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arXiv 2021年

作者： Liu, Guliu Li, Lei Liu, Guanfeng Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computing Macquarie University Australia Mininglamp Academy of Sciences Mininglamp Technology China

Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interaction between members of the group. Generally, graph pattern matching (GPM) is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this paper, a social group query problem that needs to consider the trust between members of the group is proposed. To solve this problem, we propose a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence. Aiming at the inefficiency of the NTSS algorithm when matching pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matched edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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Multi-scale Graph Convolutional Neural Network for Object Recognition from Point Cloud data 2nd

Multi-scale Graph Convolutional Neural Network for Object Re...

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2nd International Conference on Urban Intelligence and Applications, ICUIA 2020

作者： Lu, Qiang Chen, Chao Teng, Jinfeng Zhang, Chunyuan Huang, Yi Xuan, Shanli Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China School of Computer and Information Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230009 China

ISBN: (纸本)9789813346000

How to make robots understand the point cloud data which is collected from the 3D sensor and complete the recognition has become a hot research direction in recent years. In this paper, we propose a new approach to improve the critical robotic capability, semantic understanding of the environment (i.e., 3D object recognition). The convolutional neural network (CNN) method has a very good recognition result in the 2D image domain, but it has certain difficulty in applying irregular and unordered 3D point clouds data. The network for point cloud data generally uses the convolution to realize the extraction of point cloud features by finding the neighborhood features on the point set. Due to the different neighborhood scales caused by the irregularity of 3D point cloud data, we propose a CNN structure that combines multi-scale features. By finding multiple neighborhoods of the point set and establishing local graph extraction features, the stable expression of the local neighborhood is obtained. At the same time, the key point calibration method is added, so that the network can dynamically focus on key point features to improve the recognition result. In a series of analytical experiments, we demonstrate competing results that demonstrate the effectiveness of the network structure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Automatically lab.ling Aging Scenarios with a Machine Learning Approach 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. lab.ling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic lab.ling approach to manage and utilize aging scenarios. Under each class, there are lab.ls with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one lab.l, properly lab.ling it becomes a multi-lab.l text classification problem. We extend the multi-lab.l text classification model LSAN for lab.ling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Vehicle Segmentation with Coarse Distance Estimation Based on Monocular Vision

Vehicle Segmentation with Coarse Distance Estimation Based o...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Fangyuan Xie Pengshuai Yin Zehui Ke Ruizhou Sun Ke Ding Jian Chen Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

With the increasing population of drivers and the popularity of intelligent cars, traffic safety is becoming more concerned. Aiming to reduce the probability of road accidents, many works have explored how to provide an early warning when the distances between the current car (or the in-car camera) and vehicles ahead are dangerous. In this paper, benefiting from the robust pixel-level understanding of cutting-edge deep networks, we introduce a vehicle segmentation method based on monocular vision, detecting vehicles in front of the current one and generating a segmentation mask for each vehicle instance detected. Instead of concentrating our efforts on measuring distance precisely, we categorize distances into three classes (“near”, “medium”, and “far”), which are pretty enough for an early collision warning. Our model is trained and evaluated on our hand-made dataset, after which we transfer and deploy it onto an Android TV virtual machine to test its performance. All the results we got show that our method does a good job in vehicle segmentation with coarse distance estimation, being applicable to aided driving.

关键词： TV Sociology Estimation Cameras Virtual machining Safety Automobiles

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is availab.e, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Nitrogen sources and conversion processes in shallow groundwater around a plain lake (Northwest China): Evidenced by multiple isotopes and water chemistry

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Chemosphere 2023年第1期337卷 139322页

作者： Feng, Bo Zhong, Yanxia He, Jing Sha, Xiaohua Fang, Lei Xu, Zhaoxiang Qi, Yarong School of Civil and Hydraulic Engineering Ningxia University Ningxia Yinchuan750021 China Breeding Base for State Key Lab. of Land Degradation and Ecological Restoration in Northwestern China Ningxia Yinchuan750021 China Key Lab. for Restoration and Reconstruction of Degraded Ecosystems in Northwestern China of Ministry of Education Ningxia Yinchuan750021 China School of Ecology and Environment Ningxia University Ningxia Yinchuan750021 China Ningxia Vocational Technical College of Industry and Commerce Ningxia Yinchuan750021 China Hydrology Environmental Geological Survey Institute of Ningxia Hui Autonomous Region Ningxia Yinchuan750021 China

The groundwater quality is severely impacted by Nitrate (NO3−-N) pollution worldwide. Effective lake quality management depends on understanding the origin and fate of nitrogen (N) in the groundwater around lakes. This study combined data for multiple stable isotopes (δ2H–H2O and δ18O–H2O, δ15N–NO3 and δ18O–NO3) and hydrochemistry with the hydrodynamic monitoring profile and a Bayesian isotope mixing (MixSIAR) model to clarify the sources and transformation of N within shallow groundwater around Shahu Lake in the arid area plain of Northwest China. In May 2022, multiple water samples were collected from aquifers (n = 33), drainage water (n = 1), channel water (n = 1), and lake water (n = 7). The results showed that 57% of groundwater samples had high NO3−-N concentrations exceeding the World Health Organisation threshold for drinking water (10 mg/L). The high variation in δ15N–NO3 (from −9.21‰ to +27.57‰) and δ18O–NO3 (from −8.32‰ to +19.04‰) revealed multiple N sources and conversion processes. According to nitrate isotopes and the MixSIAR model, N fertilizer, soil organic N and manure, and sewage are the main sources of nitrogen in groundwater and lake water, which account for 40.61%, 35.86%, and 21.55% of groundwater NO3−-N, respectively, and 35.07%, 34.43%, and 27.49% of lake water NO3−-N. Hydrodynamic monitoring combined with water isotopes showed that upper groundwater (5–10 m) within 1.22 km of the adjacent lake shore strongly interacted with the lake. In groundwater, nitrification predominated, while local denitrification remained a possibility. In conclusion, this research offers a comprehensive approach to determining the sources and conversion of N in contaminated groundwater. © 2023

关键词： Nitrates

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I-mRMR: Incremental max-relevance, and min-redundancy feature selection 3rd

I-mRMR: Incremental max-relevance, and min-redundancy featur...

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3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Xiu, Yeliang Zhao, Suyun Chen, Hong Li, Cuiping Information of School Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China

ISBN: (纸本)9783030260743

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Redundancy

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Online Attentive Kernel-Based Temporal Difference Learning

arXiv

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arXiv 2022年

作者： Yang, Guang Chen, Xingguo Yang, Shangdong Wang, Huihui Dong, Shaokang Gao, Yang The the Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University China The State Key Laboratory for Novel Software Technology Nanjing University China The PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Effects of Mo Doping on Phase Transformation Behaviors and Magnetic Properties of Rare-Earth-Free Mnbi Alloy

SSRN

引用

SSRN 2023年

作者： Xiang, Zhen Wang, Junwei Yang, Yang Li, Chengyu Xu, Huiyu Nguyen, Truongxuan Lu, Wei Shanghai Key Lab. of D&A for Metal-Functional Materials Key Laboratory of Advanced Civil Engineering Materials Ministry of Education School of Materials Science & Engineering Tongji University Shanghai201804 China Baosteel Magnetics Co. Ltd No. 2029 Baoyang Road Baoshan District Shanghai201999 China Graduate University of Sciences and Technology Vietnam Academy of Science and Technology No.18 Hoang Quoc Viet Road Cau Giay district Hanoi Viet Nam

The high-performance rare-earth-free low-temperature phase Mn-Bi-based permanent magnet alloys (LTP-MnBi) have gained widespread attention, but they still present a challenge. Here, the LTP-MnBi alloy with Mo element doping was prepared by using arc-melting and subsequent high-vacuum annealing. The crystalline structure, phase transformation behavior, and magnetic properties were systematically investigated. The results demonstrated that Mo element doping significantly improved the stability of the low-temperature phase in MnBi alloy. The investigation revealed that the disparity in atomic radius between Mo and Mn, Bi played a pivotal role in the formation and stability of the *** the increase of Mo content in the LTP-MnBi alloy, the coercivity value remarkably increased from 8.1 kOe to 11.7 kOe with a decrease of saturation magnetization from 71.6 emu/g to 56.5 emu/g and the maximum energy product gradually reduced from 10.6 MGOe to 7.9 MGOe. The magnetic properties were closely related to the fraction and crystal structure of LTP-MnBi. Therefore, the magnetic properties of Mo-doped MnBi alloy make them an attractive candidate among the rare-earth-free permanent magnets. © 2023, The Authors. All rights reserved.

关键词： Magnetic properties

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Towards Harnessing Feature Embedding for Robust Learning with Noisy lab.ls

arXiv

引用

arXiv 2022年

作者： Zhang, Chuang Shen, Li Yang, Jian Gong, Chen Pca Lab The Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Info. of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image and Video Understanding for Social Security China Jd Explore Academy China College of Computer Science Nankai University China

The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent lab.l noise learning methods. To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy lab.ls. However, we observe that the model will make mistakes during lab.l prediction, resulting in unsatisfactory performance. By contrast, the produced features in the early stage of learning show better robustness. Inspired by this observation, in this paper, we propose a novel feature embedding-based method for deep learning with lab.l noise, termed lab.l NoiseDilution (LEND). To be specific, we first compute a similarity matrix based on current embedded features to capture the local structure of training data. Then, the noisy supervision signals carried by mislab.led data are overwhelmed by nearby correctly lab.led ones (i.e., lab.l noise dilution), of which the effectiveness is guaranteed by the inherent robustness of feature embedding. Finally, the training data with diluted lab.ls are further used to train a robust classifier. Empirically, we conduct extensive experiments on both synthetic and real-world noisy datasets by comparing our LEND with several representative robust learning approaches. The results verify the effectiveness of our LEND. © 2022, CC BY.

关键词： Deep neural networks

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