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International Conference on big data and Smart computing (bigCOMP)

作者： Rong Yan Huawei Shen Cao Qi Keting Cen Li Wang College of Big Data Taiyuan University Of Technology Taiyuan China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Beijing China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distance between the true connectivity distribution and generated connectivity distribution by Kullback-Leibler or Jensen-Shannon divergence. However, since these divergences are not continuous with respect to the generator's parameters, such methods easily lead to unstable training and poor convergence. In contrast, Wasserstein distance is continuous and differentiable almost everywhere, which means it can produce more reliable gradient, allowing the training more stable and more convergent. In this paper, we utilize Wasserstein distance to characterize the distance between the underlying true connectivity distribution and generated distribution in graph representation learning. Experimental results show that the accuracy of our method exceeds existing baselines in tasks of both node classification and link prediction.

关键词： Hafnium Conferences big data

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A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

A Diverse Biases Non-negative Latent Factorization of Tensor...

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Wu, Xuke Gou, Hang Wu, Hao Wang, Juan Chen, Minzhi Lai, Siyu Computer School China West Normal University Nanchong Sichuan637002 China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China Department of Medical Imaging North Sichuan Medical College Nanchong Sichuan637000 China

ISBN: (纸本)9781728168531

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy. © 2020 IEEE.

关键词： Factorization

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Forensicability Assessment of Questioned Images in Recapturing Detection

arXiv

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arXiv 2022年

作者： Chen, Changsheng Zhao, Lin Cai, Rizhao Yu, Zitong Huang, Jiwu Kot, Alex C. The Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Department of Electrical and Electrics Engineering Nanyang Technological University Singapore639798 Singapore

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing (FAS) and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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Fusion Strategy of Multi-sensor Based Object Detection for Self-driving Vehicles

Fusion Strategy of Multi-sensor Based Object Detection for S...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Yanqi Li Jianwei Niu Zhenchao Ouyang Hangzhou Innovation Institute Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Beijing China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

Lidar and optical camera are common sensors in the sensor layer of autopilot system. Lidar can use depth data to obtain accurate relative distance and contour information of obstacles, which is not easily affected by external light conditions. Optical camera can obtain rich object/environment semantic information through high-resolution image, which is relatively mature in technology. The two different sensors are highly complementary, and previous studies show that the fusion of laser point cloud and image data can greatly improve the efficiency of object detection in out door environment. In this paper, a deep convolutional neural network detection model based on Lidar and image information features layered fusion is studied. We try different fusion depth at the CNN model to seek the best solution according to the detection performance. The experimental results on the KITTI dataset show that the detection accuracy of the fusion based on YOLOv3 is 1.08% higher than original model. Another small scale experiment with our own self-driving platform on local area also show the final fusion model can achieve better detection accuracy in real road condition.

关键词： Feature extraction Three-dimensional displays Object detection Laser radar Cameras Sensor phenomena and characterization

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High voltage-controlled magnetic anisotropy and interface magnetoelectric effect in sputtered multilayers annealed at high temperatures

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Science China(Physics,Mechanics & Astronomy) 2020年第7期63卷 114-119页

作者： LeZhi Wang Xiang Li Taisuke Sasaki Kin Wong GuoQiang Yu ShouZhong Peng Chao Zhao Tadakatsu Ohkubo Kazuhiro Hono WeiSheng Zhao KangLong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Computer Science&Engineering Beihang UniversityBejing 100191China Department of Electrical Engineering University of CaliforniaLos AngelesLos Angeles 90095USA School of Microeletronics Beihang UniversityBeijing 100191China Inston IncLos Angeles 90095USA National Institute for Materials Science(NIMS) Tsukuba 305-0047Japan Institute of Microelectronics Chinese Academy of SciencesBejing 100029China

Voltage control of magnetism promises great energy efficiency in writing magnetic memory. Here, using Cr/Mo/CoFeB/MgO multilayers stable under high annealing temperatures up to 590°C, we significantly enhance the interfacial crystallinity, thereby the interface-originated perpendicular magnetic anisotropy(PMA), voltage-controlled magnetic anisotropy(VCMA), and interface magnetoelectric(ME) effect. High interfacial PMA of 1.35 mJ/m^2, VCMA coefficient of-138 fJ/(V m), and interface ME coefficient, which is 2-3 orders of magnitude larger than ab initio calculation results are simultaneously achieved after annealing at 500°C. These promising results enabled by the industry-applicable sputtering process will pave the way for highdensity voltage-controlled spintronic devices.

关键词： voltage-controlled magnetic anisotropy(VCMA) magnetoelectric(ME)effect perpendicular magnetic anisotropy

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Anomaly Detection for Tower Crane Alarm Surge Based on Copula Function 2

Anomaly Detection for Tower Crane Alarm Surge Based on Copul...

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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Shi, Hongyang Lu, Ming Wang, Hongjun Zhang, Yonghong Dong, Zengshou Shenyang Institute of computing technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Infrastructure & Cloud Service SSG Lenovo Beijing China R.C of Big Data and Artificial Intelligence Technology Shandong University Jinan China Beijing Zhongke Zhihe Digital Technology Co. Ltd Beijing China School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi China

ISBN: (纸本)9783800757398

In recent years, tower crane safety accidents have occurred frequently, resulting in casualties and significant losses of construction site property. In order to reduce the probability of tower crane accidents, quality supervisor needs a method that can cause anomaly detection of alarm surges in a certain time period of the tower crane. Due to the lack of generalization capability of existing methods for new equipment and the large calculation overhead and weak interpretability, quality supervisor is often unable to effectively detect the risks brought about by the improper operation of special operators. This paper adopts the unsupervised anomaly detection method based on copula function, estimates the empirical copula function according to the empirical cumulative distribution of input data through nonparametric method, and then estimates the tail probability of joint distribution in all dimensions to evaluate the anomaly. In order to evaluate the effect of the model, this paper calculates the sudden increase point by comparing with each time series value and historical value, and uses it as an anomaly label. By comparing other different classes of anomaly detection algorithms horizontally, the copula function-based anomaly detection method for the scenario in this paper has better generalization and generalization ability, and the overall performance is better than other algorithms. In addition, compared with other methods, this method has the characteristics of low calculation consumption, scalability and high interpretability. For the anomaly detection of the total amount of tower crane alarm, this method has high application value. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Function evaluation

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Comprehensive Characteristic Decomposition of Parametric Polynomial Systems 21

Comprehensive Characteristic Decomposition of Parametric Pol...

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46th International Symposium on Symbolic and Algebraic Computation, ISSAC 2021

作者： Dong, Rina Lu, Dong Mou, Chenqi Wang, Dongming Chongqing Key Laboratory of Automated Reasoning and Cognition Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Mathematical Sciences Beihang University Beijing100191 China LMIB-School of Mathematical Sciences Beihang University Beijing100191 China Centre National de la Recherche Scientifique Paris cedex 1675794 France

ISBN: (纸本)9781450383820

This paper presents an algorithm that decomposes an arbitrary set F of multivariate polynomials involving parameters into finitely many sets Gi of (lexicographical) Gröbner bases G ij such that associated with each Gi there is a system Ai of parametric constraints, all the Ai's partition the parameter space, all the Gij's in each Gi remain Gröbner bases under specialization of the parameters satisfying the constraints in Ai, and for each i the Gröbner bases Gij together with their corresponding W-characteristic sets form a normal characteristic decomposition of F. The sets of Gröbner bases computed by the algorithm provide a comprehensive characteristic decomposition of F that is structure-invariant under the associated constraints and possesses many algebraic and geometric properties, including most of the notable properties on comprehensive Gröbner systems and comprehensive triangular decomposition. Some of these properties are highlighted in the paper and advantages of the proposed algorithm are discussed briefly from the aspects of methodological simplicity and computational performance using illustrative examples and preliminary experiments. © 2021 ACM.

关键词： Polynomials

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Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction

arXiv

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arXiv 2022年

作者： Xie, Peng Ma, Minbo Li, Tianrui Ji, Shenggong Du, Shengdong Yu, Zeng Zhang, Junbo School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Enginnering Laboratory of Integrated Transportation Big Data Application Technology China Tencent Inc. Shenzhen China JD iCity JD Technology Beijing China JD Intelligent Cities Research Institute of Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, it faces two main challenges. First, different metro stations, e.g. transfer stations and non-transfer stations, have unique traffic patterns. Second, it is challenging to model complex spatio-temporal dynamic relation of metro stations. To address these challenges, we develop a spatio-temporal dynamic graph relational learning model (STDGRL) to predict urban metro station flow. First, we propose a spatio-temporal node embedding representation module to capture the traffic patterns of different stations. Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix. Finally, we provide a transformer-based long-term relationship prediction module for long-term metro flow prediction. Extensive experiments are conducted based on metro data in Beijing, Shanghai, Chongqing and Hangzhou. Experimental results show the advantages of our method beyond 11 baselines for urban metro flow prediction. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation 23rd

BEFD: Boundary Enhancement and Feature Denoising for Vessel ...

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Mo Yu, Fei Zhao, Jie Zhang, Li Li, Quanzheng Center for Data Science Peking University Beijing100871 China Center for Data Science in Health and Medicine Peking University Beijing100871 China Laboratory for Biomedical Image Analysis Beijing Institute of Big Data Research Beijing100871 China Center for Advanced Medical Computing and Analysis MGH/BWH Center for Clinical Data Science Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA02115 United States

ISBN: (纸本)9783030597214

Blood vessel segmentation is crucial for many diagnostic and research applications. In recent years, CNN-based models have leaded to breakthroughs in the task of segmentation, however, such methods usually lose high-frequency information like object boundaries and subtle structures, which are vital to vessel segmentation. To tackle this issue, we propose Boundary Enhancement and Feature Denoising (BEFD) module to facilitate the network ability of extracting boundary information in semantic segmentation, which can be integrated into arbitrary encoder-decoder architecture in an end-to-end way. By introducing Sobel edge detector, the network is able to acquire additional edge prior, thus enhancing boundary in an unsupervised manner for medical image segmentation. In addition, we also utilize a denoising block to reduce the noise hidden in the low-level features. Experimental results on retinal vessel dataset and angiocarpy dataset demonstrate the superior performance of the new BEFD module. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Collaborative filtering algorithm based on user interest change 4

Collaborative filtering algorithm based on user interest cha...

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4th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019

作者： Song, Na Lu, Qin Qilu University of Technology School of Computer Science and Technology China Cloud Computing and Big Data Research Institute Jinan China

ISBN: (纸本)9781728119076

The collaborative filtering algorithm can provide personalized service recommendations based on the user's personal interests. In view of the shortcomings of traditional similarity measurement methods for user interest changes, this paper proposes a collaborative filtering algorithm based on user interest changes. When calculating the user similarity, the time penalty function is added to the traditional Pearson correlation coefficient, and then the score prediction is used to obtain the target user's rating data to make recommendations for the user. The improved algorithm can adapt to the change of user interest under the change of time. The experimental results on the MovieLens dataset show that compared with the traditional algorithm, the proposed algorithm can make recommendations for users more accurately. © 2019 IEEE.

关键词： Collaborative filtering

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