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检索条件"机构=Shaanxi Key Laboratory of Network Data Intelligent Processing"

共 357 条记录，以下是41-50 订阅

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Moving Forward Together: A Multi-User Pattern Extraction Neural Model for Sequential Recommendations 6

Moving Forward Together: A Multi-User Pattern Extraction Neu...

引用

6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Du, Xiangpeng Li, Xiaoge Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (纸本)9798400707674

The sequential recommendation is a very important task in recommendation systems. The aim of it is to dynamic predict user's interests based on their historical behaviors. Despite recent progress, most of deep learning approaches focus on extracting the behavioral patterns of a single user. Pulling multiple users can increase the diversity of historical information. In this paper, we propose multi-user behavioral pattern extraction model (TMGN) via a combination of traditional behavioral pattern extraction and graph convolutional aggregation. Concretely, TMGN first learns the graph structure of users and items. Then it performs graph convolution aggregation to derive a representation of users to extract user interest features. We recall the users with the highest similarity by calculating the cosine similarity of each candidate user. Finally, we inject multiple users' sequence patterns and time information into the muti-head attention mechanism. Experiments on actual data (Movielens1m, Movielen20m and Amazon beauty) show that the proposed method outperforms current state-of-the-art sequential recommendation methods. TMGN achieves an average improvement of 5.67%, 5.75%, 7.32%, and 7.19% over the most robust baseline in Hit@5, Hit@10, NDCG@5, and NDCG@10, respectively. We provide extensive ablation studies to analyze the impact of each model component on TMGN. © 2023 ACM.

关键词： Convolution

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Application study of oil pipe entity recognition combined with Deep Reinforcement Learning: Named Entity Recognition combined with Deep Reinforcement Learning 6

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

引用

6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Guan, Wei Liu, Shijie Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing 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. © 2023 ACM.

关键词： Long short-term memory

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Visual Question Answering Model Based on Relationship graphs and Co-Attention 6

Visual Question Answering Model Based on Relationship graphs...

引用

6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Guan, Wei Yang, Xueli Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing 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. © 2023 ACM.

关键词： Image enhancement

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Multimodal Dialogue Generation Based on Transformer and Collaborative Attention 5

Multimodal Dialogue Generation Based on Transformer and Coll...

引用

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

作者： Guan, Wei Zhang, Zhen Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

In view of the fact that the current multimodal dialogue generation models are based on a single image for question-and-answer dialogue generation, the image information cannot be deeply integrated into the sentences, resulting in the inability to generate semantically coherent, informative visual contextual dialogue responses, which further limits the application of multimodal dialogue generation models in real scenarios. This paper proposes a Deep Collaborative Attention Model (DCAN) method for multimodal dialogue generation tasks. First, the method globally encode the dialogue context and its corresponding visual context information respectively;second, to guide the simultaneous learning of interactions between image and text multimodal representations, after the visual context features are fused with the dialogue context features through the collaborative attention mechanism, the hadamard product is used to fully fuse the multimodal features again to improve the network performance;finally, the fused features are fed into a transformer-based decoder to generate coherent, informative responses. in order to solve the problem of continuous dialogue in multimodal dialogue, the method of this paper uses the OpenVidial2.0 data set to conduct experiments. The results show that the responses generated by this model have higher correlation and diversity than existing comparison models, and it can effectively integrate visual context information. © 2022 ACM.

关键词： Image enhancement

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GA-SATIC: Semi-autoregressive Transformer Image Captioning Based on Geometric Attention 5

GA-SATIC: Semi-autoregressive Transformer Image Captioning B...

引用

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

作者： Li, Ziyi Li, Ma Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

Image captioning is a cross-domain task involving image and natural language processing. Most of the current models follow an encoder-decoder architecture, where the encoder takes image feature vectors as input, and the decoder uses an autoregressive or non-autoregressive approach for decoding. However, most models extract the feature vectors of an image through region proposals of the object detector, without considering the relative spatial relationship between objects. When decoding, the autoregressive decoding method is adopted, that is, the next word is generated based on the generated word, and the generation is performed step by step, which will lead to high delay in the inference process. To solve this problem, scholars have proposed a non-autoregressive approach, which speeds up inference by generating all words in parallel, but reduces the quality of generated captioning. Aiming at the above problems, this paper proposes a semi-autoregressive Transformer model based on geometric attention. The encoder integrates the relative spatial relationship between the detected objects through geometric attention and appearance attention, so as to enhance spatial awareness;the decoder adopts a semi-autoregressive decoding method, which is serialized globally and paralleled locally, enabling the model to achieve a better trade-off between decoding speed and captioning accuracy. Extensive experiments and ablation studies on the MSCOCO dataset have shown that the model achieves better performance compared to state-of-the-art models. © 2022 ACM.

关键词： Signal encoding

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Research on the Generation of Emotional Dialogue Statements in Generative Adversarial networks 5

Research on the Generation of Emotional Dialogue Statements ...

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

作者： Guan, Wei Huang, Mingrui Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

With the continuous progress of neural network technology in different fields, people have put forward higher requirements and prospects for the research of deep neural network technology in the field of artificial intelligence, and language intelligence is the core problem of artificial intelligence. As an important research content in the field of natural language processing, dialogue generation has been widely concerned by the academic community for a long time, and this research is also widely used in people's daily lives, such as medical Q&A, e-commerce shopping, emotional response, etc. There are still many problems in the existing model for dialogue generation research, such as easy to produce universal replies ("ok", "emm", etc.), the emotional information of the reply statement is not strong enough, and the response is not related to the subject. For this problem, we propose to use an emotional dialogue generation model based on a generative adversarial network to generate statements with emotional responses, and our network has a generator and multi-level discriminator. In terms of discriminators, multi-level discourse discriminators are introduced to guide the generation of replies and the strengthening of emotional information, to discriminate emotions at the individual word level and sentence level, to solve the problem that the same text may be different in different semantic environments and the emotional information of some words is not clear in some cases, in order to achieve maximum accuracy the emotional discrimination results are fed back to the generator and guide the generation of generator statements. In addition, compared with the traditional generative adversarial network, the task processing ability for large pieces of text is improved, and the model has been shown to produce emotional reply statements with better baseline levels than in the past. © 2022 ACM.

关键词： Generative adversarial networks

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CA-WGE: A two-view graph neural network-based knowledge graph completion approach combining common sense perception 6

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

引用

6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Guan, Wei Lian, Xiaoru Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing 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. © 2023 ACM.

关键词： Knowledge graph

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Visual Question Answering Model Based on CAM and GCN 5

Visual Question Answering Model Based on CAM and GCN

引用

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

作者： Wen, Ping Li, Ma Zhen, Zhang Ze, Wang Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

Visual Question Answering (VQA) is a challenging problem that needs to combine concepts from computer vision and natural language processing. In recent years, researchers have proposed many methods for this typical multimodal problem. Most existing methods use a two-stream strategy, i.e., compute image and question features separately and fuse them using various techniques, rarely relying on higher-level image representations, to capture semantic and spatial relationships. Based on the above problems, a visual question answering model (CAM-GCN) based on Cooperative Attention Mechanism (CAM) and Graph Convolutional network (GCN) is proposed. First, the graph learning module and the concept of graph convolution are combined to learn the problem-specific graph representation of the input image and capture the interactive image representation of the specific problem. Image region dependence, and finally, continue to optimize the fused features through feature enhancement. The test results on the VQA v2 dataset show that the CAM-GCN model achieves better classification results than the current representative models. © 2022 ACM.

关键词： Semantics

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Dialogue Generation for Heterogeneous Graph Neural on networks Incorporating External Knowledge 6

Dialogue Generation for Heterogeneous Graph Neural on Networ...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Guan, Wei Guo, Shuaiqian Ma, Li Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9798400707674

In recent years, with the improvement of hardware computing power and the rapid development of deep learning theory, the field of dialogue generation has also entered the era of deep learning. However, because it is difficult to generate content-rich and recovery statements by learning semantics only from dialogue corpus, many models are prone to generate responses that are weakly scalable to the topic of the input dialogue or generic responses that lack knowledge information. To address the above issues, a Heterogeneous Graph Neural network Incorporating External Knowledge Dialogue Generation Model (HEDG) is proposed. First, we build up the external knowledge into a knowledge graph, and find the relevant external knowledge in the knowledge graph according to the entities in the input conversation according to certain rules. This external knowledge and the historical conversation context are then used as inputs to the heterogeneous graph encoder, and the conversation content and the corresponding external knowledge are represented by a Heterogeneous Graph Neural network (HGNN). Finally, a decoder with Transformer structure as the main body is used to generate a response that is relevant to the conversation context with some knowledge extension by taking the encoded heterogeneous graphical representation as input. The experimental results show that the model has higher relevance and diversity compared with existing comparison models, and can effectively integrate external knowledge. © 2023 ACM.

关键词： Knowledge graph

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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...

引用

2020 International Conference on networking and network Applications, NaNA 2020

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

ISBN: (纸本)9781728189543

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. © 2020 IEEE.

关键词： Time series

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