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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Wang, Zhongmin Zhou, Rui Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an Shaanxi710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi710121 China

ISBN: (纸本)9781728189543

The information interaction when the brain processes emotional activities is intricate. Therefore, it is very necessary for us to explore the mechanisms of the functional coordination of various brain regions. Recent studies have demonstrated that there are hub regions in the brain that are highly connected and highly central, and are responsible for coordinating dynamic interactions between brain regions. This study aims to construct the dynamic brain networks related to emotions and examine the structure of the hubs of these networks that reflect functional connectivity. In this paper, we demonstrate that a combination of network hubs set a closely connected collective called 'rich-club',which can be used as the core network architecture for identifying different emotions. According to the phase synchronization relationship of the Electroencephalogram (EEG) in three time periods to construct the corresponding brain networks in different sub-states, we found that the dynamic connection patterns produced by different emotions are different, which is manifested in the different rich-club organization. The discovery of rich-club structure related to emotions in this study is of great significance for studying the brain mechanisms behind human emotional processes. © 2020 IEEE.

关键词： Electroencephalography

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Research on Visualization Algorithm of Association Rules Based on Concept Lattice 2020

Research on Visualization Algorithm of Association Rules Bas...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Xiajiong Shen Lijie Bao Lei Zhang Institute of Data and Knowledge Engineering Henan University Kaifeng PR China School of Computer and Information Engineering Henan University Kaifeng PR China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng PR China

ISBN: (纸本)9781450387828

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display.

关键词： Visualization Concept lattice Association rules Knowledge discovery

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Causal Invariant Hierarchical Molecular Representation for Out-of-distribution Molecular Property Prediction

Causal Invariant Hierarchical Molecular Representation for O...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinlong Wen Yifei Guo Shuoying Wei Wenhan Long Lida Zhu Rongbo Zhu Huazhong Agricultural University Wuhan China College of Informatics Huazhong Agricultural University Wuhan China Shenzhen Institute of Nutrition and Health Huazhong Agricultural University Shenzhen China Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Huazhong University of Science and Technology Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular representation learning models are prone to be influenced by spurious parts during distribution shifts (also known as out-of-distribution, or OOD), which results in models mistakenly treating these spurious parts as crucial features of molecules, thereby limiting the generalization capability of the models. To tackle this issue, a novel invariant molecular representation learning model, called Causal Invariant Hierarchical Molecular Representation Graph Neural Networks (CHiMoGNN), is proposed for OOD molecular property prediction. In CHiMoGNN, a Feature Enhancement (FE) module is designed to leverage the multi-level molecular parts to enhance the expression of invariant features, thereby enhancing the model’s capability to capture key molecular information. In addition, a Cartesian Product based Environmental Impact (EI) module is adopted to generate counterfactual samples with environmental diversity. Consequently, these samples are utilized to train a classifier that maintains consistent performance across various environments. Extensive experiments on seven real-world datasets demonstrate that CHiMoGNN outperforms 9 state-of-the-art models, achieving a 5.73% increase in average ROC-AUC, and the results also show that CHiMoGNN can effectively maintain generalization in various distribution shifts. Code and datasets are available at https://***/Chertuion/CHiMoGNN.

关键词： Representation learning Limiting Codes Predictive models Feature extraction Iron Graph neural networks Drug discovery data mining Compounds

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Msnoa-Catboost: A Novel Pan-Cancer Diagnosis Model with Feature Subset Selection and Hyperparameter Optimization

SSRN

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SSRN 2024年

作者： Xiao, Tianyun Zhang, Zichen Kong, Shanshan Liu, Fengchun Yang, Aimin Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China

Cancer, as a global epidemic disease, remains a significant threat to human life and health. Prevention and early diagnosis are widely recognized as effective ways to tackle cancer. With the development of big data technology, many data-driven cancer diagnosis models have been proposed. However, the accuracy and generalization ability of existing models often fail to meet practical application needs. In order to address these challenges, this paper proposes a novel and improved protocol for high-precision pan-cancer diagnosis based on five publicly available cancer datasets from the UCI database and Kaggle, covering different cancer types such as breast, lung, and glioma. To achieve feature subset selection and hyperparameter optimization, we first introduce the nutcracker optimization algorithm (NOA), and improve it by using a multi-strategy method (MSNOA) to enhance optimization performance and efficiency. In the comparative experiment phase, we compare several popular machine learning models and identify MSNOA-CatBoost as the proposed model. The experimental results show that MSNOA-CatBoost improves the accuracy of the five datasets by an average of 9.12% compared to the baseline optimal model, and outperforms existing models in terms of performance. In summary, this study presents a promising approach to achieving high-precision pan-cancer diagnosis by leveraging the power of big data and advanced machine learning techniques. The proposed MSNOA-CatBoost model shows remarkable performance improvement over existing models, and has the potential to be applied in clinical practice for cancer diagnosis and treatment planning. © 2024, The Authors. All rights reserved.

关键词： Optimization

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Recognition of Pest Based on Faster RCNN 6th

Recognition of Pest Based on Faster RCNN

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6th Conference on Signal and Information processing, Networking and Computers, ICSINC 2019

作者： Zhang, Miaohui Chen, Yunzhong Zhang, Bo Pang, Kangning Lv, Bingxue Key Laboratory of Data Analysis and Processing of Henan Province Henan University Kaifeng China Postdoctoral Research Station of Geography Henan University Kaifeng China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9789811541629

Effective prediction of crop pest species and quantities can provide a priori knowledge for pest control. However, there are many kinds of pests and the color and texture of different pests are similar, traditional image processing and recognition methods based on time or frequency domain features and classifiers are hard to meet the needs of high-precision recognition. In order to improve the recognition result of agricultural pests, based on the target detection algorithm Faster RCNN, we put forward a method for the classification and identification of pests on the basis of online hard sample mining and residual network. The main work is as follows: (1) According to the different size of pests, the detection ability of the network for small targets is enhanced by changing the size and number of anchors;(2) In the feature extraction stage, we use ResNet-50 with stronger feature extraction ability rather than the original VGG16 network;(3) In order to solve the imbalance of samples, online hard Sample Mining Strategy is introduced in the training stage. The experimental outcome demonstrate that our method is effective, the improved algorithm achieves about 5% improvement in accuracy, and the network has a stronger ability to detect small targets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability

arXiv

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arXiv 2022年

作者： Mao, Xudong Cao, Liujuan Gnanha, Aurele T. Yang, Zhenguo Li, Qing Ji, Rongrong Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-Sen University China School of Informatics Xiamen University China Department of Computer Science City University of Hong Kong China Department of Computer Science Guangdong University of Technology China Department of Computing Hong Kong Polytechnic University China

GAN inversion aims to invert an input image into the latent space of a pre-trained GAN. Despite the recent advances in GAN inversion, there remain challenges to mitigate the tradeoff between distortion and editability, i.e. reconstructing the input image accurately and editing the inverted image with a small visual quality drop. The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code. Here, we show that both reconstruction and editability can be improved by a proper design of the pivot code. We present a simple yet effective method, named cycle encoding, for a high-quality pivot code. The key idea of our method is to progressively train an encoder in varying spaces according to a cycle scheme: W→W+→W. This training methodology preserves the properties of both W and W+ spaces, i.e. high editability of W and low distortion of W+. To further decrease the distortion, we also propose to refine the pivot code with an optimization-based method, where a regularization term is introduced to reduce the degradation in editability. Qualitative and quantitative comparisons to several state-of-the-art methods demonstrate the superiority of our approach. Copyright © 2022, The Authors. All rights reserved.

关键词： image reconstruction

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A k-core analysis to Large-Scale Web API Collaboration Networks

A k-core Analysis to Large-Scale Web API Collaboration Netwo...

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2020 International Conference on Service Science, ICSS 2020

作者： Tang, Mingdong Lei, Wenquan Lian, Sixian Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9781728185316

The Web has become a huge programmable platform in the Web 2.0 era. More and more companies are opening their data and services to the public through Web APIs. With the increasing number and variety of Web APIs, new Web applications and value-added services can be rapidly developed by combining different APIs. The ever-growing collaborations between APIs thus raise a kind of a large-scale networks, namely Web API collaboration networks. However, the structure and evolution process of Web API collaboration networks are still unclear to people so far. This paper provides a deep analysis to the internal structure of a real-world Web API collaboration network using k-core decomposition. We firstly construct the Web API collaboration network by using the data crawled from the largest Web API registry, Programmable ***, and then employ the k-core decomposition method to obtain different subgraphs of the network with different centrality or coreness (i.e., k-cores). We give an experimental analysis to the structures of the Web API collaboration network and its k-cores by using some classic statistical tools, such as degree distribution and clustering coefficient. The analysis results not only can identify the most central APIs in the Web API collaboration network, but also provides a basis for the visualization and understanding of Web API collaboration networks. © 2020 IEEE.

关键词： Statistical mechanics

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A novel cloud game framework in dual gigabit network

A novel cloud game framework in dual gigabit network

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2020 International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2020

作者： Sun, Kai Xu, Tao Shanghai Urban Construction Vocational College Shanghai201415 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China School of Computer and Information Engineering Henan University Kaifeng475004 China

With the development of 5G, cloud computing, and artificial intelligence (AI) technology, cloud game has become a hotspot about application and innovation of Gigabit bandwidth. This paper, firstly, analyses the present situation and development of cloud game to clarify the key business and key technology of cloud game, then, proposes an operational framework of cloud game, finally, the feasibility of the proposed framework is discussed and several suggestions about the operation of cloud game are given. © Published under licence by IOP Publishing Ltd.

关键词： 5G mobile communication systems

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A Metaheuristic with Learning Mechanism for Solving the Multi-school Heterogeneous School Bus Routing Problem

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2021年第4期48卷 884页

作者： Xie, Yi Kong, Yunfeng Xiang, Hongzhen Hou, Yan-e Han, Daojun Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475000 China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Henan University Kaifeng475000 China College of Computer and Information Engineering Henan University Kaifeng475000 China

This paper presents a metaheuristic with online learning (MOL) to solve the multi-school heterogeneous-fleet school bus routing problem (MHSBRP). In the iterated local search (ILS) metaheuristic framework, an online learning mechanism is integrated with the neighborhood search heuristic. It evaluates the performance of search operators according to the historical search information, and then adjusts the selection probability of the operators in the following loops of search. The proposed algorithm is tested on a set of benchmark instances. The solution results show that MOL solves both mixed-load and single-load instances of MHSBRP effectively, and outperforms the ILS significantly. Compared with the algorithm without learning mechanism, MOL could improve the convergence of neighborhood search and the quality of solution. © 2021. IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Benchmarking

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An Orbital Angular Momentum Mode Estimation Method with an Unknown Beam Axis

An Orbital Angular Momentum Mode Estimation Method with an U...

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IEEE SENSORS

作者： Gaofeng Shu Bingxu Chen Ning Li School of Computer and Information Engineering Henan University Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Orbital angular momentum (OAM) beam is very difficult to receive due to the divergence of the beam. The position of the beam axis must be known by the existing receiving methods, including the single point method and the phase gradient method. This article proposes an OAM mode estimation method with an unknown beam axis. This method first estimates the position of the beam axis according to the phase characteristics and then uses the phase gradient method to estimate the OAM mode. The simulation results verify the effectiveness of the method.

关键词： Gradient methods Simulation Estimation Orbits Sensors

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