct the telephone interview. The third part is multi-Turn question answering dialogue. If the call is answered, the dialogue module will actively ask the respondent according to the questions in the questionnaire, and then through hybrid answer matching to determine the option matched by the respondent's answer. Finally, the dialogue module decides the question to be asked in the next turn to achieve the effect of multi-Turn dialogue. The fourth part is call detail record and table generation, which is responsible for recording the call status and answering situation during the telephone interview. In addition, the system performs post-processing according to the telephone interview results of all lines after the execution, so that we can quickly understand the situation of the telephone interview survey. © 2021 IEEE.

关键词： Surveys

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A Stochastic Proximal WMMSE for Ergodic Sum Rate Maximization

A Stochastic Proximal WMMSE for Ergodic Sum Rate Maximizatio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaotong Zhao Xi Wang Juncheng Wang Qingjiang Shi School of Software Engineering Tongji University Shanghai China Department of Computer Science Hong Kong Baptist University Hong Kong China Shenzhen Research Institute of Big Data Shenzhen China

We consider ergodic weighted sum rate (WSR) maximization in a massive multi-user multiple-input multiple-output system. Existing solutions iteratively minimize the average WSR based on all the historical information, and use bisection search to satisfy the power constraint at each iteration, resulting in both high storage burden and high computational complexity. In contrast, we propose an efficient stochastic proximal weighted minimum mean-square error (SPWMMSE) algorithm, which updates the precoder only based on the current single channel realization, without checking the power constraint at each iteration. Furthermore, we propose a novel proximal term to incorporate all the previous channel and surrogate function information in precoder updates. Our analysis shows that SPWMMSE converges to the stationary point of the original ergodic WSR maximization problem almost surely. Simulation results demonstrate the effectiveness of SPWMMSE over the current best alternatives.

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Smart water grid technology based on deep learning: a review

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International Journal of Wireless and Mobile Computing 2022年第3-4期22卷 338-349页

作者： Wu, Huan Peng, Lin Jiang, Feng Cheng, Shuiping Chen, Jie Yan, Linda College of Environmental Science and Engineering Tongji University Shanghai China Co. Ltd. Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Finance and Management Chongqing Business Vocational College Chongqing China College of Environment and Ecology Chongqing University Chongqing China School of Engineering University of Portsmouth Hampshire PortsmouthPO1 2UP United Kingdom

In recent years, the development of deep learning technology has made breakthroughs in computer vision, natural language processing and other fields. The Smart Water Grid (SWG) technology based on deep learning has also been a hot area of research in recent years. It has achieved better performance in the related detection and prediction of urban pipe networks. Therefore, this survey paper presents an extensive review of the application of deep learning to several different issues related to the SWG. This paper emphasises feasibility studies and summarises the state-of-the-art development in this field from a technical point of view, which consists of pipeline leakage and burst detection, contamination source identification and water demand forecasting. Furthermore, this paper also proposes challenges and future directions in these key research areas, demonstrating that deep learning-based SWG technology is still an emerging and encouraging research field. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Forecasting

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A Solution Framework for All-to-All Comparison data Distribution Strategy Based on Tabu Search 6

A Solution Framework for All-to-All Comparison Data Distribu...

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6th IEEE International Conference on Cloud Computing and big data Analytics, ICCCBDA 2021

作者： Deng, Dan Li, Leixiao Gao, Haoyu Lin, Hao Yang, Yanyan Meng, Chuang Wang, Yongsheng Li, Shaoxu College of Data Science and Application Inner Mongolia University of Technology Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot China

ISBN: (纸本)9780738105338

All-to-All Comparison (ATAC) is a key computing mode to improve computing performance and is applied in many disciplines. Through the research on the data distribution strategy of the ATAC calculation, a novel solution framework of the ATAC data distribution strategy based on tabu search is proposed for the first time. An ATAC data distribution model is constructed, and the related algorithms of the model are designed based on the tabu search, and it is implemented on MATLAB. The framework consists of three parts: 1) the driver module responsible for interacting with users;2) the load balancing module that solves the data distribution strategy that meets the load balancing situation;3) the storage optimization module that solves the data distribution strategy that meets the load balancing and optimized storage. Experiments prove that the framework for solving the ATAC data distribution strategy based on tabu search has the following advantages: A) The data distribution strategy obtained by the framework enables the nodes of the distributed system participating in the ATAC calculation to achieve load balancing and complete data localization. B) The data distribution scheme can reduce 40%-50% of the storage space in the distributed system. C) The framework is more advanced than the existing ATAC data distribution algorithm in calculation speed. © 2021 IEEE.

关键词： Tabu search

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Intelligent larval zebrafish phenotype recognition via attention mechanism for high-throughput screening

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Computers in Biology and Medicine 2025年 188卷 109892-109892页

作者： Wang, Baihua Sun, Qi Liu, Yujia Zhang, Jiheng Li, Gaozheng Wu, Sifang Zheng, Houbing Ye, Jialin Zhou, Meihua Zheng, Haisu Yu, Yongqiang Zhong, Yi Wu, Yuanzi Huang, Da Wang, Biao Weng, Zuquan College of Biological Science and Engineering Fuzhou University Fujian Fuzhou China Department of Plastic and Cosmetic Surgery The First Affiliated Hospital of Fujian Medical University Fujian Fuzhou China The Center for Big Data Research in Burns and Trauma College of Computer and Data Science/College of Software Fuzhou University Fujian China

Background: Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects is time-consuming and requires specialized knowledge. Method: We proposed a deep network model called RECNet, which combines attention mechanisms and residual structures. In terms of data processing, we applied the mixup data augmentation technique and accumulated a collection of 6805 larval zebrafish phenotype images, mostly generated from our laboratory. Our proposed model was deployed to execute two distinct tasks, including a four-classification of zebrafish phenotypes and a seven-classification involving mixed labels for abnormalities. Results: In the four-class classification task, the RECNet model achieved an accuracy of 0.949, with a mean area under the curve of 0.986 and an F1-score of 0.966. Through interpretable research, attention mechanisms enable the model to focus more accurately on regions of interest. In the mixed-label seven-classification task for anomalies, our model achieved an accuracy of 0.913 and a mean average precision value of 0.847 by employing the weighted loss function (DFBLoss). Furthermore, in a new test dataset, the RECNet model achieved accuracy rates of 0.924 and 0.876 for the two tasks, respectively. Our RECNet model was trained by orders of magnitude larger dataset than previous studies and also showed better accuracy rates. Conclusions: Our method holds promise for diverse applications within zebrafish laboratories and fields such as toxicology, providing indispensable support to scientific research. © 2025 Elsevier Ltd

关键词： Invertebrates

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing software

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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