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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices 38

DapperFL: Domain Adaptive Federated Learning with Model Fusi...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Jia, Yongzhe Zhang, Xuyun Hu, Hongsheng Choo, Kim-Kwang Raymond Qi, Lianyong Xu, Xiaolong Beheshti, Amin Dou, Wanchun State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computing Macquarie University Australia School of Information and Physical Sciences University of Newcastle Australia Department of Information Systems and Cyber Security University of Texas San Antonio United States College of Computer Science and Technology China University of Petroleum [East China China School of Computer and Software Nanjing University of Information Science and Technology China

Federated learning (FL) has emerged as a prominent machine learning paradigm in edge computing environments, enabling edge devices to collaboratively optimize a global model without sharing their private data. However, existing FL frameworks suffer from efficacy deterioration due to the system heterogeneity inherent in edge computing, especially in the presence of domain shifts across local data. In this paper, we propose a heterogeneous FL framework DapperFL, to enhance model performance across multiple domains. In DapperFL, we introduce a dedicated Model Fusion Pruning (MFP) module to produce personalized compact local models for clients to address the system heterogeneity challenges. The MFP module prunes local models with fused knowledge obtained from both local and remaining domains, ensuring robustness to domain shifts. Additionally, we design a Domain Adaptive Regularization (DAR) module to further improve the overall performance of DapperFL. The DAR module employs regularization generated by the pruned model, aiming to learn robust representations across domains. Furthermore, we introduce a specific aggregation algorithm for aggregating heterogeneous local models with tailored architectures and weights. We implement DapperFL on a real-world FL platform with heterogeneous clients. Experimental results on benchmark datasets with multiple domains demonstrate that DapperFL outperforms several state-of-the-art FL frameworks by up to 2.28%, while significantly achieving model volume reductions ranging from 20% to 80%. Our code is available at: https://***/jyzgh/DapperFL. © 2024 Neural information processing systems foundation. All rights reserved.

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HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion 38

HairDiffusion: Vivid Multi-Colored Hair Editing via Latent D...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Zeng, Yu Zhang, Yang Liu, Jiachen Shen, Linlin Deng, Kaijun He, Weizhao Wang, Jinbao Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images. © 2024 Neural information processing systems foundation. All rights reserved.

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques 6th

A Mathematical Model for the Selection of Software Requireme...

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6th International Conference on information systems and Management Science, ISMS 2023

作者： Saxena, Virat Raj Yadav, Rajeev Parveen, Azra Sadiq, Mohd. Department of Computer Science and Engineering Mangalayatan University Uttar Pradesh Aligarh202001 India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (纸本)9783031664090

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： software testing

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software Defect Prediction Harnessing on Multi 1-Dimensional Convolutional Neural Network Structure

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computers, Materials & Continua 2022年第4期71卷 1521-1546页

作者： Zuhaira Muhammad Zain Sapiah Sakri Nurul Halimatul Asmak Ismail Reza M.Parizi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Information Technology College of CommunityPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia College of Computing and Software Engineering Kennesaw State UniversityMariettaGA30060USA

Developing successful software with no defects is one of the main goals of software *** order to provide a software project with the anticipated software quality,the prediction of software defects plays a vital *** learning,and particularly deep learning,have been advocated for predicting software defects,however both suffer from inadequate accuracy,overfitting,and complicated *** this paper,we aim to address such issues in predicting software *** propose a novel structure of 1-Dimensional Convolutional Neural Network(1D-CNN),a deep learning architecture to extract useful knowledge,identifying and modelling the knowledge in the data sequence,reduce overfitting,and finally,predict whether the units of code are defects *** design large-scale empirical studies to reveal the proposed model’s effectiveness by comparing four established traditional machine learning baseline models and four state-of-the-art baselines in software defect prediction based on the NASA *** experimental results demonstrate that in terms of f-measure,an optimal and modest 1DCNN with a dropout layer outperforms baseline and state-of-the-art models by 66.79%and 23.88%,respectively,in ways that minimize overfitting and improving prediction performance for software *** to the results,1D-CNN seems to be successful in predicting software defects and may be applied and adopted for a practical problem in software ***,in turn,could lead to saving software development resources and producing more reliable software.

关键词： Defects software defect prediction deep learning convolutional neural network machine learning

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

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computers, Materials & Continua 2023年第4期75卷 165-182页

作者： Muhammad Azhar Sehat Ullah Khalil Ullah Habib Shah Abdallah Namoun Khaliq Ur Rahman Department of Computer Science&IT University of MalakandChakdara18800Pakistan Department of Software Engineering University of MalakandChakdara18800Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time *** (50) volunteers of different ages participated in the experimental *** the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

关键词： Age estimation gait biometrics classical linear regression model

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques 14

A Mathematical Model for the Selection of Software Requireme...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Saxena, Virat Raj Yadav, Rajeev Parveen, Azra Sadiq, Mohd. Mangalayatan University Department of Computer Science and Engineering Uttar Pradesh Aligarh202001 India Indraprastha Institute of Information Sciences Private Limited Indraprastha Research Laboratory New Delhi110025 India Upfet Jamia Millia Islamia A Central University Software Engineering Laboratory Computer Engineering Section New Delhi110025 India

ISBN: (纸本)9798350344837

关键词： information systems

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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Success Factors of Requirement Elicitation in the Field of software engineering 4

Success Factors of Requirement Elicitation in the Field of S...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Attanayaka, Buddhima Nawinna, Dasuni Manathunga, Kalpani Abeygunawardhana, Pradeep K.W. Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Systems & Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors. © 2022 IEEE.

关键词： Requirements engineering

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