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arXiv 2025年

作者： Comeau, Jacob Bazinet, Mathieu Germain, Pascal Subakan, Cem Computer Science and Software Engineering Department Laval University Canada Mila - Quebec Artificial Intelligence Institute Canada Computer Science and Software Engineering Department Concordia University Canada

Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existing continual learning approaches in the literature rely on heuristics and do not provide learning guarantees for the continual learning setup. In this paper, we present a new method called ‘Continual Pick-to-Learn’ (CoP2L), which is able to retain the most representative samples for each task in an efficient way. The algorithm is adapted from the Pick-to-Learn algorithm, rooted in the sample compression theory. This allows us to provide high-confidence upper bounds on the generalization loss of the learned predictors, numerically computable after every update of the learned model. We also empirically show on several standard continual learning benchmarks that our algorithm is able to outperform standard experience replay, significantly mitigating catastrophic forgetting. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Enhancing Threat Detection Accuracy in IIoT Networks using AI-based Models

Enhancing Threat Detection Accuracy in IIoT Networks using A...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Omer Waseem Al-Zaidi Mohammed Al-Hubaishi Department of Software Engineering Halic University Istanbul Turkey Department of Computer Engineering Halic University Istanbul Turkey

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

software-Defined Networking (SDN) and Artificial Intelligence (AI) have the potential to address challenges in Industrial Internet of Things (IIoT) networks, such as security, scalability, and network performance, and this paper proposes a robust framework that leverages SDN and AI to enhance network management, real-time monitoring, and efficient resource utilization. SDN decouples the control and data planes, providing centralized control and simplifying the management because AI techniques, including machine learning and reinforcement learning, enable intelligent threat detection, optimized routing, and data protection. The synergistic fusion of these technologies ensures timely and secure data transmission, reduces latency and prevents data loss. Thus, the proposed framework enhances IIoT deployments through automation, predictive analytics, and proactive threat detection and mitigation. This paper demonstrates the integration of SDN and AI to create secure, scalable, and seamless IIoT networks that meet the requirements of modern industrial operations and information security standards. Therefore, it highlights the potential of SDN and AI to revolutionize IIoT networks, providing a comprehensive framework for future IIoT deployments.

关键词： Computational modeling Scalability Routing Threat assessment Real-time systems Computational efficiency Artificial intelligence software defined networking Standards Industrial Internet of Things

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Accurate 3D Grapevine Structure Extraction from High-Resolution Point Clouds

arXiv

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arXiv 2025年

作者： Dobbs, Harry Peat, Casey Batchelor, Oliver Atlas, James Green, Richard Department of Computer Science and Software Engineering University of Canterbury New Zealand

Accurate 3D modelling of grapevines is crucial for precision viticulture, particularly for informed pruning decisions and automated management techniques. However, the intricate structure of grapevines poses significant challenges for traditional skeletonization algorithms. This paper presents an adaptation of the Smart-Tree algorithm for 3D grapevine modelling, addressing the unique characteristics of grapevine structures. We introduce a graph-based method for disambiguating skeletonization. Our method delineates individual cane skeletons, which are crucial for precise analysis and management. We validate our approach using annotated real-world grapevine point clouds, demonstrating an improvement of 15.8% in the F1 score compared to the original Smart-Tree algorithm. This research contributes to advancing 3D grapevine modelling techniques, potentially enhancing both the sustainability and profitability of grape production through more precise and automated viticulture practices. © 2025, CC BY.

关键词： Musculoskeletal system

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Testing Refactoring Engine via Historical Bug Report driven LLM

arXiv

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arXiv 2025年

作者： Wang, Haibo Xu, Zhuolin Tan, Shin Hwei Department of Computer Science & Software Engineering Concordia University Montreal Canada

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed. © 2025, CC BY-NC-SA.

关键词： software testing

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Online Credibility Assessment of Equipment Digital Twin for Discrete Manufacturing

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14763-14774页

作者： Lu, Han Zhang, Lin Deen, M. Jamal Wang, Kunyu Cheng, Hongbo Yang, Laurence T. Beihang University Hangzhou International Innovation Institute Zhejiang 311115 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China AI Atlas Inc Canada Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China St. Francis Xavier University Department of Computer Science Antigonish Canada

The equipment digital twins (EDTs) for discrete manufacturing should be calibrated quickly to avoid irreversible physical damage to the equipment caused by biased control commands. Therefore, an online credibility assessment method for EDTs is urgently needed. However, existing assessment approaches consume too much time, and thus could not reveal dynamic faults in time. In this paper, the dynamic relationship between online evolution and actual applications of EDTs is investigated. Then, two steps are proposed to accelerate the assessment process significantly. One involves pre-constructing a performance-deviation-agent (PDA), and the other involves dynamically fitting the application-time-window (ATW) probability distribution. The methodology is applicable to discrete manufacturing processes. The dynamic credibility of EDT evolution process can be updated after every iteration of the model evolution. Sorting manufacturing equipment was used as a case study to demonstrate the effectiveness of this method. The time consumption was reduced by 90% compared with traditional assessment methods in the case. © 2025 IEEE.

关键词： Fabrication

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Development and evaluation of an early childhood caries prediction model: a deep learning-based hybrid statistical modelling approach

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European Archives of Paediatric Dentistry 2025年 1-12页

作者： Eusufzai, S.Z. Jamayet, N.B. Ahmed, S. Islam, M.B. Ahmad, W.M.A.W. Alam, M.K. Department of Biostatistics Universiti Sains Malaysia Kota Bharu Malaysia School of Dentistry IMU University Kuala Lumpur Malaysia Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Computing and Software Engineering Florida Gulf Coast University Florida United States College of Dentistry Jouf University Sakaka Saudi Arabia

Purpose: An effective Deep learning (DL) based Early Childhood Caries (ECC) prediction model is crucial for early detection of ECC. This study aims to develop and evaluate a deep learning (DL) based hybrid statistical model for ECC prediction. Methods: The study employed a computational cross-sectional design, conducted over a three-year period from March 2021 to March 2024. Data analysis was carried out using a hybrid statistical approach that integrated bootstrap methods, Logistic Regression Modelling (LRM), and Multilayer Feed-Forward Neural Networks (MLFFNN). The sample comprised 157 parent–child pairs, providing a robust dataset for examining the research questions. Results: In the current study, the predictors named, “mother’s education” (β₁: 0.423;p < 0.25), “parent’s knowledge of bottle-feeding habit during sleep can cause tooth decay” (β₂: −1.264;p < 0.25), “attitude towards the importance of oral health as general health” (β₄: −1.052;p < 0.25) and “parent’s self-reported oral pain among their children” (β₅: −2.107;p < 0.25) showed significant association with ECC. For this model, the Mean Absolute Deviation (MAD) was 0.02211, Predictive Mean Squared Error (PMSE) was 0.07909, and the accuracy level was 99.98%. No significant difference was observed from the t-test between the actual values and the predicted values of the model (p > 0.05). Conclusion: It has been shown that this unique deep learning-based ECC prediction model appears an effective tool with high accuracy and interpretability for ECC prediction. After implementing the oral health intervention program, focusing on the potential predictors of ECC obtained from this innovative model, policymakers could be able to evaluate their prediction models comparing their results with the findings of the current study. This comparison will guide them in understanding, designing, and implementing a more effective intervention program for ECC prevention. © The Autho

关键词： Artificial intelligence Caries prediction model Early childhood caries Machine learning Neural networks

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Evaluating Logical Expressions by Membrane Systems

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Chinese Journal of Electronics 2025年第2期23卷 278-283页

作者： Ping Guo Jinfang Ji Haizhu Chen Ran Liu College of Computer Science Chongqing University Chongqing China Chongging Key Laboratory of Software Theory and Technology Chongqing China Department of Software Engineering Chongqing College of Electronic Engineering Chongqing China

Boolean and relational operations, which are defined for solving mathematically logical problems, are always required in computing models. Membrane computing is a kind of distributed parallel computing model. In this paper, we design different membranes for implementing primary Boolean and relational operations respectively. And based on these membranes, a membrane system can be constructed by a present algorithm for evaluating a logical expression. Some examples are given to illustrate how to perform the Boolean, relational operations and evaluate the logical expression correctly in these membrane systems.

关键词： Computational modeling Parallel processing Mathematical models

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Arithmetic Expression Evaluations with Membranes

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Chinese Journal of Electronics 2025年第1期23卷 55-60页

作者： Ping Guo Haizhu Chen Hui Zheng College of Computer Science Chongqing University Chongqing China Chongqing Key Laboratory of Software Theory and Technology Chongqing China Department of Software Engineering Chongqing College of Electronic Engineering Chongqing China

Arithmetic operations and expression evaluations are fundamental in computing models. This paper firstly designs arithmetic membranes without priority rules for basic arithmetic operations, and then proposes an algorithm to construct expression P systems based on several of such membranes after designing synchronous and asynchronous transmission strategies among the membranes. For any arithmetic expression, an expression P system can be built to evaluate it effectively. Finally, we discuss different parallelism strategies through which different expression P systems can be built for an arithmetic expression.

关键词： Computational modeling Biological system modeling Parallel processing Biomembranes Compounds Arithmetic

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Artificial intelligence in qualitative analysis: a practical guide and reflections based on results from using GPT to analyze interview data in a substance use program

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Quality and Quantity 2025年 1-24页

作者： Yang, Yang Ma, Liran Institute of Behavioral Research Texas Christian University 2800 South University Drive Fort Worth 76109 TX United States Department of Computer Science and Software Engineering Miami University Oxford OH United States

Language-based text provide valuable insights into people’s lived experiences. While traditional qualitative analysis is used to capture these nuances, new paradigms are needed to scale qualitative research effectively. Artificial intelligence presents an unprecedented opportunity to expand the sale of analysis for obtaining such nuances. The study tests the application of GPT-4—a large language modeling—in qualitative data analysis using an existing set of text data derived from 60 qualitative interviews. Specifically, the study provides a practical guide for social and behavioral researchers, illustrating core elements and key processes, demonstrating its reliability by comparing GPT-generated codes with researchers’ codes, and evaluating its capacity for theory-driven qualitative analysis. The study followed a three-step approach: (1) prompt engineering, (2) reliability assessment by comparison of GPT-generated codes with researchers’ codes, and (3) evaluation of theory-driven thematic analysis on psychological constructs. The study underscores the utility of GPT’s capabilities in coding and analyzing text data with established qualitative methods while highlighting the need for qualitative expertise to guide GPT applications. Recommendations for further exploration are also discussed. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Artificial intelligence ChatGPT and GPT Large language models Qualitative research

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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