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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Wei Guan Xueli Yang Li Ma Shaanxi provincial key laboratory of network data analysis and intelligent processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Visual Question Answering is a very challenging tasks in the fiield of AI. The existing model is not accurate enough to judge the relationship between problem words, image objects and the association between problem and image objects, meanwhile, the interpretability of the model needs to be improved. In response, this paper proposes a visual question answering model based on relationship graph and co-attention. Relational features can be obtained by constructing a relational graph using the relationships between the problem and the image, and between the objects of the image, and by dynamically updating the relational graph using a graph attention network guided by the problem. Then, the obtained features are fused using co- attention to obtain richer ***, these features are used to make answer prediction. The model is experimented on the VQA2.0 dataset, and the experimental results show that the model improved the accuracy of the visual question answering model to some extent compared with the existing models.

关键词： Co-Attention Mechanism Relationship Graph,Feature Fusion Visual Question Answering

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CA-WGE: A two-view graph neural network-based knowledge graph completion approach combining common sense perception 23

CA-WGE: A two-view graph neural network-based knowledge grap...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Wei Guan Xiaoru Lian Li Ma Shaanxi provincial key laboratory of network data analysis and intelligent processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The knowledge graph completion algorithm can make the knowledge graph more complete and is currently a research hotspot in the field of artificial intelligence. The knowledge graph completion model is mainly defined in three aspects, the way of negative example generation, the design of scoring function and the design of loss function. The previous knowledge graph completion models only rely on factual view data to predict the missing links between entities and ignore the valuable common sense knowledge, and there is invalid negative sampling in knowledge graph embedding techniques; on the other hand, the existing graph neural network-based knowledge graph embedding models mainly consider capturing the graph structure around entities, and the relational representation is only used to update the entity embedding, which may miss the potentially useful information about the relational structure of potentially useful information. To address the above challenges, this paper proposes a two-view graph neural network-based knowledge graph completion model combined with common sense awareness. Common knowledge is first automatically extracted from fact triples with entity concepts to facilitate high-quality negative sampling, and then positive and weighted negative triples are fed into the two-view graph neural network-based knowledge graph embedding model to capture entity- and relationship-centric graph structures and learn vector representations of entities and relationships, and then the learned entity and relationship representations are fed into a weighted score function to return the final the final score. Extensive experimental and ablation studies on four datasets, FB15K, FB15K237, NELL995, and DBpedia-242, show that the model achieves better performance compared to the state-of-the-art models.

关键词： Common Sense Awareness Graph Neural network Knowledge Graph Completion Knowledge Graph Embedding Negative Sampling

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Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks

Anomaly detection frameworks for outlier and pattern anomaly...

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International Conference on networking and network Applications (NaNA)

作者： Cong Gao Yanping Chen Zhongmin Wang Hong Xia Ning Lv Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xian China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

As a major form of data, the collection and analysis of time series have been widely used in many fields. In practice, wireless sensor network is a popular mechanism for data collection and it is suitable for time series. However, due to the diversity deployment area for a wireless sensor network, the external environmental factors pose a great challenge on the quality of data collected by a sensor node. Besides, the accuracy and reliability of collected data are also affected by internal factors of a sensor node and the performance of the whole wireless sensor network. Common internal factors are resource limitation such as battery capacity, computing ability, and memory size. Moreover, a wireless sensor network deployed for production use is very likely to suffer from malicious attacks from both inside and outside. Therefore, anomaly is inevitable for time series collected by a wireless sensor network. In the paper, we make an extensive review about two types of anomaly detection methods in time series for wireless sensor networks: outlier detection and pattern anomaly detection. For outlier detection, six categories of detection methods are elaborated. For pattern anomaly detection, two main ideas are discussed. Finally, we present the main drawbacks of existing approaches to outlier detection and pattern anomaly detection for time series. The possible future research directions are also summarized.

关键词： Wireless sensor networks Time series analysis Production data collection Environmental factors Reliability Anomaly detection

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Application study of oil pipe entity recognition combined with Deep Reinforcement Learning 23

Application study of oil pipe entity recognition combined wi...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Wei Guan Shijie Liu Li Ma Shaanxi provincial key laboratory of network data analysis and intelligent processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Aiming at the problems of inaccurate extraction of entity feature information and mislabeling of recognition results in the construction of knowledge graphs in the field of oil pipe failure using traditional methods, this paper proposes a research method of BERT-BiLSTM-CRF named entity recognition combined with deep reinforcement learning. The method firstly uses the BERT (Bidirectional encoder representations from transformers) pre-training model to obtain the word vectors of the input sequence semantics; secondly, the trained word vectors are fed into the bi-directional long short- term memory network (BiLSTM) to further obtain contextual features and output the maximum probability sequence annotation results according to the labeling rules of conditional random fields (CRF) and sequence decoding ability; finally, the BERT-BiLSTM-CRF model is supplemented with the deep reinforcement learning-based labeling Finally, the BERT-BiLSTM-CRF-DRL model is added to the BERT-BiLSTM-CRF model to form the framework of named entity recognition model in oil pipe failure domain. The model is compared with other 2 named entity recognition models on a self-built oil pipe data-set, and the experimental results show that the accuracy, recall and F1 values of the model reach 90.5%, 92.7% and 91.9%, respectively, and the entity recognition is better than the other 2 models.

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A new model based on differential evolutionary algorithm and Markov clustering for identifying protein complexes

A new model based on differential evolutionary algorithm and...

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第42届中国控制会议

作者： Zengyu Feng Shouheng Tuo Tianrui Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing

As the material basis for all life activities, proteins play a crucial role in performing life activities. Most real-life proteins perform various functions in the form of protein complexes, so it is essential for understanding of life activities to accurately identify protein complexes. Most of the existing approaches learn protein interactions directly from protein topology,ignoring the biological characteristics of the actual proteins. Nevertheless, actual protein complexes are usually composed of protein cores and protein attachments. In this study, a new MP-DE algorithm is designed from the core-attachment structure to generate protein complex cores using Markov clustering, while searching for attached proteins in the second-order neighborhood of core protein complexes based on differential evolution(DE) algorithm. The experimental results show that the proposed method has high detection accuracy and efficiency on the current mainstream protein-protein interaction(PPI) database.

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural network

Drug-Protein Interaction Prediction by Fusion of Attention a...

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第42届中国控制会议

作者： Tianrui Chen Shouheng Tuo Zengyu Feng School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing

The identification of drug-protein interactions(DTIs) is a critical step in drug development and ***,detecting these interactions using scientific methods presents a formidable *** models often struggle to extract relevant information from drug-protein sequences,and they fail to consider pre-existing interactions between ***,we introduce a deep learning model,DPAG,based on attention mechanisms and graph neural networks,which overcomes these *** leverages information from both the drug and protein sequences,as well as the association between drug-protein pairs,to predict *** conducted multiple experiments to evaluate the performance of the model in benchmark datasets,and the results showed that DPAG achieved excellent performance in terms of AUC,AUPR,precision,and *** findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and *** is available at https://***/veres se/DPAG.

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A Model that Learns Discriminative Features for Automatic Sleep Stage Classification Based on Raw Single-Channel EEG

SSRN

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SSRN 2023年

作者： Heng, Xia Wang, Miao Wang, Zhong-Min Zhang, Jie He, Lang Fan, Lin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China

Sleep staging is the basis of sleep quality assessment. In the process of scoring each sleep stage, some automatic sleep staging models often fail to effectively capture the more accurate long-range correlation coupling between the input sleep EEG signals and the output sleep stage, which leads to the extracted features cannot effectively distinguish the different sleep stage. In order to alleviate the long-term time series problem of sleep EEG signals and learn the discriminative features of each sleep stage in sleep EEG signals, we propose an end-to-end automatic sleep stage classification method based on raw single-channel sleep EEG signals, which can improve the attention to the discriminative features of each sleep stage. In the construction of the model, in order to learn the source domain information of the key part of the sleep EEG signal, SE-Block is added to Convolutional Neural network (CNN) to highlight the time-frequency domain features related to the sleep stage. Meanwhile, the attention mechanism is added to the decoding part of Bidirectional Recurrent Unit (Bi-GRU) to enhance the long-term memory ability of Bi-GRU and learn the conversion rules between sleep stages. To validate the accuracy and stability of the model, the Fpz-Cz channel and Pz-Oz channel EEG signals in the Sleep-EDF sleep dataset are used for 10-fold cross-validation. The classification accuracies obtained are 88.48% and 87.56%, respectively. The results show that our model has stronger ability to extract discriminative features, better representation ability, more stable performance and relatively simple structure. © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Siamese network Visual Tracking Algorithm Based on GCT Attention and Dual-Template Update 5

Siamese Network Visual Tracking Algorithm Based on GCT Atten...

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5th International Conference on Natural Language Processing, ICNLP 2023

作者： Ma, Sugang Sun, Siwei Pu, Lei Yang, Xiaobao Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Rocket Force Engineering University School of Operational Support Xi'an China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an China

ISBN: (纸本)9798350302219

To address the problem of insufficient representational capability and lack of online update of the Fully-convolutional Siamese network (SiamFC) tracker in complex scenes, this paper proposes a siamese network visual tracking algorithm based on GCT attention and dual-template update mechanism. First, the feature extraction network is constructed by replacing AlexNet with the VGG16 network and SoftPool is used to replace the maximum pooling layer. Secondly, the attention module is added after the backbone network to enhance the network's ability to extract object features. Finally, a dual-template update mechanism is designed for response map fusion. Average Peak-to-Correlation Energy (APCE) is used to determine whether to update the dynamic templates, effectively improving the tracking robustness. The proposed algorithm is trained on the Got-10k dataset and tested on the OTB2015 and VOT2018 datasets. The experimental results show that, compared with SiamFC, the success rate and accuracy reach 0.663 and 0.891 on the OTB2015, which improve respectively 7.6% and 11.9%;On the VOT2018 dataset, the tracking accuracy, robustness and EAO are improved respectively by 2.9%, 29% and 14%. The proposed algorithm achieves high tracking accuracy in complex scenes and the tracking speed reaches 52.6 Fps, which meets the real-time tracking requirements. © 2023 IEEE.

关键词： Complex networks

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A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

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Journal of Southeast University(English Edition) 2018年第1期34卷 28-35页

作者： Sun Jiaze Wang Gang School of Computer Science and Technology Xi’anUniversity of Posts and TelecommunicatioosXi’an 710121China Shaanxi Key Laboratory of Network Data Intelligent Processing X’an University of Posts and TelecommunicationsX’an 710121China

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is ***,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea ***,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test *** typical open source projects are selected as benchmark *** and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.

关键词： test case prioritization integer linear programming(I LP) genetic algorithm time constraint

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FF-KGAT: Feature Fusion Based Knowledge Graph Attention network for Recommendation 5

FF-KGAT: Feature Fusion Based Knowledge Graph Attention Netw...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tao, Li Xiaoge, Li Chaodong, Wang Xianliang, Li Shuai, Gao Dan, Han Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China Inspur Software Technology Co Ltd Software Development Project Management China Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Communication and Information Engineering Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (纸本)9781450396899

It is commonly agreed that a recommender system based on knowledge graph (KG) should not only use user-item interactions, but also take side information into account to deal with the problem of data sparsity. However, existing KG-based models present unique challenges that have the shortcoming of cold start and redundant iterations. To address the issue, we propose a Feature Fusion-based Knowledge Graph Attention network (FF-KGAT) for the recommendation. FF-KGAT handles the cold-start problem through introducing user characteristics to fully explore the information of users. Additionally, FF-KGAT introduces the feature fusion graph, which removes slight nodes to obtain fewer iterations. Experimental results show that the proposed model significantly outperforms baseline methods. Particularly, the ndcg is increased by 9.83% and 10.25% on two public datasets, compared with the best performance of existing methods. © 2022 ACM.

关键词： Recommender systems

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