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Self‐training maximum classifier discrepancy for EEG emotion recognition

CAAI Transactions on Intelligence technology 2023年第4期8卷 1480-1491页

作者： Xu Zhang Dengbing Huang Hanyu Li Youjia Zhang Ying Xia Jinzhuo Liu School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismChongqingChina School of Electrical and Computer Engineering Inha UniversityIncheonSouth Korea School of Software Yunnan UniversityYunnanChina

Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming *** study proposed to solve the limited label problem via domain adaptation ***,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this *** proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'***,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is ***,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is *** experiments on SEED and SEED‐IV are *** experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

关键词： artificial intelligence bioinformatics domain adaptation EEG neural network pattern classification

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Online Learning-Based Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks

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China Communications 2024年第3期21卷 230-246页

作者： Tong Minglei Li Song Han Wanjiang Wang Xiaoxiang School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Mobile edge computing(MEC)-enabled satellite-terrestrial networks(STNs)can provide Internet of Things(IoT)devices with global computing ***,the network state information is uncertain or *** deal with this situation,we investigate online learning-based offloading decision and resource allocation in MEC-enabled STNs in this *** problem of minimizing the average sum task completion delay of all IoT devices over all time periods is *** decompose this optimization problem into a task offloading decision problem and a computing resource allocation problem.A joint optimization scheme of offloading decision and resource allocation is then proposed,which consists of a task offloading decision algorithm based on the devices cooperation aided upper confidence bound(UCB)algorithm and a computing resource allocation algorithm based on the Lagrange multiplier *** results validate that the proposed scheme performs better than other baseline schemes.

关键词： computing resource allocation mobile edge computing satellite-terrestrial networks task offloading decision

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Enhanced Brain Tumor Detection Using Transfer Learning with Fine-Tuned Efficientnet-B3 Blocks and Graph-Based Spatial Attention Capsule Networks 21

Enhanced Brain Tumor Detection Using Transfer Learning with ...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Aggrey, Esther Stacy E.B Zhen, Qin Kodjiku, Seth Larweh Fiasem, Delali Linda Sey, Collins Ejiyi, Chukwuebuka Joseph Ntia, Nsikakabasi Williamson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9798331519254

Brain tumors are critical medical conditions that require timely and accurate diagnosis to improve survival outcomes. This research proposes a cutting-edge framework for brain tumor classification, combining fine-tuned EfficientNet-B3 blocks with graph-based spatial attention capsule networks. By addressing challenges such as limited data availability, overfitting, and the complex nature of tumor structures, the framework employs partial transfer learning, advanced feature extraction, and attention mechanisms to effectively model spatial relationships in MRI images. Evaluated on a dataset of 3,064 contrast-enhanced brain MRI images, the model demonstrated exceptional performance, achieving 99.85% classification accuracy and notable improvements in precision, recall, and F1 scores for glioma, meningioma, and pituitary tumours. The use of advanced data augmentation techniques significantly improved the model's ability to generalize, while its computational efficiency makes it well-suited for clinical diagnostic applications. © 2024 IEEE.

关键词： Dynamic contrast enhanced MRI

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Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

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Operations Research Forum 2024年第3期5卷 58页

作者： Wei, Li Solihin, Mahmud Iwan Saruchi, Sarah ‘Atifah Astuti, Winda Hong, Lim Wei Kit, Ang Chun School of Computer Science and Technology (School of Software) Guangxi University of Science and Technology Guangxi 545006 China Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur 56000 Malaysia Faculty of Manufacturing and Mechatronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Computer Engineering Department Automotive and Robotics Engineering Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia

High-precision cylindrical parts are critical components across various industries including aerospace, automotive, and manufacturing. Since these parts play a pivotal role in the performance and safety of the systems they are integrated into, they are often subject to stringent quality control measures. Defects on the interior and exterior wall surfaces of these cylindrical parts can severely undermine their function, leading to degraded performance, increased wear, and even catastrophic failures in extreme cases. This article aims to comprehensively summarize the task definition, challenges, mainstream methods, public datasets, evaluation metrics, and other aspects of surface defect detection for high-precision cylindrical parts, in order to help researchers quickly grasp this field. Specifically, the background and characteristics of industrial defect detection are first introduced. Owing to the unique geometric features of cylindrical part surfaces, algorithms and equipment for image data acquisition used in surface defect detection are elaborated in detail. This article presents an extensive overview of state-of-the-art surface defect detection techniques designed for high-precision cylindrical components, all rooted in deep learning. The methods are systematically classified into three main categories: fully supervised, unsupervised, and alternative approaches, based on their data labeling strategies. Additionally, the paper conducts a comprehensive analysis within each category, shedding light on their unique strengths, limitations, and practical use cases. Concluding the discussion, the paper provides insights into future development trends and potential research directions in this field that will lead to manufacturing innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection computer vision Deep learning Defect detection High-precision cylindrical parts Image processing Optical illumination

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios 20

A Method Based on Genetic Programming to Automatically Const...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ma, Yan Zhang, Changsheng Gao, Yan Guo, Ying School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (纸本)9798350356328

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated. © 2024 IEEE.

关键词： Genetic programming

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Huang, Shizhao Wu, Yimin Lu, Jinwei Deng, Chao School of Software Engineering South China University of Technology Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Engineering Guangdong Ocean University at yangjiang Yangjiang China

Stack Overflow is a widely-used community Q&A website for programming-related queries. In such a platform, providing related questions as suggestions to the users can significantly enhance their search experience. Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited because the semantic and interaction features of the sentences may be lost. In this paper, we propose a novel method to predict the relatedness between questions based on semantic matching. We adopt the Interaction Feature Extractor to capture the interaction information and fuse it through a fusion mechanism to enhance the interaction between questions. Our experimental results demonstrate that our proposed method achieves state-of-the-art performance in terms of Precision, Recall, and F1-score evaluation metrics, outperforming the baseline approaches. Furthermore, we show that our model also performs well in other semantic matching tasks in software fields, indicating its generalization ability and robustness. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Semantics

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Discussion on the Influence of Anti-normalization Parameter Selection of BP Neural Network on Mending Seepage Pressure Data of Tailing Dam

Discussion on the Influence of Anti-normalization Parameter ...

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2022 International Joint Conference on Information and Communication engineering, JCICE 2022

作者： Zhu, Hongli Jin, Meiqi Wen, Yumeng Lu, Ling Liaoning University of Science and Technology School of Computer and Software Engineering Department of Software Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Department of Network Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Liaoning Anshan China

ISBN: (数字)9781665460675

ISBN: (纸本)9781665460675

The height of Infiltration line of tailings dam is a very important parameter for the safety of tailings reservoir. Infiltration line is the intersection line between the free water surface and the cross section of the dam body, that the free water surface is formed by the infiltration of water from the earth dam to the downstream. When the buried depth of the pressure sensors at each monitoring point could be obtained and the groundwater seepage pressure of the tailings dam could be measured too, the height of the free water surface can be calculated then. The commonly used pressure sensor is vibrating wire sensor. Among the many seepage pressure monitoring points, because of the lines, power supply or some other reasons, it often occurs that one or some points do not collect groundwater seepage pressure data for a period of time. For the missing data, BP neural network can be used to mend the missed data. Of course, the patched data should be marked to distinguish it from the real collected data. The mending method of missing seepage pressure data using BP neural network model is analyzed. The influence of anti-normalization parameters on mending data is analyzed. In the process of anti-normalization, the maximum and minimum values of the output data of the training data set are not put to use. Instead, a sequences is formed with the existing output data of some range before and after the mission data segment. The maximum and minimum values of the sequence are selected to use as the anti-normalization parameters. This will make the patched data values closer to the actual collected values. © 2022 IEEE.

关键词： Groundwater

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Two-stage image inpainting method based on transformer and Fast Fourier convolution 2

Two-stage image inpainting method based on transformer and F...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Li, Changxing Chen, Zhuang Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing China Chongqing University of Technology School of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798350331417

In view of the insufficient ability of the currently existing deep learning-based methods to repair image high-frequency information and the small sensory field of the traditional convolutional methods. A two-stage image repair model is proposed. First, Transformer is used to repair the image initially;second, Fourier convolution is used to construct a generative network to refine the high-frequency details of the image;finally, the repair results are input into the discriminator for training to obtain the final image repair model. The experimental results on street view datasets show that the model in the paper has better restoration results in high-frequency details as well as semantic information;compared with mainstream methods, It achieves better results in peak signal-to-noise ratio and structural similarity. © 2023 IEEE.

关键词： Generative adversarial network image inpainting multiple attention Receptive field

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Ensemble Graph and Device Clustering Method based on Attention Mechanism for Decomposing Monolithic to Microservices 22

Ensemble Graph and Device Clustering Method based on Attenti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Deng, Qingyong Li, Qiuming Zuo, Qinghua Tian, Shujuan Long, Saiqin Guangxi Normal University School of Computer Science and Engineering The School of Software Guilin China Xiangtan University School of Computer Science Xiangtan China Jinan University College of Information Science and Technology Guangzhou China

ISBN: (纸本)9798331509712

Existing methods for decomposing monolithic applications into microservices in cloud environments primarily rely on the call relationships within itself. However, these methods are difficult to apply directly in resource-constrained and distributed edge network scenarios without considering the heterogeneity of the device. Therefore, this paper proposes a clustering method that ensembles graph structures and device features based on attention mechanism, which utilizes attention encoders to learn node embeddings and employs a spectral clustering algorithm to obtain decomposition results, optimizing the affinity and matching degree between microservices and devices. Experimental results demonstrate that the proposed method exhibits excellent performance in terms of functional independence, modularity, and adaptability of microservices. © 2024 IEEE.

关键词： Clustering algorithms

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