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Crowdsourced Requirements engineering Challenges and Solutions:A software Industry Perspective

computer Systems science & engineering 2021年第11期39卷 221-236页

作者： Huma Hayat Khan Muhammad Noman Malik Youseef Alotaibi Abdulmajeed Alsufyani Saleh Alghamdi Department of Software Engineering National University of Modern LanguagesIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityPO Box 11099TaifSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software ***,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and *** the requirements are not clear to the development team,it has a significant effect on the quality of the software *** study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)***,solutions to overcome these challenges are also *** data analysis is performed on the interview data collected from software industry ***,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven *** study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

关键词： software crowdsourced requirements engineering software industry software development survey challenges

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Traffic Sign Recognition using You Look Only Once Version 8 with Vision Transformer

Traffic Sign Recognition using You Look Only Once Version 8 ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Mohammed Ihsan Ramesh Babu N Navamani C Narendra Chennupati P. Vinayasree Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering Nandha Engineering College Erode India Hindsight Software Solutions Inc Frisco TX Department of Computer Science and Engineering School of Engineering Anurag University Hyderabad India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

In recent years, Traffic Sign Recognition (TSR) has become an emerging computer vision task which allows vehicles to detect and interpret road signs from captured images that facilitates for safe navigation. Even though, previous researchers have developed effective models to classify and recognize traffic signs under varying conditions, but still there are challenges such as occlusions, motion blur and luminous conditions. To overcome these challenges, this research proposes a hybrid approach using the proposed You Look Only Once Version 8 with Vision Transformer (YOLOV8-ViT) model. Initially, the data is collected from Tsinghua-Tencent 100K (TT100K) dataset which comprises of vast traffic signs in varying luminous and weather conditions. Then, the collected data is preprocessed with data augmentation and Contrast Limited Adaptive Histogram Equalization (CLAHE) for improving generalizability and visibility in poor lighting conditions respectively. Further, Convolution with Attention Network (CoAtNet) is utilized for extraction of feature which has the ability to capture spatial and long-range dependencies. Finally, the proposed YOLOV8-ViT is incorporated for TSR as it handles complex backgrounds and occlusions effectively. The proposed YOLOV8-ViT acquired better results with 98.9% accuracy, 97.9% precision, 96.6% recall, 97.5% F1-score respectively when compared with the existing TrafficSignNet model.

关键词： Adaptation models computer vision Histograms Image recognition Computational modeling Transformers Feature extraction Data augmentation Data models Integrated circuit modeling

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Dual Branch CNN and Transformer for Cardiac Atherosclerotic Plaque Classification

Dual Branch CNN and Transformer for Cardiac Atherosclerotic ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Zhao, Jia Tong, Guanjie Zhang, Longjiang Su, Huaqiang Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University Jinling Hospital Medical School of Nanjing University Department of Radiology Nanjing China Shenzhen University School of Biomedical Engineering Shenzhen University Medical School Shenzhen China

ISBN: (纸本)9798350337488

Accurate classification of coronary artery plaques can provide effective assistance for the diagnosis of coronary artery disease(CAD). The task of coronary artery plaque classification remains extremely challenging due to the complex anatomical structure and background of coronary arteries. 3D convolution still has limitations in feature modeling, so this study builds a dual branch bridge network based on convolution neural network (CNN) and Transformer framework, and fused the local feature extraction ability of convolution and the global modeling ability of Transformer through the bridge communication module. By using a shift attention (SA) module at the intersection of dual branch information to utilizes minimal computational complexity to fuse feature maps from both branches. The ghost plus (GP) module was aimed at balancing the enormous computational power issues of building rich semantic information and training difficulties in 3D Transformers. The proposed method have demonstrated the effectiveness through a large number of comparative and ablation experiment. © 2023 IEEE.

关键词： Coronary artery classification Feature fusion Transformer

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Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ma, Long Hou, Junhui Liu, Yuan Wang, Wenping The School of Software Shandong University Jinan250100 China The Department of Computer Science City University of Hong Kong Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore The Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States

As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero-level set, would result in sub-optimal reconstruction quality. In this paper, we propose the scaled-squared distance function (S2DF), a novel implicit surface representation for modeling arbitrary surface types. S2DF does not distinguish between inside and outside regions while effectively addressing the non-differentiability issue of UDF at the zero-level set. We demonstrate that S2DF satisfies a second-order partial differential equation of Monge-Ampere-type, allowing us to develop a learning pipeline that leverages a novel Monge-Ampere regularization to directly learn S2DF from raw unoriented point clouds without supervision from ground-truth S2DF values. Extensive experiments across multiple datasets show that our method significantly outperforms state-of-the-art supervised approaches that require ground-truth surface information as supervision for training. The code will be publicly available at https://***/chuanxiang-yang/S2DF. © 2025 IEEE. All rights reserved.

关键词： Supervised learning

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AutoDebias Based on NeuralCF

AutoDebias Based on NeuralCF

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作者： Meng Li Zhuoya Gu Haolun Zheng Yening He Department of Science University of Auckland Department of Computer Science Xidian University School of Communication Engineering Xidian University College of Computer Science and Technology College of SoftwareZhejiang University of Technology

Recommendation systems rely on user behavior data(ratings,clicks,etc.) to build personalized ***,the data collected was always observational rather than experimental,which can lead to a variety of *** deviations will directly affect the operation results of the recommendation *** may not favor the items recommended by the system to them,so this is an imperative problem that must be resolved as soon as *** majority of current efforts to debias recommendation systems focus only on one or two specific biases and are not universally *** a result,Chen et *** their group developed a general debiasing framework(AutoDebias) containing most of the debias strategies,which overcomes the problem of low flexibility of most of today's debiasing ***,the model still has room for *** et *** Matrix Factorization(MF) as the benchmark model for *** our study,however,we found that NeuralCF model may be a better alternative to ***,the goal of our work is to replace the MF base model in AutoDebias with the NeuralCF model,and to conduct relevant experiments in order to validate our *** to experiments,when using NLL as evaluation metrics,NeuralCF has better performance than MF,which improved 14.3% in final results.

关键词： AutoDebias MF Model NeuralCF Model

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Predicting software Reusability Using Classification Techniques

Predicting Software Reusability Using Classification Techniq...

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International Conference on Information and Communication Systems (ICICS)

作者： Neda’a Alsalman Raed Shatnawi Ammar Jalamneh Omar AlZoubi Computer Science Jordan University of Science and Technology Irbid Jordan Software Engineering Department Jordan University of Science and Technology College of Arts & Science Applied Science University Bahrain

ISBN: (数字)9798331540760

ISBN: (纸本)9798331540777

This study predicts software reusability at the class level using machine learning and a dataset of 65 Java applications. The reuse rates were calculated using cohesion, coupling, complexity, inheritance, documentation, and size metrics. The data was segmented by project size (all, small, medium, and big). Several classifiers were tested once the labels were changed to multi-class labels (low: 0, moderate: 1, high: 2). On large projects, the voting model was 93% accurate. The results demonstrate how well machine learning predicts software reusability and provide recommendations for increasing software quality measures.

关键词： Q-factor Machine learning software quality Predictive models Complexity theory Maintenance software measurement

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Multiobjective Many-Tasking Evolutionary Optimization using Diversified Gaussian-Based Knowledge Transfer

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IEEE Transactions on Evolutionary Computation 2024年

作者： Lin, Qiuzhen Wang, Qianhui Chen, Baihao Ye, Yulong Ma, Lijia Tan, Kay Chen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong Hong Kong

Multiobjective multitasking evolutionary algorithms have shown promising performance for tackling a set of multiobjective optimization tasks simultaneously, as the optimization experience gained within one task can be transferred to accelerate the solving of others. However, most studies only select similar transfer tasks based on their designed metrics, which become less efficient when tackling a large number of optimization tasks, as their transferred knowledge may be insufficiently diversified. To alleviate this issue, this article proposes a Multiobjective Many-Tasking Evolutionary Algorithm using Diversified Gaussian-based knowledge Transfer, named MMaTEA-DGT. In this algorithm, a diversified transfer selection strategy is presented to choose a number of similar and complementary source tasks for knowledge transfer. Then, based on the above diversified source tasks, a Gaussian-based transfer strategy is designed to transfer their various optimization knowledge. In this way, MMaTEA-DGT is more effective in transferring optimization knowledge to speed up the solving of many tasks. Experimental studies on both the benchmark suites and a real-world dynamic vaccine prioritization problem have indicated the superiority of MMaTEA-DGT over some recently proposed multiobjective many-tasking evolutionary algorithms. © 1997-2012 IEEE.

关键词： Optimization algorithms

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An empirical study of deep learning-based feature extractor models for imbalanced image classification

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Advances in Computational Intelligence 2023年第6期3卷 1-16页

作者： Khan, Ammara Rasheed, Muhammad Tahir Khan, Hufsa Department of Botany University of the Punjab Lahore Pakistan College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen university Shenzhen China

Deep learning has played an important role in many real-life applications, especially in image classification. It is often found that some domain data are highly skewed, i.e., most of the data belongs to a handful of majority classes, and the minority classes only contain small amounts of information. It is important to acknowledge that skewed class distribution poses a significant challenge to machine learning algorithms. Due to which in case of imbalanced data distribution, the majority of machine and deep learning algorithms are not effective or may fail when it is highly imbalanced. In this study, a comprehensive analysis in case of imbalanced dataset is performed by considering deep learning based well known models. In particular, the best feature extractor model is identified and the current trend of latest feature extraction model is investigated. Moreover, to determine the global scientific research on the image classification of imbalanced mushroom dataset, a bibliometric analysis is conducted from 1991 to 2022. In summary, our findings may offer researchers a quick benchmarking reference and alternative approach to assessing trends in imbalanced data distributions in image classification research.

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Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

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computers, Materials & Continua 2021年第12期69卷 4151-4168页

作者： Vinay Arora Karun Verma Rohan Singh Leekha Kyungroul Lee Chang Choi Takshi Gupta Kashish Bhatia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaPunjabIndia Associate Application ITConcentrixGurugramHaryanaIndia School of Computer Software Daegu Catholic UniversityGyeongsanKorea Department of Computer Engineering Gachon UniversitySeongnam13120Korea Information Security Engineering Soonchunhyang UniversityKorea Department of Computer Engineering University College of EngineeringPunjabi UniversityPatialaPunjabIndia

The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension,irregular cardiac functioning,and heart ***-based learning of heart sound is an efficient technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac ***(PCG)and electrocardiogram(ECG)waveforms provide the much-needed information for the diagnosis of these *** this work,the researchers have converted the heart sound signal into its corresponding repeating pattern-based *** 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform *** existing models,***,Xception,Visual Geometry Group(VGG16),ResNet,DenseNet,and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and *** PhysioNet 2016,DenseNet has outperformed its peer models with an accuracy of 89.04 percent,whereas for PASCAL 2011,VGG has outperformed its peer approaches with an accuracy of 92.96 percent.

关键词： PCG signals transfer learning repeating pattern-based spectrogram biomedical signals internet of things(IoT)

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WHISPER TURNS STRONGER: AUGMENTING WAV2VEC 2.0 FOR SUPERIOR ASR IN LOW-RESOURCE LANGUAGES

arXiv

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arXiv 2024年

作者： Anidjar, Or Haim Marbel, Revital Yozevitch, Roi Department of Computer and Software Engineering Ariel University Ariel Israel Department of Electrical Engineering Ariel University Ariel Israel School of Computer Science College of Management Rishon Le'Tzion Israel Department of Computer Science Holon Institue of Technology Holon Israel

Approaching Speech-to-Text and Automatic Speech Recognition problems in low-resource languages is notoriously challenging due to the scarcity of validated datasets and the diversity of dialects. Arabic, Russian, and Portuguese exemplify these difficulties, being low-resource languages due to the many dialects of these languages across different continents worldwide. Moreover, the variety of accents and pronunciations of such languages complicate ASR models' success. With the increasing popularity of Deep Learning and Transformers, acoustic models like the renowned Wav2Vec2 have achieved superior performance in the Speech Recognition field compared to state-of-the-art approaches. However, despite Wav2Vec2's improved efficiency over traditional methods, its performance significantly declines for under-represented languages, even though it requires significantly less labeled data. This paper introduces an end-to-end framework that enhances ASR systems fine-tuned on Wav2Vec2 through data augmentation techniques. To validate our framework's effectiveness, we conducted a detailed experimental evaluation using three datasets from Mozilla's Common Voice project in Arabic, Russian, and Portuguese. Additionally, the framework presented in this paper demonstrates robustness to different diacritics. Ultimately, our approach outperforms two previous baseline models, which are the pre-trained Wav2Vec2 and the well-known Whisper ASR model, resulting in an average relative improvement of 33.9% in Word Error Rate and a 53.2% relative improvement in Character Error Rate. © 2024, CC BY.

关键词： Speech recognition

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