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SSRN 2024年

作者： Huang, Yiwang Huang, Yuxin Tang, Zhengyi Liu, Jianxiao School of Data Science Tongren University Tongren China School of Computer Science and Mathematics Fujian University of Technology Fuzhou China College of Informatics Huazhong Agricultural University Wuhan China

Noise data sets are easier to obtain and closer to real scenarios than clean data sets, but deep neural networks are prone to overfitting noise label data, which leads to declined generalization performance of network. Although the correction of noise labels can alleviate overfitting of the model, the existing methods usually rely on small clean data sets, and the memory behavior of the model itself in the late stage of training still causes it tend to learn the noise label data. In order to solve this problem, a general self-distillation-based ensemble label correction (SDELC) is proposed. Consisting of a self-distillation model and a multi-layer perceptron (MLP), SDELC combines the prediction output of each training stage with the original noise label to form an ensemble label for updating the original noise label, and continuously reduces the proportion of the original noise labels in the ensemble labels during the training, thereby minimizing the influence of noise labels on the model. Experiments have proved that SDELC can gradually reduce the influence of noise labels on the model during training, and exhibited competitive strength on both the CIFAR10 and CIFAR100 data sets. On the CIFAR100 data set with a symmetrical noise coefficient of 20% especially, the model classification accuracy reached 76.21%. © 2024, The Authors. All rights reserved.

关键词： Distillation

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Decoder-Only Image Registration

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Jia, Xi Lu, Wenqi Cheng, Xinxing Duan, Jinming University of Birmingham School of Computer Science United Kingdom Manchester Metropolitan University Department of Computing and Mathematics United Kingdom University of Manchester Division of Informatics Imaging and Data Sciences United Kingdom

In unsupervised medical image registration, encoder-decoder architectures are widely used to predict dense, full-resolution displacement fields from paired images. Despite their popularity, we question the necessity of making both the encoder and decoder learnable. To address this, we propose LessNet, a simplified network architecture with only a learnable decoder, while completely omitting a learnable encoder. Instead, LessNet replaces the encoder with simple, handcrafted features, eliminating the need to optimize encoder parameters. This results in a compact, efficient, and decoder-only architecture for 3D medical image registration. We evaluate our decoder-only LessNet on five registration tasks: 1) inter-subject brain registration using the OASIS-1 dataset, 2) atlas-based brain registration using the IXI dataset, 3) cardiac ES-ED registration using the ACDC dataset, 4) inter-subject abdominal MR registration using the CHAOS dataset, and 5) multi-study, multi-site brain registration using images from 13 public datasets. Our results demonstrate that LessNet can effectively and efficiently learn both dense displacement and diffeomorphic deformation fields. Furthermore, our decoder-only LessNet can achieve comparable registration performance to benchmarking methods such as Voxel-Morph and TransMorph, while requiring significantly fewer computational resources. © 1982-2012 IEEE.

关键词： Image registration

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Optimal half-metal band structure for large thermoelectric performance

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Physical Review B 2024年第3期110卷 035150-035150页

作者： Finantius E. M. Rahangiar Adam B. Cahaya Melania S. Muntini Isa Anshori Eddwi H. Hasdeo Department of Physics Research Center for Quantum Physics Department of Physics Faculty of Mathematics and Natural Sciences Department of Physics Faculty of Science and Data Analytics School of Electrical Engineering and Informatics Research Center for Nanosciences and Nanotechnology Department of Physics and Material Science

Half-metal ferromagnets were predicted [Cahaya, Tretiakov, and Bauer, IEEE Trans. Magn. 51, 1 (2015)] to give large thermoelectric performance in antiparallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin valve enhancement factor. We test this theory and find a mismatch of parameters that gives large thermoelectric performance and large spin valve enhancement factor. As a result, we show that the spin valve setup is useful only for half-metal ferromagnets with initially poor thermoelectric performance. To obtain the largest thermoelectric performance, one still needs to open the band gap.

关键词： Thermal energy harvesting devices Transport phenomena Boltzmann theory

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Povm-Based Quantum Self-Attention Neural Network 21

Povm-Based Quantum Self-Attention Neural Network

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21st International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Wei, Jiachun He, Zhimin Chen, Chuangtao Deng, Maijie Situ, Haozhen School of Mathematics and Big data Foshan University Foshan528000 China School of Electronic and Information Engineering Foshan University Foshan528000 China Macau University of Science and Technology Faculty of Innovation Engineering Macau China School of Mechatronic Engineering and Automation Foshan University Foshan528000 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

ISBN: (纸本)9798350303810

Quantum Self-Attention Neural Network (QSANN) has demonstrated remarkable potential. However, it is limited to measuring only partial information from qubits, thereby restricting its information extraction capability. Moreover, the measurement process requires multiple measurements for each qubit, leading to inefficiency in utilizing the feature space. To address these challenges, this paper introduces a novel approach called Positive Operator-Valued Measure based Quantum Self-Attention Neural Network (POVM-QSANN). It leverages POVM operators as observables, enabling the extraction of comprehensive QKV (Query-Key-Value) feature vectors from each qubit. This innovative mechanism significantly enhances information extraction capability and efficiently utilizes the feature space. The experimental results underscore the considerable advancements of POVM-QSANN on MC and RP datasets. Particularly noteworthy is its remarkable accuracy increase of 9.68% compared to QSANN on the RP dataset. © 2023 IEEE.

关键词： Positive Operator-Valued Measure Quantum machine learning Self-attention mechanism Variational quantum algorithm

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Mobile Islamic Banking Consumer Behaviour in Indonesia

Mobile Islamic Banking Consumer Behaviour in Indonesia

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International Conference on Cyber and IT Service Management (CITSM)

作者： Nur Hidayah Haryatih Tira Mutiara Agus Salim Ahmad Ihsan Maulana Faculty of Economics and Business Syarif Hidayatullah State Islamic University Jakarta Indonesia School of Data Science Mathematics and Informatics IPB University Bogor Indonesia

ISBN: (数字)9798331528799

ISBN: (纸本)9798331528805

The challenge of managing digital banks is in managing technology and convincing consumers to use it. The purpose of this study is to analyze the intention and behavior of using Islamic mobile banking by adopting the UTAUT 2 variables. data were collected by distributing online questionnaires using Google Form. The data collected from 117 respondents were then analyzed using the Structural Equation Model Partial Least Square (SEM - PLS) technique using SmartPLS Software version 3.3.0. The results of the study show that social influence, experience and habits have a significant influence on the intention to use Islamic mobile banking. Performance expectancy, effort expectancy, and price values have a positive relationship, but their influence is insignificant. While facilitating conditions and hedonic motivation have a negative relationship, their influence is insignificant. The intention to use mobile banking has a significant effect on the behavior of using Islamic mobile banking. Facilitating conditions have a positive relationship but the effect is not significant. Islamic banks need to focus on setting strategies to increase social influence and user experience to encourage customer intentions to use Islamic mobile banking, which will ultimately affect Islamic mobile banking behavior.

关键词： Analytical models Consumer behavior Numerical analysis Government Banking User experience Mathematical models Software data models Internet

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A modified FCN-based method for Left Ventricle endocardium and epicardium segmentation with new block modules 8

A modified FCN-based method for Left Ventricle endocardium a...

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8th NAFOSTED Conference on Information and Computer science, NICS 2021

作者： Nham, Do-Hai-Ninh Trinh, Minh-Nhat Tran, Tien-Thanh Pham, Van-Truong Tran, Thi-Thao Hanoi University of Science and Technology School of Applied Mathematics and Informatics Vingroup Big Data Institute Smart Health Center

ISBN: (纸本)9781665410014

Cardiac segmentation of medical magnetic resonance images has been crucial nowadays owing to its necessity for cardiac problems diagnosis. In the increasing demand of advanced procedures for cardiac disease diagnosis and inspired by the structure of receptive field block;in this paper, we propose a new block module then further assembling into a deep fully convolutional neural network to deal with automated left ventricle segmentation. With only one learning stage, our proposed model is trained end-to-end, pixels-to-pixels and validated on two popular cardiac MRI benchmarks, ACDC and SunnyBrook datasets. Several experiments have proved that our new model architecture has a better performance than previous segmentation methods with enhanced feature discriminability and robustness, despite having much less training parameters. © 2021 IEEE.

关键词： Pixels

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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Computer Modeling in Engineering & sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Spatiotemporal information conversion machine for time-series forecasting

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Fundamental Research 2024年第6期4卷 1674-1687页

作者： Hao Peng Pei Chen Rui Liu Luonan Chen School of Mathematics South China University of TechnologyGuangzhou 510640China Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghai 200031China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Guangdong Institute of Intelligence Science and Technology Zhuhai 519031China West China Biomedical Big Data Center Med-X center for informaticsWest China HospitalSichuan UniversityChengdu 610041China

Making time-series forecasting in a robust way is a difficult task only based on the observed data of a non-linear *** this work,a neural network computing framework,the spatiotemporal information conver-sion machine(STICM),was developed to efficiently and accurately render a forecasting of a time series by employing a spatial-temporal information(STI)*** combines the advantages of both the STI equation and the temporal convolutional network,which maps the high-dimensional/spatial data to the future temporal values of a target variable,thus naturally providing the forecasting of the target *** the observed variables,the STICM also infers the causal factors of the target variable in the sense of Granger causality,which are in turn selected as effective spatial information to improve the robustness of time-series *** STICM was successfully applied to both benchmark systems and real-world datasets,all of which show superior and robust performance in timeseries forecasting,even when the data were perturbed by *** both theoretical and computational viewpoints,the STICM has great potential in practical applications in artificial intelligence or as a model-free method based only on the observed data,and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning.

关键词： Spatiotemporal information conversion network Robust time-series forecasting High-dimensional time series Takens’embedding theory Causal inference

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data-Driven Design for Anomaly Detection in Network Access Control Systems

Data-Driven Design for Anomaly Detection in Network Access C...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Musa Abubakar Muhammad Fabio Caraffini Adebamigbe Fasanmade Olabayo Ishola Kabiru Mohammed Jarrad Morden School of Computing Mathematics and Data Science Coventry University Coventry UK Department of Computer Science Swansea University Swansea UK School of Computer Science and Informatics De Montfort University Leicester UK

Current network access control systems can contain unpredictable interactions between multiple device models, multiple network protocol layers (e.g. TCP, UDP and ICMP), hardware, and clock-skew-specific influences, and cannot detect or identify abnormal behaviours based on the type of device. To complicate things further, the ‘bring your own device’ policy is increasing security threats, vulnerabilities, and risks to enterprise network environments, making intrusion detection and prevention systems unable to detect illegal and unauthorised access to devices in the enterprise network. The consequences can be disastrous. In this light, this work propose a simple but effective clustering approach capable of separating normal and abnormal network traffic patterns to detect such challenges (anomalies). The work in this paper applies this approach to single devices and aggregations of data per device type. Additionally, this work propose plotting the notched box for each cluster to acquire a better understanding of their data distributions and measuring the clusters’ performance using the Adjusted Rand Index. The results show that the proposed method is valid, can be used in several contexts, and features a 95% confidence that most single device and device type distributions overlap, which makes them equivalently usable for anomaly detection purposes.

关键词： Performance evaluation Access control Protocols Clustering algorithms Telecommunication traffic Partitioning algorithms Classification algorithms

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Price Prediction of Seasonal Items Using Time Series Analysis

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Computer Systems science & Engineering 2023年第7期46卷 445-460页

作者： Ahmed Salah Mahmoud Bekhit Esraa Eldesouky Ahmed Ali Ahmed Fathalla Faculty of Computers and Informatics Zagazig UniversitySharkeya44523Egypt Information Technology Department the University of Technology and Applied SciencesIbriOman School of Electrical and Data Engineering University of Technology SydneySydney2007Australia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Computer Science Department Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Higher Future Institute for Specialized Technological Studies Cairo3044Egypt Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

The price prediction task is a well-studied problem due to its impact on the business *** are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal *** lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series *** proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based ***,this research tuned a set of well-known predictive models on a real-life *** models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear ***,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are ***,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined *** obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

关键词： Deep learning price prediction seasonal goods time series analysis

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