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Enhanced oracle bone corrosion detection using attention-guided YOLO with ghost convolution

Discover Artificial Intelligence 2024年第1期4卷 1-13页

作者： Gao, Feng Yang, Ziheng Liu, Qiyu Zhang, Zhan Li, Bang Zhang, Han Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Anyang455000 China School of Computer & Information Engineering Anyang Normal University Henan Anyang455000 China

Oracle bone inscriptions (OBIs), as the important records of early Chinese characters, possess profound cultural and historical significance. However, These fragments are susceptible to further damage due to corrosion. Consequently, the identification and localization of corroded regions on these fragments to prevent further deterioration has emerged as a critical task. Current object detection algorithms exhibit limitations in identifying corroded regions on oracle bone fragments, which is manifested by suboptimal detection accuracy, F1 scores, and average precision (AP) values. This deficiency hinders their capability to effectively manage the complexity and diversity of corrosion patterns present on oracle bone fragments. To tackle this challenge, this study incorporates advanced attention mechanisms, including Squeeze-and-Excitation Networks (SE), Coordinate attention, and Convolutional Block Attention Module (CBAM), into the YOLOv5 detection architecture. Additionally, we introduce Ghost convolution into the backbone network to effectively retain critical feature map information while optimizing computational efficiency. Our experimental results indicate that the integration of CBAM attention and Ghost convolution within the YOLOv5 backbone markedly improves the detection accuracy of corroded regions on oracle bone fragments. Specifically, compared to the baseline YOLOv5 model, the proposed models incorporating SE, CBAM, and Ghost convolution obtain the improvements of 2.2%, 6.3%, and 8.5% in AP, respectively. This improvement in detection performance not only facilitates the identification of corroded areas but also contributes to the preservation of oracle bone heritage and the continuity of cultural knowledge. © The Author(s) 2024.

关键词： Object detection

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REMOTE SENSING IMAGES CHANGE DETECTION USING THE SIAMESE NETWORK COMBINED WITH PURE SWIN TRANSFORMER

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2024年第4期2024卷 241-252页

作者： Song, Xu Tong, Xinyu Hajamydeen, Asif Iqbal School of Computer and Information Engineering Anyang Normal University Anyang455000 China School of Graduate Studies Management and Science University Shah Alam40100 Malaysia

While Transformers have become prevalent in detecting changes in remote sensing imagery, several challenges hinder their broader adoption in the field, such as missing detection, low precision of contour boundary detection and the complexity of calculation in the data processing. Addressing these challenges, our research introduces the SiamFormer architecture, which employs a layered Swin Transformer encoder coupled with a cascaded decoder for feature fusion, specifically designed for change detection within remote sensing imagery. First, our approach integrates the Siamese network framework with a pure Swin Transformer, crafting a decoder with a hierarchical layout to bolster its capabilities for pixel-level change detection in remote sensing images. Second, we improve the interconnection status between decoder layers by top-down cascade paths and dense cascades to produce high-quality high-resolution image semantic feature change outputs. Furthermore, to mitigate the loss of edge details in change objects caused by high-dimensional downsampling, we implement a convolutional decoding classifier. This classifier efficiently reduces the channel dimensions of the merged change feature map to the bare minimum. Our experimental analysis, conducted on the CDD and LEVIR-CD datasets, demonstrates that our proposed methodology outperforms existing change detection techniques for remote sensing imagery in terms of effectiveness. © 2024, Politechnica university of Bucharest. All rights reserved.

关键词： Change detection

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Research on Oracle Bone Inscriptions Recognition From Multi-Source Data Based on Deep Learning 7

Research on Oracle Bone Inscriptions Recognition From Multi-...

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7th IEEE Advanced information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Wang, Nan Qi, Zhenyang Du, Fengxia Anyang Normal University School of Computer & Information Engineering Henan Anyang455000 China Ministry of Education of China Key Laboratory of Oracle Bone Inscriptions Information Processing Henan Anyang455000 China Anyang Normal University School of Fine Art Henan Province Anyang455000 China

ISBN: (纸本)9798350339161

How to carry out recognition process in a quick and effective way is important to the oracle bone inscriptions research area. In this paper, we proposed a recognition method which adopt a skeletonization process to normalize handwriting and rubbing forms of oracle bone inscriptions into the same expression. Then by using models based on CNN such as VGG19, AlexNet and ResNet34, the proposed method can simultaneously handle recognition work with mixed input. The experiment results showed that our method achieve a good performance applied in the real oracle bone inscriptions data set. © 2024 IEEE.

关键词： Deep learning

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Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

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Intelligent Automation & Soft Computing 2023年第7期37卷 1-16页

作者： Jixun Gao Bingyi Hu Jialei Liu Huaichen Wang Quanzhen Huang Yuanyuan Zhao School of Computer Henan University of EngineeringZhengzhou451191China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China School of Computer Hubei University of Arts and ScienceXiangyang441053China School of Software Engineering Anyang Normal UniversityAnyang455000China School of Computer and Information Engineering Henan Normal UniversityXinxiang453003China The Publicity Department Zhengzhou University of TechnologyZhengzhou450044China

Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service ***,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste *** the backup virtual machine under the above circumstances can effectively improve resource ***,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for *** deployment locations have different resource utilization and average service response *** want toﬁnd a balanced solution that minimizes the average service response time of the IoT application while maximizing resource *** this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment ***,the simulation results show that the TDA can signiﬁcantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical *** experimental results conﬁrm that the perfor-mance of TDA is better than that of other two methods.

关键词： Mobile edge computing overbooking resource utilization service response time task deployment algorithm

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The Research on Remote Sensing Image Change Detection Based on Deep Learning

The Research on Remote Sensing Image Change Detection Based ...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Song, Xu Zhou, Hongyu Wu, Liang School of Computer & Information Engineering Anyang Normal University Anyang China Management and Science University School of Graduate Studies Shah Alam Malaysia

In the past, remote sensing image(RSI) change detection often ignored the importance of shallow information with high resolution and fine grained features, and there were problems of missing detection and rough detection boundary. This caused the uncertainty of the edge pixels of the target and the loss of information of the small target. To solve such problem, we construct a new network under the CDD data set for RSI change detection. In addition, in order to better integrate the information from the bottom and top layers, a channel attention module is introduced to conduct in-depth supervision. The testing results indicated that the accuracy of the evaluation index reaches 88%, the recall rate reaches 86%, and the F1 value reaches 83%, which better solves the problems of the uncertainty of the target edge pixel and the information missing of the small target. © Published under licence by IOP Publishing Ltd.

关键词： Change detection

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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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A Novel Adaptive Linear Neuron Based on DNA Strand Displacement Reaction Network

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022年第3期19卷 1424-1434页

作者： Zou, Chengye Wei, Xiaopeng Zhang, Qiang Zhou, Changjun Anyang Normal University School of Mathematics and Statistics Anyang455000 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Dalian Minzu University College of Computer Science and Engineering Dalian116024 China

Analog DNA strand displacement circuits can be used to build artificial neural network due to the continuity of dynamic behavior. In this study, DNA implementations of novel catalysis, novel degradation and adjustment reaction modules are designed and used to build an analog DNA strand displacement reaction network. A novel adaptive linear neuron (ADALINE) is constructed by the ordinary differential equations of an ideal formal chemical reaction network, which is built by reaction modules. When reaction network approaches equilibrium, the weights of the ADALINE are updated without learning algorithm. Simulation results indicate that, ADALINE based on the analog DNA strand displacement circuit has ability to implement the learning function of the ADALINE based on the ideal formal chemical reaction networks, and fit a class of linear function. © 2004-2012 IEEE.

关键词： Degradation

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OraclePoints: A Hybrid Neural Representation for Oracle Character 23

OraclePoints: A Hybrid Neural Representation for Oracle Char...

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31st ACM International Conference on Multimedia, MM 2023

作者： Jiang, Runhua Liu, Yongge Zhang, Boyuan Chen, Xu Li, Deng Han, Yahong College of Intelligence and Computing Tianjin University Tianjin China School of Computer and Information Engineering Anyang Normal University Anyang China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Anyang China

ISBN: (纸本)9798400701085

Oracle Bone Inscriptions (OBI) are ancient hieroglyphs originated in China and are considered one of the most famous writing systems in the world. Up to now, thousands of OBIs have been discovered, which require deciphering by experts to understand their contents. Experts typically need to restore, classify, and compare each character with previous inscriptions. Although existing research can assist with one of these operations, their performance falls short of practical requirements. In this work, we propose the OraclePoints framework, which represents OBI images as hybrid neural representations comprising features of images and point sets. The image representation provides inscription appearance and character structure, while the point representation makes it easy and effective to distinguish characters and noises. In addition, we demonstrate that OraclePoints can be easily integrated with existing models in a plug-and-play manner. Comprehensive experiments demonstrate that the proposed hybrid neural representation framework supports a range of OBI tasks, including character image retrieval, recognition, and denoising. It is also demonstrated that OraclePoints is helpful for deciphering OBIs by linking ancient characters to modern Chinese characters. Our codes are available at https://***/. © 2023 ACM.

关键词： Geometry

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