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Data and Ensemble Machine Learning Fusion Based Intelligent software Defect Prediction System

computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

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SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment 31

SLAM: Towards Efficient Multilingual Reasoning via Selective...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Fan, Yuchun Mu, Yongyu Wang, Yilin Huang, Lei Ruan, Junhao Li, Bei Xiao, Tong Huang, Shujian Feng, Xiaocheng Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China Harbin Institute of Technology Harbin China Meituan Inc. National Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798891761964

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. However, this method suffers from both substantial computational resource computing and catastrophic forgetting. The fundamental cause is that, with the primary goal of enhancing multilingual comprehension, an excessive number of irrelevant layers and parameters are tuned during the first stage. Given our findings that the representation learning of languages is merely conducted in lower-level layers, we propose an efficient multilingual reasoning alignment approach that precisely identifies and fine-tunes the layers responsible for handling multilingualism. Experimental results show that our method, SLAM, only tunes 6 layers' feed-forward sub-layers including 6.5−8% of all parameters within 7B and 13B LLMs, achieving superior average performance than all strong baselines across 10 languages. Meanwhile, SLAM only involves one training stage, reducing training time by 4.1 − 11.9× compared to the two-stage method. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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EBCFL: An Efficient BlockChain-based Federated Learning Framework for Internet of Vehicles

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IEEE Network 2025年

作者： Cao, Dun Liao, Yonghui Alqahtani, Fayez Wang, Jin School of Computer and Communication Engineering Changsha University of Science and Technology China Changsha University of Science and Technology China Software Engineering Department College of Computer and Information Sciences King Saud University Saudi Arabia Hunan University of Science and Technology China

Federated Learning (FL) can train a shared model while protecting vehicle data privacy, which has significant advantages in improving driving safety and efficiency. However, there are still many challenges in applying FL to Internet of Vehicles (IoV). First, the varying computing capabilities of moving vehicles result in slow convergence speed due to underperforming vehicles. Second, malicious vehicles may tamper with model parameters before uploading them to the central server, which is itself vulnerable to single-point failure. To solve the above challenges, we design an efficient and decentralized blockchain FL framework (Efficient BlockChain-based Federated Learning Framework, EBCFL). This framework effectively prevents possible malicious behaviors in the vehicular network nodes and improves the efficiency of FL through a well-designed security verification mechanism. The experimental results show that EBCFL outperforms other schemes in terms of average test accuracy and average total time cost, indicating that EBCFL significantly improves the learning efficiency while bearing an acceptable additional time cost for blockchain communication, demonstrating its strong ability to guard against malicious behaviours and ensure robustness. © 1986-2012 IEEE.

关键词： Blockchain Federated learning IoV Security verification

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Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services

Seed Feature Maps-based CNN Models for LEO Satellite Remote ...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Lu, Zhichao Ding, Chuntao Wang, Shangguang Cheng, Ran Juefei-Xu, Felix Boddeti, Vishnu Naresh Sun Yat-sen University School of Software Engineering Zhuhai China Beijing Jiaotong University School of Computer and Information Technology Beijing China Beijing University of Posts and Telecommunications Key Laboratory of Networking and Switching Technology Beijing China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China New York University New YorkNY United States Michigan State University Department of Computer Science and Engineering East LansingMI United States

ISBN: (纸本)9798350304855

Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3 × fewer parameters and 2.2 × fewer FLOPs. © 2023 IEEE.

关键词： Neural network models

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Wireless Channel Estimation and Equalization Based on Deep Learning

Wireless Channel Estimation and Equalization Based on Deep L...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Xueting Huang Linshu Chen Xiaoyan Chen Mo Wang Yuxin Liu Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

Nowadays, the research on wireless communication is hot, and it has achieved leapfrog development in the past few decades and has made astonishing breakthroughs in emerging applications such as 5G technology, Internet of Things (lOT), and smart cities. Channel estimation and equalization in wireless communication are very important technologies, which are cru-cial for ensuring reliable data transmission and improving system performance. However, with the increasing demand for wireless high-speed data communication, traditional channel estimation and equalization technologies are facing enormous challenges. Nowadays, with the development of artificial intelligence, deep learning has been successfully applied to wireless communication systems. Compared to traditional channel estimation and equalization techniques, deep learning-based channel estimation and equalization techniques have many advantages. For example, traditional channel estimation and equalization methods typically require modeling of channel models and are only applicable to specific channel conditions, making it difficult to achieve optimal performance under different channel conditions. Deep learning technology can improve the performance of channel estimation and equalization by learning a large amount of data and training under different channel conditions, especially in nonlinear and complex channel conditions. Therefore, this article will provide a detailed description of the research on channel estimation and equalization based on deep learning, in order to demonstrate its ultra-high performance and development prospects.

关键词： Wireless communication Deep learning Training Smart cities System performance Channel estimation Data communication

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A multi-objective reinforcement learning algorithm for deadline constrained scientific workflow scheduling in clouds

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Frontiers of computer science 2021年第5期15卷 25-36页

作者： Yao QIN Hua WANG Shanwen YI Xiaole LI Linbo ZHAI School of Computer Science and Technology Shandong UniversityJinan 250101China Shanghai Police College Shanghai 200137China School of Software Shandong UniversityJinan 250101China School of Information Science and Engineering Linyi UniversityLinyi 276005China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China

Recently,a growing number of scientific applications have been migrated into the *** deal with the problems brought by clouds,more and more researchers start to consider multiple optimization goals in workflow ***,the previous works ignore some details,which are challenging but *** existing multi-objective work-flow scheduling algorithms overlook weight selection,which may result in the quality degradation of ***,we find that the famous partial critical path(PCP)strategy,which has been widely used to meet the deadline constraint,can not accurately reflect the situation of each time ***-flow scheduling is an NP-hard problem,so self-optimizing algorithms are more suitable to solve *** this paper,the aim is to solve a workflow scheduling problem with a deadline *** design a deadline constrained scientific workflow scheduling algorithm based on multi-objective reinforcement learning(RL)called *** uses the Chebyshev scalarization function to scalarize its *** method is good at choosing weights for *** propose an improved version of the PCP strategy called *** sub-deadlines in MPCP regularly update during the scheduling phase,so they can accurately reflect the situation of each time *** optimization objectives in this paper include minimizing the execution cost and energy consumption within a given ***,we use four scientific workflows to compare DCMORL and several representa-tive scheduling *** results indicate that DCMORL outperforms the above *** far as we know,it is the first time to apply RL to a deadline constrained workflow scheduling problem.

关键词： workflow scheduling energy saving multiobjective reinforcement learning deadline constrained cloud computing

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Importance-Aware Transformer: Addressing Intra-Class Heterogeneity in Weakly Supervised Brain Tumor Segmentation 25

Importance-Aware Transformer: Addressing Intra-Class Heterog...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Lyu, Yiheng Xu, Lian Bennamoun, Mohammed Boussaid, Farid Dwivedi, Girish The University of Western Australia Department of Computer Science and Software Engineering Perth Australia The University of Western Australia Department of Electrical Electronic and Computer Engineering Perth Australia Harry Perkins Institute of Medical Research Perth Australia Medical School The University of Western Australia Perth Australia Fiona Stanley Hospital Department of Cardiology Perth Australia

ISBN: (纸本)9798350379037

Weakly supervised brain tumor segmentation is a critical challenge in medical image analysis, characterized by inherent biological complexity and the scarcity of annotations. For segmentation on magnetic resonance imaging from image-level annotations, the intrinsic intra-class heterogeneity hampers accurate segmentation on distal images. In this paper, we propose a transformer-based model for weakly supervised brain tumor segmentation. Our approach tackles intra-class heterogeneity by utilizing an Importance-Aware Class Prediction strategy to improve segmentation accuracy on positive images. Additionally, we integrate Affine Transformation Equivariant Regularization to ensure consistent tumor localization. Finally, we introduce a Cross-Plane Refinement method to refine inaccurate segmentations using high-confidence images within the same volume. Evaluated on the Brain Tumor Segmentation 2023 dataset, our model demonstrates state-of-the-art performance on native Tl and T2-weighted sequences. © 2024 IEEE.

关键词： Semantic Segmentation

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An Ensemble of CapsNet and KSVM Using Feature Fusion: Application to COVID-19 Detection 3

An Ensemble of CapsNet and KSVM Using Feature Fusion: Applic...

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3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Aggrey, Esther Stacy E. B. Zhen, Qin Kodjiku, Seth Larweh Aidoo, Evans Fiasam, Linda Delali Mensah, Acheampong Edward University of Electronic Science & Technology of China School of Information & Software Engineering Chengdu China University of Electronic Science & Technology of China School of Information & Software Engineering Network and Data Security Key Lab of Sichuan Province Chengdu China Zhejiang Gongshang University School of Computer & Information Engineering Hangzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural Networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

关键词： COVID-19

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A high-resolution numerical investigation of unsteady wake vortices for coaxial rotors in hover

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Chinese Journal of Aeronautics 2024年第11期37卷 278-299页

作者： Shaoqiang HAN Wenping SONG Zhonghua HAN Jianhua XU Tianfu Engineering-oriented Numerical&Software Innovation Center College of Computer ScienceSichuan UniversityChengdu 610065China National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi’an 710072China

High-resolution numerical simulations for wake vortical flows have long been a challenge in rotor aerodynamics.A novel spectrum-optimized sixth-order Weighted Essentially NonOscillatory(WENO)scheme is proposed to discretize inviscid fluxes on moving overset grids,and the Improved Delayed Detached Eddy Simulation(IDDES)is employed to resolve turbulent *** integration of these methods facilitates a comprehensive numerical investigation into the unsteady vortical flows over coaxial rotors in *** results highlight the substantial improvement in numerical resolution,in terms of both spatial structure and temporal evolution of unsteady multiscale wake *** rotors in hover manifest three primary scales of wake vortex structures:(A)the helical evolution of primary blade tip vortices and the periodic occurrence of strong Blade-Vortex Interactions(BVI);(B)the continuous shedding of small-scale horseshoeshaped vortices from the trailing edges of rotor blades,forming the vortex sheets;(C)the emergence of small-scale secondary vortex braids induced by interactions between rotor tip vortices and the vortex *** vortex structures and their interactions cause high-frequency oscillations in rotor disk loads and induce unsteady perturbations in the local flow *** among these primary vortices,coupled with the generation of secondary vortices,result in the dissipation,distortion,and breakup of the rotor tip vortices,ultimately forming a vortex ***,a substantial quantity of seemingly weak small-scale secondary vortex braids significantly contribute to energy dissipation during the evolution of wake vortices for coaxial rotors in hover.ó2024 Chinese Society of Aeronautics and *** and hosting by Elsevier *** is an open access article under the CC BY-NC-ND license(http://***/licenses/by-nc-nd/4.0/).

关键词： Coaxial rotor Blade tip vortex Secondary vortex High-order scheme IDDES Moving overset grid

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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Shumeng Qi, Lei Yu, Qian Huo, Jing Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China School of Computer Science and Engineering China Shandong Women's University School of Data and Computer Science China

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Stitching, Fine-tuning, and Re-training (SFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our stitching strategy mitigates the mismatch between natural and 3D medical images. The stitched images are then used for fine-tuning SAM, providing robust initialization of pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our SFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. We also develop an extended framework SFR+ with selective fine-tuning and re-training through confidence estimation. Extensive experiments validate that our SFR and SFR+ achieve significant improvements in both moderate annotation and scarce annotation across five datasets. In particular, SFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset. The code is available at https://***/ShumengLI/SFR. © 1982-2012 IEEE.

关键词： Image segmentation

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