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6th International Conference on computer Network, electronic and Automation, ICCNEA 2023

作者： Chen, Yupeng Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

A large amount of data is collected during geological hazard monitoring, which is extremely valuable for further data mining, hazard monitoring and decision analysis. However, in the process of data collection and transmission may be affected by interference and other factors, resulting in the generation of abnormal data. To extract higher value information from these basic data, it is necessary to improve the quality of source data first, so it is necessary to detect the abnormal data. The monitoring data of the geological disaster are time-series data, which have the characteristics of time-series correlation. The nearest neighbor difference jump anomaly detection algorithm is suitable for monitoring anomalous data of geological disaster system, but there are shortcomings in selecting floating values and correlation perception. To address these problems, the nearest neighbor difference jumping algorithm is improved and a algorithm is proposed to detect anomalous data of geological disasters, i.e., the data series are segmented by using sliding windows, the selection of floating values is improved, the calculation range of difference values is expanded to enhance the correlation of data, and the concept of change speed is incorporated to better perceive the trend of change before and after the data, and finally the anomaly is determined by using the anomaly probability and correlation. After the experimental comparison and analysis, the accuracy and recall rate of the proposed method on the detection of geological disaster data are improved and meet the expected results. © 2023 IEEE.

关键词： Time series

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Improving the Accuracy of Iris Recognition Based on Attention Mechanism 6

Improving the Accuracy of Iris Recognition Based on Attentio...

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6th International Conference on computer Network, electronic and Automation, ICCNEA 2023

作者： Wang, Zike Wang, Jianguo Zhong, Lei Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

Iris recognition algorithms are becoming more and more widely used in daily life, and the accuracy requirements for iris recognition algorithms are getting higher and higher. Therefore, in this paper, an improved Shufflenetv2 algorithm is proposed as a way to improve the speed and accuracy of current iris recognition algorithms in daily life. In this paper, deep and shallow features are filtered by using the channel attention mechanism, which enables the model to acquire feature representations of both underlying and high-level semantics. In addition, in the loss function, this paper uses the focal loss function instead of the cross entropy loss function to achieve a solution to the imbalance between the number of positive and negative samples and prevent too much learning to negative sample features. By validating on the CASIA-Iris-Thousand dataset, which is commonly used in iris recognition tasks, the proposed model achieves an equal error rate (EER) of 1.66% compared to the Shufflenetv2 model, which has an EER of 5.3%, and optimizes by 3.64%. The model proposed in this paper can effectively improve the accuracy of iris recognition. © 2023 IEEE.

关键词： Convolutional neural networks

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Control Flow Divergence Optimization by Exploiting Tensor Cores 24

Control Flow Divergence Optimization by Exploiting Tensor Co...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Pang, Weiguang Jiang, Xu Liu, Songran Qiao, Lei Fu, Kexue Gao, Longxiang Yi, Wang University of Electronic Science and Technology of China China Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Engineering Northeastern University China Beijing Institute of Control Engineering Beijing China Uppsala University Sweden

ISBN: (纸本)9798400706011

Kernels are scheduled on Graphics Processing Units (GPUs) in the granularity of GPU warp, which is a bunch of threads that must be scheduled together. When executing kernels with conditional branches, the threads within a warp may execute different branches sequentially, resulting in a considerable utilization loss and unpredictable execution time. This problem is known as the control flow divergence. In this work, we propose a novel method to predict threads' execution path before the launch of the kernel by deploying a branch prediction network on the GPU's tensor cores, which can efficiently parallel run with the kernels on CUDA cores, so that the divergence problem can be eased in a large extent with the lowest overhead. Combined with a well-designed thread data reorganization algorithm, this solution can better mitigate GPUs' control flow divergence problem. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Tensors

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Unidirectional Identity-Based Proxy Re-Signature with Key Insulation in EHR Sharing System

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computer Modeling in engineering & sciences 2022年第6期131卷 1497-1513页

作者： Yanan Chen Ting Yao Haiping Ren Zehao Gan School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Basic Course Teaching Department Jiangxi University of Science and TechnologyNanchang330013China Institute of Electronic and Information Engineering University of Electronic Science and Technology of ChinaDongguan523808China Network and Data Security Key Laboratory of Sichuan Province Unversity of Electronic Science and Technology of ChinaChengdu610054China

The introduction of the electronic medical record(EHR)sharing system has made a great contribution to the management and sharing of healthcare *** referral treatment for patients,the original signature needs to be converted into a re-signature that can be verified by the new *** re-signature(PRS)can be applied to this scenario so that authenticity and nonrepudiation can still be insured for ***,the existing PRS schemes cannot realize forward and backward ***,this paper proposes the first PRS scheme that can provide key-insulated property,which can guarantee both the forward and backward security of the *** the leakage of the private key occurs at a certain moment,the forward and backward key will not be ***,the purpose of key insulation is ***’s more,it can update different corresponding private keys in infinite time periods without changing the identity information of the user as the public ***,the unforgeability of our scheme is proved based on the extended Computational Diffie-Hellman assumption in the random oracle ***,the experimental simulation demonstrates that our scheme is feasible and in possession of promising properties.

关键词： Proxy re-signature key insulation electronic medical record(EHR) random oracle model

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Received Signal Strength Prediction Using Generative Adversarial Networks for Indoor Localization

Received Signal Strength Prediction Using Generative Adversa...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Wu, Haochang Qin, Hao Ma, Siteng Lang, Hans-Dieter Zhang, Xingqi School of Electrical and Electronic Engineering University College Dublin Dublin Ireland School of Computer Science University College Dublin Insight Centre for Data Analytics Dublin Ireland Eastern Switzerland University of Applied Sciences OST SG Rapperswil Switzerland University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350312843

The popularity of WiFi networks has led to the adoption of fingerprint-based WiFi localization as one primary method for indoor location tracking. However, this technique requires a significant amount of time and effort to collect data at numerous reference points (RPs) to ensure accuracy. To reduce the cost and improve efficiency, generative models can be used to generate received signal strength (RSS) fingerprints. This paper proposes a Deep Convolutional Generative Adversarial Network (DCGAN) based model for building RSS fingerprint maps that can generate a comprehensive fingerprint database using only the location of a wireless access point. The proposed approach is expected to lower the cost and effort involved in the collection of RSS fingerprints while maintaining a high level of accuracy. © 2023 IEEE.

关键词： Generative adversarial networks

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Metaheuristic Based Clustering with Deep Learning Model for Big data Classification

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computer Systems science & engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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A Cognitive Memory-Augmented Network for Visual Anomaly Detection

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1296-1307页

作者： Tian Wang Xing Xu Fumin Shen Yang Yang the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731 the Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808China

With the rapid development of automated visual analysis,visual analysis systems have become a popular research topic in the field of computer vision and automated *** analysis systems can assist humans to detect anomalous events(e.g.,fighting,walking alone on the grass,etc).In general,the existing methods for visual anomaly detection are usually based on an autoencoder architecture,i.e.,reconstructing the current frame or predicting the future ***,the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or *** flaws of the existing methods are that abnormal samples can also be reconstructed *** this paper,inspired by the human memory ability,we propose a novel deep neural network(DNN)based model termed cognitive memory-augmented network(CMAN)for the visual anomaly detection *** proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected ***,in the proposed CMAN model,we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data *** reconstruction errors and the novelty scores are used to distinguish abnormal events from *** addition,we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve *** experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts *** implementation code of our CMAN method can be accessed at https://***/CMANcode/CMAN_pytorch.

关键词： Cognitive computing density estimation memory visual analysis systems visual anomaly detection.

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Deep Evidential Active Learning with Uncertainty-Aware Determinantal Point Process 27th

Deep Evidential Active Learning with Uncertainty-Aware Dete...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhou, Yuxian Yue, Xiaodong Chen, Yufei Xie, Shaorong School of Computer Engineering and Science Shanghai University Shanghai China Artificial Intelligence Institute Shanghai University Shanghai China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9783031781063

Deep learning method requires a substantial amount of labeled data to achieve the state-of-the-art performance. However, annotating a large volume of data is often costly and impractical. Active Learning is a approach that reduces labeling costs by intelligently selecting and annotating the most crucial data points, which benefits from the integration of uncertainty and diversity as key criteria for sampling. Existing uncertainty-based methods often fall short in capturing the distinct sources of uncertainty, resulting in a diminished quality of uncertainty estimation. Additionally, clustering is commonly used to ensure diversity, which requires multiple iterations but overlooks the global correlations present throughout the entire unlabeled dataset. As a result, a well-designed ad hoc combination is essential to balance uncertainty and diversity. To address above limitations, we propose Evidential Uncertainty-aware Determinantal Point Process active learning method. Specifically, we employ the theory of Subjective Logic to measure multifaceted uncertainty including vacuity and dissonance. On these grounds, we first focus on the samples with high dissonance and employ Determinantal Point Process to select the samples considering both vacuity and diversity. The proposed method explores and exploits the information associated with the latent feature space near the decision boundary to select the most valuable samples. The experimental results on various real-world datasets reveal the superiority of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Active learning

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Task Offloading Optimization for AGVs with Fixed Routes in Industrial IoT Environment

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China Communications 2023年第5期20卷 302-314页

作者： Peng Liu Zifu Wu Hangguan Shan Fei Lin Qi Wang Qingshan Wang School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou 310018China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Hefei University of Technology School of MathematicsHefei 230009China

In order to solve the delay requirements of computing intensive tasks in industrial Internet of things,edge computing is moving from theoretical research to practical *** servers(ESs)have been deployed in factories,and on-site auto guided vehicles(AGVs),besides doing their regular transportation tasks,can partly act as mobile collectors and distributors of computing data and *** AGVs may offload tasks to the same ES if they have overlapping path segments,resource allocation conflicts are *** this paper,we study the problem of efficient task offloading from AGVs to ESs,along their fixed *** propose a multi-AGV task offloading optimization algorithm(MATO),which first uses the weighted polling algorithm to preliminarily allocate tasks for individual AGVs based on load balancing,and then uses the Deep Q-Network(DQN)model to obtain the updated offloading strategy for the AGV *** simulation results show that,compared with the existing methods,the proposed MATO algorithm can significantly reduce the maximum completion time of tasks and be stable under various parameter settings.

关键词： industrial Internet of Things task offloading optimization auto guided vehicles reinforcement learning

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Bayesian Detection for Colocated MIMO Radar with Limited Training data 2

Bayesian Detection for Colocated MIMO Radar with Limited Tra...

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2nd International Symposium on Computing and Artificial Intelligence, ISCAI 2023

作者： Liu, Weijian Zhou, Zhe Gong, Pengcheng Wu, Yuntao The First Department Wuhan Electronic Information Institute Wuhan China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798400708954

Utilizing waveform diversity, colocated multiple-input multiple-output (MIMO) radar offers increased degrees of freedom (DOF) and potential enhancements in target detection and identification compared to traditional phased array radar. However, the expanded DOF in colocated MIMO radar systems also necessitates a significant increase in training samples for target detection, particularly in the presence of strong clutter. Acquiring a sufficient number of training samples in practical environments can pose challenges. This paper addresses the issue of target detection using colocated MIMO radar with limited training data. To overcome this challenge, a knowledge-based approach is employed. The unknown clutter covariance matrix is modeled as an inverse Wishart random distribution, with the DOF serving as an adjustable system parameter. The scalar matrix can be obtained through antenna configuration or historical data. Three effective Bayesian detectors are proposed based on three commonly used detection criteria, namely the Bayesian GLRT, Bayesian Rao test, and Bayesian Wald test. Through computer simulations, the effectiveness of these Bayesian detectors is validated, demonstrating their remarkable target detection capability even with limited training data. Furthermore, these proposed Bayesian detectors outperform existing detectors even when abundant training samples are available. Among the three detectors proposed, the Bayesian GLRT exhibits the most superior detection performance. © 2023 ACM.

关键词： Inverse problems

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