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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Song, ZiAn Deng, ZhengJie Hainan Normal University Haikou571158 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9783030786205

Convolutional neural networks have been applied in image recognition widely, and acquired well-known results. However, the discovery of adversarial examples in recent years has attracted many researcher’s attention on the safety and reliability of convolutional neural networks. Adversarial examples are small perturbations added only to the clean image data, which could cause the trained model makes wrong identifications, while people could still recognize. In this paper, we propose a new convolutional neural network to defense the adversarial examples. It breaks into two branches after inputting an image. One branch likes a normal image recognition model, while another branch distills the low-frequency information of the image firstly, and then uses the low-frequency image to train. The two branches combine to make the output of the model. The presented model uses the image’s low-frequency information, through which people often recognizes an object. The experiments show that the presented model suppresses the success rate of the attack in the face of the adversarial examples, and improves the model’s recognition success rate, comparing to some other methods. © 2021, Springer Nature Switzerland AG.

关键词： image recognition

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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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Efficient Construction of Delaunay Triangulations on Riemannian Manifolds with the Bowyer-Watson Method

Efficient Construction of Delaunay Triangulations on Riemann...

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International Conference on Digital Home (ICDH)

作者： Dejiangmo Qian Wang Li Zhou Xinyun Zhang Dawei Luo Northwest Minzu University Colleage of Mathematics and Computer Science Lanzhou China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

This paper explores the application of the Bowyer-Watson algorithm for constructing Delaunay triangulations on Riemannian manifolds, with a particular focus on karst terrain and channel detection scenarios. We define geodesic disks with sufficiently small radii and demonstrate the existence of a net within these disks that allows for the construction of triangles satisfying the Delaunay condition. By analyzing the insertion of points both inside and outside of triangles, we ensure proper point placement and perform edge flips as necessary to maintain the Delaunay property. Compared to other algorithms, the Bowyer-Watson approach offers notable advantages for achieving Delau-nay triangulation on Riemannian manifolds due to its flexibility and locality. This method effectively manages complex geometric structures by adaptively adjusting the triangulation, ensuring both accuracy and efficiency in non-Euclidean spaces. It is particularly well-suited for modeling karst terrain features and detecting channels, providing a crucial theoretical foundation and practical guidance for future numerical computations and geometric modeling on curved surfaces.

关键词： Manifolds graphics Accuracy Data analysis Scientific computing image edge detection Geometric modeling Focusing Feature extraction Numerical models

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Research on Collaborative Filtering Algorithm of E-commerce Based on Flink

Research on Collaborative Filtering Algorithm of E-commerce ...

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International Conference on intelligent Control and Information processing (ICICIP)

作者： Haohao Zheng Xiaonan Luo Xiaoshu Zhu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

In online shopping, a person’s interest in a product is closely related to whether they will purchase it Analyzing people’s interest in various products on time, along with product recommendations, can increase purchase rates. However, in many cases, We only know user implicit feedback, such as browsing or favoriting items, and cannot directly access user explicit feedback, such as product ratings. Moreover, users have limited exposure to products, resulting in a sparse user-item matrix. The traditional Alternating Least Squares (ALS) algorithm can partially address the is of of matrix sparsity but its accuracy in recommending products based on implicit feedback alone is insufficient. This paper proposes an improved ALS algorithm that converts implicit feedback into explicit ratings. We calculate the browsing frequency of a product based on user browsing counts. For a given product, we represent user preference by suimming the browsing frequencies of all products with browsing frequencies less than or equal to that of the product. Then, by multiplying the preference level by the product’s maximum rating, we derive a product rating, achieving a transformation from implicit feedback to explicit scores. Additionally, traditional collaborative filtering algorithms often use offline data, which may not align with user current preferences. To improve the algorithm’s accuracy, we also utilize Flink for real-time data processing, allowing us to capture the most recent feedback data. This study tests the approach using the UserBehavior dataset from the publicly available Tianchi dataset. Experimental results indicate improvements in both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

关键词： Accuracy Collaborative filtering Prediction algorithms Real-time systems Electronic commerce Sparse matrices User preference Matrix converters Root mean square intelligent control

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A Convolutional Knowledge Tracing Method based on Graph Embedding

A Convolutional Knowledge Tracing Method based on Graph Embe...

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Computer Engineering and intelligent Communications (ISCEIC), International Symposium on

作者： Guimin Huang Ying Li Qingkai Guo Nanxiao Deng Jianxing Lin School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

With online education rapidly developing, it is a significant issue to evaluate students’ grasp of knowledge more accurately. Knowledge tracing models are good at it, in which convolutional knowledge tracing (CKT) based on convolutional neural networks(CNN) gets better performance. However, we found that the CKT model ignores the relation between the knowledge points, which we believe has an important impact on the correctness of students’ answers. Therefore, we put forward a graph embedding based convolutional knowledge tracing model (CKT-GE), which feeds the relation between the knowledge points with other inputs into model for improving the effectiveness of knowledge tracing. Our model outperforms existing models on two classic datasets, and improves the performance over the original CKT model, allowing for more accurate assessment of student performance.

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An Analysis Model of English Text Coherence Based on RST Dependency Relationship

An Analysis Model of English Text Coherence Based on RST Dep...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Guimin Huang Wanying Shuai Qingkai Guo Nanxiao Deng Jianxing Lin Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively.

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Object Detection Algorithm Based on Coordinate Attention and Context Feature Enhancement 22

Object Detection Algorithm Based on Coordinate Attention and...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Lingzhi Liu Baohua Qiang Yuanchun Wang Xianyi Yang Jubo Tian Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

In recent years, object detection has been widely used in various fields such as face detection, remote sensing image detection and pedestrian detection. Due to the complex environment in the actual scene, we need to fully obtain the feature information in the image to improve the accuracy of object detection. This paper proposes an object detection algorithm based on coordinate attention and contextual feature enhancement. We design a multi-scale attention feature pyramid network, which first uses multi-branch atrous convolution to capture multi-scale context information, and then fuses the coordinate attention mechanism to embed location information into channel attention, and finally uses a bidirectional feature pyramid structure to effectively fuse high-level features and low-level features. We also adopt the GIoU loss function to further improve the accuracy of object detection. The experimental results show that the proposed method has certain advantages compared with other detection algorithms in the PASCAL VOC datasets.

关键词： Feature pyramid network Object detection Coordinate attention Atrous convolution

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A Deep Knowledge Tracking Model Based on Forgetting Features and Self-Attention Mechanism

A Deep Knowledge Tracking Model Based on Forgetting Features...

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Computer Engineering and intelligent Communications (ISCEIC), International Symposium on

作者： Guimin Huang Xiong Wei Qingkai Guo Nanxiao Deng Jianxing Lin School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

The integration of large education and artificial intelligence technologies is gradually deepening, and how to provide personalized user profiling services for learners is an important research problem. In response to the long-term dependency problem and lack of learning features of today’s deep knowledge tracking models, a deep knowledge tracking model (FSA-MuLSTM) based on forgetting features and the self-attentiveness mechanism is proposed. Firstly, the model combines the learner’s historical learning interaction tuple sequences with learning times and memory residuals to obtain the input vector based on the LSTM; secondly, the correlation weights between tuples are captured using the self-attention mechanism; finally, the deep information between tuple sequences is captured using the stacking of multilayer LSTMs. The probability of correctly answering the current question is derived from the hidden vectors and the self-attentive weights of the output of the multilayer LSTM. Two public datasets are used for the experiments, and the combined analysis outperforms existing knowledge-tracking models in terms of performance and improves the accuracy of prediction.

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 22

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition

作者： Pingping Sun Baohua Qiang Zhiguang Liu Xianyi Yang Guangyong Xi Weigang Liu Ruidong Chen Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.

关键词： Semantic Relevance CLIP Cross-Modal Retrieval Modal Alignment

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