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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gong, Cheng Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Population initialization is always needed in evolutionary multi-objective optimization (EMO) algorithms. Intuitively, a well-designed initialization method can help facilitate the evolutionary process and improve the performance of EMO algorithms. However, very few studies have investigated the effects of initialization methods on the performance of EMO algorithms. Many existing EMO algorithms randomly generate an initial population to start the evolutionary process. To fill this research gap and attract more attention from EMO researchers to this important yet under-explored issue, in this paper, we examine the effects of various initialization methods that may become promising alternatives to the commonly-used random initialization method. Each initialization method is evaluated through computational experiments on test problems of various sizes with 5-1000 decision variables. Experimental results clearly demonstrate the advantage of well-designed initialization methods over the random initialization method. This study provides useful insights into EMO algorithm design and motivates further research on population initialization. © 2023 IEEE.

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Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction 27th

Leveraging Data from Vast Unexplored Seas: Positive Unlabel...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Konii, Haruki Nakano, Teppei Miyazawa, Yasumasa Ogawa, Tetsuji Department of Communications and Computer Engineering Waseda University Tokyo Japan Intelligent Framework Laboratory Tokyo Japan Application Laboratory Japan Agency for Marine-Earth Science and Technology Yokohama Japan

ISBN: (纸本)9783031781919

This paper proposes a Positive Unlabeled (PU) learning approach to narrow down the prediction area in the context of good fishing ground prediction. PU learning, a type of semi-supervised learning, is particularly suitable for scenarios characterized by limited positive examples and abundant unlabeled data, as often encountered in fishing ground prediction tasks, where the explored sea areas identified as productive fishing spots are treated as positive instances and the vast unexplored sea areas as unlabeled data. Conventional methods often struggle to accurately model the characteristics of good fishing grounds, resulting in overly broad prediction areas or overly restrictive constraints. To tackle this challenge, we present a PU learning-based method designed to identify negative examples from the unlabeled data and consequently refine the area predicted as positive. Specifically, we train a prediction model for fishing duration, which can be considered a surrogate indicator of good fishing grounds. Subsequently, we apply this model to predict the fishing duration for unexplored areas;those areas exhibiting short fishing durations are deemed reliable negative examples. By incorporating both past positive examples and selected negative examples into a binary classification framework for predicting good fishing grounds, we aim to fine-tune the prediction area. To the best of our knowledge, this study represents the first application of PU learning in the domain of good fishing ground prediction. Experimental comparisons conducted using data from bullet tuna trolling validate the efficacy of the proposed methodology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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The Astrometric Performance Test of 80 cm Telescope at Yaoan Station and Precise CCD Positions of Apophis

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Research in Astronomy and Astrophysics 2022年第5期22卷 56-67页

作者： Bifeng Guo Qingyu Peng Ying Chen Zhongjie Zheng Yijia Shang Dan Li Xiao Chen Department of Computer Science Jinan UniversityGuangzhou 510632China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China

The 80 cm azimuthal telescope has newly been mounted at Yaoan Station,Purple Mountain Observatory *** astrometric performance of the telescope is tested in the following three aspects.(a)The geometric distortion of its CCD *** is stable in both a single epoch and multi *** distortion solutions are derived over about one *** maximum values range from 0.75 to 0.79 pixel and the median values range from 0.14 to 0.16 pixel.(b)The limit magnitude of *** 20.5 mag(Gaia-G)stars can be detected with Johnson-V filter exposured in 300 *** astrometric error of about 20.5 mag stars is estimated at O".14 using the fitted sigmoidal function.(c)The astrometric accuracy and the precision of stacked fast-moving faint object.24 stacked frames of the potentially hazardous asteroid(99942)Apophis were derived on 2021 April 14 and 15(fainter than18 mag)based on the ephemeris *** data reduction,the newest Gaia EDR3 Catalog and Jet Propulsion laboratory Horizons ephemeris are referenced as theoretical positions of stars and Apophis,*** results show that the mean(O-C)s(observed minus computed)of Apophis are-O".018 and O".020 in *** decl.,and the dispersions are estimated at O".094 and O".085,respectively,which show the consistency of the stacked results by Astrometrica.

关键词： astronomical instrumentation astrometry-asteroid belt-astronomy data analysis astronomy data reduction

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On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets 1

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12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022

作者： Henouda, Salah Eddine Laallam, Fatima Zohra Kazar, Okba Harous, Saad Houfani, Djihane Department of Computer Science LINATI Laboratory Kasdi Merbah University Ouargla Algeria Department of Computer Science LINFI Laboratory Mohamed Khider University Biskra Algeria Department of Information Systems and Security College of Information Technology United Arab Emirate University Abu Dhabi United Arab Emirates Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9783031253447

ISBN: (纸本)9783031253430

The present work strives to investigate the effect of using dimensionality reduction techniques (DRTs) on breast cancer (BC) classification problem. Primarily, we focused on the following five (DRTs): Auto-Encoders (AE), T-Distributed Stochastic Neighbor Embedding (T- SNE), Recursive Feature Elimination (RFE), Isometric Feature Mapping (Isomap), and Principle Component analysis (PCA). These methods are combined with two famous classifiers that are Support Vector Machine (SVM) and Multilayer perceptron (MLP). They are used for BC classification. Breast Cancer Wisconsin Diagnostic (WDBC) data set was used to validate the experiments of this work. The former was provided by the University of California, Irvine (UCI) machine learning repository. The results demonstrated that combining MLP with the chosen (DRTs) methods increased the classification accuracy for almost all built models by at least 0.7%. In addition, they revealed a decrease in the classification accuracy using SVM as a classifier for almost all built models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Breast cancer prediction Deep learning Dimensionality reduction Machine learning Medical dataset Neural networks

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Medical Image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 IEEE.

关键词： Frequency domain analysis

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Automated Classification of Inherited Retinal Diseases in Optical Coherence Tomography Images Using Few-shot Learning

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Biomedical and Environmental sciences 2023年第5期36卷 431-440页

作者： ZHAO Qi MAI Si Wei LI Qian HUANG Guan Chong GAO Ming Chen YANG Wen Li WANG Ge MA Ya LI Lei PENG Xiao Yan Department of Ophthalmology Beijing Tongren Eye CenterBeijing Tongren HospitalCapital Medical UniversityBeijing Key Laboratory of Ophthalmology and Visual SciencesBeijing 100730China Department of Computer Science RutgersThe State University of New JerseyNew Brunswick 08901USA Department of Computer Science and Engineering University at BuffaloBuffalo 14260USA Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Science KeyLaboratoryBeijing 100730China

Objective To develop a few-shot learning(FSL) approach for classifying optical coherence tomography(OCT) images in patients with inherited retinal disorders(IRDs).Methods In this study, an FSL model based on a student–teacher learning framework was designed to classify images. 2,317 images from 189 participants were included. Of these, 1,126 images revealed IRDs, 533 were normal samples, and 658 were control *** The FSL model achieved a total accuracy of 0.974–0.983, total sensitivity of 0.934–0.957, total specificity of 0.984–0.990, and total F1 score of 0.935–0.957, which were superior to the total accuracy of the baseline model of 0.943–0.954, total sensitivity of 0.866–0.886, total specificity of 0.962–0.971,and total F1 score of 0.859–0.885. The performance of most subclassifications also exhibited advantages. Moreover, the FSL model had a higher area under curves(AUC) of the receiver operating characteristic(ROC) curves in most *** This study demonstrates the effective use of the FSL model for the classification of OCT images from patients with IRDs, normal, and control participants with a smaller volume of data. The general principle and similar network architectures can also be applied to other retinal diseases with a low prevalence.

关键词： Few-shot learning Student-teacher learning Knowledge distillation Transfer learning Optical coherence tomography Retinal degeneration Inherited retinal diseases

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MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

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computers, Materials & Continua 2023年第6期75卷 5573-5595页

作者： Zhanyang Xu Jianchun Cheng Luofei Cheng Xiaolong Xu Muhammad Bilal School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory Novel Software Technology Nanjing UniversityNanjing210023China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siGyeonggi-do17035Korea

Federated learning has been used extensively in business inno-vation scenarios in various *** research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment ***,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise *** proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model ***,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and *** addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global *** results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify ***,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

关键词： Federated learning feature selection credit risk assessment MSEs

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Sparse is Enough in Fine-tuning Pre-trained Large Language Models 41

Sparse is Enough in Fine-tuning Pre-trained Large Language M...

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41st International Conference on Machine Learning, ICML 2024

作者： Song, Weixi Li, Zuchao Zhang, Lefei Zhao, Hai Du, Bo National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for low-cost adaptation. Although PEFT has demonstrated effectiveness and been widely applied, the underlying principles are still unclear. In this paper, we adopt the PAC-Bayesian generalization error bound, viewing pre-training as a shift of prior distribution which leads to a tighter bound for generalization error. We validate this shift from the perspectives of oscillations in the loss landscape and the quasi-sparsity in gradient distribution. Based on this, we propose a gradient-based sparse finetuning algorithm, named Sparse Increment Fine-Tuning (SIFT), and validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning. The code is accessible at https://***/song-wx/SIFT. Copyright 2024 by the author(s)

关键词： Benchmarking

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QNTN: Establishing a Regional Quantum Network in Tennessee

QNTN: Establishing a Regional Quantum Network in Tennessee

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Shaban, Mohamed Ismail, Muhammad Kiran, Mariam Computer Science Department TN United States Alexandria University Faculty of Education Department of Mathematics Egypt Oak Ridge National Laboratory TN United States

ISBN: (纸本)9798350355543

This paper investigates the design of a regional Quantum Network in Tennessee (QNTN) that will connect three quantum local area networks in different cities. We explore two approaches for achieving this interconnection: deploying a satellite constellation in the space layer and employing high-altitude platforms (HAPs) in the aerial layer. Our comparison reveals that a space-ground architecture that uses 108 satellites provides 55.17% coverage of the day and handles 57.75% of entanglement distribution requests with an average fidelity of 0.96. In contrast, the air-ground architecture delivers full-day coverage, fulfills 100% of requests, and achieves a higher average fidelity of 0.98. However, HAPs face significant challenges such as limited operational time, sensitivity to vibrations and weather conditions, and the need for continuous maintenance. This paper contributes to the understanding of optimal architecture for regional quantum networks, highlighting the trade-offs between satellite-based and air-ground approaches. © 2024 IEEE.

关键词： entanglement distribution entanglement fidelity high-altitude platforms quantum communications Quantum Internet regional quantum networks satellites

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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