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A Relation-Constraint Link Prediction Model for Dynamic knowledge Graphs with Entity Drift

ACM Transactions on knowledge Discovery from data 1000年

作者： Xiulin Zheng Peipei Li Zan Zhang Jia Wu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology China and School of Computer Science and Information Engineering Hefei University of Technology China School of Computing Faculty of Science and Engineering Macquarie University Australia

knowledge Graphs (KGs) often suffer from incompleteness and this issue motivates the task of knowledge Graph Completion (KGC). Traditional KGC models mainly concentrate on static KGs with a fixed set of entities and relations, or dynamic KGs with temporal characteristics, faltering in their generalization to constantly evolving KGs with possible irregular entity drift. Thus, in this paper, we propose a novel link prediction model based on the embedding representation to handle the incompleteness of KGs with entity drift, termed as DCEL. Unlike traditional link prediction, DCEL could generate precise embeddings for drifted entity without imposing any regular temporal characteristic. The drifted entity is added into the KG with its links to the existing entity predicted in an incremental fashion with no requirement to retrain the whole KG for computational efficiency. In terms of DCEL model, it fully takes advantages of unstructured textual description, and is composed of four modules, namely MRC (Machine Reading Comprehension), RCAA (Relation Constraint Attentive Aggregator), RSA (Relation Specific Alignment) and RCEO (Relation Constraint Embedding Optimization). Specifically, the MRC module is first employed to extract short texts from long and redundant descriptions. Then, RCAA is used to aggregate the embeddings of textual description of drifted entity and the pre-trained word embeddings learned from corpus to a single text-based entity embedding while shielding the impact of noise and irrelevant information. After that, RSA is applied to align the text-based entity embedding to graph-based space to obtain the corresponding graph-based entity embedding, and then the learned embeddings are fed into the gate structure to be optimized based on the RCEO to improve the accuracy of representation learning. Finally, the graph-based model TransE is used to perform link prediction for drifted entity. Extensive experiments conducted on benchmark datasets in terms of evaluat

关键词： dynamic knowledge graph entity drift link prediction relation constraint

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Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

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Genomics, Proteomics & Bioinformatics 2019年第4期17卷 441-452页

作者： Zhenyu Liu Jiangang Liu Huijuan Yuan Taiyuan Liu Xingwei Cui Zhenchao Tang Yang Du Meiyun Wang Yusong Lin Jie Tian CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Department of Endocrinology and Metabolism Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Department of Radiology Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Cooperative Innovation Center for Internet Healthcare&School of Software Zhengzhou UniversityZhengzhou 450003China School of Mechanical Electrical&Information EngineeringShandong University(Weihai)Weihai 264209China University of Chinese Academy of Sciences Beijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly ***,the underlying neural mechanism of these cognitive impairments remains *** aimed to investigate the correlation between whole brain resting state functional connections(RSFCs)and the cognitive status in 95 patients with *** constructed an elastic net model to estimate the Montreal Cognitive Assessment(MoCA)scores,which served as an index of the cognitive status of the patients,and to select the RSFCs for further ***,we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected *** estimated and chronological MoCA scores were significantly correlated with R=0.81 and the mean absolute error(MAE)=***,cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54%and the area under the receiver operating characteristic(ROC)curve(AUC)of *** connectivity pattern not only included the connections between regions within the default mode network(DMN),but also the functional connectivity between the task-positive networks and the DMN,as well as those within the task-positive *** results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.

关键词： Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA

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Microphysical Characteristics of the 2020 Record-Breaking Meiyu Rainfall in Anhui, China

SSRN

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SSRN 2024年

作者： Yang, Qiqi Zhang, Shuliang Chen, Yiheng Jin, Yuhan Fang, Hongyuan Nanjing Normal University China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou China Key Laboratory of VGE Ministry of Education Nanjing Normal University Nanjing China Suzhou Big Data Research Institute Suzhou China School of Water Conservancy and Transportation Zhengzhou University Zhengzhou China Department of Civil Engineering University of Bristol Bristol United Kingdom

The 2020 Meiyu season in Anhui, China, brought unprecedented rainfall, causing severe socio-economic and ecological disruptions. This study investigates the microphysical characteristics of raindrops during the 2020 record-breaking Meiyu event using minute-level data from six disdrometer stations. Light rain events lasted longer but contributed little to total rainfall, while brief, intense rain events delivered most of the precipitation. Throughout the entire event, the mean drop diameter (Dm) increased from 1.18 mm to 1.9 mm, and mean drop concentration (log10 Nw) rose from 2.92 to 4.08 with increasing rain rate. Novel μ–Λ and Z−R relationships tailored for this extreme event uncovered significant regional variability. The Z−R relationships for total, stratiform, and convective rainfall types revealed higher coefficients than those found in other Meiyu studies, indicating denser raindrop concentrations and larger drop sizes. Cutting-edge dual-polarization radar rainfall prediction models were developed, with relationships between Zdr, Zh, Kdp, and rain rate (R) showing exceptional accuracy, as evidenced by correlation coefficients nearing 1.0 and low RMSE and NMAE values. Furthermore, various KE–R relationships were established for estimating rainfall kinetic energy (KE), with Power Law relationships providing the most accurate representation for KEtime–R and Logarithmic relationships being most effective for KEmm–R, particularly for total and stratiform rainfall. These findings enhance understanding of rainfall microphysics in extreme Meiyu events, offering critical insights for improving flood management and soil erosion control in eastern China. © 2024, The Authors. All rights reserved.

关键词： Rain

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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A Profit-Oriented FDI Attack Strategy in Real-Time Peer-to-Peer Energy Trading Market

A Profit-Oriented FDI Attack Strategy in Real-Time Peer-to-P...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Aichao Zhang Shunbo Lei Chongyang Wan Haoran Zhang Yuan Luo School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The emergence of peer-to-peer (P2P) energy trading market brings both benefits and risks to modern power system. Relying on information and communication technology (ICT), transaction in P2P energy trading market is vulnerable to false data injection attacks (FDIAs). Specifically, the malicious redistribution of short-term forecasting load data will mislead the economic dispatch (ED) in the ex-ante real-time (RT) market. Hence, the actual generation outputs deviate from the schedule, and the corrupt result of ED can affect the trading volume in RT market through a P2P framework. Accordingly, targeting on a specific agent in P2P market, we propose a strategy to inject a malicious forecasting data in order to maximize the trading profits for this agent. Moreover, since the P2P model often solved in a distributed way, which brings heavy computational burden, we conduct a preferred locational marginal price (LMP) to give a one-time solution. Eventually, we demonstrate the effectiveness of our attacking strategy based on the IEEE 9-bus test system.

关键词： Economics Schedules Energy Internet System integration Electricity supply industry Real-time systems Peer-to-peer computing Information and communication technology Forecasting Optimization

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Multi-modal Prediction of Solar Power Generation in Various Provinces of China

Multi-modal Prediction of Solar Power Generation in Various ...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Chongyang Wan Jiyuan Wang Weimin Wu Aichao Zhang Yuan Luo Shunbo Lei School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network School of Science and Engineering The Chinese University of Hong Kong Shenzhen P.R. China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen P.R. China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

This study pioneers a forecasting model for solar power generation tailored specifically for the provinces of China, employing a multi-modal data fusion strategy. Diverging from conventional models that rely solely on single-source data, this approach integrates a diverse array of data types including time-series power generation, stock market prices, and textual information from policy documents. This integration enhances the predictive capabilities of our model by leveraging the unique information provided by each data type. We propose a novel approach called Multi-modal Information Fusion (Multiformer), which utilizes an encoder-decoder architecture augmented with a self-attention layer to effectively capture and synthesize the interdependencies among the varied data inputs. This architectural innovation significantly reduces error propagation and improves model accuracy. Empirical validation of our model was conducted through monthly forecasts of solar power generation across various provinces, revealing that Multiformer outperforms existing state-of-the-art methods, including ITransformer and Informer, with a substantial 35% improvement in performance for a six-month forecast period. The success of Multiformer underscores its potential to generate accurate predictions that are crucial for energy policy-making and strategic planning.

关键词： Technological innovation Accuracy data integration Solar energy Predictive models Strategic planning Transformers data models Stock markets Forecasting

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Towards artificial general intelligence via a multimodal foundation model

arXiv

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arXiv 2021年

作者： Fei, Nanyi Lu, Zhiwu Gao, Yizhao Yang, Guoxing Huo, Yuqi Wen, Jingyuan Lu, Haoyu Song, Ruihua Gao, Xin Xiang, Tao Sun, Hao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China School of Information Renmin University of China Beijing China Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Electrical and Electronic Engineering University of Surrey Guildford United Kingdom

The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI". © 2021, CC BY.

关键词： Semantics

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xitong Ma Yuansen Kuang Lin Yuan Cheng Tian Yijie Zeng Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation; 2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily; 3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models.

关键词： knowledge engineering Visualization Terminology Semantics MIMICs Memory management Radiology Feature extraction data models Biomedical imaging

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt engineering

An Entity Relation Extraction Framework Based on Large Langu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haibin Geng Chenglong Shi Xuesong Jiang Zan Kong Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models.

关键词： Large language models Semantics Noise Merging knowledge graphs Multitasking Natural language processing Iterative methods Prompt engineering data mining

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Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization

arXiv

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arXiv 2023年

作者： Wang, Wen Ni, Wanli Tian, Hui Eldar, Yonina C. Zhang, Rui The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Math and CS Faculty Weizmann Institute of Science Rehovot7610001 Israel School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

In this paper, we propose and study a multifunctional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that only reflects signals, the proposed MF-RIS simultaneously supports multiple functions with one surface, including reflection, refraction, amplification, and energy harvesting of wireless signals. As such, the proposed MF-RIS is capable of significantly enhancing RIS signal coverage by amplifying the signal reflected/refracted by the RIS with the energy harvested. We present the signal model of the proposed MF-RIS, and formulate an optimization problem to maximize the sum-rate of multiple users in an MF-RIS-aided non-orthogonal multiple access network. We jointly optimize the transmit beamforming, power allocations as well as the operating modes and parameters for different elements of the MF-RIS and its deployment location, via an efficient iterative algorithm. Simulation results are provided which show significant performance gains of the MF-RIS over SF-RISs with only some of its functions available. Moreover, we demonstrate that there exists a fundamental trade-off between sum-rate maximization and harvested energy maximization. In contrast to SF-RISs which can be deployed near either the transmitter or receiver, the proposed MF-RIS should be deployed closer to the transmitter for maximizing its communication throughput with more energy harvested. Copyright © 2023, The Authors. All rights reserved.

关键词： Transmitters

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