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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Large-Scale network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： network security

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Fusion of Dynamic Hypergraph and Clinical Event for Sequential Diagnosis Prediction 29

Fusion of Dynamic Hypergraph and Clinical Event for Sequenti...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Xin Peng, Xueping Guan, Hongjiao Zhao, Long Qiao, Xinxiao Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

ISBN: (纸本)9798350330717

Sequential diagnosis prediction (SDP) is a challenging task, aiming to predict patients' future diagnoses based on their historical medical records. While methods based on graph neural networks (GNNs) have proven successful for this task, they typically focus on modeling pairwise diseases using a global disease combination graph. However, these approaches neglect the fine-grained higher-order relations among persistent and emerging diseases within a single visit, which may contain crucial clues to predict the next diagnosis. Additionally, they fail to fully leverage patient-related clinical information present in electronic health records (EHRs). To address these challenges, this paper proposes a novel approach called the fusion of Dynamic Hypergraph and Clinical Event (DHCE) for sequential diagnosis prediction. The proposed method aims to exploit the fine-grained higher-order relations among diagnoses within a visit and leverage clinical event information from EHRs to improve the accuracy of predicting the next diagnosis. Specifically, DHCE categorizes diagnoses within a single visit in a fine-grained granularity into persistent and emerging categories based on a patient's historical diagnoses. It then constructs dynamic hypergraphs to capture higher-order disease relations within each visit. Next, we design a transition function to extract the transitional context from previous visits in order to generate the visit representation. Furthermore, to fully leverage patient-related clinical events in a visit, we utilize Bio-Clinical BERT to encode them and generate the clinical event representation for each visit. Finally, we combine the visit representation and event representation to generate a comprehensive patient representation, which is then used to predict the patient's next diagnosis. Experimental results on two benchmark datasets consistently demonstrate that DHCE outperforms state-of-the-art methods. © 2023 IEEE.

关键词： Bio-Clinical BERT clinical event dynamic hypergraph sequential diagnosis prediction

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Research on Classroom Behavior Target Detection Algorithm based on YOLOv7 6

Research on Classroom Behavior Target Detection Algorithm ba...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Qiu, Wenke Wu, Xiaoming Liu, Xiangzhi Computer Science and Qilu University of Technology Shandong Academy of Sciences ShanDong Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350316520

The integration of video information for the purpose of detecting students' behavior in the classroom, with the aim of enhancing teaching efficiency and maintaining a stable classroom environment, offers significant assistance. However, existing models usually struggle to achieve good accuracy due to the lack of datasets, dense targets, interclass occlusion, and other issues. To address these difficulties, the CD-YOLO classroom behavior detection algorithm is proposed in this paper. And the classroom behavior dataset (DSL-Dataset) is created. We propose a loss function named VAIoU, which can be combined with vector ratio to filter the prediction box. To further improve the detection accuracy of occluded targets, we propose an attention module CM module with command feature fusion. Moreover, in this paper, we improve the convolutional kernel to design a lightweight convolutional kernel FFconv. Experimental results on DSL-Dataset show that CD-YOLO outperforms existing one-stage detection models in terms of detection accuracy. © 2024 IEEE.

关键词： Convolution

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Petri Net-Based Attack Modeling for Industrial Control System networks 3rd

Petri Net-Based Attack Modeling for Industrial Control Syste...

引用

3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Wang, Yichuan Lv, Zhaoqi Zhang, Yaling Xiao, Yeqiu Hei, Xinhong School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819903009

Industrial control systems have been deeply involved in industrial facilities and infrastructure. With the integrated development of industry and information technology, industrial control systems are tied to the Internet and may be threatened by cyber attacks. Modeling analysis and security evaluation are significant to guarantee industrial control system security. For in-depth research of security in the industrial control network, we first provide a cyber attack strategy for programmable logic controllers with respect to critical nodes in industrial control systems. Then, we explore and validate the viability of attacks based on the proposed strategy through simulations and visualization. Finally, Petri net modeling is utilized to depict both cyber attacks and defensive techniques. The results in this paper demonstrate that the proposed attack strategy gives new ideas for us to investigate industrial control cyber assaults, while the modeling and analytic methodologies in this paper are also useful for system weaknesses analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： network security

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Competitive Swarm Optimizer Based on Individual Learning Mechanism

Competitive Swarm Optimizer Based on Individual Learning Mec...

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High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： JingQi Tang Wei Li Lei Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

In view of the disadvantages of the traditional competitive swarm optimizer (CSO), such as falling into local minimization or poor convergence accuracy, this paper proposed an enhanced CSO algorithm called competitive swarm optimizer based on individual learning mechanism (ILCSO). Firstly, the individual selection rate and the learning rate are designed to dynamically select the winner and loser. The losers are updated by the precise competitive learning strategy to improve the algorithm exploitation ability. Secondly, the mutation strategy based on performance improvement is introduced, which improves the search ability of algorithm. The proposed algorithm effectively balances the local search and global exploration with the individual learning mechanism, and improves the probability of finding the global optimal solution. Finally, ILCSO is compared with six classical meta-heuristic algorithms on CEC2014 benchmark functions. The Wilcoxon rank-sum test is used to demonstrate that the ILCSO is effective. Experimental results and statistical analysis show that ILCSO has higher convergence speed and convergence accuracy.

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A Driving Risk Prediction Approach Based on Generative Adversarial networks and VANET for Autonomous Trams

A Driving Risk Prediction Approach Based on Generative Adver...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Ji, Wenjiang Yang, Jiangcheng Wang, Yichuan Zhu, Lei Qiu, Yuan Hei, Xinhong College of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Shaanxi Key Laboratory for Net work Computing and Security T echnology Xi'an China

ISBN: (纸本)9781665441582

Driving safety is an essential prerequisite to the rapid development of autonomous trams. However, the relationship of driving risk factors is nonlinear, which makes modeling difficult. To improve the accuracy of driving risk prediction, a data driven approach based on Generative Adversarial networks was proposed. First of all, a communication and alarming scenario of Vehicular Ad-hoc networks was demonstrated, in which the original data sets can be collected and transmitted by the help of sensors and Road Side Units. Then the RFE feature selection algorithm was used to keep the key features. To deal the sample asymmetry problem, a DCGAN model was designed for sparse samples expansion. At last, the XGBoost algorithm was used to classification and output the risk prediction result. During the experiment implemented with the public and real data sets, the risk prediction accuracy of proposed approach can up to 97.24%, for which takes the advantages in generating of the sparse samples. © 2021 IEEE.

关键词： Generative adversarial networks

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Text Information Mining in Cyberspace: An Information Extraction Method Based on T5 and KeyBERT 9

Text Information Mining in Cyberspace: An Information Extrac...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Liu, Kuan Li, Yumei Qi, Yuanhua Qi, Ning Zhai, Mengran Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Institute of Economy and Informatization Development Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Shandong Institute of Economy and Informatization Development Jinan China

ISBN: (纸本)9798350391367

With the development of the digital economy and the advent of the big data era, the rapid growth of textual information in cyberspace has placed higher demands on information processing technology. This paper proposes an information extraction technique based on the Text-to-Text Transfer Transformer, known as T5, and KeyBERT models, aiming to efficiently distill the key content from textual information in cyberspace. This method combines automatic summarization and keyword extraction to form information briefs. The experiments selected representative policy documents related to data elements as input data and employed information entropy and ROUGE scores as metrics to evaluate the automatic summarization model. The results were compared with the outputs of similar models. Experimental results indicate that the T5 model outperforms other models in terms of summarization effectiveness, and the proposed information extraction method shows significant advantages in readability and processing efficiency, demonstrating practical application value. © 2024 IEEE.

关键词： Spatio-temporal data

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Prediction of miRNA family based on class-incremental learning

Prediction of miRNA family based on class-incremental learni...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Lulu Qiu Aimin Li Rong Fei Junhuai Li Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Department of Mechanical Engineering Department of Computer Science Division of Biomedical Engineering Saskatoon Canada

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the development of deep sequencing, recent studies indicate that a miRNA precursor can generate multiple miRNA isoforms (isomiRs). The family prediction of canonical miRNAs and isomiRs could provide a basis for miRNA functional research. In this study, we propose a novel method for family identification of canonical miRNA and isomiRs based on incremental learning. First, a benchmark dataset is constructed by processing data based on miRNA sequences and their family annotation. Moreover, sequence embedding and RNN are used for capturing essential features and inherent dependencies within a sequence. Finally, incremental learning is applied to accommodate the continuous influx of miRNA sequencing data, enabling RNN to stay relevant and effective over time. Comparative experiments and ablation studies illustrate the effectiveness of our model, which can help to comprehensively understand miRNA’s function.

关键词： Sequential analysis Incremental learning Annotations Biological system modeling Benchmark testing Bioinformatics

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Petri net modeling and vulnerability analysis of the Heartbleed

Petri net modeling and vulnerability analysis of the Heartbl...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Wang, Yichuan Wang, He Hei, Xinhong Ji, Wenjiang Zhu, Lei Xi'an University of technology School of computer science and engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (纸本)9781665441582

In recent years, a variety of network attacks emerge in an endless stream, and network attacks gradually show the characteristics of higher secrecy and greater harm. At present, the analysis of system vulnerabilities is generally focused on the characteristics analysis and impact hazard level, and lack of formal modeling and vulnerability analysis methods. In this paper, we model the OpenSSL service's Heartbleed vulnerability based on Petri net. We build a formal model combined with the source code and system state, analyze the vulnerability of the system running state, and propose an automatic vulnerability repair scheme. Experiments show that this model can carry out fine-grained formal analysis and simulation of the Heartbleed, which is of great significance to explore the system vulnerability modeling method. © 2021 IEEE.

关键词： Petri nets

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