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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zheng, Yan Wu, Song Ren, Yazhou Pu, Xiaorong Xu, Zenglin He, Lifang Yi, Zhang University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Lehigh University Department of Computer Science And Engineering Bethlehem United States Sichuan University Intelligent Interdisciplinary Research Center College of Computer Science Chengdu China

ISBN: (纸本)9798350359145

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data's complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets. © 2023 IEEE.

关键词： Ordinary differential equations

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Research on the Application of improved AMCL Algorithm in Robot Obstacle Avoidance in Logistics Sorting Scene 7

Research on the Application of improved AMCL Algorithm in Ro...

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2024 7th International Conference on Circuits, Systems and Simulation, ICCSS 2024

作者： Shang, Gongyu Gong, Gu Hu, Xiaoting Liu, Xintong Zhu, Hua School of Computer Science and Technology Jiangsu Normal University Xuzhou China School of Mechatronic Engineering China University of Mining and Technology Xuzhou China

Compared with the normal scenes, the positions of obstacles, delivery boxes and sorting stations in the logistics sorting scene are relatively fixed, what’s more, the number of static obstacles in the logistics sorting scene is large. It requires higher navigation accuracy and pose adjustment of the robot. The author proposes an Adaptive Monte Carlo Localization (AMCL) algorithm that integrates the Dynamic Window Approach (DWA) algorithm to improve the accuracy and efficiency of robots in real-time positioning, navigation in static indoor environments, and obstacle avoidance efficiency. Using this method, the data collection of lidar sensor are optimized, reducing positioning calculation. By adjusting the parameters’ value, the adaptive positioning accuracy and real-time positioning rate of the logistics robot are improved. The experimental results show that the Adaptive Monte Carlo Localization integrated with Dynamic Window Approach algorithm is about 13.682 % higher than the normal Adaptive Monte Carlo Localization algorithm in the obstacle avoidance rate, which effectively makes the number of particles collected by lidar more standardized and rational. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Robots

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Memristor-Based Genetic Algorithm for Image Restoration

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Journal of Electronic science and technology 2022年第2期20卷 149-158页

作者： Yong-Bin Yu Chen Zhou Quan-Xin Deng Yuan-Jing-Yang Zhong Man Cheng Zheng-Fei Kang the School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054 the School of Computer Science Chengdu College of University of Electronic Science and Technology of ChinaChengdu 611731

This paper explores a way of deploying the classical algorithm named genetic algorithm(GA) with the memristor. The memristor is a type of circuit device with both characteristics of storage and computing, which provides the similarity between electronic devices and biological components, such as neurons, and the structure of the memristor-based array is similar to that of chromosomes in genetics. Besides, it provides the similarity to the image gray-value matrix that can be applied to image restoration with GA. Thus, memristor-based GA is proposed and the experiment about image restoration using memristor-based GA is carried out in this paper. And parameters,such as the size of initial population and the number of iterations, are also set different values in the experiment,which demonstrates the feasibility of implementing GA with memristors.

关键词： Image image similarity

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FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Privacy-Preserving Incentive Mechanism for Crowdsensing 5

FAB-D2PIM: A Full Anonymous Blockchain-based Distributed Pri...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Cheng, Jiameng Zhang, Hongwei Tianjin University of Technology School of Computer Science and Engineering Tianjin China National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350325294

Privacy-preserving incentive mechanism in crowd-sensing systems is an ongoing research challenge, partly due to the untrusted centralized servers and sensitive data leakage. In this paper, we focus on a secure incentive way with blockchain. First, we analyze the limitations in traditional crowdsensing systems with a trusted third party (TTP) and identify three ways privacy in the incentive mechanism (IM) of crowdsensing: identity, data, and revenues. Then, we propose a novel decentralized system for IM based on a full anonymous blockchain called FAB-D2PIM. In the proposed system, we use a one-way aggregate signature scheme (Boneh et al. EUROCRYPT 2003) for identity privacy and homomorphic Public Key Encryption (Boneh et al. TCC 2005) for sensing data and reward privacy. The proposed scheme not only protects the privacy of the workers but also decreases the operation rate of the task publishing center. © 2023 IEEE.

关键词： Blockchain

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Hardware-friendly Cycle-GAN and reconfigurable design

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The Journal of China Universities of Posts and Telecommunications 2023年第4期30卷 10-20页

作者： Xie Xiaoyan Chai Miaomiao Deng Junyong Du Zhuolin Yang Kun Yin Shaorun School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'an 710121China School of Electronic Engineering Xi'an University of Posts and TelecommunicationsXi'an 710121China College of Safety Science and Engineering Xi'an University of Science and TechnologyXi'an 710054China

As a kind of generative adversarial network(GAN),Cycle-GAN shows an apparent superiority in image style *** more complicated architectures with large number of parameters and huge computational complexities,cause a big challenge in deployment on resource-constrained *** make full use of the parallelism of hardware under guaranteed image quality,this paper improves the generator network to a hardware-friendly Inception *** optimized framework is named simplified Cycle-GAN(S-CycleGAN),with greatly reduced parameters of convolution,while avoiding the degradation of image quality from structural *** with the apple2organge and horse2zebra datasets,the experiment results show that the images generated by S-CycleGAN outperform the baseline and other *** number of parameters reduces by 19.54%,memory usage cuts down by 9.11%,theoretical amount of multiply-adds(Madds)decreases by 17.96%,and floating-point operations per second(FLOPS)diminishes by 18.91%.Finally,the S-CycleGAN was mapped on the dynamic programmable reconfigurable array processor(DPRAP),which calculate the convolution and deconvolution in a unified architecture,and support flexible runtime *** prototype systems are implemented on Xilinx field programmable gate array(FPGA)XC6 VLX550 *** synthesized results show that,with 150 MHz,the hardware resource consumption is reduced by 52%compared to the recent FPGA scheme.

关键词： Cycle-GAN generator Inception hardware-friendly reconfiguration

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GEOMETRY-AWARE PROJECTIVE MAPPING FOR UNBOUNDED NEURAL RADIANCE FIELDS 12

GEOMETRY-AWARE PROJECTIVE MAPPING FOR UNBOUNDED NEURAL RADIA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Junoh Jung, Hyunjun Park, Jin-Hwi Bae, Inhwan Jeon, Hae-Gon School of Electrical Engineering and Computer Science Gwangju Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

Estimating neural radiance fields (NeRFs) is able to generate novel views of a scene from known imagery. Recent approaches have afforded dramatic progress on small bounded regions of the scene. For an unbounded scene where cameras point in any direction and contents exist at any distance, certain mapping functions are used to represent it within a bounded space, yet they either work in object-centric scenes or focus on objects close to the camera. The goal of this paper is to understand how to design a proper mapping function that considers per-scene optimization, which remains unexplored. We first present a geometric understanding of existing mapping functions that express the relation between the bounded and unbounded scenes. Here, we exploit a stereographic projection method to explain failures of the mapping functions, where input ray samples are too sparse to account for scene geometry in unbounded regions. To overcome the failures, we propose a novel mapping function based on a p-norm distance, allowing to adaptively sample the rays by adjusting the p-value according to scene geometry, even in unbounded regions. To take the advantage of our mapping function, we also introduce a new ray parameterization to properly allocate ray samples in the geometry of unbounded regions. Through the incorporation of both the novel mapping function and the ray parameterization within existing NeRF frameworks, our method achieves state-ofthe-art novel view synthesis results on a variety of challenging datasets. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Geometry

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Non-Hermitian polarization quantized by time-reversal symmetry

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science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 141-147页

作者： Jing Cheng Jinbing Hu Zhigao Hu Lin Chen Minghui Lu Songlin Zhuang Technical Center for Multifunctional Magneto-Optical Spectroscopy(Shanghai) Engineering Research Center of Nanophotonics&Advanced Instrument(Ministry of Education)Department of Physics and Electronic ScienceEast of China Normal UniversityShanghai200241China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China National Laboratory of Solid State Microstructures Science and Engineering Nanjing UniversityNanjing210093China

It is well known that in one-dimensional(1D) crystalline insulators,the electric polarization is a manifestation of Berry phase,which can not be quantized by time-reversal symmetry(TRS) as in Hermitian physics TRS does not induce any topological phase in one *** this paper we report that even though associated with complex eigenenergies a 1D non-Hermitian insulator obeying only TRS is capable of presenting quantized bulk *** underlying physical reason is unveiled:TRS guarantees the complex energies to come in pair(E,E*),and the corresponding decaying and amplifying wave functions also come in pair and have the same variation rate,hence,giving rise to a stable wannier *** electron transport is performed by means of charge pumping process,which verifies the physical mechanism *** last,we discuss the possible experimental implementation of the proposed model by means of twisted-π gauge flux.

关键词： non-Hermitian polarization time-reversal symmetry complex hopping

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Machine Learning Approaches for Predicting Heart Attacks 7

Machine Learning Approaches for Predicting Heart Attacks

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Raj, Raunak Kaur, Veerpal Kaur, Gagandeep Lovely Professional University School of Computer Science and Engineering Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350067

Heart attacks are the leading cause of deaths worldwide. However, diagnosing them can be difficult as their symptoms can resemble those of other illnesses. There are several types of heart diseases, which makes identifying symptoms challenging. Mistaking these symptoms for something else can be very harmful to the patient. Heart disease is caused by various factors such as high bp, unhealthy diet, smoking, and lack of physical activity. New approaches are required to address heart problems since more young people are suffering from heart attacks, and current medical tools and diagnostic procedures are insufficient. This research aims to investigate the use of Machine Learning (ML) algorithms in heart attack prediction, emphasizing early detection and thereby increasing life expectancy and decreasing mortality rate. This research tries to determine the most successful models for heart attack prediction by analyzing KNN, Logistic Regression, XGBClassifier and ExtraTreeClassifier approaches. It also discusses the impact of these technologies in changing healthcare practices. The work synthesizes findings from 2016 to 2023, aiming to provide a comprehensive overview of the past works done in the field of heart attack detection and also proposes a methodology to predict heart disease using machine learning. © 2024 IEEE.

关键词： Diagnosis

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DecGAN: Decoupling Generative Adversarial Network for Detecting Abnormal Neural Circuits in Alzheimer's Disease

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5050-5063页

作者： Pan, Junren Zuo, Qiankun Wang, Bingchuan Chen, C.L. Philip Lei, Baiying Wang, Shuqiang Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China Hubei University of Economics Hubei Key Laboratory of Digital Finance Innovation School of Information Engineering Hubei Internet Finance Information Engineering Technology Research Center Hubei Wuhan430205 China Central South University School of Automation Changsha410083 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China

One of the main reasons for Alzheimer's disease (AD) is the disorder of some neural circuits. Existing methods for AD prediction have achieved great success, however, detecting abnormal neural circuits from the perspective of brain networks is still a big challenge. In this work, a novel decoupling generative adversarial network (DecGAN) is proposed to detect abnormal neural circuits for AD. Concretely, a decoupling module is designed to decompose a brain network into two parts: one part is composed of a few sparse graphs that represent the neural circuits largely determining the development of AD;the other part is a supplement graph, whose influence on AD can be ignored. Furthermore, the adversarial strategy is utilized to guide the decoupling module to extract the feature more related to AD. Meanwhile, by encoding the detected neural circuits to hypergraph data, an analytic module associated with the hyperedge neurons algorithm is designed to identify the neural circuits. More importantly, a novel sparse capacity loss based on the spatial-spectral hypergraph similarity is developed to minimize the intrinsic topological distribution of neural circuits, which can significantly improve the accuracy and robustness of the proposed model. Experimental results demonstrate that the proposed model can effectively detect the abnormal neural circuits at different stages of AD, which is helpful for pathological study and early treatment. © 2020 IEEE.

关键词： Brain

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Towards more reliable evaluation in pedestrian detection by rethinking“ignore regions”

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Visual Intelligence 2024年第1期2卷 39-51页

作者： Gang Li Xiang Li Shanshan Zhang Jian Yang Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina Jiangsu Key Laboratory of Image and Video Understanding for Social Safety School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina VCIP Nankai UniversityTianjingChina

It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors *** we perform an error analysis based on the traditional evaluation strategy,we find that it produces many misleading false positives,which in fact cover occluded *** reason for this is that we usually have two kinds of annotations in the dataset:regular pedestrians(detection targets)labeled by full-body boxes and ignored pedestrians(NOT detection targets)labeled by visible *** pedestrians are labeled as an additional category termed the“ignore region”.Nevertheless,our detectors always predict a full-body box for each *** gap results in the following case:when a detector successfully predicts a full-body box for those ignored pedestrians,a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored *** becomes even more harmful as the detector improves and becomes more capable of locating occluded *** alleviate this issue,we devise a new pedestrian detection pipeline,which considers the additional visible box at both the detection and evaluation *** detection,we predict an extra visible box apart from the full-body box for every instance;during evaluation,we employ visible boxes instead of full-body boxes to match the“ignore region”.We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results.

关键词： Pedestrian detection Object detection Occlusion handling Evaluation metric

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