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intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Guimin Huang Xiangqian Qin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. Therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods.

关键词： Knowledge engineering Human computer interaction Computational modeling Education Predictive models Data models

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Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Ya Zhou Chen Chen Guimin Huang School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%.

关键词： Knowledge engineering Accuracy Semantics Knowledge based systems Syntactics Natural language processing Software engineering

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Computer Science and Blockchain (CCSB), International Conference on

作者： Guimin Huang Canjun Shi Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance.

关键词： Computational modeling Semantics Knowledge based systems Syntactics Predictive models Feature extraction Vectors Natural language processing Convolutional neural networks Mutual information

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AN ADVERSARIAL AND DEEP HASHING-BASED HIERARCHICAL SUPERVISED CROSS-MODAL image AND TEXT RETRIEVAL ALGORITHM

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International Journal of Robotics and Automation 2024年第1期39卷 77-86页

作者： Chen, Ruidong Qiang, Baohua Zhou, Mingliang Zhang, Shihao Zheng, Hong Tang, Chenghua Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science Chongqing University Chongqing400044 China

With the rapid development of robotics and sensor technology, vast amounts of valuable multimodal data are collected. It is extremely critical for a variety of robots performing automated tasks to find relevant multimodal information quickly and efficiently in large amounts of data. In this paper, we propose an adversarial and deep hashing-based hierarchical supervised cross-modal image and text retrieval algorithm to perform semantic analysis and association modelling on image and text by making full use of the rich semantic information of the label hierarchy. First, the modal adversarial block and the modal differentiation network both perform adversarial learning to keep different modalities with the same semantics closest to each other in a common subspace. Second, the intra-label layer similarity loss and inter-label layer correlation loss are used to fully exploit the intrinsic similarity existing in each label layer and the correlation existing between label layers. Finally, an objective function for different semantic data is redesigned to keep data with different semantics away from each other in a common subspace, thus avoiding interference of retrieval by data of different semantics. The experimental results on two cross-modal retrieval datasets with hierarchically supervised information show that the proposed method substantially enhances retrieval performance and consistently outperforms other state-of-the-art methods. © 2024 Acta Press. All rights reserved.

关键词： Semantics

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Multiple-instance CNN Improved by S3TA for Colon Cancer Classification with Unannotated Histopathological images 11

Multiple-instance CNN Improved by S3TA for Colon Cancer Clas...

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11th International Conference on intelligent Control and Information processing, ICICIP 2021

作者： Ye, Tiange Lan, Rushi Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9781665425155

In this paper, we propose a new method for colon cancer classification from histopathological images, which can automatically analyze a given whole slide image (WSI). We usually classify cancer classification by referring a WSI, which is typically 20000 × 20000 pixels. The cost of obtaining WSIs with annotating cancer regions is very high. Multiple-instance learning (MIL) is a variant of supervised learning in which the instances in a bag share a single class label. That is, MIL only needs unannotated WSI. In recent years, MIL has developed a hard attention mechanism which has achieved good performance. However, this hard attention mechanism cannot notice the interior of each patch, i.e., it lacks soft attention mechanism. In this paper, a soft, sequential, spatial, top-down attention mechanism (which we abbreviate as S3TA) is used to make up for the lack of MIL attention mechanism. Finally, our experiments show that by varying the number of attention steps in S3TA, we achieved a better accuracy of 93.6% than the old model. © 2021 IEEE.

关键词： image enhancement

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Chinese Event Extraction Method Based on Roformer Model

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Qiang, Baohua Zhou, Xiangyu Wang, Yufeng Yang, Xianyi Wang, Yuemeng Tian, Jubo Chen, Peng Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC Shijiazhuang050081 China

Event extraction is an important research direction in the field of natural language processing. The current Chinese event extraction field still suffers from errors in the pretraining and fine-tuning stages, inability to directly handle texts with more than 512 tokens, and inaccurate event extraction due to insufficient semantic sample diversity. In this paper, we propose a Chinese event extraction method RoformerFC (Roformer model with FGM and CRF) based on the Roformer model to address the above problems. Firstly, our method utilizes the Roformer model based on rotary position embedding, which both moderates the errors in the pretraining and fine-tuning phases and allows the model to directly handle texts with more than 512 tokens;then, the adversarial networks based on FGM (fast gradient method) are realized to increase the diversity of semantic feature samples;finally, the classical CRF (conditional random fields) model is used to decode and identify the event element entity and its corresponding event role and event type. On the short text DuEE dataset, the microP, microR, and microF of our method improved 1.26%, 4.01%, and 2.68%, respectively, over the classical Chinese event extraction method BERT-CRF. On the long text JsEE dataset, the microP, microR, and microF of our method improved 2.26%, 5.03%, and 3.72%, respectively, over the classical Chinese event extraction method BERT-CRF. © 2023 Baohua Qiang et al.

关键词： Semantics

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Siamese Interaction and Fine-Tuning Representation of Chinese Semantic Matching Algorithm Based on RoBERTa-wwm-ext

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Mobile Information Systems 2023年第1期2023卷

作者： Qiang, Baohua Xi, Guangyong Wang, Yufeng Yang, Xianyi Wang, Yuemeng Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC Shijiazhuang050081 China

Semantic matching research is the cornerstone of research in the fields of natural language similarity measurement and sensor ontology matching (OM). In the existing Chinese semantic matching methods, there are some shortcomings, such as the single dimension of semantic expression, the insufficient expression of context semantic relations, and the insufficient interaction of semantic information between different sentences. This paper proposes a Chinese semantic matching algorithm based on RoBERTa-wwm-ext with Siamese interaction and fine-tuning representation (RSIFR). The RSIFR model initializes the model with RoBERTa-wwm-ext as a vector of text. Firstly, a Siamese structure with embedded soft alignment attention mechanism and BiLSTM is constructed to realize the information interaction between two sentences. Secondly, LSTM-BiLSTM network structure is constructed to enhance the expression of semantic logic before and after sentences. Then, build a training model with fine-tuning mechanism. Fine tune the text's eigenvector parameters through label supervision. Finally, the fusion vectors of the sentence pairs are inserted into the MLP network layer, resulting in semantic matching results. RSIFR model starts from a variety of dimensions, strengthens the expression ability of vectors to text semantic relations, deeply mines the semantic similarities and differences between different sentences, and generally improves the Chinese semantic matching performance. Experiments on the public dataset LCQMC show that our model outperforms existing Chinese semantic matching models. © 2023 Baohua Qiang et al.

关键词： Semantics

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Non-intrusive load monitoring based on a combination of Transformer and CNN

Non-intrusive load monitoring based on a combination of Tran...

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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Yiqi Cheng Yanru Zhong College of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Image And Graphic Intelligent Processing Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Non-intrusive load monitoring (NILM) estimates the energy usage of individual devices by analyzing the total power meter data from a household. To address the issue of decreased monitoring accuracy with an increasing number of electricity meters, this paper proposes a solution for single appliance load monitoring (Tra-CNN). The solution uses the UK-DALE dataset, which contains high and low frequencies electricity consumption data of multiple households in the UK and the electricity consumption data of each household appliance, and uses the active power of the appliance as the input of the algorithm. The first part of the model consists of a convolutional layer, a normalization layer, and a dropout layer, with the primary task of capturing local dependencies in the data. The second part consists of a Transformer, whose self-attention mechanism associates features at different positions in the sequence to achieve modeling of long-term dependencies. The third part introduces a convolutional neural network, which uses a sliding window for convolution operations to extract local features from different positions and further extract features from sequence data. The final decoder consists of a transposed convolutional layer and a normalization layer, designed to revert the learned features to their original dimensions. Through experimental verification, the effectiveness and superiority of Tra-CNN are shown, which provides important ideas and methods for the further development of NILM technology.

关键词： Meters Home appliances Load monitoring Electricity Computational modeling Transformers Feature extraction Convolutional neural networks Data mining Load modeling

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HDMacBERT-FGM-CRF Model-Based Chinese Event Element Extraction 14th

HDMacBERT-FGM-CRF Model-Based Chinese Event Element Extracti...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Chen, Peng Qiang, Baohua Wang, Yufeng Wang, Chen Chen, Ruidong Li, Baolian Peng, Bo Zhou, Xiangyu Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050002 China

ISBN: (纸本)9789811684296

Because of the inaccuracy of event element extraction caused by long text, the errors of pre-training and fine-tuning stages, and the single sample of semantic features in the current Chinese event element extraction model, Hierarchical Decomposition MacBERT and Fast Gradient Method-based Chinese event element extraction algorithm by jointing Conditional Random Field (HDMacBERT-FGM-CRF) is proposed. This model extends absolute location encoding based on hierarchical decomposition so that the HDMacBERT model can directly process more than 512 Token texts. The HDMacBERT model based on the Mask strategy of similar words is used to mitigate errors. The FGM-based approach implements adversarial networks and increases the diversity of semantic feature samples. The P, R and F 1 of the HDMacBERT-FGM-CRF model proposed in this paper are 2.52%, 4.03% and 3.26% higher than those of the classical Chinese event element extraction model BERT-CRF on the DuEE dataset. The experimental results show that the proposed model can learn more comprehensive deep-level semantic information of text and improve Chinese event element extraction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extraction

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Spatiality-Aligned Graph Convolution Hashing for Unsupervised Cross-Modal Retrieval

Spatiality-Aligned Graph Convolution Hashing for Unsupervise...

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Signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Fang Li Liulian Dai Rushi Lan Rui Yang Xiaonan Luo Center for Applied Mathematics of Guangxi(GUET) Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Unsupervised cross-modal retrieval leveraging hash learning has attracted a lot of interest from academics, primarily due to its minimal storage requirements, swift retrieval speeds, and label-free nature, making it a prime candidate for real-world applications. Nevertheless, the inherent "heterogeneous differences" and "semantic gap" within cross-modal data features pose significant challenges. Despite various attempts by scholars to address these issues, the existing solutions often fall short in terms of effectiveness. This can be attributed to several reasons: Firstly, image features, in contrast to text features, possess a plethora of attributes, making it challenging to extract features that possess strong expressive capabilities. Secondly, each modality of data exhibits not only inter-node correlations but also intricate relationships between individual nodes and their neighboring nodes. Consequently, a straightforward semantic correlation matrix is inadequate in capturing the intricate structural relationships within the data. Thirdly, the neglect of internal structural associations results in insufficient representation capabilities of binary hash codes in the Hamming space, hampering the learning of effective hash functions. To address these limitations, we introduce a Spatiality-Aligned Graph Convolution Hashing (SAGCH) algorithm. Extensive experiments demonstrate that our SAGCH method excels in both "image-to-text retrieval" and "text-to-image retrieval" tasks.

关键词： Hash functions Correlation Codes Convolution Graph convolutional networks Semantics Text to image Feature extraction Data processing Cross modal retrieval

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