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IEEE Advanced information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Nan Wang Zan Liu School of Computer & Information Engineering Anyang Normal University Anyang China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang China Key Laboratory of Oracle Information Processing in Henan Province Anyang China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Food material management plays an important role in catering enterprises. Accurately predicting the consumption of each kind of food materials in the future can reduce the cost and improve the operation efficiency of catering enterprises. Based on ARMA and ARIMA models, we took the actual operation data of a big hot pot restaurant as the research object, and tried to forecast the food materials consumption of the restaurant in a relatively short future time. The experimental results show that although there is still room for improvement, the time series model can be used to help the material management of each catering enterprise.

关键词： Conferences Time series analysis Predictive models Prediction algorithms Data models information technology Business

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Graph Contrastive Learning with Decoupled Augmentation

Graph Contrastive Learning with Decoupled Augmentation

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shihao Gao Caoshuo Li Cunli Mao Xulong Zhang Xiaoyang Qu Taisong Jin Jianzong Wang Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Ping An Technology Co. Ltd. Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph contrastive learning based on augmentation strategies has recently demonstrated remarkable performance. Existing methods typically jointly leverage attribute and structural augmentations to generate graph views, learning data invariance information through contrasting sample pairs. However, this joint approach may deviate from the expectation of semantically similar before and after augmentation. The propagation of attribute information in graphs usually occurs through their structure, meaning that structural and attribute augmentations can interfere with each other and potentially distort the graph’s semantics. To address this, we propose a decoupled augmentation framework for graph contrastive learning, which eliminates the mutual interference between the two levels of augmentation while fully exploring graph information. Specifically, our framework employs separate encoders to learn data invariance under different augmentation levels, and it considers the positive gains generated between these levels. Experimental results on five public datasets show that the proposed method is more competitive than state-of-the-art approaches.

关键词： Acoustic distortion Semantics Contrastive learning Interference Signal processing Acoustics Speech processing

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Dynamic Clustering Convolutional Neural Network 39

Dynamic Clustering Convolutional Neural Network

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Tanzhe Zhang, Baochang Lyu, Jiayi Zheng, Xiawu Guo, Guodong Jin, Taisong Key Laboratory of Multimedia Trusted Perception and Effcient Computing Ministry of Education of China Xiamen University China School of Informatics Xiamen University China Hangzhou Research Institute School of Artificial Intelligence Beihang University China Nanchang Institute of Technology China School of Engineering Science University of Chinese Academy of Sciences China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China

ISBN: (纸本)157735897X

Convolutional neural networks (CNNs) have been playing a dominant role in computer vision. However, the existing approaches of using local window modeling in popular CNNs lack flexibility and hinder their ability to capture long-range dependencies of objects in an image. To overcome these limitations, we propose a novel CNN architecture, termed Dynamic Clustering Convolutional Neural Network (DCCNeXt). The proposed DCCNeXt takes a unique approach by employing global clustering to group image patches with similar semantics into clusters that are then convolved using the shared convolution kernels. To address the high computational complexity of global clustering, the feature vectors from each patch's subspace are extracted for efficient clustering, which makes the proposed model widely compatible with the downstream vision tasks. The extensive experiments of image classification, object detection, instance segmentation, and semantic segmentation on the benchmark datasets demonstrate that the proposed DCCNeXt outperforms the mainstream Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Vision Multi-layer Per-ceptrons (MLPs), Vision Graph Neural Networks (GNNs), and Vision Mambas (ViMs). We anticipate that this study will provide a new perspective and a promising avenue for the design of convolutional neural networks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convolutional neural networks

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oracle bone inscriptions Components Analysis Based on Image Similarity

Oracle Bone Inscriptions Components Analysis Based on Image ...

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IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Nan wang Zhenquan Zhao Qingju Jiao School of Computer & Information Engineering Anyang Normal University Anyang Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Henan China Key Laboratory of Oracle Information Processing in Henan Province Anyang Henan China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

Components analysis is a common way for the glyph structure research of ancient Chinese characters. Traditional methods require researchers to have a very rich knowledge reserve which is time-consuming and labor-consuming. We proposed a method based on image similarity to study the oracle bone inscriptions components. This method extracted and compressed the component skeleton, and introduced a pixel orientation mechanism, so as to compare the similarity of different components from different characters. The results show that our method can be applied to the components comparison. It will be more effective at processing single simple lines oracle bone inscriptions and for the complex characters the result is slightly worse.

关键词： Image coding Conferences bones information technology Artificial intelligence

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oracle bone inscriptions Detection in Rubbings Based on Deep Learning

Oracle Bone Inscriptions Detection in Rubbings Based on Deep...

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IEEE Joint International information Technology and Artificial Intelligence Conference (ITAIC)

作者： Nan Wang Qingyu Sun Qingju Jiao Jinxian Ma School of Computer & Information Engineering Anyang Normal University Anyang Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Henan China Key Laboratory of Oracle Information Processing in Henan Province Anyang Henan China Anyang Information Center Anyang Henan China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

The rubbings of oracle bone inscriptions are an important carrier of oracle bone inscriptions, which are often used by ancient Chinese language expert. oracle bone inscriptions detection in rubbings is a basic step for studying oracle bone inscriptions by using computer technology. Because of the remote times, some of the oracle bones have been worn seriously or broken, the content in the rubbings may be interfered by noise. How to effectively detect the oracle bone inscriptions in rubbings is the focus of oracle bone inscriptions research based on rubbings. In this paper we applied YOLOv4 model to the oracle bone inscriptions detection. The experimental results show that the YOLOv4 model can be effectively used to detect the characters in rubbings, but there are still some deficiencies in the ability to distinguish noise.

关键词： Deep learning Conferences Data preprocessing bones Data models Joints information technology

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Spatial Pyramid Block for oracle bone Inscription Detection 2020

Spatial Pyramid Block for Oracle Bone Inscription Detection

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Proceedings of the 2020 9th International Conference on Software and Computer Applications

作者： Guoying Liu Jici Xing Jing Xiong School of Computer and information Engineering Anyang Normal University Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education

ISBN: (纸本)9781450376655

The detection of oracle bone Inscription (OBI) is one of the most fundamental aspects of oracle bone morphology. However, the detection method depending on experts' experience requires longterm learning and accumulation for professional knowledge. This paper investigated the performance of the deep-learning-based object detection framework in the OBI dataset, then selected the one with the best performance as the baseline and made a series of optimization. Specifically, we first redesigned the sizes and ratios of the anchor box according to the data characteristics by using K- means clustering. Secondly, we extracted some typical noises from OBI for data augmentation. Finally, Focal Loss and Mixed-precision are used to improve the model precision and compress the memory footprint. To further improve the performance, the Spatial Pyramid Block is proposed, which can stabilize features and suppress noise interference. Experiments on our OBI benchmarks validate the superiority of the proposed method that achieves 82.1% F-measure suppressing several mainstream object detectors. Our dataset and algorithms will soon be available at http://***.

关键词： oracle Detection bone Inscription

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OBC306: A large-scale oracle bone character recognition dataset 15

OBC306: A large-scale oracle bone character recognition data...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Huang, Shuangping Wang, Haobin Liu, Yongge Shi, Xiaosong Jin, Lianwen South China University of Technology Anyang Normal University China Henan Key Laboratory of Oracle Bone Inscriptions Information Processing Anyang Normal University China

ISBN: (纸本)9781728128610

The oracle bone script from ancient China is among the world's most famous ancient writing systems. Identifying and deciphering oracle bone scripts is one of the most important topics in oracle bone study and requires a deep familiarity with the culture of ancient China. This task remains very challenging for two reasons. The first is that it is executed mainly by humans and requires a high level of experience, aptitude, and commitment. The second is due to the scarcity of domain-specific data, which hinders the advancement of automatic recognition research. A collection of well-labeled oracle-bone data is necessary to bridge the oracle bone and information processing fields;however, such a dataset has not yet been presented. Hence, in this paper, we construct a new large-scale dataset of oracle bone characters called OBC306. We also present the standard deep convolutional neural network-based evaluation for this dataset to serve as a benchmark. Through statistical and visual analyses, we describe the inherent difficulties of oracle bone recognition and propose future challenges for and extensions of oracle bone study using information processing. This dataset contains more than 300,000 character-level samples cropped from oracle-bone rubbings or images. It covers 306 glyph classes and is the largest existing raw oracle-bone character set, to the best of our knowledge. It is anticipated the publication of this dataset will facilitate the development of oracle bone research and lead to optimal algorithmic solutions. © 2019 IEEE.

关键词： Deep neural networks

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OBC306: A Large-Scale oracle bone Character Recognition Dataset

OBC306: A Large-Scale Oracle Bone Character Recognition Data...

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International Conference on Document Analysis and Recognition

作者： Shuangping Huang Haobin Wang Yongge Liu Xiaosong Shi Lianwen Jin South China University of Technology Henan Key Laboratory of Oracle Bone Inscriptions Information Processing Anyang Normal University

The oracle bone script from ancient China is among the world's most famous ancient writing systems. Identifying and deciphering oracle bone scripts is one of the most important topics in oracle bone study and requires a deep familiarity with the culture of ancient China. This task remains very challenging for two reasons. The first is that it is executed mainly by humans and requires a high level of experience, aptitude, and commitment. The second is due to the scarcity of domain-specific data, which hinders the advancement of automatic recognition research. A collection of well-labeled oracle-bone data is necessary to bridge the oracle bone and information processing fields; however, such a dataset has not yet been presented. Hence, in this paper, we construct a new large-scale dataset of oracle bone characters called OBC306. We also present the standard deep convolutional neural network-based evaluation for this dataset to serve as a benchmark. Through statistical and visual analyses, we describe the inherent difficulties of oracle bone recognition and propose future challenges for and extensions of oracle bone study using information processing. This dataset contains more than 300,000 character-level samples cropped from oracle-bone rubbings or images. It covers 306 glyph classes and is the largest existing raw oracle-bone character set, to the best of our knowledge. It is anticipated the publication of this dataset will facilitate the development of oracle bone research and lead to optimal algorithmic solutions.

关键词： bones Dictionaries Character recognition Task analysis Tools Visualization

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oracle character recognition by nearest neighbor classification with deep metric learning 15

Oracle character recognition by nearest neighbor classificat...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Zhang, Yi-Kang Zhang, Heng Liu, Yong-Ge Yang, Qing Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing China School of Computer & Information Engineering Anyang Normal University Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan China

ISBN: (纸本)9781728128610

oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset oracle-20K and outperforms CNN with softmax layer on a new dataset oracle-AYNU. © 2019 IEEE.

关键词： Character recognition

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HWOBC-A Handwriting oracle bone Character Recognition Database

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Bang Li Qianwen Dai Feng Gao Weiye Zhu Qiang Li Yongge Liu School of Computer & Information Engineering Anyang Normal University Anyang Henan 455000 China Key Laboratory of Oracle Information Processing in Henan Province Anyang Henan 455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Henan 455000 China School of Information Engineering Zhengzhou University Zhengzhou Henan 450000 China

The oracle bone character (OBC) from ancient China is the most famous ancient writing systems around the world. Identifying and deciphering OBCs is one of the most important topics in oracle bone study. In research, one of the challenges is that the literature review usually leads to a huge cost of time and manpower. Therefore, the digitazation of OBC literature through the automatic recognition is the inevitable trend of future development. However, the OBCs in the literature are usually writing characters while the database of handwriting OBC has not yet been presented. In this paper, we establish a handwriting oracle bone character database called HWOBC, containing 83,245 character-level samples which are grouped into 3881-character categories. We also present the performance of several baseline DCNN-based methods, in which Melnyk-Net exhibits the best accuracy of 97.64%. It is anticipated that the publication of this database will facilitate the development of OBC research.

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