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arXiv 2020年

作者： Wang, Shuai Kong, Qingchao Wang, Yuqi Wang, Lei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shenzhen Artificial Intelligence and Data Science Institute Longhua） China State Information Center Beijing100045 China

Social media, such as Facebook and Twitter, has become one of the most important channels for information dissemination. However, these social media platforms are often misused to spread rumors, which has brought about severe social problems, and consequently, there are urgent needs for automatic rumor detection techniques. Existing work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying rumors. Previous works which have considered structural information, only utilize limited propagation structures. Moreover, few related research has considered the dynamic evolution of diffusion structures. To address these issues, in this paper, we propose a Neural Model using Dynamic Propagation Structures (NM-DPS) for rumor detection in social media. Firstly, we propose a partition approach to model the dynamic evolution of propagation structure and then use temporal attention based neural model to learn a representation for the dynamic structure. Finally, we fuse the structure representation and content features into a unified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure information and the effectiveness of our proposed method in capturing the dynamic structure. Copyright © 2020, The Authors. All rights reserved.

关键词： Social networking (online)

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Identification of muscle morphology with noncontact capacitive sensing: Preliminary study

Identification of muscle morphology with noncontact capaciti...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Enhao Zheng Jiacheng Wan Dongfang Xu Qining Wang Hong Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Information Engineering China university of geosciences (Beijing) Beijing China The Robotics Research Group College of Engineering Peking University Beijing China

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Human-machine interface with muscle signals serves as an important role in the field of wearable robotics. To compensate for the limitations of the existing surface Electromyography (sEMG) based technologies, we previously proposed a noncontact capacitive sensing approach that could record the limb shape changes. The sensing approach frees the human skin from contacting to the metal electrodes, thus enabling the measurement of muscle signals by dressing the sensing front-ends outside of the clothes. We validated the capacitive sensing in human motion intent recognition tasks with the wearable robots and produced comparable results to existing studies. However, the biological significance of the capacitance signals is still unrevealed, which is an indispensable issue for robot intuitive control. In this study, we address the problems of identifying the relationships between the muscle morphological parameters and the capacitance signals. We constructed a measurement system that recorded the noncontact capacitive sensing signals and the muscle ultrasound (US) images simultaneously. With the designed device, five subjects were employed and the US images from the gastrocnemius muscle (GM) and the tibialis anterior (TA) muscle during level walking were sampled. We fitted the calculated muscle morphological parameters (the pinnation angles and the muscle fascicle length) and the capacitance signals of the same gait phases. The results demonstrated that at least one-channel capacitance signal strongly correlated to the muscle morphological parameters (R 2 > 0.5, quadratic regression). The average R 2 s of the most correlated channels were up to 0.86 for pinnation angles and 0.83 for the muscle fascicle length changes. The interesting findings in this preliminary study suggest the biological physical significance of the capacitance signals during human locomotion. Future efforts are worth being paid in this new research direction for more promising results.

关键词： Muscles Robot sensing systems Capacitance Legged locomotion Electrodes Ultrasonic variables measurement

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MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divi...

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IEEE International Conference on Networking, Sensing and control

作者： Ying Tang Shengtao Sun Ben Wu Rowan University Glassboro USA State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process.

关键词： Training Surface reconstruction Three-dimensional displays Databases Knowledge based systems Feature extraction Strain

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Improving the Data Quality for Credit Card Fraud Detection

Improving the Data Quality for Credit Card Fraud Detection

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Rongrong Jing Hu Tian Yidi Li Xingwei Zhang Xiaolong Zheng Zhu Zhang Daniel Zeng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shenzhen Artificial Intelligence and Data Science Institude (Longhua) Shenzhen China

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

Label imbalance and data missing are two major challenges in the problem of credit card fraud detection. However, existing matrix completion algorithms are generally difficult and cannot be easily applied to real-world credit card fraud detection since the scale of the normally used dataset is oversized. In this paper, we develop a spectral regularization algorithm to complete the large-scale sparse matrices, and further utilize an over-sampling algorithm to tackle the problem of the imbalance between positive and negative samples. Experimental results on a real-world dataset demonstrate that our model can outperform the state-of-the-art baseline methods. The proposed method could also be extended to other large-scale scenarios where data is missing or labels are imbalanced.

关键词： Data integrity Conferences Credit cards Sparse matrices Security Informatics

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Differential Time-variant Traffic Flow Prediction Based on Deep Learning

Differential Time-variant Traffic Flow Prediction Based on D...

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International Conference on Intelligent Transportation

作者： Wei Zhang Fenghua Zhu Yuanyuan Chen Xiao Wang Gang Xiong Fei-Yue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Clouding Computing Center Chinese Academy of Sciences Dongguan Guangdong China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

The accuracy of traffic flow prediction significantly impacts the operation of Intelligent Transportation systems (ITS). In this paper, we propose a Differential Time-variant (DT) Traffic Flow Prediction method, which can remarkably improve the accuracy and reduce the variance of traffic flow forecast based on deep learning models. To extract the temporal trend of the traffic flow at different locations, we apply data difference to preprocess the raw traffic data. This method can better eliminate the uncertainties of traffic flow series like volatility and anomaly. Then, time information is introduced in the form of One-Hot Encoding to effectively model the temporal patterns of traffic flow. Necessary analysis is presented to demonstrate the rationality. Three popular deep neural networks are applied to test our method, and experimental results on PeMS data sets indicate that it can make more accurate prediction compared with the same model.

关键词： Deep learning Predictive models Feature extraction Time series analysis Market research Data models Uncertainty

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Learning category-level implicit 3D rotation representations for 6D pose estimation from RGB Images

Learning category-level implicit 3D rotation representations...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Li, Xiaocan Cai, Yinghao Wang, Shuo Lu, Tao Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781728163215

We exploit the embedding ability of a de-noising autoencoder for an implicit 3D rotation representation learning at the category level. Contrast to the exact 3D reconstruction model of each instance-level physical object, we leverage the inexact CAD/Reconstruction models of an object as the representative model for some category. Under our assumptions that objects within the same category share resemble geometry, we train a de-noising autoencoder on synthetic 3D views of category-level objects to extract the homogenous features at the bottleneck layer. The latent representation is agnostic not only to heterogeneous textures, colors, and illuminations, but also ambiguous pose caused by object symmetry. To extend the instance-level 3D translation estimation to the category level, we considered the 3D diagonal length ratio between the source and target object. We achieved a frame rate of 17Hz. © 2019 IEEE.

关键词： Object detection

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Application and Future of Modern Energy Internet Based on Artificial Intelligence

Application and Future of Modern Energy Internet Based on Ar...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xiaojing Yang Tianlu Gao Shiqian Ma Xiaoyan Gong Zhigang Huang Dazhi Hao Power Dispatching and Control Center State Grid Tianjin Electric Power Company Tianjin China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China Electric Power Research Institute State Grid Tianjin Electric Power Company Tianjin China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

The emergence of deep learning is undoubtedly a landmark breakthrough in artificial intelligence, and the emergence of this technology makes it possible for us to realize the real smart grid. A major challenge currently facing deep learning technology is the requirement of enough training samples to ensure the accuracy of results of the learning. But for the current power grid, training data can be obtained because a lot of real time data is collected to maintain the stability of its operation. With the deep learning technology, there is no doubt that these data can be utilized to realize the intelligent operation of the power grid. First, this paper gives a brief introduction to the current artificial intelligence, followed by the introduction of deep learning technology. Some potential applications of the deep learning in the daily operation of the power grid are proposed. And in the end, there is a brief description of some of the features that future smart grids should have.

关键词： Deep learning Training Training data Learning (artificial intelligence) Power system stability Real-time systems Smart grids

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Neural Network Tracking of Switched Robotic Manipulators with Mode-Dependent Average Dwell Time

Neural Network Tracking of Switched Robotic Manipulators wit...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Chao Ma Wei Wu Yidao Ji Wei Wang School of Mechanical Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper is concerned with the tracking control for robotic manipulators with switching loads and parameter uncertainties. More precisely, the neural network approximation method is adopted with adaptive control to deal with the unmodeled dynamics. Multiple Lyapunov function method is used based on mode-dependent average dwell time, such that the tracking error can converge with desired region. A numerical example is finally provided for illustrating our proposed tracking strategy.

关键词： Switches Robots Manipulators Neural networks control systems Manipulator dynamics Uncertain systems

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Attacking DNN-based Intrusion Detection Models

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IFAC-PapersOnLine 2020年第5期53卷 415-419页

作者： Xingwei Zhang Xiaolong Zheng Desheng Dash Wu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China and also with the Stockholm Business School Stockholm University SE-106 91 Stockholm Sweden

Intrusion detection plays an important role in public security domains. Dynamic deep neural network(DNN)-based intrusion detection models have been demonstrated to show effective performance for timely detecting network intrusions. While DNN-based intrusion detection models have shown powerful performance, in this paper, we verify that they could be easily attacked by well-designed small adversarial perturbations. We design an effective procedure to employ commonly used adversarial perturbations for attacking well-trained DNN detection models on NSL-KDD dataset. We further find that the performance of DNN models for recognizing real labels of abnormal data suffers more from attacks compared with that on normal samples.

关键词： public security intrusion detection deep neural networks adversarial perturbations

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Neural-based Inverse Kinematical Analysis for Bio-inspired Cable-driven Robotic Manipulator

Neural-based Inverse Kinematical Analysis for Bio-inspired C...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Hang Fu Yang Liu Wei Wu Xiaoqing Li School of Mechanical Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

In this paper, the inverse kinematical analysis problem is investigated for a class of bio-inspired cable-driven robotic manipulator. Moreover, the modeling of the redundant cable-driven robotic manipulator is performed. In particular, the RBF neural network is adopted to deal with the unmodeled parameters uncertainties such that the inverse kinematic analysis precision can be improved. In the end, an illustrative example is presented for showing the usefulness of the developed analysis approach.

关键词： Manipulators Clustering algorithms Entropy Robot kinematics Kinematics Training Radial basis function networks

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