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arXiv 2023年

作者： Wang, Shiping Wu, Zhihao Chen, Yuhong Chen, Yong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China School of Computer Science Beijing University of Posts and Telecommunications China

Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. However, few work provide a general view to interpret various GCNs and guide GCNs’ designs. In this paper, by revisiting the original GCN, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs, such as APPNP, JKNet, DAGNN, and GNN-LF/HF. Further, under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture topological and semantic structures from graph data. Since the derived learning rule for tsGCN contains an inverse of a large matrix and thus is time-consuming, we leverage the Woodbury matrix identity and low-rank approximation tricks to successfully decrease the high computational complexity of computing infinite-order graph convolutions. Extensive experiments on eight public datasets demonstrate that tsGCN achieves superior performance against quite a few state-of-the-art competitors w.r.t. classification *** Codes 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Fine-grained pseudo-code generation method via code feature extraction and transformer

arXiv

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arXiv 2021年

作者： Yang, Guang Zhou, Yanlin Chen, Xiang Yu, Chi School of Information Science and Technology Nantong University China Ministry of Industry and Information Technology China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China

Pseudo-code written by natural language is helpful for novice developers’ program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

arXiv

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arXiv 2023年

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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

作者： Chengjia Jiang Tao Wang Sien Li Jinyang Wang Shirui Wang Antonios Antoniou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

关键词：

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Extensive Techniques to Detect Defects in Tyres through Radiography

Extensive Techniques to Detect Defects in Tyres through Radi...

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IEEE International Conference on Multi Topic

作者： Muhammad Ahmad Shabir Muhammad Umair Hassan Xiangru Yu Jinping Li Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Jinan China School of Information Science and Engineering Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (数字)9781728140018

ISBN: (纸本)9781728140025

The present paper highlights the experimentation procedure and decomposition of vehicle tyres through the extensive technique of radiography by the incorporation of total changes occurring in the image, transformation of curvelet that is based on image enlargement, and moreover, canny edge detection will examine the patterns through outer surfaces and poor defects in different types of tyres. Keeping into knowledge, the feature appeared after proceeding radiography are decomposed into pattern recognition. In the present paper, the splitting of the tyre into various segments of pattern recognition through image processing is focused on the clear image for selection to detect defects. It is done because the images occurred in the form of segments and recognition to detect the defects in continuity becomes simpler. Suitable modifications is made around the corners of the image appeared and is enhanced by the help of curvelet coefficients initially before the image detection algorithm is carried out. In addition, the result of the analysis is also drawn by incorporating the method of detection called, canny edge and is helpful for the detection of abnormal textures. Moreover, the operator of Sobel and many other detection methods like canny edge algorithms are effective for making comparative and precise discussion on the results of the analysis in brief. It is indicated by the results of analysis through X-ray detection method that outer surfaces of tyre and weak defects inside the tyres are the two different types of detection procedures located clearly by the help of proposed methods.

关键词： Tires Radiography Feature extraction Image edge detection Wires Image segmentation Electronics packaging

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Tyre Defect Detection Based on GLCM and Gabor Filter

Tyre Defect Detection Based on GLCM and Gabor Filter

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IEEE International Conference on Multi Topic

ISBN: (数字)9781728140018

ISBN: (纸本)9781728140025

Defect detection is a crucial issue at industrial level, and generally, the designers have to find out a way to select suitable features to detect the defects in textured material images. This work reports this issue using the gray-level co-occurrence matrix (GLCM) and Gabor filter texture segmentation. We first analyze the defects in the corresponding tyre texture images using GLCM and then we apply Gabor filter texture segmentation to illustrate further the impurities found in the material. Our proposed method matches the abnormal textured images with standard texture images, and we use the 5 GLCM features to calculate the score for an abnormal texture image. Further, our proposed method decides the defected tyres and presents the texture segmentation of abnormal image. We found that our proposed method performs well when compares with other methods of tyre images data.

关键词： Gabor filters Feature extraction Tires Inspection Image segmentation Fabrics Filter banks

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Financial Big Data Hot and Cold Separation Scheme Based on HBase and Redis

Financial Big Data Hot and Cold Separation Scheme Based on H...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Kunhui Li Kun Guo Hong Guo Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining Information Sharing Ministry of Education Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In the era of big data, HBase has been widely used in scenarios of massive unstructured data. For the financial big data, due to the integrity and timing of it, unreasonable data storage and management usually lead to hot spots that decreases the query performance. In practice, the separation of hot and cold financial data will improve data query performance and utilization rate of cluster resources. In this paper, a hot and cold data separation scheme is designed, to store infrequently queried financial data to HBase, and frequently queried one to Redis. The cold data is reasonably planned and managed through pre-partitioning and row key design for HBase. A hot data cache based on Redis is realized to improve the query speed and reduces the pressure of HBase. In addition, due to the lack of Redis's inherent cache elimination strategy, we propose a caching strategy based on the frequencies of updating and querying operations. The experimental results show that the scheme can effectively avoid the hot storage problem, and improve the query performance, and improve the cache hit ratio of Redis. Therefore, the number of cold data access requests can be effectively reduced.

关键词： Big Data Distributed databases Memory Time series analysis Stock markets

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Mixed Word Embedding Method Based on Knowledge Graph Augment for Text Classification

Mixed Word Embedding Method Based on Knowledge Graph Augment...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Hongzhong Wang Kun Guo Zhanghui Liu Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

This paper presents a self-training word embedding text classification model based on knowledge graph expansion for text classification. Current mixed word embedding methods are overly dependent on the Fasttext pre-training model and here is still a problem of missing words with rich semantic information are not mapped. First, we propose a method for extracting missing nouns based on shape near word filtering. Second, we design a self-training word embedding method based on knowledge graph that mixes with pre-training word embedding to obtain a high-quality mixed word vector with rich semantics and rich semantics. Third, we designed a GRU model based on improved mixed word embedding to improve the quality of text classification. Experiments conducted on multiple text classification datasets demonstrate that our methods can effectively improve the text classification accuracy.

关键词： Text categorization Context modeling Feature extraction Training Task analysis Predictive models

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Few-Shot Object Detection Based on Generalized Features

Few-Shot Object Detection Based on Generalized Features

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Artificial Intelligence and intelligent information processing (AIIIP), International Conference on

作者： Qiuqin Chen Xiao Ke Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou Fuzhou China

Few-shot object detection aims to rapidly detect novel classes of objects using a minimal number of annotated instances. Compared to methods such as meta-learning, few-shot object detection based on transfer learning can achieve better performance. However, when training on base-class datasets, the model gradually improves its detection performance on base classes but does not enhance its generalization. A base-class model lacking generalization tends to result in lower detection performance for novel classes during fine-tuning. To address the aforementioned issues, this paper tackles the problem from two aspects: firstly, we introduce the Confusion-Dropblock Module (CDM) to perturb the model's features, thus enhancing feature generalization. Secondly, we incorporate Deformable Convolution Modules to mitigate the impact of object shapes and sizes on detection performance, thereby improving overall detection performance. Experimental results on the COCO dataset demonstrate the effectiveness of the proposed algorithm, outperforming the compared methods.

关键词：

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

引用

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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