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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

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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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Research on Crowdsourcing Truth Inference Method Based on Graph Embedding

Research on Crowdsourcing Truth Inference Method Based on Gr...

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IEEE International Conference on Big knowledge (ICBK)

作者： Liangzhu Zhou Xingrui Zhuo Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation strategies to deal with the labels of different workers to improve the utility of crowdsourcing data. However, most of these studies are based on probabilistic graphical models, which have problems such as difficulty in setting initial parameters. This paper proposes a novel crowdsourcing method Truth Inference based on Graph Embedding (TIGE) for single-choice questions, the method draws on the idea of graph autoencoder, constructs feature vectors for each crowdsourcing task, embeds the relationship between crowdsourcing tasks and workers in graphs, then uses graph neural networks to convert crowdsourcing problems into graph node prediction problems. The feature vectors are continuously optimized in the convolutional layer to obtain the final result. Compared with the six state-of-the-art algorithms on real-world datasets, our method has significant advantages in accuracy and F1-score.

关键词： Crowdsourcing Training Graphical models Probabilistic logic Prediction algorithms Graph neural networks Robustness

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Design of Wireless data Center Network Structure Based on ExCCC-DCN 6th

Design of Wireless Data Center Network Structure Based on Ex...

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6th International Conference on data Science, ICDS 2019

作者： Jing, Yanhao Han, Zhijie Du, Xiaoyu Zhang, Qingfang School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

A data center is a cluster of servers, which is typically an organic collection of tens of thousands of servers. The sheer number of servers determines how well its performance is related to how it is interconnected. Just like the topology of Ethernet determines its network capacity and communication characteristics, the network structure of the data center has a great impact on its performance and capacity. The research object ExCCC-DCN in this paper has excellent node capacity, and its construction cost and operating energy consumption are relatively low, which is very suitable for deploying large-scale data centers. However, ExCCC-DCN is deployed on a wired link, and a large number of wired links can make maintenance and upgrade of the data center more difficult. Therefore, this paper utilizes the advantages of high transmission rate, strong anti-interference and high security of 60 GHz millimeter wave to wirelessly transform ExCCC-DCN, making it more flexible in the case of maintaining communication efficiency. © Springer Nature Singapore Pte Ltd 2020.

关键词： Millimeter waves

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Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification

Gaussian Model-Based Fully Convolutional Networks for Multiv...

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IEEE International Conference on Big knowledge (ICBK)

作者： Changyang Tai Ze Yang Huicheng Zhang Gongqing Wu Junwei Lv Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input.

关键词： Training knowledge engineering Deep learning Correlation Conferences Time series analysis data models

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MTSC-GE: A Novel Graph based Method for Multivariate Time Series Clustering

MTSC-GE: A Novel Graph based Method for Multivariate Time Se...

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IEEE International Conference on Big knowledge (ICBK)

作者： Ze Yang Changyang Tai Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance.

关键词： Deep learning Conferences Clustering methods Time series analysis Clustering algorithms Benchmark testing Stability analysis

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Cloud-assisted Road Condition Monitoring with Privacy Protection in VANETs

Cloud-assisted Road Condition Monitoring with Privacy Protec...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lemei Da Yujue Wang Yong Ding Bo Qin Xiaochun Zhou Hai Liang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Pengcheng Laboratory Cyberspace Security Research Center Shenzhen China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education School of Information Renmin University of China Beijing China Shenzhen Olym Information Security Technology Co. Ltd. Shenzhen China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

关键词： Condition monitoring Privacy data privacy Roads Vehicular ad hoc networks Forgery Sensors

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A New Type Wireless data Center of Comb Topology 6th

A New Type Wireless Data Center of Comb Topology

引用

6th International Conference on data Science, ICDS 2019

作者： Zhang, Qingfang Han, Zhijie Du, Xiaoyu School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

In recent years, group communication has become more and more popular. In the current data center network, multicast plays an extremely important role in group communication. Traditional data center network is designed based on wires. Researchers have proposed Dcell, Bcube and other data center topologies successively. However, in the traditional data center network, the wired topology structure has the disadvantages of high overhead, complex deployment, high maintenance cost, slow heat dissipation and so on. Moreover, the existing wireless data center Flyways structure and Graphite structure have the problems of low connectivity and poor scalability. So this paper proposes and designs a new data center network topology Comb structure (Comb model is a multi-layer cellular structure model, which is a multi-layer structure composed of single-layer honeycomb-like structure interlaced with each other). The structure can effectively improve the connectivity of the data center, shorten the routing path, reduce the probability of node failure in the network, and ensure the smooth network. © Springer Nature Singapore Pte Ltd 2020.

关键词： Topology

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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Robust Misinformation Detection by Visiting Potential Commonsense Conflict

arXiv

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arXiv 2025年

作者： Wang, Bing Li, Ximing Li, Changchun Zhao, Bingrui Fu, Bo Guan, Renchu Wang, Shengsheng College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of the MoE Jilin University China School of Computer Science and Artificial Intelligence Liaoning Normal University China

The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Misinformation Detection (MD), aiming to detect online misinformation automatically, emerges as a rapidly growing research topic in the community. In this paper, we propose a novel plug-and-play augmentation method for the MD task, namely Misinformation Detection with Potential Commonsense Conflict (MD-PCC). We take inspiration from the prior studies indicating that fake articles are more likely to involve commonsense conflict. Accordingly, we construct commonsense expressions for articles, serving to express potential commonsense conflicts inferred by the difference between extracted commonsense triplet and golden ones inferred by the well-established commonsense reasoning tool COMET. These expressions are then specified for each article as augmentation. Any specific MD methods can be then trained on those commonsense-augmented articles. Besides, we also collect a novel commonsense-oriented dataset named CoMis, whose all fake articles are caused by commonsense conflict. We integrate MD-PCC with various existing MD backbones and compare them across 4 public benchmark datasets and CoMis. Empirical results demonstrate that MD-PCC can consistently outperform the existing MD baselines. Copyright © 2025, The Authors. All rights reserved.

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