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

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

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

Sampling topic representative users by integrating node degree and edge weight 5

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Sampling topic representative users by integrating node degr...

5th IEEE International Conference on data Science in Cyberspace, DSC 2020

作者： Wang, Jiangwen Ban, Zhijie Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering College of Computer Science Inner Mongolia University Huhhot China China Banking and Insurance Information Technology Management Co. Ltd Beijing China

ISBN: (纸本)9781728195582

Finding a subset of users to statistically represent the original social network in terms of attributes has received a lot of attention recently. The existing literature had proved that the problem is NP-Hard and proposed some sampling models, but representative degrees aren't systematically studied. Taking node degree and edge weight into account, we present a topic sampling model for the whole social network in this paper. Firstly, by statistically calculating node degrees for one real dataset, we find that one node is more likely to be a representative user if its degree is high. We then give some definitions about representative degrees. Based on statistical stratified sample, we finally give a sampling model of topic representative users who are from most or all attribute groups in the network. Experimental results show that our algorithm significantly outperforms other six baseline methods and is also effective in terms of time complexity. © 2020 IEEE.

关键词： Sampling

Semimetal Contacts to Monolayer Semiconductor: Weak Metalization as an Effective Mechanism to Schottky Barrier Lowering

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arXiv 2022年

作者： Su, Tong Li, Yueyan Wang, Qianqian Zhao, Weiwei Cao, Liemao Ang, Yee Sin Sauvage Laboratory for Smart Materials School of Materials Science and Engineering Harbin Institute of Technology Shenzhen518055 China Shenzhen Key Laboratory of Flexible Printed Electronics Technology Harbin Institute of Technology Shenzhen518055 China Science Mathematics and Technology Singapore University of Technology and Design Singapore487372 Singapore School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China College of Physics and Electronic Engineering Hengyang Normal University Hengyang421002 China

Recent experiment has uncovered semimetal bismuth (Bi) as an excellent electrical contact to monolayer MoS2 with ultralow contact resistance. The contact physics of the broader semimetal/monolayer-semiconductor family beyond Bi/MoS2, however, remains largely unexplored thus far. Here we perform a comprehensive first-principle density functional theory investigation on the electrical contact properties between six archetypal two-dimensional (2D) transition metal dichalcogenide (TMDC) semiconductors, i.e. MoS2, WS2, MoSe2, WSe2, MoTe2 and WTe2, and two representative types of semimetals, Bi and antimony (Sb). As Bi and Sb work functions energetically aligns well with the TMDC conduction band edge, Ohmic or nearly-Ohmic n-type contacts are prevalent. The interlayer distance of semimetal/TMDC contacts are significantly larger than that of the metal/TMDC counterparts, which results in only weak metalization of TMDC upon contact formation. Intriguingly, such weak metalization generates semimetal-induced gap states (MIGS) that extends below the conduction band minimum, thus offering an effective mechanism to reduce or eliminate the n-type Schottky barrier height (SBH) while still preserving the electronic structures of 2D TMDC. A modified Schottky-Mott rule that takes into account SMIGS, interface dipole potential, and Fermi level shifting is proposed, which provides an improved agreement with the DFT-simulated SBH. We further show that the tunneling-specific resistivity of Sb/TMDC contacts are generally lower than the Bi counterparts, thus indicating a better charge injection efficiency can be achieved through Sb contacts. Our findings reveal the promising potential of Bi and Sb as excellent companion electrode materials for advancing 2D semiconductor device technology. Copyright © 2022, The Authors. All rights reserved.

关键词： Molybdenum compounds

ER: Equivariance Regularizer for knowledge Graph Completion

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arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the "size" of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. The experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods. © 2022, CC BY.

关键词： knowledge graph

knowledge Fragment Cleaning in a Genealogy knowledge Graph

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Knowledge Fragment Cleaning in a Genealogy Knowledge Graph

IEEE International Conference on Big knowledge (ICBK)

作者： Guliu Liu Lei Li Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As an important topic of artificial intelligence, knowledge graphs have a wide range of applications such as semantic search, intelligent question answering, and visual decision support. Among them, the genealogy knowledge graph, as a kind of domain knowledge graph, has important application value in genetic disease analysis, population behavior analysis, etc. In the case of multiple data sources and multi-person collaboration, the construction of a genealogy knowledge graph involves the techniques of knowledge representation, knowledge acquisition, and knowledge fusion. In the knowledge fusion process, there are many situations such as the lack and chaos of a relationship, redundant entities, the isolation of some entities and knowledge fragments. How to effectively detect and process these problematic knowledge fragments is significant to the construction of a genealogy knowledge graph. In response to this problem, we propose a method for cleaning the problematic knowledge fragments in a genealogy knowledge graph. The method consists of three phases. In phase 1, we propose a method for detecting and analyzing the problematic knowledge fragments. In phase 2, we design a method for supplementing the entity-relationship of people for different error patterns and a method fusion method for the aligned entity. In phase 3, for the cleaning of isolated knowledge fragments, we propose an entity alignment method based on the father-son relationship and people's names and a connection method of isolated knowledge fragments. Finally, we conduct experiments on a family tree dataset of the Huapu System, and the experimental results indicate the effectiveness and practicality of the method.

关键词： Cleaning Big data knowledge acquisition Redundancy Organizations databases

The Development Trends of Fuel Cell Technologies Based on Patent Analysis

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The Development Trends of Fuel Cell Technologies Based on Pa...

2019 International Conference on Building Energy Conservation, Thermal Safety and Environmental Pollution Control, ICBTE 2019

作者： Tan Xinyu Zhang Yingxuan Deng Sanhong School of Information Management Nanjing university Key Laboratory of Data Engineering and Knowledge Services in Jiangsu Province

Fuel cells are made from fuel and *** of its low pollution,high energy conversion efficiency and high reliability,fuel cell has become the future direction of new energy application,the technological development path in the field of fuel cell research has great significance to the development of technological and energy *** the patent analysis method,this paper analyses the patent data from Derwent Innovation Index quantitively to study the state of application for patents,core technologies,highly cited patents and the main *** shows that auxiliary device and related methods were a research hotspot in recent years;as the biggest patent holder of fuel cell technologies,Toyota,Honda motor *** Nissan motor *** an *** paper has discovered some potential problems behind the phenomena and some suggestions are put forward finally.

关键词： Fuel cells

Neural Collaborative Recommendation with knowledge Graph

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Neural Collaborative Recommendation with Knowledge Graph

IEEE International Conference on Big knowledge (ICBK)

作者： Lei Sang Lei Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities' long-term relational dependencies for the recommendation. In this paper, a two-channel neural interaction method named knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose a Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.

关键词： Collaboration Recurrent neural networks knowledge engineering Semantics Motion pictures Context modeling Filtering

Geometry Interaction knowledge Graph Embeddings

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arXiv 2022年

作者： Cao, Zongsheng Xu, Qianqian Yang, Zhiyong Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

knowledge graph (KG) embeddings have shown great power in learning representations of entities and relations for link prediction tasks. Previous work usually embeds KGs into a single geometric space such as Euclidean space (zero curved), hyperbolic space (negatively curved) or hyperspherical space (positively curved) to maintain their specific geometric structures (e.g., chain, hierarchy and ring structures). However, the topological structure of KGs appears to be complicated, since it may contain multiple types of geometric structures simultaneously. Therefore, embedding KGs in a single space, no matter the Euclidean space, hyperbolic space or hyperspheric space, cannot capture the complex structures of KGs accurately. To overcome this challenge, we propose Geometry Interaction knowledge graph Embeddings (GIE), which learns spatial structures interactively between the Euclidean, hyperbolic and hyperspherical spaces. Theoretically, our proposed GIE can capture a richer set of relational information, model key inference patterns, and enable expressive semantic matching across entities. Experimental results on three well-established knowledge graph completion benchmarks show that our GIE achieves the state-of-the-art performance with fewer parameters. © 2022, CC BY.

关键词： knowledge graph

Disentangled Representation Learning with Transmitted Information Bottleneck

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arXiv 2023年

作者： Dang, Zhuohang Luo, Minnan Jia, Chengyou Dai, Guang Wang, Jihong Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi’an710049 China The SGIT AI Laboratory Xi’an710048 China The State Grid Shaanxi Electric Power Company Ltd. State Grid Corporation of China Xi’an710048 China The School of Information Science and Technology University of Science and Technology of China Hefei230026 China Abu Dhabi United Arab Emirates Baidu Inc Beijing100085 China

Encoding only the task-related information from the raw data, i.e., disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Although significant advances have been made by regularizing the information in representations with information theory, two major challenges remain: 1) the representation compression inevitably leads to performance drop;2) the disentanglement constraints on representations are in complicated optimization. To these issues, we introduce Bayesian networks with transmitted information to formulate the interaction among input and representations during disentanglement. Building upon this framework, we propose DisTIB (Transmitted Information Bottleneck for Disentangled representation learning), a novel objective that navigates the balance between information compression and preservation. We employ variational inference to derive a tractable estimation for DisTIB. This estimation can be simply optimized via standard gradient descent with a reparameterization trick. Moreover, we theoretically prove that DisTIB can achieve optimal disentanglement, underscoring its superior efficacy. To solidify our claims, we conduct extensive experiments on various downstream tasks to demonstrate the appealing efficacy of DisTIB and validate our theoretical analyses. Copyright © 2023, The Authors. All rights reserved.

关键词： Information theory