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ContextAug:model-domain failing test augmentation with contextual information

Frontiers of computer Science 2024年第2期18卷 43-60页

作者： Zhuo ZHANG Jianxin XUE Deheng YANG Xiaoguang MAO School of Information Technology and Engineering Guangzhou College of CommerceGuangzhou 511363China School of Computer and Information Engineering Institute for Artificial IntelligenceShanghai Polytechnic UniversityShanghai 201209China College of Computer National University of Defense TechnologyChangsha 410073China

In the process of software development,the ability to localize faults is crucial for improving the efficiency of *** speaking,detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development *** have tried to utilize various methods to locate the faulty ***,failing test cases usually account for a small portion of the test suite,which inevitably leads to the class-imbalance phenomenon and hampers the effectiveness of fault ***,in this work,we propose a new fault localization approach named *** obtaining dynamic execution through test cases,ContextAug traces these executions to build an information model;subsequently,it constructs a failure context with propagation dependencies to intersect with new model-domain failing test samples synthesized by the minimum variability of the minority feature *** contrast to traditional test generation directly from the input domain,ContextAug seeks a new perspective to synthesize failing test samples from the model domain,which is much easier to augment test *** conducting empirical research on real large-sized programs with 13 state-of-the-art fault localization approaches,ContextAug could significantly improve fault localization effectiveness with up to 54.53%.Thus,ContextAug is verified as able to improve fault localization effectiveness.

关键词： context fault localization test cases

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Masked Face Detection and Recognition Using a Unified Feature Extractor 5

Masked Face Detection and Recognition Using a Unified Featur...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Iftikhar, Aroobah Shaukat, Arslan Tariq, Rimsha College of Eme Dept of Computer and Software Engineering Islamabad Pakistan Information Technology University Dept of Computer Engineering Lahore Pakistan

ISBN: (纸本)9798350394764

The research field of computer vision has recently taken an interest in the active computer vision problem of masked face recognition due to the COVID-19 pandemic. The use of face masks as a preventative measure against the spread of COVID-19 has presented a new challenge for the technology of face recognition. Masked Face Recognition (MFR) is a specific type of facial occlusion issues that obstruct vital facial features such as the mouth, nose, or chin. The purpose of research on Masked Face Detection and Recognition is to fine-tune a pre-trained model that can more accurately recognize masked faces and detect whether the person is wearing a mask or not, which can be advantageous in various applications, like security and surveillance, healthcare, retail, law enforcement, the workplace, and social media. This paper proposes an approach to enhance the performance of the single neural network architecture as a unified model capable of both detection and recognition of masked face images by achieving 99% and 98% respectively, on the MFR2 dataset. Also, a detection model is proposed that is evaluated on publicly available datasets for the detection of masked faces, which are the MDMFR dataset, the Kaggle face mask detection dataset, and the Facedatahybrid dataset. The findings of this research offer valuable insights into the potential of a pre-trained network with transfer learning to make an efficient masked face detection and recognition system and pave the way for future research in this area. © 2024 IEEE.

关键词： Face recognition

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Luminescence properties and optical sensing behaviors of Sr_(2)GdSbO_(6):Eu^(3+) phosphors

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Journal of Rare Earths 2024年第8期42卷 1458-1469,I0002页

作者： Youfa Tai Ruirui Cui Jun Zhang Chunkai Wang Tingyu Zhao Beiqiao Zhang Chaoyong Deng Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025China College of Computer and Information Engineering Guizhou University of CommerceGuiyang550014China School of Electronics and Information Engineering Guiyang UniversityGuiyang550005China

Novel orange-red Sr_(2)GdSbO_(6):xEu^(3+)(x=0,0.05,0.1,0.2,0.3,0.4,0.5 and 0.6) phospho rs were successfully prepared by the traditional high-temperature solid-state *** results of Rietveld refinement,energy dispersive spectroscopy(EDS) spectrum and elemental mapping demonstrate that Eu^(3+) successfully replaces the Gd^(3+) sites and distributes uniformly in the particles of *** luminescence properties of Sr_(2)GdSbO_(6):Eu_(3+)phosphors were investigated in *** emission spectra of the strongest emission peak is the ^(5)D_(0)→^(7)F_(1)(593 nm) transition,which can emit orange-red light under393 nm *** the doping concentration of Eu3+ions is x=0.2,the luminescence intensity of the phosphors reaches the *** detailed mechanism of concentration quenching is attributed to dipole-dipole *** thermal stability values of Sr_(2)GdSbO_(6):0.2Eu^(3+) phosphors are 87%,82% and114% under 393,467 and 527 nm excitations,*** causes of the abnormal thermal quenching under 527 nm excitation were *** on the abnormal thermal quenching under527 nm excitation,the optical thermometry properties of Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors were investigated by fluorescence intensity ratio(FIR) technique,and appreciable relative sensitivity was *** results suggest that Sr_(2)GdSbO_(6):0.2Eu^(3+)phosphors can be potentially applied to w-LEDs and optical thermometers.

关键词： Sr_(2)GdSbO_(6):Eu^(3+) Phosphors Luminescence properties Abnormal thermal quenching Rare earths

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A probabilistic generative model for tracking multi-knowledge concept mastery probability

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Frontiers of computer Science 2024年第3期18卷 155-170页

作者： Hengyu LIU Tiancheng ZHANG Fan LI Minghe YU Ge YU School of Computer Science and Engineering Northeastern UniversityShenyang 110169China Software College Northeastern UniversityShenyang 110169China

Knowledge tracing aims to track students’knowledge status over time to predict students’future performance *** a real environment,teachers expect knowledge tracing models to provide the interpretable result of knowledge *** chain-based knowledge tracing(MCKT)models,such as Bayesian Knowledge Tracing,can track knowledge concept mastery probability over ***,as the number of tracked knowledge concepts increases,the time complexity of MCKT predicting student performance increases exponentially(also called explaining away problem).When the number of tracked knowledge concepts is large,we cannot utilize MCKT to track knowledge concept mastery probability over *** addition,the existing MCKT models only consider the relationship between students’knowledge status and problems when modeling students’responses but ignore the relationship between knowledge concepts in the same *** address these challenges,we propose an inTerpretable pRobAbilistiC gEnerative moDel(TRACED),which can track students’numerous knowledge concepts mastery probabilities over *** solve explain away problem,we design long and short-term memory(LSTM)-based networks to approximate the posterior distribution,predict students’future performance,and propose a heuristic algorithm to train LSTMs and probabilistic graphical model *** better model students’exercise responses,we proposed a logarithmic linear model with three interactive strategies,which models students’exercise responses by considering the relationship among students’knowledge status,knowledge concept,and *** conduct experiments with four real-world datasets in three knowledge-driven *** experimental results show that TRACED outperforms existing knowledge tracing methods in predicting students’future performance and can learn the relationship among students,knowledge concepts,and problems from students’exercise *** also conduct several case *** case studies show that

关键词： probabilistic graphical model deep learning knowledge tracing learner modeling

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A reliable and secure video steganography method based on 4d-logistic mapping

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Multimedia Tools and Applications 2025年 1-31页

作者： Sha, Mohemmed Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj16278 Saudi Arabia

Data security is becoming more and more crucial due to developments in communication and information technology, particularly when it comes to video transmission. This research provides a unique approach that combines 4D Logistic Mapping (4DLM) with MASK cryptography technology to improve video data security. The five steps of the suggested approach are as follows: gathering video, extracting frames, compressing data using the LZW technique, encrypting it using MASK, and embedding it using 4DLM. To protect the video frames, MASK encryption uses a single matrix and four arrays, advancing through matrix initialization, key scheduling, and substitution/diffusion. 4DLM is used after encryption to improve the system's overall security and effectiveness. This model's major goal is to keep performance high while safeguarding video data from unwanted access. Data confidentiality is significantly improved by the suggested strategy, as shown by simulations performed with the Python program. For the example video sequences, the Structural Similarity Index (SSIM) was close to 99%, which indicates better performance than current methods. MASK and 4D Logistic Mapping together provide a powerful answer for today's data security issues by providing an effective and safe method of video data protection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10035-10044页

作者： Nagamani, T. Logeswari, S. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. © 2024 – IOS Press.

关键词： Diseases

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Framework for Effective Utilization of Distributed Scrum in software Projects

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computer Systems Science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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Intelligent Inspection of Electronic Devices in Specific Environments via a Novel Cascade Network of Combining Mixed Sampling and Nonstrided Convolution

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3287-3299页

作者： Liu, Bo Guo, Jing Wang, Yaowei Yang, Chengrong Nan, Fengtao Yang, Yun Yunnan University School of Software Yunnan Provincial Key Laboratory of Software Engineering Kunming650504 China Northwest A&F University College of Information Engineering Xianyang712100 China

In environments where intelligent video surveillance systems (IVSSs) are deployed, particularly in review room, the detection of electronic devices constitutes a crucial task. Nevertheless, this task presents significant challenges attributed to the high rates of false positives and false negatives in electronic device detection (EDD), compounded by the low resolution of objects when viewed from multiple *** address these challenges, we propose a deep learning-based cascaded detection framework. Specifically, we design a mixed region sampling (MRS) method to enhance the foreground perception with background information and image details. We design a nonstrided downsampling method (ASDP) to map the attention spatial features to depth and improve the detection of low-resolution objects with fine-grained features. We enhance the model’s robustness to different viewing angles by feature perturbation during training. Moreover, we use a cascaded strategy to reduce false positives. To evaluate our method, we construct a real review room dataset (EDD) with 28,000 images from multiple angles. Our method improves the multiview generalization performance by 4.48% mAP and 5.62% mAR. On the public datasets Pascal VOC-2007 and visDrone-2019, our method is also superior to other suboptimal methods. We propose a framework for review environment detection, which is accurate, fast, and generalizable to other scenarios. © 2013 IEEE.

关键词： Feature extraction Reviews Detectors Training Object detection Video surveillance Convolution Cameras Visualization Inspection

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Multimodality in online education: a comparative study

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Multimedia Tools and Applications 2025年 1-34页

作者： Immadisetty, Praneeta Rajesh, Pooja Gupta, Akshita M. R, Dr. Anala A, Dr. Soumya Subramanya, Dr. K. N. Computer Science and Engineering R V College of Engineering Karnataka Bengaluru India Information Science and Engineering R V College of Engineering Karnataka Bengaluru India Principal and Professor R V College of Engineering Karnataka Bengaluru India

The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world. With a surge in the usage of online video conferencing platforms and tools to better gauge student understanding, there needs to be a mechanism to assess whether instructors can grasp the extent to which students understand the subject and their response to the educational stimuli. The current systems consider only a single cue with a lack of focus in the educational domain. Thus, there is a necessity for the measurement of an all-encompassing holistic overview of the students’ reaction to the subject matter. This paper highlights the need for a multimodal approach to affect recognition and its deployment in the online classroom while considering four cues, posture and gesture, facial, eye tracking and verbal recognition. In this paper, various machine learning models suitable for each cue are analyzed based on key metrics such as accuracy, ease of data collection, sensitivity, and limitations. The comparison of models revealed the most effective techniques for each cue. A multimodal approach derived from weighted majority voting is proposed by combining the top-performing models for each aspect of student engagement in online learning environments. This paper proposes a novel approach that integrates multiple modalities for affect recognition, aiming to improve the accuracy of student response assessments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Video conferencing

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Building LOD representation for 3D urban scenes

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ISPRS Journal of Photogrammetry and Remote Sensing 2025年 226卷 16-32页

作者： Pan, Shanshan Zhang, Runze Liu, Yilin Gong, Minglun Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Guelph Guelph Canada

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms. Therefore, the generation of appropriate levels-of-detail (LOD) representations for these models is crucial. Additionally, automatically reconstructed 3D models tend to suffer from noise and lack semantic information. Dealing with these issues and creating LOD representations that are robust against noise while capturing semantic meaning present significant challenges. In this paper, we propose a novel algorithm to address these challenges. We begin by analyzing the properties of planar primitives detected from the input and group these primitives into multiple level sets by forming meaningful 3D structures. These level sets form the nodes of our innovative LOD-Tree. By selecting nodes at appropriate depths within the LOD-Tree, different LOD representations can be generated. Experimental results on real and complex urban scenes demonstrate the merits of our approach in generating clean, accurate, and semantically meaningful LOD representations. © 2025

关键词： Three dimensional computer graphics

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