检索结果-内蒙古大学图书馆

Bridging Trajectory-Aware Evolutionary Graph Learning and Large Language Models for Enhancing Navigability in Social Internet of Things

学校读者我要写书评

暂无评论

IEEE Internet of Things Journal 2025年

作者： Bi, Xin Han, Zhubin Yao, Xin Zhao, Xiangguo Wang, Yu-Ping Yuan, Ye State Key Laboratory of Intelligent Deep Metal Mining and Equipment China Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University College of Software Shenyang110819 China Beijing Institute of Technology School of Computer Science and Technology Beijing100091 China

Social Internet of Things (SIoT) has emerged as a novel paradigm that enhances IoT service capabilities by leveraging device-level social relationships. However, the explosive growth of heterogeneous devices, the dynamic mobile device trajectories, and complex spatiotemporal interaction patterns severely hinder SIoT network navigability. Particularly, the device mobility and contextual diversity pose significant challenges to social relation classification, a critical task for efficient routing and service discovery. Existing approaches, primarily designed for static or homogeneous networks, fail to adequately capture the evolving contextual dependencies and the spatiotemporal heterogeneity in SIoT. To address these challenges, we propose Trajectory-Aware Graph LLM (TAGLLM), a novel framework that enhances SIoT navigability through context-aware relation classification. TAGLLM introduces a multi-feature fusion trajectory evolutionary graph encoder to jointly model complex device attributes, social relations, and dynamic trajectories. Furthermore, a structural graph-text token alignment strategy is designed to exploit the generalization ability and contextual understanding capabilities of Large Language Models (LLMs), enabling more effective modeling of heterogeneous and dynamic SIoT scenarios. Extensive experiments on real-world SIoT datasets demonstrate that TAGLLM outperforms state-of-the-art baselines across multiple evaluation metrics, highlighting its potential to push the frontier of graph learning and LLM integration in SIoT applications. © 2014 IEEE.

关键词： Network theory (graphs)

Simulating the Dilution of Sulfuric Acid on Mixed Reality Platform 8

学校读者我要写书评

暂无评论

Simulating the Dilution of Sulfuric Acid on Mixed Reality Pl...

8th International Conference on Virtual Reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. Virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

Scientific Paper Classification Based on Graph Neural Network with Hypergraph Self-attention Mechanism

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Liu, Jiashun Xue, Zhe Li, Ang School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

The number of scientific papers has increased rapidly in recent years. How to make good use of scientific papers for research is very important. Through the high-quality classification of scientific papers, researchers can quickly find the resource content they need from the massive scientific resources. The classification of scientific papers will effectively help researchers filter redundant information, obtain search results quickly and accurately, and improve the search quality, which is necessary for scientific resource management. This paper proposed a science-technique paper classification method based on hypergraph neural network(SPHNN). In the heterogeneous information network of scientific papers, the repeated high-order subgraphs are modeled as hyperedges composed of multiple related nodes. Then the whole heterogeneous information network is transformed into a hypergraph composed of different hyperedges. The graph convolution operation is carried out on the hypergraph structure, and the hyperedges self-attention mechanism is introduced to aggregate different types of nodes in the hypergraph, so that the final node representation can effectively maintain high-order nearest neighbor relationships and complex semantic information. Finally, by comparing with other methods, we proved that the model proposed in this paper has improved its performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wang, Benzhi Liang, Meiyu Kou, Feifei Xu, Mingying School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

Science and technology big data contain a lot of cross-media *** are images and texts in the scientific *** s ingle modal search method cannot well meet the needs of scientific *** paper proposes a cross-media scientific research achievements retrieval method based on deep language model (CARDL).It achieves a unified cross-media semantic representation by learning the semantic association between different modal data, and is applied to the generation of text semantic vector of scientific research achievements, and then cross-media retrieval is realized through semantic similarity matching between different modal *** results show that the proposed CARDL method achieves better cross-modal retrieval performance than existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Modal analysis

Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zeng, Yuyao Du, Junping Xue, Zhe Li, Ang School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

关键词： Knowledge graph

A Novel Topic Extraction Model for Science and technology Demand Data 7

学校读者我要写书评

暂无评论

A Novel Topic Extraction Model for Science and Technology De...

7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Cui, Haiyan Xue, Zhe Du, Junping Xu, Xin Xi, Junqing Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing100876 China Ministry of Justice Information Center Beijing100020 China

ISBN: (纸本)9781665441490

There are few studies focus on enterprise science and technology demand data, which is very important for enterprise development and innovation. These data are scattered on several websites and contain a lot of noise, which make it difficult to accurately analyze their topic. In this paper, the topic extraction algorithm based on deep learning is proposed to obtain the topic of demand in various industries. We adopt topic features clustering method to refine the classification of science and technology demand data. keyword extraction method is proposed to filter the extracted theme words. The extracted topics are combined with time series to analyze the evolution of the topics and show the applicability of the extracted results of the science and technology demand data. A lot of experiments are conducted to verify the effectiveness of our algorithm. The optimal parameters and the number of topics are also analyzed in the experiments. © 2021 IEEE.

关键词： Deep learning

An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wang, Yuhui Du, Junping Shao, Yingxia School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

Patent texts contain a large amount of entity information. Through named entity recognition, intellectual property entity information containing key information can be extracted from it, helping researchers to understand the patent content faster. Therefore, it is difficult for existing named entity extraction methods to make full use of the semantic information at the word level brought about by professional vocabulary changes. This paper proposes a method for extracting intellectual property entities based on Transformer and technical word information, and provides accurate word vector representation in combination with the BERT language method. In the process of word vector generation, the technical word information extracted by IDCNN is added to improve the understanding of intellectual property entities Representation ability. Finally, the Transformer encoder that introduces relative position encoding is used to learn the deep semantic information of the text from the sequence of word vectors, and realize entity label prediction. Experimental results on public datasets and annotated patent datasets show that the method improves the accuracy of entity recognition. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal encoding

Research Scholar Interest Mining Method based on Load Centrality

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Jiang, Yang Xue, Zhe Li, Ang School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

In the era of big data, it is possible to carry out cooperative research on the research results of researchers through papers, patents and other data, so as to study the role of researchers, and produce results in the analysis of results. For the important problems found in the research and application of reality, this paper also proposes a research scholar interest mining algorithm based on load centrality (LCBIM), which can accurately solve the problem according to the researcher's research papers and patent data. Graphs of creative algorithms in various fields of the study aggregated ideas, generated topic graphs by aggregating neighborhoods, used the generated topic information to construct with similar or similar topic spaces, and utilize keywords to construct one or more topics. The regional structure of each topic can be used to closely calculate the weight of the centrality research model of the node, which can analyze the field in the complete coverage principle. The scientific research cooperation based on the load rate center proposed in this paper can effectively extract the interests of scientific research scholars from papers and corpus. Copyright © 2022, The Authors. All rights reserved.

关键词： Data mining

A Relational Triple Extraction Method Based on Feature Reasoning for Technological Patents

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fang, Runze Du, Junping Shao, Yingxia Guan, Zeli School of Computer Science Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

The relation triples extraction method based on table filling can address the issues of relation overlap and bias propagation. However, most of them only establish separate table features for each relationship, which ignores the implicit relationship between different entity pairs and different relationship features. Therefore, a feature reasoning relational triple extraction method based on table filling for technological patents is proposed to explore the integration of entity recognition and entity relationship, and to extract entity relationship triples from multi-source scientific and technological patents data. Compared with the previous methods, the method we proposed for relational triple extraction has the following advantages: 1) The table filling method that saves more running space enhances the speed and efficiency of the model. 2) Based on the features of existing token pairs and table relations, reasoning the implicit relationship features, and improve the accuracy of triple extraction. On five benchmark datasets, we evaluated the model we suggested. The result suggest that our model is advanced and effective, and it performed well on most of these datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction