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检索条件"主题词=Software algorithms"

共 12479 条记录，以下是121-130 订阅

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Improving software Reliability Through Bug Detection and Automated Error Repair Using Transformer-Based Models

Improving Software Reliability Through Bug Detection and Aut...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nedal Nwasra Jamal Zaraqu Zahid Hussain Qaisar Computer Science Department University of Petra Amman Jordan Computing & Emerging Technologies Emerson University Multan Multan Pakistan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Automated bug detection and repair are critical in determining the reliability and cost of software development. To address these concerns, this research uses a transformer-based sequence-to-sequence model called CodeT5, together with the Defects4J dataset, which contains real Java programs with officially identified buggy and fixed versions. The approach involves retrieving buggy and fixed code pairs, pretraining CodeT5 for sequence-to-sequence learning, and then evaluating the model using the BLEU, EM, and ED measures. This model got translated sentences' BLEU 78.4%, Exact Match, rate 64.2% and accuracy 82.0%. While having an average edit distance of 12.3 operations, the generated fixes suggest how slight modifications can be made to produce semantically and syntactically correct fixes with little human intuition or input. This work shows that program repair can benefit from applying state-of-the-art deep learning algorithms and realistic benchmark datasets. The outcomes point to possible uses for enhancing directions of software creation and shortening the hours spent on debugging. Future work will be dedicated to using similar techniques in different languages and handling different types of bugs.

关键词： Java Computational modeling Computer bugs software algorithms Maintenance engineering Transformers software software reliability software engineering software development management

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Information and software Support for the Multifactor Authentication Process using Biometric Data in Linux Kernel-Based Operating Systems

Information and Software Support for the Multifactor Authent...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Alexander A. Zakharov Andrey M. Shabalin Roman V. Tikhonov Department of Smart City Technologies Information Security Tyumen State University Tyumen Russia Information Security Department Tyumen State University Tyumen Russia Department of information technology Akvatechnik Tyumen Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The article substantiates the necessity of of implementing multifactor authentication based on user biometric data to enhance the security level of computer systems. Traditional methods such as password authentication are not always capable of providing the required level of protection for the account and user data. The authors analyze modern approaches to biometric authentication, identify the advantages of using them within multifactor systems, and address current issues related to the secure storage of biometric data. As a result of their research, they have developed a software architecture that uses the proposed mechanisms for integration in Linux kernel-based operating systems.

关键词： Biometrics software architecture Linux software algorithms Authentication Personal voice assistants Protection Open source software Secure storage Testing

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An Ensemble Learning Method Based on Neighborhood Granularity Discrimination Index and Its Application in software Defect Prediction

An Ensemble Learning Method Based on Neighborhood Granularit...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Yuqi Sha Feng Jiang Qiang Hu Yifan He College of Information Science and Technology Qingdao University of Science and Technology Qingdao P. R. China Institute of Cosmetic Regulatory Science Beijing Technology and Business University Beijing P.R. China

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

software defect prediction (SDP) is a primary field of study in software engineering, aiming to optimize test resource allocation by highlighting the defect-prone software modules. Over the last few years, ensemble learning method has been extensively adopted in SDP. However, how to strengthen the diversity of base learners is an issue in ensemble learning. In this paper, we consider the problem of diversity in the eye of feature space perturbation. First, we propose the notion of neighborhood granularity discrimination index (NGDI), by combining the neighborhood knowledge granularity with the neighborhood discrimination index within the framework of neighborhood rough sets. NGDI can not only measure the uncertainty of feature subsets' discriminant capability, but also characterize the granularity of neighborhood knowledge induced by feature subsets. Second, we propose an ensemble learning algorithm, EL-NGDI, established on the NGDI. ELNGDI disturbs the feature space using multiple NGDI-based neighborhood approximate reducts. Third, we use ELNGDI to predict software defects. ELNGDI and the Synthetic Minority Oversampling Technique (SMOTE) are combined in order to handle the class imbalance issue in SDP, and propose a mechanism called SMOTE-ELNGDI. Experimental results on 20 datasets demonstrate that ELNGDI effectively improves the performance of SDP compared with existing ensemble learning methods.

关键词： Uncertainty software algorithms Diversity reception Rough sets Prediction algorithms Approximation algorithms software Ensemble learning Indexes software engineering

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Self-Interference Cancellation for software-Defined Radar Using Coupling Properties of Channels

Self-Interference Cancellation for Software-Defined Radar Us...

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European Conference on Antennas and Propagation, EuCAP

作者： Siyu Xia Xiao Ma Qi Wu School of Electronics and Information Engineering Beihang University Beijing China

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

This paper presents a new software-defined radar with multiple frequency continuous waves (MFCW) and a self-interference cancellation algorithm. The presented radar is implemented as a highly reconfigurable system that is made of a USRP X310, two patch antennas and the GNU Radio software. Most of the radar operations can be configured and controlled by the software. The transmitter and receiver channels are in one radio-frequency frontend of the USRP X310, thus releasing the remaining channels for other purposes. The radar is experimentally evaluated in indoor scenarios, operating at a frequency of 4.25 GHz while transmitting MFCW signals within a narrow bandwidth. Experimental results demonstrate that the implemented algorithm enables the SDRadar to effectively measure short-range targets.

关键词： Radio frequency Interference cancellation Patch antennas software algorithms Radio transmitters Europe Radar Receivers Radar antennas software

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Towards Data-Centric and Context-Aware Decision Making in software-Defined Vehicles

Towards Data-Centric and Context-Aware Decision Making in So...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Ella Peltonen Vishaka Basnayake Nada Elgendy Benjamin Kämä Pertti Seppänen Tero Päivärinta University of Oulu Oulu Finland

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

As vehicular computing environments move towards software-defined paradigms and microservice architectures, more data becomes available from the in-vehicular sensors for various applications to be developed. Combining such local vehicular information with feedback from drivers and passengers, the environment, and external data sources opens new avenues for application development and enchanting vehicular capabilities, services, and systems. However, in managing more data effectively, reliably, securely, and privately, more focus is given to data analysis, machine learning, and artificial intelligence tasks and how they are integrated into vehicle-cloud architectures. This paper considers a vision for a data-centric architecture that utilizes open-source building blocks. We aim to bring together data from humans, vehicles, and environments to support novel application development and context-aware decision-making.

关键词： software architecture Architecture Soft sensors Decision making software algorithms Microservice architectures Computer architecture Machine learning Reliability Vehicles

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Key Applications and Challenges of Virtual software Simulation Technology in Data Resource Searching Schemes

Key Applications and Challenges of Virtual Software Simulati...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Jiao Yueyue Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

With the continuous development of big data and complex software systems, virtual software simulation technology plays an increasingly important role in the fields of data resource search and security prevention applications. Inspired by this, this study first analyzes the basic principles of virtual software simulation technology, and proposes a new virtual simulation platform model by combining resource allocation strategy, search algorithm optimization and real-time interactivity, and conducts systematic extensibility research from the perspective of DDos detection. At the practical level, this study introduces a virtual simulation model based on the VERICUT platform to analyze the complex data search process and improve the modeling. The core methods of this research framework include the following three aspects: 1) Proposing a new collaborative management model for automated testing based on data flow; 2) Performing coverage testing of multi-dimensional data input and intermediate state parameterization at the practical level; 3) Proposing a new DDoS attack analysis and detection algorithm as an extension study. From the experimental results, it can be seen that in 50 sets of independent repeated tests, the DDos detection accuracy of the proposed algorithm is stable between 98% and 100%, with a small fluctuation rang. This shows the stability of the proposed model.

关键词： Analytical models Accuracy software algorithms Data models Stability analysis Real-time systems Security Resource management Optimization Testing

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Improving software Defects Detection: An In-Depth Analysis of Machine Learning Methods and Static Analysis Tools for Greater Accuracy

Improving Software Defects Detection: An In-Depth Analysis o...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Veer Bobade Chetan Puri Department of Artificial Intelligence and Machine Learning Faculty of Engineering And Technology DMIHER Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering And Technology DMIHER Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, software Defect Prediction (SDP) uses machine learning approaches to anticipate and address any problems early in the development cycle. Numerous machine learning techniques, such as Support Vector Machines (SVM), Random Forest (RF), Logistic Regression, Naïve Bayes, Multilayer Perceptron (MLP), Decision Stump, J48, Lazy IBK, and ZeroR, are thoroughly examined in this work. With an emphasis on feature selection as a crucial step in increasing prediction accuracy and lower computing costs, these approaches are assessed for their capacity to detect software flaws precisely. This research study demonstrates how these models may improve fault identification and prevention procedures using historical records from sources such as the PROMISE repository. The study highlights the best practices for machine learning-based software quality assurance, lowering expenses while guaranteeing superior results.

关键词： Support vector machines Accuracy Machine learning algorithms software algorithms Machine learning software quality Multilayer perceptrons Feature extraction Prediction algorithms Random forests

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Efficient Load Balancing in software Defined Networks Using Unsupervised Meta-Reinforcement Learning

Efficient Load Balancing in Software Defined Networks Using ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Prerita Kulkarni Nitika Vats Doohan Department of Computer Science Medicaps University Indore India Department of Computer Science Sage University Indore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

New technologies, such as cloud computing and big data, make traditional network management more difficult, while the network's user base continues to expand. Consequently, changes to the traditional network architecture are required. To address this issue and enhance network management compliance, a new idea called software-defined network (SDN) has been proposed. Due to limited network resources and to satisfy the criteria of quality of service, it is necessary to address numerous difficulties, one of which is load balancing (LB), which distributes data traffic among several resources to maximize their efficiency and reliability. The local information of a network is used to design LB in a typical network. Consequently, it is not very accurate. Still, software-defined network (SDN) controllers can see the network from anywhere in the world and make better LB. We provide a set of unsupervised meta- RL algorithms to build smart optimization architecture for SDN LB.

关键词： Uncertainty software algorithms Computer architecture Telecommunication traffic Load management Inference algorithms Reliability software defined networking Optimization Standards

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Towards Classical software Verification using Quantum Computers

Towards Classical Software Verification using Quantum Comput...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Sebastian Issel Kilian Tscharke Pascal Debus Fraunhofer AISEC Garching Germany

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

We explore the possibility of accelerating the formal verification of classical programs with a quantum computer.A common source of security flaws stems from the existence of common programming errors like use after free, null-pointer dereference, or division by zero. To aid in the discovery of such errors, we try to verify that no such flaws *** our approach, for some code snippet and undesired behavior, a SAT instance is generated, which is satisfiable precisely if the behavior is present in the code. It is in turn converted to an optimization problem, that is solved on a quantum computer. This approach holds the potential of an asymptotically polynomial *** examples of common errors, like out-of-bounds and overflows, but also synthetic instances with special properties, specific number of solutions, or structure, are tested with different solvers and tried on a quantum *** use the near-standard Quantum Approximation Optimization Algorithm, an application of the Grover algorithm, and the Quantum Singular Value Transformation to find the optimal solution, and with it a satisfying assignment.

关键词： Quantum computing Codes software algorithms Noise Approximation algorithms Hardware Encoding software Optimization Formal verification

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Developing a New MapReduce Framework with SVM-based software Solution for Lyme Lesion Impact on Alzheimer's Disease Prevention

Developing a New MapReduce Framework with SVM-based Software...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Saravanan. M.S G. Sreehitha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences and Assistant Professor Sri Padmavati Mahila Visvavidyalayam Tirupathi Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Alzheimer's disease has many symptoms to occur, one of the latest research studies identified that Lyme disease, caused by “Borrelia burgdorferi” also one of the reasons indirectly affect the neuroinflammation. The dataset used for this research study is supplied by many web sources, that are involved in feature extraction to apply on machine learning techniques for extracting meaningful knowledge. This work proposing a new MapReduce framework-based Support Vector Machine (SVM) model for better prediction of impact of Lyme on Alzheimer's disease. The MapReduce based framework using SVM model creates more impact on prediction of Alzheimer's disease at the earliest. The clinical data with images are collected from distributed environments and applied on preprocessing and feature extraction stages then applied to proposed MapReduce based SVM model for better accuracy and optimal classification for complex patters on Lyme image dataset. From these results the Alzheimer's risk factors are predicted for early treatment process. The results obtained by this study will give early decision making for highly available sources of treatment procedures and gives more insight on preventive care on each and every patient involved in diagnosis of Lyme disease prediction, lead to identify the Alzheimer's disease prediction.

关键词： Support vector machines Accuracy Machine learning algorithms software algorithms Machine learning Predictive models Feature extraction software Classification algorithms Alzheimer's disease

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