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arXiv 2024年

作者： Zhu, Hongwei Li, Shitao Shi, Minjia Xia, Shu-Tao Solé, Patrick Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China Centrale Marseille CNRS I2M Aix Marseille University Marseille13009 France State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

The size of the Hamming distance spectrum of a code has received great attention in recent research. The main objective of this paper is to extend these significant theories to the b-symbol distance spectrum. We examine this question for various types of codes, including unrestricted codes, additive codes, linear codes, and cyclic codes, successively. For the first three cases, we determine the maximum size of the b-symbol distance spectra of these codes smoothly. For the case of cyclic codes, we introduce three approaches to characterize the upper bound for the cardinality of the b-symbol weight spectrum of cyclic codes, namely the period distribution approach, the primitive idempotent approach, and the b-symbol weight formula approach. As two by-products of this paper, the maximum number of symplectic weights of linear codes is determined, and a basic inequality among the parameters [n, k, dH(C)]q of cyclic codes is provided. © 2024, CC0.

关键词： Hamming distance

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CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Xiao Li, Chenglong Jiang, Bo Zhu, Lin Huang, Zhixiang Tian, Yonghong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China School of Artificial Intelligence Anhui University China Beijing Institute of Technology Beijing China School of Electronic and Information Engineering Anhui University China Peng Cheng Laboratory Shenzhen China School of Computer Science Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China

Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution (346 × 260) is low for practical applications. Actually, only visible cameras are deployed in many practical systems, and the newly designed neuromorphic cameras may have different resolutions. The latest neuromorphic sensors can output high-definition event streams, but it is very difficult to achieve strict alignment between events and frames on both spatial and temporal views. Therefore, how to achieve accurate tracking with unaligned neuromorphic and visible sensors is a valuable but unresearched problem. In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras. We build the first unaligned frame-event dataset CRSOT collected with a specially built data acquisition system, which contains 1,030 high-definition RGB-Event video pairs, 304,974 video frames. In addition, we propose a novel unaligned object tracking framework that can realize robust tracking even using the loosely aligned RGB-Event data. Specifically, we extract the template and search regions of RGB and Event data and feed them into a unified ViT backbone for feature embedding. Then, we propose uncertainty perception modules to encode the RGB and Event features, respectively, then, we propose a modality uncertainty fusion module to aggregate the two modalities. These three branches are jointly optimized in the training phase. Extensive experiments demonstrate that our tracker can collaborate the dual modalities for high-performance track- Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Interactive Prototype Learning and Self-Learning for Few-Shot Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Song, Yuhui Xu, Chenchu Wang, Boyan Du, Xiuquan Chen, Jie Zhang, Yanping Li, Shuo School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230051 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Engineering Case Western Reserve University Cleveland44106 United States

Few-shot learning alleviates the heavy dependence of medical image segmentation tasks on large-scale labeled data, but it shows a strong performance gap when dealing with new targets and new tasks compared with traditional deep learning. Existing few-shot medical image segmentation models mainly realize segmentation by learning the semantic class knowledge of a few known (support) samples and extending it to unknown (query) samples. However, the neglect of the differences between known and unknown samples leads to a certain deviation in the transfer of class knowledge, and the trained model tends to overfit the class knowledge of training classes, which greatly reduces the segmentation and generalization performance of few-shot model. In this work, we propose an interactive prototype learning and self-learning network to alleviate the above problems. Specifically, we propose an interactive prototype learning network to promote feature consistency between support images and query images to improve the accuracy of prototype metric learning. In addition, to avoid overfitting the training class knowledge and reduce the over-dependence of query segmentation on the class knowledge, we introduce a query features-guided self-learning network to achieve the finer segmentation of query images through its rich feature information. Our model obtained the competitive segmentation results on three multi-organ and multi-modal public datasets that show a substantial improvement in generalization ability. © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Double circulant self-dual and LCD codes over Galois rings

arXiv

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arXiv 2018年

作者： Shi, Minjia Huang, Daitao Sok, Lin Solé, Patrick School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Anhui University Hefei230601 China CNRS/LAGA University of Paris 8 2 rue de la Liberté Saint-Denis93 526 France

This paper investigates the existence, enumeration and asymptotic performance of self-dual and LCD double circulant codes over Galois rings of characteristic p2 and order p4 with p and odd prime. When p ≡ 3 (mod 4), we give an algorithm to construct a duality preserving bijective Gray map from such a Galois ring to Z2p2. Using random coding, we obtain families of asymptotically good self-dual and LCD codes over Zp2, for the metric induced by the standard Fp-valued Gray maps. Copyright © 2018, The Authors. All rights reserved.

关键词： Codes (symbols)

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A Secure and Efficient Scheme for Digital Gift Certificates

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Journal of Information Hiding and Multimedia signal processing 2015年第3期6卷 416-429页

作者： Liu, Yanjun Chang, Chin-Chen Sun, Chin-Yu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Department of Electrical Engineering Anhui University 111 Jiu-Long road Hefei230039 China Department of Computer Science and Information Engineering Asia University 500 Liou-feng Road Taichung413 Taiwan Department of Information Engineering and Computer Science Feng Chia University 100 Wen-hwa Road Taichung407 Taiwan Department of Computer Science National Tsing-Hua University 101 Kuang-Fu Road Hsinchu300 Taiwan

With the increasingly rapid developments in e-commerce, schemes for digital gift certificates have become prevalent electronic payment systems due to their practicality and simplicity. In 2002, Chan and Chang introduced the concept of digital gift certificates and proposed the first scheme associated with them. Recently, Chang and Chang (2005) proposed a more practical scheme, called DGC-US (digital gift certificates with unconstrained spending), which can reduce the computational cost of all entities involved in Chan and Chang‘s scheme, especially the computational cost of the electronic department store. However, the asymmetric encryption/decryption operations required in the DGC-US scheme still waste a significant amount of time. In this paper, we propose a novel authentication scheme for digital gift certificates based on Chebyshev chaotic maps and the blind signature. Our proposed scheme can satisfy the essential security requirements and withstand various well-known attacks. Our analyses have shown that our proposed scheme maintains all of the merits of the DGC-US scheme but is more efficient in terms of computational cost. © 2015, Ubiquitous International. All rights reserved.

关键词： Authentication

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

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

引用

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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Quantum simulation of the Fokker-Planck equation via Schrödingerization

arXiv

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arXiv 2024年

作者： Jin, Shi Liu, Nana Yu, Yue School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China

This paper studies a quantum simulation technique for solving the Fokker-Planck equation. Traditional semi-discretization methods often fail to preserve the underlying Hamiltonian dynamics and may even modify the Hamiltonian structure, particularly when incorporating boundary conditions. We address this challenge by employing the Schrödingerization method - it converts any linear partial and ordinary differential equation with non-Hermitian dynamics into systems of Schrödinger-type equations. It does so via the so-called warped phase transformation that maps the equation into one higher dimension. We explore the application in two distinct forms of the Fokker-Planck equation. For the conservation form, we show that the semi-discretization-based Schrödingerization is preferable, especially when dealing with non-periodic boundary conditions. Additionally, we analyze the Schrödingerization approach for unstable systems that possess positive eigenvalues in the real part of the coefficient matrix or differential operator. Our analysis reveals that the direct use of Schrödingerization has the same effect as a stabilization procedure. For the heat equation form, we propose a quantum simulation procedure based on the time-splitting technique, and give explicitly its corresponding quantum circuit. We discuss the relationship between operator splitting in the Schrödingerization method and its application directly to the original problem, illustrating how the Schrödingerization method accurately reproduces the time-splitting solutions at each step. Furthermore, we explore finite difference discretizations of the heat equation form using shift operators. Utilizing Fourier bases, we diagonalize the shift operators, enabling efficient simulation in the frequency space. Providing additional guidance on implementing the diagonal unitary operators, we conduct a comparative analysis between diagonalizations in the Bell and the Fourier bases, and show that the former generally exhibit

关键词： Hamiltonians

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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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Fil-Cad: A Fault Detection Method for Iiot Based on Federated Incremental Learning with Class Accuracy Distillation

SSRN

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SSRN 2024年

作者： Liu, Yanhua Fang, Wenyu Huang, Wei Wang, Xiaofeng Zhao, Baokang Liu, Ximeng College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer National University of Defense Technology Changsha410073 China

Fault detection is a research topic attracting much attention in IIoT. However, the continuous accumulation and growth of IIoT data leads to the catastrophic forgetting problem in the fault detection model and data heterogeneity, which creates a bottleneck for the performance and generalization of federated learning in fault detection. To improve the fault detection performance of federated learning models in data incremental scenarios, we propose a federatedincremental-learning-based fault detection method for IIoT, namely FIL-CAD. Firstly, comparing the knowledge distillation, the accuracies of different data classes in the global and local models are chosen as the model lightweight learning features, and the loss function is weighted. Secondly, we introduce the concept of data learning depth. It computes different model weights in the aggregation based on their degrees of data sample learning and model training participation and obtains the global model through weighted aggregation. Additionally, FIL-CAD supports the combination of differential privacy to protect data privacy. The proposed method is compared with four benchmark methods on four datasets, which include CWRU and MFPT bearing datasets. Then, the experimental results show that FIL-CAD performs optimally in accuracy and F1-Score, and the average accuracy is improved by 1.32%-18.52%, demonstrating the superiority of FIL-CAD. © 2024, The Authors. All rights reserved.

关键词： Fault detection

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3d Surface Detection and Segmentation from Point Clouds Via Quadric Fits Based on Dbscan Clustering

SSRN

引用

SSRN 2024年

作者： Xie, Tingting Chen, Hui Liu, Wanquan Zhou, Rongyu Li, Qilin College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University BentleyWA Australia

Extracting surfaces from 3D point clouds is significant in reconstructing and transforming these discrete points into their corresponding models. Scannedpoint clouds are often accompanied by noise, and the existing methods mainly rely on local feature similarities for surface extractions. Errors in estimatingthe feature information may lead to incorrect surface detection. In this paper we propose a surface extraction and boundary detection method based onclustering technique. The method can be described in three steps: In the first step, a normal correction is carried out using the information from theneighbourhood of points with sharp features. The second step is to cluster the points that meet the coplanar condition of the local quadric surface (LQS).In the third step, surface merger is performed by merging the local surfaces satisfying the merging conditions. Experimental validation is carried outto determine the effectiveness of the proposed method. The experimental results show improved surface extraction accuracy of the proposed methodin comparison to RANSAC, RG, LCCP, C2NO and HT methods. © 2024, The Authors. All rights reserved.

关键词： Merging

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