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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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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Prototype Guided Personalized Federated Intrusion Detection system

Prototype Guided Personalized Federated Intrusion Detection ...

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Smart World Congress (SWC), IEEE

作者： Long Cheng Huiru Yan Hanlin Zhou Ying Wang Haichuan Tang Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Chinese Academy of Sciences Institute of Computing Technology Beijing China AI Lab of CRRC Academy Beijing China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Preventing network attacks and protecting user privacy are consistently hot research topics in the Internet of Things (IoT) and edge computing fields. Recent advancements in Federated Learning (FL) have shown promise in addressing these challenges. FL allows various clients to collaboratively build an intrusion detection system (IDS) without sharing their private data. However, most existing methods train a single intrusion detection model for all clients and assume that the training and test data distribution are identical, leading to unsatisfactory detection accuracy and generalization abilities in practice. To overcome these challenges, this paper introduces a prototype-guided personalized FL approach named PG-FedIDS. We propose two novel mechanisms within this method. Firstly, we utilize class prototypes as auxiliary information carriers to generate personalized models for each client, rather than generating a single global model as in previous works. Secondly, we propose a prototype-guided ensemble learning strategy, which can leverage the global knowledge in prototypes to enhance detection accuracy and generalization abilities for each client. We conduct extensive experiments on two benchmark datasets with different evaluation test settings. The results demonstrate that our PG-FedIDS achieves promising detection accuracy and consistently outperforms other FL baselines.

关键词： Training Privacy Accuracy Federated learning Image edge detection Prototypes Intrusion detection Data models Ensemble learning Internet of Things

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LOG-LIO2: A LiDAR-Inertial Odometry with Efficient Uncertainty Analysis

arXiv

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arXiv 2024年

作者： Huang, Kai Zhao, Junqiao Lin, Jiaye Zhu, Zhongyang Song, Shuangfu Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncertainty is often overlooked by the community. Moreover, the existing uncertainty propagation methods suffer from computational inefficiency. This paper proposes a comprehensive point uncertainty model that accounts for both the uncertainties from LiDAR measurements and surface characteristics, along with an efficient local uncertainty analytical method for LiDAR-based state estimation problem. We employ a projection operator that separates the uncertainty into the ray direction and its orthogonal plane. Then, we derive incremental Jacobian matrices of eigenvalues and eigenvectors w.r.t. points, which enables a fast approximation of uncertainty propagation. This approach eliminates the requirement for redundant traversal of points, significantly reducing the time complexity of uncertainty propagation from O(n) to O(1) when a new point is added. Simulations and experiments on public datasets are conducted to validate the accuracy and efficiency of our formulations. The proposed methods have been integrated into a LIO system, which is available at https://***/tiev-tongji/LOG-LIO2. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition

arXiv

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arXiv 2023年

作者： Zhao, Junqiao Zhang, Fenglin Cai, Yingfeng Tian, Gengxuan Mu, Wenjie Ye, Chen Feng, Tiantian Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to retrieve frames from a geotagged database that are located at the same place as the query frame. To improve the robustness of VPR in perceptually aliasing scenarios, sequence-based VPR methods are proposed. These methods are either based on matching between frame sequences or extracting sequence descriptors for direct retrieval. However, the former is usually based on the assumption of constant velocity, which is difficult to hold in practice, and is computationally expensive and subject to sequence length. Although the latter overcomes these problems, existing sequence descriptors are constructed by aggregating features of multiple frames only, without interaction on temporal information, and thus cannot obtain descriptors with spatio-temporal discrimination. In this paper, we propose a sequence descriptor that effectively incorporates spatio-temporal information. Specifically, spatial attention within the same frame is utilized to learn spatial feature patterns, while attention in corresponding local regions of different frames is utilized to learn the persistence or change of features over time. We use a sliding window to control the temporal range of attention and use relative positional encoding to construct sequential relationships between different features. This allows our descriptors to capture the intrinsic dynamics in a sequence of frames. Comprehensive experiments on challenging benchmark datasets show that the proposed approach outperforms recent state-of-the-art methods. The code is available at https://***/tiev-tongji/Spatio-Temporal-SeqVPR. Copyright © 2023, The Authors. All rights reserved.

关键词： Query processing

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Structure Enhanced Prototypical Alignment for Unsupervised Cross-Domain Node Classification

SSRN

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SSRN 2023年

作者： Liu, Meihan Zhang, Zhen Ma, Ning Gu, Ming Wang, Haishuai Zhou, Sheng Bu, Jiajun College of Computer Science Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University Hangzhou310027 China School of Software Technology Zhejiang University Ningbo315048 China School of Computing National University of Singapore 117417 Singapore

Graph Neural Networks (GNNs) have demonstrated remarkable success in graph node classification task. However, their performance heavily relies on the availability of high-quality labeled data, which can be time-consuming and labor-intensive to acquire for graph-structured data. Therefore, the task of transferring knowledge from a label-rich graph (source domain) to a completely unlabeled graph (target domain) becomes crucial. In this paper, we propose a novel unsupervised graph domain adaptation framework called Structure Enhanced Prototypical Alignment (SEPA), which aims to learn domain-invariant representations on non-IID (non-independent and identically distributed) data. Specifically, SEPA captures class-wise semantics by constructing a prototype-based graph and introduces an explicit domain discrepancy metric to align the source and target domains. The proposed SPEA framework is optimized in an end-to-end manner, which could be incorporated into various GNN architectures. Experimental results on several real-world datasets demonstrate that our proposed framework outperforms recent state-of-the-art baselines with different gains. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

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

引用

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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A twin-multi-ring channel model for Massive MIMO system

A twin-multi-ring channel model for Massive MIMO system

引用

International Symposium on Communications and Information Technologies (ISCIT)

作者： Ying Chen Yue Li Songlin Sun Xinzhou Cheng Xuqi Chen School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) China Unicom Network Technology Research Institute China Posts & Telecommunications Planning and Designing Institute Co. Ltd China

ISBN: (纸本)9781509041008

A Massive MIMO system is equipped with more antennas than traditional MIMO system. Channel modeling for MIMO system has been well studied. However, study for Massive MIMO system channel modeling has just begun. In this paper, we prove the correctness of the spherical wave assumption and the non-stationary properties on both time and array axes. Then, we propose a novel twin-multi-ring channel model for massive MIMO. This model is based on the extension of a multi-ring model for MIMO. Furthermore, we consider the appearance and disappearance of clusters by “Cluster generation algorithm”. Finally, we make the simple numerical analysis for the model, and we find the characteristic is well matching the measurements.

关键词： MIMO Antenna arrays Receiving antennas Channel models Fading channels Transmitting antennas Wireless communication

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Dynamic Bayesian network based framework for continuous speech recognition and its token passing model

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2008年第11期45卷 1882-1891页

作者： Miao, Duoqian Wang, Ruizhi Ran, Wei Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai 201804 China Department of Computer Science and Technology Tongji University Shanghai 201804 China

Recently, dynamic Bayesian network (DBN) based speech recognition has aroused an increasing interest, because of its interpretability, factorization and extensibility, which hidden Markov models (HMMs) lack. Although a huge success of the introduction of DBNs into speech recognition in many areas and DBNs has been presented with promising potential to overcome inherent limitations of HMMs in speech recognition, previous work on DBN based speech recognition mainly focuses on isolated word speech recognition, and the frameworks and recognition algorithms for DBN based continuous speech recognition are not as mature and flexible as those for HMM based one. This paper is trying to address the problems of flexibility and extensibility in DBN based continuous speech recognition. To achieve this purpose, the token passing model, which works very well to address the above problems for HMM based continuous speech recognition, is adapted for DBN based one, and a general framework based on it is proposed. In this framework, the advantages of both token passing model and DBN are combined. A novel recognition algorithm independent of the upper layer language model is proposed under this framework, and a toolkit DTK for building DBN based speech recognition under this framework is developed.

关键词： Stochastic models

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Optimal Power Allocation of Cooperative NOMA with Finite Blocklength Codes

Optimal Power Allocation of Cooperative NOMA with Finite Blo...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Siling Liu Zhengchuan Chen Jinfeng Xie Liang Liang Min Wang Yunjian Jia The Key Laboratory of Dependable Service Computing in Cyber-Physical Society Ministry of Education and School of Microelectronics and Communication Engineering Chongqing University Chongqing China Chongqing Academy of Informatioon and Communications Technology Chongqing China School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728172361

ISBN: (纸本)9781728172378

Compared with orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) has enormous advantages in enhancing fairness and spectral efficiency for downlink transmission. To further improve the performance of NOMA, cooperative NOMA (C-NOMA) has attracted attention while power allocation of C-NOMA is of great importance to ameliorate the system performance. To guarantee the requirements of ultra-reliable and low-latency communication (URLLC) nowadays, the code length is supposed to be short enough where finite blocklength (FBL) codes are adopted. This paper presents a two-user half-duplex downlink C-NOMA scheme with low latency constraint. To minimize the error probability, power allocation problems are formulated. For this purpose, the properties of power allocation to objective functions are investigated. First, the error probability is analysed in terms of signal-to-noise ratio. Afterwards, the convexity of the power allocation problems under certain conditions are shown and optimal algorithms that solve the problems are proposed. Numerical results demonstrate that the proposed C-NOMA with finite blocklength codes successfully achieve lower error probability with regard to the conventional NOMA.

关键词： Wireless communication Signal processing Conferences

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