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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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An Adaptive Sampling Fourier Single-Pixel Imaging Method Based on Discrete Coefficients

An Adaptive Sampling Fourier Single-Pixel Imaging Method Bas...

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International Conference on Automation and computing (ICAC)

作者： Guangyao Chen Yuanping Xu Jiliu Zhou Zhijie Xu Chaolong Zhang Chao Kong Tukun Li Qiuyan Gai School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield UK

Fourier Single-Pixel Imaging (FSPI) can directly obtain the Fourier spectrum of a target scene and reconstruct it. However, the existing FSPI spectrum sampling methods are mostly constrained within a prior range, which makes it challenging to balance the image details and noise suppression under limited samples and short reconstruction time. One of the crucial issues in the practical application of SPI technology is how to enhance its imaging speed while ensuring the quality of the imaging. Therefore, this study proposed an adaptive sampling FSPI method based on discrete coefficients. This method utilizes the characteristic of energy concentration in the Fourier spectrum to plan circular and rectangular sampling paths in the frequency domain. Measurements are taken along the planned sampling paths, and the mean and standard deviation of each segment are calculated to obtain the discrete coefficients. When the discrete coefficient exceeds the threshold, the sampling stops automatically. Then, the target image is reconstructed by performing the inverse Fourier transform on the acquired spectrum. The method proposed in this article has significantly improved the imaging efficiency of SPI.

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SACK: Enabling Environmental Situation-Aware Access Control for Vehicles in Linux Kernel

SACK: Enabling Environmental Situation-Aware Access Control ...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Chen, Boyan Shen, Qingni Xue, Lei She, Jiarui Zhang, Xiaolei Luo, Xiapu Zhang, Xin Chen, Wei Wu, Zhonghai School of Software and Microelectronic Peking University China Peking University National Engineering Research Center for Software Engineering China School of Cyber Science and Technology Sun Yat-sen University China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9783982674100

Connected and autonomous vehicles (CAVs) operate in open and evolving environments, which require timely and adaptive permission restriction to address dynamic risks that arise from changes in environmental situations (hereinafter referred to as situations), such as emergency situations due to vehicle crashes. Enforcing situation-aware access control is an effective approach to support adaptive permission restriction. Current works mainly implement situation-aware access control in the permission framework and API monitoring in user space. They are vulnerable to being bypassed and are coarse-grained. Autonomous systems have widely adopted mandatory access control (MAC) to configure and enforce system-wide and fine-grained access control policies. However, the MA C supported by Linux security modules (LSM) relies on predefined security contexts (e.g., type) and relatively fixed permission transition conditions (e.g., exec syscall), which lacks consideration of environmental factors. To address these issues, we propose a Situation-aware Access Control framework in the Kernel (SACK), which enforces adaptive permission restriction based on environmental factors for CAVs. Incorporating environmental situations into the LSM framework is not straightforward. SACK introduces situation states as a new security context for abstracting environmental factors in the kernel. Subsequently, SACK utilizes a situation state machine to implement new adaptive permission transitions triggered by situation events. In addition, SACK provides a novel situation-aware policy language that links specific user space permissions to MAC rules while maintaining compatibility with other LSMs such as AppArmor. We develop two prototypes: an independent SACK with its own policies and a SACK-enhanced AppArmor that adaptively updates the corresponding policies of AppArmor. The experimental results demonstrate that SACK can efficiently enforce situation-adaptive permissions with negliaible runtime overh

关键词： Authorization

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A Joint Scoring and Classification for Rehabilitation Training Poses Based on Convolutional Neural Networks

A Joint Scoring and Classification for Rehabilitation Traini...

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2022 International Symposium on Robotics, Artificial Intelligence, and Information engineering, RAIIE 2022

作者： Long, Jingxuan Ma, Chuang Zhan, Fengke School of Computing Technologies Royal Melbourne Institute of Technology University MelbourneVIC3000 Australia School of Software Engineering Guangxi University of science and technology Liuzhou545000 China

ISBN: (纸本)9781510660137

Telemedicine is likely to be an increasingly available medical mean for modern patients and those who are inconvenient to go to hospitals. As a kind of Chinese traditional rehabilitation training, eight-section brocade is more suitable for patients of most ages and they can promote physical quality. However, it is difficult for non-professional patients to complete standard posture without doctor guidance. Therefore, in this work, a method of remote data collection, posture scoring, and classification based on convolutional neural network of Siamese Network will be presented. 1, 204 images are used as template pose data and other 1, 204 pictures are patient’s poses. To train the network to have a better understanding of the image's sophistication, a Convolutional Neural Network (CNN) is used in this work, which can take an input image and apply learnable weights and biases to the image's numerous objects. Meanwhile, Siamese network is mentioned as a method to compare two vectors with different outputs so that posture classification and evolution can be realized. The average similarity of the score is 0.2172, as can be shown. The score range is -1 to 1. The closer the score is to 0, the more the patient's activities resemble the template. © 2022 SPIE.

关键词： Telemedicine

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BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance 26

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Feiyu Zhou, Jian-Tao Guo, Xu Inner Mongolia University College of Computer Science Inner Mongolia Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software China

ISBN: (纸本)9798350331684

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme's safety and do simulation experiments to show that the scheme has practicality. © 2023 IEEE.

关键词： Smart contract

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Automated Characterization in Cryocon 22C at Wide Temperature Range using LabVIEW

Automated Characterization in Cryocon 22C at Wide Temperatur...

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International Conference on Computer Applications In Electrical engineering-Recent Advances (CERA)

作者： Jyotiska Bharadwaj Lip Bogdanović Lovro Marković Software Engineer Independent Researcher Jorhat India Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

This paper presents the development of an algorithm using LabVIEW, a B2912B SMU, and a Model 22C Cryogenic temperature controller for automating the characterization of various semiconductor devices. The algorithm is carried out using LabVIEW. By removing human error and enhancing precision through temperature control, the research aims to streamline the measurement procedure. Users enter the desired temperature values into the LabVIEW program. The Cryocon 22C is then automatically turned on by the algorithm to use a PID control method to stabilize the temperature near the sample. Once the temperature has stabilized, the software performs automatic characterization of the sample, including Gummel plot and Gummel plot negative sweep. To accurately capture the temperature-dependent features of the sample, measurements are performed at various temperatures. The method has the potential to increase productivity in a variety of semiconductor characterization and research applications due to its success in achieving temperature stability and precise measurements.

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3D Object Tracking with Adaptively Weighted Local Bundles

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Journal of Computer Science & Technology 2021年第3期36卷 555-571页

作者： Jia-Chen Li Fan Zhong Song-Hua Xu Xue-Ying Qin School of Software Shandong UniversityJinan 250101China School of Computer Science and Technology Shandong UniversityQingdao 266237China College of Engineering and Computing University of South CarolinaColumbia 29208U.S.A.

The 3D object tracking from a monocular RGB image is a challenging *** popular color and edgebased methods have been well studied,they are only applicable to certain cases and new solutions to the challenges in real environment must be *** this paper,we propose a robust 3D object tracking method with adaptively weighted local bundles called AWLB tracker to handle more complicated *** bundle represents a local region containing a set of local *** alleviate the negative effect of the features in low-confidence regions,the bundles are adaptively weighted using a spatially-variant weighting function based on the confidence values of the involved energy ***,in each frame,the weights of the energy items in each bundle are adapted to different situations and different regions of the same *** show that the proposed method can improve the overall accuracy in challenging *** then verify the effectiveness of the proposed confidence-based adaptive weighting method using ablation studies and show that the proposed method overperforms the existing single-feature methods and multi-feature methods without adaptive weighting.

关键词： 3D tracking local bundle feature fusion confidence map

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Secure Mutual Learning with Low Interactions for Deep Model Training

Secure Mutual Learning with Low Interactions for Deep Model ...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Wenxing Zhu Xiangxue Li Shanghai Key Laboratory of Trustworthy Computing School of Software Engineering East China Normal University Shanghai China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

The paper proposes SMuLe, a secure mutual learning protocol for two-party deep model training, to support low demand for interaction complexity and communication overhead. The strategy is that two parties exchange their blind predictions securely on each other's dataset and the underlying models can thereby benefit from not only the true label of the data but the prediction of the other party, as being different from federated learning and prior art that counts on secure multi-party computation. After this protocol, each participant occupies a well trained plaintext model privately. The contributions include the following: (i) the communication cost of SMuLe is lower than state-of-the-art two-party training protocols; (ii) our solution is flexible with different secure inference schemes; (iii) SMuLe can resist malicious attacks through poisoning samples. The experiments show that on CifarlO, SMuLe can obtain desirable accuracy even in a small convnet and the communication cost in each epoch is less than 75 MB as expected.

关键词： Training Protocols Costs Accuracy Computational modeling Resists Predictive models

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Robust Length of Stay Prediction Model for Indoor Patients

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Computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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Predicting the Performance of DNNs to Support Efficient Resource Allocation

Predicting the Performance of DNNs to Support Efficient Reso...

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International Conference on Network and Service Management

作者： Sarah Shah Yasaman Amannejad Diwakar Krishnamurthy Electrical and Software Engineering University of Calgary Calgary Canada Mathematics and Computing Mount Royal University Calgary Canada

Numerous organizations are adopting sophisticated Machine Learning (ML) algorithms for their operations. To ensure the optimal performance of ML systems, organizations require insights into the response time of such systems under realistic user workloads. However, despite the widespread adoption of ML models, research on predicting the response time of a system serving an ML model under varying resources and user workloads is limited. In this paper, we address this gap by proposing a modeling approach to predict response times of multiple well-known Deep Neural Networks (DNNs) under simultaneously varying resource settings and user workloads. We join a classifier and a regressor to identify the optimal resource setting for meeting a DNN's response time target, and to predict the response time under the allocated resource setting. Our technique enables performance modeling without the need to collect extensive data during system operation, thus empowering pre-deployment predictions. The results demonstrate that our approach can generalize to unseen resource and workload scenarios, guaranteeing accurate predictions of compliance with response time targets 98.05% of the time and offering response time predictions with a mean prediction error of 9.10%.

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