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

作者： Zhai, Sheping Gao, Mingyang Yang, Rui Kang, Xinnian Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Unlike the traditional unimodal sentiment analysis, multimodal sentiment analysis can jointly process features between different modalities. Existing multimodal sentiment analysis methods simply extract features from text and images ignoring the influence of emoticons that may be contained in text and scene information in images on sentiment polarity judgment. At the same time, the unimodal feature fusion stage does not fully fuse the features of each modality resulting in low accuracy of sentiment analysis. Therefore, this paper proposes a multimodal sentiment analysis model based on multicategorical relative fusion of images and texts. which separates text content and emoji in the word embedding stage in the feature extraction stage, the traditional emoji sentiment dictionary is queried to obtain their sentiment scores and attention mechanism is introduced to extract scene features and object features of images respectively;in the feature fusion stage, the emoji sentiment scores. mixed image. The final multimodal features are obtained by fusion the emoji sentiment scores. mixed image features and text features with layer normalization. The experiments on the dataset Yelp show that the improvement of the model in the feature extraction and feature fusion stages effectively improves the accuracy and F1 value of multimodal sentiment analysis in which the accuracy increases by 4.7 percentage points on the New York dataset and the F1 value reaches 85.7% on the San Francisco dataset. © 2023 ACM.

关键词： Sentiment analysis

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SolBERT: Advancing solidity smart contract similarity analysis via self-supervised pre-training and contrastive fine-tuning

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Information and Software Technology 2025年 184卷

作者： Zhenzhou Tian Yudong Teng Xianqun Ke Yanping Chen Lingwei Chen School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an 710121 Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an 710121 Shaanxi China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an University of Posts and Telecommunications Xi’an 710121 Shaanxi China Department of Computer Science and Engineering Wright State University Dayton 45435 OH USA

Context: Reliable and effective similarity analysis for the smart contracts facilitates the maintenance and quality assurance of the smart contract ecosystem. However, existing signature-based methods and code representation learning-based methods suffer from limitations such as heavy-weight program analysis payloads or suboptimal contract encodings. Objective: This paper aims to design a fully unsupervised language model for better capturing the syntactic and semantic richness of Solidity code, and utilizes it for advancing the effectiveness of smart contract similarity analysis. Methods: Inspired by the impressive semantic learning capability of pre-trained language models (PLMs), we propose SolBERT, a PLM specifically tailored for enhancing Solidity smart contracts similarity detection. To ensure it produces high-quality encodings, SolBERT leverages BERT-style pre-training with the masked language modeling (MLM) and token type prediction (TTP) tasks applied on code-structure-aware token sequences derived from the contracts’ abstract syntax trees (ASTs) through structure-retaining tree linearization and light-weight normalization to learn a base model. On this basis, self-supervised contrastive fine-tuning and unsupervised whitening operations are further performed to optimize contract encoding generation. Results: Experiments are conducted on three contract similarity-related tasks, including contract clone detection, bug detection, and code clustering. The results indicate that SolBERT significantly outperforms state-of-the-art approaches with average absolute gains of 21.33% and 21.50% in terms of F1, and 17.78% and 26.60% in terms of accuracy for the clone detection and bug detection tasks, respectively; and an average absolute gain of 17.97% for code clustering task. When applying both contrastive fine-tuning and whitening optimizations, SolBERT also shows superior performance than the case of lacking any of them. Conclusion: The proposed approach, SolBERT, c

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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...

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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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Density PSO-based software module clustering algorithm

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The Journal of China Universities of Posts and Telecommunications 2018年第4期25卷 38-47页

作者： Sun Jiaze Ling Beilei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

Software module clustering is to divide the complex software system into many subsystems to enhance the intelligibility and maintainability of software systems. To increase convergence speed and optimize clustering solution,density PSO-based( DPSO) software module clustering algorithm is proposed. Firstly,the software system is converted into complex network diagram,and then the particle swarm optimization( PSO) algorithm is *** shortest path method is used to initialize the swarm,and the probability selection approach is used to update the particle positions. Furthermore,density-based modularization quality( DMQ) function is designed to evaluate the clustering quality. Five typical open source projects are selected as benchmark programs to verify the efficiency of the DPSO algorithm. Hill climbing( HC) algorithm,genetic algorithm( GA),PSO and DPSO algorithm are compared in the modularization quality( MQ) and DMQ. The experimental results show that the DPSO is more stable and more convergent than the other three traditional algorithms. The DMQ standard is more reasonable than MQ standard in guiding software module clustering.

关键词： software module clustering complex network PSO MQ modularity density

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

Visual Question Answering Model Based on Relationship graphs...

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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...

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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...

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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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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...

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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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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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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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