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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Lin, Xiaohui Li, Zuoyong Huang, Xunhua Chen, Xinwei Fan, Haoyi Fuzhou University College of Computer and Data Science Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Minjiang University Fujian Engineering and Research Center of New Chinese Lacquer Materials Fuzhou350121 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

ISBN: (纸本)9798350310153

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE. © 2022 IEEE.

关键词： Contamination

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ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

arXiv

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arXiv 2022年

作者： Jiang, Yuezihan Cheng, Yu Zhao, Hanyu Zhang, Wentao Miao, Xupeng He, Yu Wang, Liang Yang, Zhi Cui, Bin School of Software and Microelectronics Peking University China School of CS Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Alibaba Group China

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. ZOOMER is designed for tackling two challenges presented by the massive user data at Taobao: low training/serving efficiency due to the huge scale of the graphs, and low recommendation quality due to the information overload which distracts the recommendation model from specific user intentions. ZOOMER achieves this by introducing a key concept, Region of Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the graph with significant relevance to a strong user intention. ZOOMER narrows the focus from the whole graph and "zooms in" on the more relevant ROIs, thereby reducing the training/serving cost and mitigating the information overload at the same time. With carefully designed mechanisms, ZOOMER identifies the interest expressed by each recommendation request, constructs an ROI subgraph by sampling with respect to the interest, and guides the GNN to reweigh different parts of the ROI towards the interest by a multi-level attention module. Deployed as a large-scale distributed system, ZOOMER supports graphs with billions of nodes for training and thousands of requests per second for serving. ZOOMER achieves up to 14x speedup when downsizing sampling scales with comparable (even better) AUC performance than baseline methods. Besides, both the offline evaluation and online A/B test demonstrate the effectiveness of ZOOMER. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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Mechanical Vibration Signal Compression Based on Speech Codecs for Intelligent Manufacturing

SSRN

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SSRN 2023年

作者： Tang, Lili Tian, Hui Chang, Chin-Chen Huang, Hui College of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen361021 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

How to effectively compress mechanical signals so that they can support remote and real-time health monitoring is a hot issue in the context of intelligent manufacturing. Therefore, this paper presents a novel mechanical vibration signal compression scheme based on speech codecs, which can realize signal compression in many scenarios such as tool wear detection and bearing fault diagnosis. In this work, signal compression is first transformed into a multi-objective optimization problem, and the feasibility of solving this problem from the perspective of speech coding is deeply analyzed. Further, this paper presents five compression algorithms with different compression ratios. The presented algorithms are evaluated with tool wear detection in stone processing and bearing fault diagnosis. The experimental results show that the proposed algorithms can achieve higher compression ratios (4.0~24.6) while ensuring low time overhead (average time not more than 0.0194ms) and high reconstruction quality (MSE index are all lower than 0.008). © 2023, The Authors. All rights reserved.

关键词： Fault detection

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and knowledge Distillation

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

引用

Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Chengjia Jiang Tao Wang Sien Li Jinyang Wang Shirui Wang Antonios Antoniou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS.

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

引用

International Conference on Computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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Cascaded Aggregation Convolution Network for Saliency Detection Of Stored-Grain Pests

SSRN

引用

SSRN 2023年

作者： Yu, Junwei Zhai, Fupin Liu, Nan Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China College of Information Scinence and Engineering Henan University of Technology Zhengzhou450001 China PLA SSF Information Engineering University Zhengzhou450001 China

The behaviors exhibited by stored grain pests, including feeding, respiration, excretion, and reproduction, engender multifarious threats to grain storage. Consequently, it is imperative to diligently undertake the task of detecting and effectively controlling these pests. At present, Great progress has made in both traditional methods and deep learning techniques for salient object detection. It is still a challenge, especially for small objects and non-salient images, biological vision. This paper presents a grain pests detection method based on cascaded strous convolution . We collet(collect) a novel dataset GrainPest, which comprises 500 images covering various salient pests in grains. We develop a simple and effective framework named cascaded aggregation convolution network(CACNet). CACNet outperforms various state-of-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research in COD. Finally, we conduct a large-scale COD study, evaluating 13 cutting-edge models, providing some interesting findings, and showing several potential applications. Our research offers the community an opportunity to explore more in this new field. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Initial years'neutron-induced cross-section measurements at the CSNS Back-n white neutron source

引用

Chinese Physics C 2021年第6期45卷 1-14页

作者： Jingyu Tang Rong Liu Guohui Zhang Xichao Ruan Xiaoguang Wu Qi An Jiangbo Bai Jie Bao Yu Bao Ping Cao Haolei Chen Qiping Chen Yonghao Chen Zhen Chen Zengqi Cui Ruirui Fan Changqing Feng Lin Gan Keqing Gao Xiaolong Gao Minhao Gu Changcai Han Zijie Han Guozhu He Yongcheng He Yang Hong Yiwei Hu Hanxiong Huang Xiru Huang Haoyu Jiang Wei Jiang Zhijie Jiang Hantao Jing Ling Kang Bo Li Chao Li Jiawen Li Qiang Li Xiao Li Yang Li Jie Liu Minliang Liu Shubin Liu Xingyan Liu Ze Long Guangyuan Luan Changjun Ning Mengchen Niu Binbin Qi Jie Ren Zhizhou Ren Zhaohui Song Kang Sun Zhijia Sun Zhixin Tan Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Zhaohui Wang Jie Wen Zhongwei Wen Xuan Wu Likun Xie Xiaoyun Yang Yiwei Yang Han Yi Li Yu Tao Yu Yongji Yu Linhao Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Luping Zhou Zhihao Zhou Kejun Zhu State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China University of Chinese Academy of Sciences Beijing 100049China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China Northwest Institute of Nuclear Technology Xi'an 710024China Institute of Modern Physics Chinese Academy of Sciences(CAS)Lanzhou 730000China

The Back-n white neutron source(known as Back-n)is based on back-streaming neutrons from the spallation target at the China Spallation Neutron Source(CSNS).With its excellent beam properties,e.g.,a neutron flux of approximately 1.8×107 n/cm2/s at 55 m from the spallation target,energy range spanning from 0.5 eV to 200 MeV,and time-of-flight resolution of a few per thousand,along with the equipped physical spectrometers,Back-n is considered to be among the best facilities in the world for carrying out nuclear data *** its completion and commencement of operation in May 2018,five types of cross-section measurements concerning neutron capture cross-sections,fission cross-sections,total cross-sections,light charged particle emissions,in-beam gamma spectra,and more than forty nuclides have been *** article presents an overview of the experimental setup and result analysis on the neutron-induced cross-section measurements and gamma spectroscopy at Back-n in the initial years.

关键词： white neutron beam experimental spectrometers nuclear data measurements nuclides data analysis

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Complementary knowledge Compilation Using the Hyper Extension Rule

引用

Chinese Journal of Electronics 2019年第2期28卷 259-265页

作者： NIU Dangdang LIU Lei LYU Shuai XU Yue College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University

We introduce the concept of Complementary formula(COMF), which is a new and non-equivalent way for knowledge compilation(KC). Based on the Hyper extension rule(HER) which is an expansion of Extension rule(ER), we design a compilation algorithm which can formula compile each Conjunctive normal form(CNF)formula to complementary Fully complementary connected diagram(c-FCCD), named as C2C(CNF formula to cFCCD). Theoretically, c-FCCD is a kind of complementary formulae of the input formulae and can support all queries and partial transformations in KC map. Experimentally,C2C is competitive with the EPCCL compilers KCER,C2E, UKCHER, DKCHER and IKCHER.

关键词： knowledge compilation Extension rule Hyper extension rule Compilation language Each pair contains complementary literals(EPCCL) theory

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Measurement of differential cross sections of neutron-induced deuteron production reactions on carbon from 25 to 52 MeV

引用

Chinese Physics C 2021年第6期45卷 109-120页

作者： Zengqi Cui Haoyu Jiang Kang Sun Guohui Zhang Ruirui Fan Wei Jiang Huaiyong Bai Yiwei Hu Jie Liu Han Yi Changjun Ning Zhijia Sun Jingyu Tang Qi An Jie Bao Yu Bao Ping Cao Haolei Chen Qiping Chen Yonghao Chen Yukai Chen Zhen Chen Changqing Feng Keqing Gao Minhao Gu Changcai Han Zijie Han Guozhu He Yongcheng He Yang Hong Hanxiong Huang Weiling Huang Xiru Huang Xiaolu Ji Xuyang Ji Zhijie Jiang Hantao Jing Ling Kang Mingtao Kang Bo Li Chao Li Jiawen Li Lun Li Qiang Li Xiao Li Yang Li Rong Liu Shubin Liu Xingyan Liu Guangyuan Luan Qili Mu Binbin Qi Jie Ren Zhizhou Ren Xichao Ruan Zhaohui Song Yingpeng Song Hong Sun Xiaoyang Sun Zhixin Tan Hongqing Tang Xinyi Tang Binbin Tian Lijiao Wang Pengcheng Wang Qi Wang Taofeng Wang Zhaohui Wang Jie Wen Zhongwei Wen Qingbiao Wu Xiaoguang Wu Xuan Wu Likun Xie Yiwei Yang Li Yu Tao Yu Yongji Yu Linhao Zhang Qiwei Zhang Xianpeng Zhang Yuliang Zhang Zhiyong Zhang Yubin Zhao Luping Zhou Zuying Zhou Danyang Zhu Kejun Zhu Peng Zhu State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Particle Detection and Electronics Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Northwest Institute of Nuclear Technology Xi'an 710024China Department of Engineering and Applied Physics University of Science and Technology of ChinaHefei 230026China School of Physics Beihang UniversityBeijing 100083China

The angle-differential cross sections of neutron-induced deuteron production from carbon were measured at six neutron energies from 25 to 52 MeV relative to those of n-p elastic scattering at the China Spallation Neutron Source(CSNS)Back-n white neutron *** employing theΔE-E telescopes of the Light-charged Particle Detector Array(LPDA)system at 15.1°to 55.0°in the laboratory system,ratios of the angle-differential cross sections of the ^(12)C(n,xd)reactions to those of the n-p scattering were measured,and then,the angle-differential cross sections of the ^(12)C(n,xd)reactions were obtained using the angle-differential cross sections of the n-p elastic scattering from the JENDL-4.0/HE-2015 library as the *** obtained results are compared with data from previous measurements,all of which are based on mono-energic neutrons,the evaluated data from the JENDL-4.0/HE-2015 library and the ENDF-B/VIII.0 library,and those from theoretical calculations based on INCA code and Talys-1.9 *** the first white-neutron-source-based systematic measurement of the angle-differential cross sections of neutron-induced deuteron production reactions on carbon in several tens of MeV,the present work can provide a reference to the data library considering the lack of experimental data.

关键词： ^(12)C(n,xd)reactions relative angle-differential cross sections CSNS Back-n white neutron source LPDA

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High-Frequency Stereo Matching Network

High-Frequency Stereo Matching Network

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China

In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg. Code and demos are available at: https://***/David-Zhao-1997/High-frequency-Stereo-Matching-Network.

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