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IEEE Transactions on Field Robotics 2025年 2卷 270-287页

作者： Olivier Gamache Jean-Michel Fortin Matěj Boxan François Pomerleau Philippe Giguère Department of Computer Science and Software Engineering Université Laval Québec QC Canada

Reproducibility is a cornerstone of scientific progress, as it enables fair comparisons between algorithms through the development of detailed solutions and datasets. However, standard datasets often present limitations, particularly due to the fixed nature of input data sensors, which makes it difficult to compare methods that actively adjust sensor parameters to suit environmental conditions. This is the case with automatic-exposure (AE) methods, which rely on environmental factors to influence the image acquisition process. As a result, AE methods have traditionally been benchmarked in an online manner, rendering experiments nonreproducible. Building on our previous work, we propose a methodology that utilizes an emulator capable of generating images at any exposure time. This approach leverages BorealHDR, a unique multiexposure stereo dataset, along with its new extension, in which data were acquired along a repeated trajectory at different times of the day to assess the impact of changing illumination. In total, BorealHDR covers 13.4km over 59 trajectories in challenging lighting conditions. The dataset also includes lidar-inertial odometry-based maps with pose estimation for each image frame, as well as global navigation satellite system (GNSS) data for comparison. We demonstrate that by using images acquired at various exposure times, we can emulate realistic images with a root-mean-square error (RMSE) below 1.78% compared to ground truth images. Using this offline approach, we benchmarked eight AE methods, concluding that the classical AE method remains the field’s best performer. To further support reproducibility, we provide in-depth details on the development of our backpack acquisition platform, including hardware, electrical components, and performance specifications. In addition, we share valuable lessons learned from deploying the backpack over more than 25 km across various environments. Our code and dataset are available online at this link: https:/

关键词： Cameras Trajectory Simultaneous localization and mapping Visualization Benchmark testing Reproducibility of results Lighting Heuristic algorithms Global navigation satellite system Emulation

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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An Online Integrated Development Environment for Automated Programming Assessment Systems 17

An Online Integrated Development Environment for Automated P...

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17th International Conference on computer Supported Education, CSEDU 2025

作者： Frankford, Eduard Crazzolara, Daniel Vierhauser, Michael Meißner, Niklas Krusche, Stephan Breu, Ruth University of Innsbruck Department of Computer Science Austria University of Stuttgart Institute of Software Engineering Germany Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9789897587467

The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student programming exercises at scale. However, these tools often do not provide feature-rich online editors compared to their traditional integrated development environments (IDEs) counterparts. This absence of key features, such as syntax highlighting and autocompletion, can negatively impact the learning experience, as these tools are crucial for effective coding practice. To address this gap, this research contributes to the field of programming education by extracting and defining requirements for an online IDE in an educational context and presenting a prototypical implementation of an open-source solution for a scalable and secure online IDE. The usability of the new online IDE was assessed using the Technology Acceptance Model (TAM), gathering feedback from 27 first-year students through a structured survey. In addition to these qualitative insights, quantitative measures such as memory (RAM) usage were evaluated to determine the efficiency and scalability of the tool under varying usage conditions. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Teaching

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning

DocTTT: Test-Time Training for Handwritten Document Recognit...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Wenhao Gu Li Gu Ziqiang Wang Ching Yee Suen Yang Wang Department of Computer Science and Software Engineering Concordia University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particularly in scenarios with minimal annotated data available. In this paper, we introduce the DocTTT framework to address these challenges. The key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder (MAE). During testing, we adapt the visual representation parameters using a self-supervised MAE loss. During training, we learn the model parameters using a meta-learning framework, so that the model parameters are learned to adapt to a new input effectively. Experimental results show that our proposed method significantly outperforms existing state-of-the-art approaches on benchmark datasets.

关键词： Training Metalearning Adaptation models Handwriting recognition Visualization Technological innovation computer vision Text recognition Computational modeling Layout

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Improving Federated Learning Efficiency with PruneFL: A Healthcare Perspective 24

Improving Federated Learning Efficiency with PruneFL: A Heal...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Shahid, Alvina Arshad, Muhammad Muneeb Azam, Farooque Anwar, Muhammad Waseem Shafi, Imran Department of Computer and Software Engineering EME College National University of Sciences and Technology Punjab Islamabad Pakistan School of Interdisciplinary Engineering and Sciences National University of Sciences and Technology Punjab Islamabad Pakistan

ISBN: (纸本)9798400717482

In healthcare, accessing diverse and large datasets for machine learning poses challenges due to data privacy concerns. Federated learning (FL) addresses this by training models on decentralized data while preserving privacy. We propose PruneFL, a FL paradigm that leverages model pruning to reduce complexity and enhance efficiency. PruneFL strikes a balance between model accuracy and efficiency by reducing parameters, accelerating convergence, and minimizing communication overhead. Our approach addresses critical FL challenges and demonstrates efficacy in resource-constrained environments like healthcare. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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TSMS-InceptionNeXt: A Framework for Image-Based Combustion State Recognition in Counterflow Burners via Feature Extraction Optimization

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computers, Materials and Continua 2025年第3期83卷 4329-4352页

作者： Huiling Yu Xibei Jia Yongfeng Niu Yizhuo Zhang Software Engineering Department of Computer Science Changzhou University Changzhou 213146 China Electrical Engineering Department of Computer Science Changzhou University Changzhou 213146 China

The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose a Triple-Scale Multi-Stage InceptionNeXt (TSMS-InceptionNeXt) combustion state recognition method based on feature extraction optimization. First, to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images, we introduce Triplet Attention, which applies attention to the width, height, and Red Green Blue (RGB) dimensions of the flame images to enhance its ability to model dynamic features. Secondly, to address the issue of key information loss in the Inception deep convolution layers, we propose a Similarity-based Feature Concentration (SimC) mechanism to enhance the model’s capability to concentrate on critical features. Next, to address the insufficient receptive field of the model, we propose a Multi-Scale Dilated Channel Parallel Integration (MDCPI) mechanism to enhance the model’s ability to extract multi-scale contextual information. Finally, to address the issue of the model’s Multi-Layer Perceptron Head (MlpHead)neglecting channel interactions, we propose a Channel Shuffle-Guided Channel-Spatial Attention (ShuffleCS) mechanism, which integrates information from different channels to further enhance the representational power of the input features. To validate the effectiveness of the method, experiments are conducted on the counterflow

关键词： Counterflow burner combustion state recognition InceptionNeXt dilated convolution channel shuffling

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Enhancing market trend prediction using convolutional neural networks on Japanese candlestick patterns

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PeerJ computer Science 2025年 11卷 e2719-e2719页

作者： Mersal, Edrees Ramadan Karaoğlan, Kürşat Mustafa Kutucu, Hakan Department of Computer Engineering Karabuk University Karabuk Turkey Department of Software Engineering Karabuk University Karabuk Turkey

This study discusses using Japanese candlestick (JC) patterns to predict future price movements in financial markets. The history of candlestick trading dates back to the 17th century and involves the analysis of patterns formed during JC trading. Candlestick patterns are practical tools for the technical analysis of traders in financial markets. They may serve as indicators of traders’ documents of a potential change in market sentiment and trend direction. This study aimed to predict the following candle-trend-based JC charts using convolutional neural networks (CNNs). In order to enhance the accuracy of predicting the directional movement of subsequent financial candlesticks, a rich dataset has been constructed by following a structured three-step process, and a CNN model has been trained. Initially, the dataset was analyzed, and sub-charts were generated using a sliding window technique. Subsequently, the Ta-lib library was used to identify whether predefined patterns were present within the windows. The third phase involved the classification of each window’s directional tendency, which was substantiated by employing various technical indicators to validate the direction of the trend. Following the data preparation and analysis phases, a CNN model was developed to extract features from sub-charts and facilitate precise predictions effectively. The experimental results of this approach demonstrated a remarkable predictive accuracy of up to 99.3%. Implementing cross-validation techniques is essential to verify the reliability and overall performance of the model. To achieve this goal, the dataset was divided into several small subsets. Subsequently, the model was trained and evaluated multiple times using different combinations of these subsets. This method allows for a more accurate assessment of the model’s predictive capabilities by examining its performance on unseen data. © (2025), (PeerJ Inc.). All rights reserved.

关键词： Convolution

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