检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Wang, Naibang Shang, Deyong Gong, Yan Hu, Xiaoxi Song, Ziying Yang, Lei Huang, Yuhan Wang, Xiaoyu Lu, Jianli Beijing100083 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Mechatronics Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin China Department of Electronic & Electrical Engineering University of Bath United Kingdom

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://***/frankwnb/Collaborative-Perceptio n-datasets-for-Autonomous-Driving. © 2025, CC BY-SA.

关键词： Sensor data fusion

来源：评论

学校读者我要写书评

暂无评论

Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

arXiv

引用

arXiv 2024年

作者： Cheng, Zhiyong Dong, Jianhua Liu, Fan Zhu, Lei Yang, Xun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology No. 485 Danxia Road Anhui Hefei230009 China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences No. 19 Keyuan Road Shandong Jinan250014 China School of Computing National University of Singapore 21 Lower Kent Ridge Road Singapore119077 Singapore School of Electronic and Information Engineering University of Tongji No. 4800 Caoan Road Shanghai201804 China School of Information Science and Technology University of Science and Technology of China No. 443 Huangshan Road Anhui Hefei230027 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Centery No. 485 Danxia Road Anhui Hefei230009 China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation on benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

A parameter adaptive de algorithm on real-parameter optimization

A parameter adaptive de algorithm on real-parameter optimiza...

引用

作者： Pan, Jeng-Shyang Yang, Cheng Meng, Fanjia Chen, Yuxin Meng, Zhenyu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Institute of Artificial Intelligence Fujian University of Technology Fuzhou China Guanzhuang Central Primary School of Zhangqiu District Jinan China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China

Differential Evolution (DE) algorithm generates a population of individuals by encoding with a floating point vector, and it is a simple and effective population-based stochastic optimization algorithm for global optimization of continuous space. Because of its excellent performance, DE variants can be applied in a wide range of applications in science and engineering. However, the performance of DE is sensitive to the choice of trial vector generation strategy and the associated control parameters. Therefore, it is necessary to choose appropriate mutation strategy and control parameters when tackling optimization applications. In this paper, an adaptive update mechanism is proposed to update control parameters F and Cr. The experimental results are verified on the CEC 2013 test suite which contains 28 benchmark functions for the evaluation of single objective real parameter optimization. The proposed algorithm is compared with jDE, iwPSO and ccPSO, and experiment results show its good performance. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Active Learning with Multi-granular Graph Auto-Encoder

Active Learning with Multi-granular Graph Auto-Encoder

引用

IEEE International Conference on data mining (ICDM)

作者： Yi He Xu Yuan Nian-Feng Tzeng Xindong Wu Center for Advanced Computer Studies University of Louisiana Lafayette Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Mininglamp Academy of Sciences Mininglamp Technology

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Predictive modeling of networked data finds many real-world applications, such as fraud detection in social networks, drug discovery in biomedical networks, paper topic classification in citation networks, and so forth. Although the advanced machine learning approaches can help build reasonably accurate predictive models, their applicability is immensely hindered by the data labeling tasks, which are onerous, time-consuming, and error-prone. In this paper, we propose a novel active learning paradigm for networked data, named topology-and-content-aware (TACA) active learning, aiming to minimize the number of labels while achieving a desirable level of model accuracy. Overall, TACA advances existing works from two aspects: (1) TACA makes no assumption on the network property, whereas most existing works only perform effectively on a locally consistent network in which linked nodes are expected to share the same labels and (2) TACA generates queries without relying on model performance, thereby enjoying robust predictive results even when noises exist in the queried labels. Both theoretical and empirical evidences are presented, substantiating the effectiveness of and optimism our approach.

关键词： Social networking (online) Neural networks Predictive models data models Labeling Noise measurement Task analysis

来源：评论

学校读者我要写书评

暂无评论

Characterizing Oil Reservoir from Three-Dimensional Surface Deformation Using Multi-Track Insar: A Case Study in the Liaohe Oilfield, Northeast China

SSRN

引用

SSRN 2023年

作者： Tang, Wei Gong, Zhiqiang Sun, Xiubo Liu, Yu'an Motagh, Mahdi Li, Zhicai Li, Jing Malinowska, Agnieszka Jiang, Jinbao Wei, Lianhuan Zhang, Xin Wei, Xing Li, Hui Geng, Xu College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing China Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region Ministry of Natural Resources China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Shenyang Center of China Geological Survey Shenyang China National Marine Environmental Monitoring Center Dalian China GFZ German Research Centre for Geosciences Department of Geodesy Section of Remote Sensing and Geoinformatics Potsdam Germany Institute of Photogrammetry and GeoInformation Leibniz University Hannover Hannover Germany AGH University of Science and Technology Faculty of Geo-Data Science Geodesy and Environmental Engineering Krakow Poland School of Resources and Civil Engineering Northeastern University Shenyang China Shenyang Geotechnical Investigation & Surveying Research Institute Co. Ltd. Shenyang China Chemical Geological Prospecting Institute of Liaoning Province Co. Ltd. Jinzhou China

Oil productions can result in land subsidence, causing damages to oil production facilities and surface infrastructures. The surface displacement is predominantly vertical subsidence, which has a great impact on coastal plains near sea level, leading to permanent inundation. The Liaohe oilfield, located in Liaohe River Delta (LRD), northeast of China, is one of the most significant subsidence areas in China as a direct consequence of oil extraction from the reservoir. In this work, we investigate the production-induced surface displacement in the LRD region by taking advantage of multiple geometries of Sentinel-1 radar images. Images from two ascending and two descending tracks are processed by an Interferometric Synthetic Aperture Radar (InSAR) time series analysis over the 2017 to 2021 period, providing displacement rate maps and displacement time series in the radar line-of-sight (LOS) *** studies carried out in this area assumed the oil production-induced displacement retrieved from InSAR corresponds only to vertical deformation. Here, for the first time, we proposed a method to retrieve the full three-dimensional (3D) displacement field over the oilfield. We retrieved the vertical and east-west displacement components by combining the multiple InSAR geometries LOS observations and retrieved the north-south component based on the assumption of a physical relationship between the horizontal and vertical *** derived 3D displacement fields over Shuguang oilfield exhibit a circular subsidence bowl with a maximum subsiding rate reaching 212 mm/year, accompanied by a centripetal pattern of horizontal displacements with maximum rates up to 50-60 mm/year moving towards the subsidence center. The retrieved-3D displacements are in good agreement with predictions from the geomechanical modeling by assuming a disk-shaped reservoir subject to a uniform reduction in pore fluid pressure. Finally, we show the importance of knowing both the vertical a

关键词： Subsidence

来源：评论

学校读者我要写书评

暂无评论

Iterative context-aware graph inference for visual dialog

arXiv

引用

arXiv 2020年

作者： Guo, Dan Wang, Hui Zhang, Hanwang Zha, Zheng-Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Nanyang Technological University University of Science and Technology of China

Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inference in a graphical model with sparse contexts and unknown graph structure (relation descriptor), and how to model the underlying context-aware relation inference is critical. To this end, we propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. The graph structure (relations in dialog) is iteratively updated using an adaptive top-K message passing mechanism. Specifically, in every message passing step, each node selects the most K relevant nodes, and only receives messages from them. Then, after the update, we impose graph attention on all the nodes to get the final graph embedding and infer the answer. In CAG, each node has dynamic relations in the graph (different related K neighbor nodes), and only the most relevant nodes are attributive to the context-aware relational graph inference. Experimental results on VisDial v0.9 and v1.0 datasets show that CAG outperforms comparative methods. Visualization results further validate the interpretability of our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Message passing

来源：评论

学校读者我要写书评

暂无评论

Balanced Tree Partitioning with Succinct Logic

Balanced Tree Partitioning with Succinct Logic

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Xindong Wu Shaojing Sheng Peng Zhou Key Laboratory of Knowledge Engineering with Big Data (Heifei Unversity of Technology) Ministry of Education Heifei China School of Compuster Science and Technology Anhui University Heifei China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.

关键词： Partitioning algorithms data visualization Convergence data analysis Distributed databases Big data

来源：评论

学校读者我要写书评

暂无评论

Review of Deep Learning Parallelization and Its Application in Spatial data mining

Review of Deep Learning Parallelization and Its Application ...

引用

Big data, Artificial Intelligence and Internet of Things engineering (ICBAIE), International Conference on

作者： Wei Hu Fenghua Huang Digital China Research Institute (Fujian) Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou China Fujian University Engineering Research Center of Spatial Data Mining and Application Yango University Fuzhou China

ISBN: (数字)9781728164991

ISBN: (纸本)9781728165004

In recent years, deep learning has achieved great success in the field of artificial intelligence. The rise of deep learning has accelerated the advent of the intelligent era. However, with the deepening of the layers of deep neural networks and the increasing complexity of algorithm models, the size of the data set used for training is also getting larger and larger, which causes the time required for training to continue to increase, and the parallelization of deep neural networks can effectively solve this problem. Therefore, the research on the parallelization of deep learning becomes more and more important. This article summarizes the current research status of deep learning and its parallelization implementation, and also introduces the related research progress in spatial data processing especially.

关键词： Machine learning Spatial databases Big data Internet of Things Learning (artificial intelligence) Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Towards efficient local causal structure learning

arXiv

引用

arXiv 2021年

作者： Yang, Shuai Wang, Hao Yu, Kui Cao, Fuyuan Wu, Xindong The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China The School of Computer and Information Technology Shanxi University Taiyuan030006 China The Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing100102 China

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Iterative Context-Aware Graph Inference for Visual Dialog

Iterative Context-Aware Graph Inference for Visual Dialog

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dan Guo Hui Wang Hanwang Zhang Zheng-Jun Zha Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Nanyang Technological University University of Science and Technology of China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

关键词： Visualization History Task analysis Semantics Message passing Neural networks Cognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：