检索结果-内蒙古大学图书馆

Enhancing rumor detection in social media using dynamic propagation structures

学校读者我要写书评

暂无评论

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)

Fast and Robust RGB-D Multiple Human Tracking Based on Part Model for Mobile Robots

学校读者我要写书评

暂无评论

Fast and Robust RGB-D Multiple Human Tracking Based on Part ...

第三十八届中国控制会议

作者： Shiying Sun Xiaoguang Zhao Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

In this paper, we address the problem of multiple human tracking for mobile robots and proposed a fast and robust RGB-D multiple human tracking approach based on part model. Firstly, a simplified deformable part model method is used for fast human detection using RGB-D information. Then the partial occlusion is detected with depth information. Finally, part model and partial occlusion information are combined into data association process and an on-line appearance classification method with dynamic occlusion handling is proposed for robust tracking. The performance of the proposed method is evaluated on the public dataset, and experiment results have demonstrated the effectiveness of the proposed method.

关键词： Multiple Human Tracking Deformable Part Model Human Detection Occlusion Handling On-line Classification

ContourRend: A Segmentation Method for Improving Contours by Rendering

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Chen, Junwen Lu, Yi Chen, Yaran Zhao, Dongbin Pang, Zhonghua State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing100144 China

A good object segmentation should contain clear contours and complete regions. However, mask-based segmentation can not handle contour features well on a coarse prediction grid, thus causing problems of blurry edges. While contour-based segmentation provides contours directly, but misses contours' details. In order to obtain fine contours, we propose a segmentation method named ContourRend which adopts a contour renderer to refine segmentation contours. And we implement our method on a segmentation model based on graph convolutional network (GCN). For the single object segmentation task on cityscapes dataset, the GCN-based segmentation contour is used to generate a contour of a single object, then our contour renderer focuses on the pixels around the contour and predicts the category at high resolution. By rendering the contour result, our method reaches 72.41% mean intersection over union (IoU) and surpasses baseline Polygon-GCN by 1.22%. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization

学校读者我要写书评

暂无评论

GPO: Global Plane Optimization for Fast and Accurate Monocul...

IEEE International Conference on Robotics and automation (ICRA)

作者： Sicong Du Hengkai Guo Yao Chen Yilun Lin Xiangbing Meng Linfu Wen Fei-Yue Wang PA2R2T Group the State Key Laboratory of Complex System Management and Control Institute of Automation Chinese Academy of Sciences Beijing China ByteDance AI Lab Beijing China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the ne-tuned baselines in both accuracy and real-time.

关键词： Cameras Simultaneous localization and mapping Optimization Matrix decomposition Transmission line matrix methods Estimation Three-dimensional displays

Application and Future of Modern Energy Internet Based on Artificial Intelligence

学校读者我要写书评

暂无评论

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

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

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Chen, Long Li, Yuchen Huang, Chao Li, Bai Xing, Yang Tian, Daxin Li, Li Hu, Zhongxu Na, Xiaoxiang Li, Zixuan Teng, Siyu Lv, Chen Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd United Kingdom BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The Department of Industrial and System Engineering Hong Kong Polytechnical University Hong Kong The College of Mechanical and Vehicle Engineering Hunan University China The School of Aerospace Transport and Manufacturing Cranfield University United Kingdom The School of Transportation Science and Engineering Beihang University China The Department of Automation Tsinghua University China The Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore The Centre for Sustainable Road Freight University of Cambridge United Kingdom The School of Mechanical Engineering Tsinghua University China The College of Artificial Intelligence Xi’an Jiaotong University China

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

Group feature learning and domain adversarial neural network for aMCI diagnosis system based on EEG

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Fan, Chen-Chen Xie, Haiqun Peng, Liang Yang, Hongjun Ni, Zhen-Liang Wang, Guanan Zhou, Yan-Jie Chen, Sheng Fang, Zhijie Huang, Shuyun Hou, Zeng-Guang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China Department of Neurology First People’s Hospital of Foshan FoshanGuangdong528000 China

— Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer’s disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system. © 2021, CC BY.

关键词： Diagnosis

K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

学校读者我要写书评

暂无评论

K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

IEEE International Conference on Networking, Sensing and control

作者： Xiaoyan Gong Ying Tang Xiwei Liu Sifeng Jing Wei Cui Joleen Liang Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

Nowadays, AI education from Kindergarten to 9 th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts.

关键词： Ethics Systematics Education Government Tools Sensors Artificial intelligence

A Parallel Education Based Intelligent Tutoring systems Framework

学校读者我要写书评

暂无评论

A Parallel Education Based Intelligent Tutoring Systems Fram...

IEEE International Conference on Networking, Sensing and control

作者： Sifeng Jing Ying Tang Xiwei Liu Xiaoyan Gong Wei Cui Joleen Liang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA Squirrel AI Learning Shanghai China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

although online education has improved efficiency of learners' access to high-quality educational resources, real-time interaction between instructors and learners have not yet been achieved in online personalized learning. Intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research & development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed.

关键词： Human computer interaction Conferences Education Market research Sensors Artificial intelligence