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Congress on Evolutionary Computation

作者： G.S. Cowan R.G. Reynolds George S. Cowan and Associates Ypsilanti MI USA Computer Science Department Wayne State University Detroit MI USA

The Metrics Apprentice processes a domain knowledge base of software quality concepts with a form of evolutionary computation in order to learn software metrics for a delimited application or software development environment. The evolutionary computation method that is used, the Cultural Algorithm, uses beliefs about the performance of individual population members in order to enhance the evolutionary learning process. In the Metrics Apprentice, these beliefs are an integrated part of the domain knowledge base, and the ones that are most useful in the learning process persist for reuse in future learning tasks. The semantic network that encodes the domain of software quality issues and concepts is displayed using an extension of expandable outlines called the Outline Knowledge Display.

关键词： Cultural differences software algorithms software systems software quality software metrics Application software Genetic programming Evolutionary computation Learning systems Displays

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software Tool for Evaluation of Traffic Prioritisation algorithms in 6LOWPAN Network

Software Tool for Evaluation of Traffic Prioritisation Algor...

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International Symposium on Electrical Apparatus and Technologies (SIELA)

作者： Aydan Haka Veneta Aleksieva Hristo Valchanov Faculty of Computer Sciences and Automation Technical University of Varna Varna Bulgaria

ISBN: (数字)9781728143460

ISBN: (纸本)9781728143477

The expected expansion of IoT networks and their virtualisation require improved quality of service. This paper introduces enhancements to the simulation product for 6LoWPAN networks proposed by authors, which enables service quality research. The influence of different algorithms for prioritising traffic on throughput, delay, packet delivery ratio and packet loss ratio considered.

关键词： Delays Quality of service Throughput software algorithms Mobile handsets software tools Packet loss

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Incorporating an Integrated software System for Stroke Prediction using Machine Learning algorithms and Artificial Neural Network

Incorporating an Integrated Software System for Stroke Predi...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Md. Jalal Uddin Chowdhury Ashab Hussan Dewan Ashraful Islam Hridoy Abu Sayed Sikder Dept. of CSE Leading University Sylhet Bangladesh

A stroke is a condition where there is no flow of blood in the brain, depriving it of oxygen and resulting in long-term brain damage and loss of function. Most typically, a clot in an artery delivering blood to the brain is the culprit. Hemorrhage, in which blood leaks into the brain due to a ruptured vessel, is another possible reason. A stroke can result in long-term harm, such as partial paralysis, speech impairment, cognitive impairment, and memory loss. The second leading cause of mortality is a stroke, which is also a significant contributor to disability worldwide. According to the World Stroke Organization (WSO), a stroke will occur in 1 out of every four adults over the age of 25. The number of stroke victims worldwide exceeds 110 million. The objective of the thesis is to incorporate an integrated software system that will predict stroke using artificial neural networks and machine learning algorithms like logistic regression, decision tree classification, random forest classification, K-nearest neighbors, support vector machines, and Nave Bayes classification. The rate of strokes is already alarming. Having an intuitive system to conveniently predict stroke could help reduce the rate of stroke by educating people, which is the focus of the research.

关键词： Support vector machines Visualization Machine learning algorithms software algorithms Artificial neural networks Stroke (medical condition) Prediction algorithms

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A Measurement of software Trustworthiness Based on Approximation algorithms

A Measurement of Software Trustworthiness Based on Approxima...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Jie Zhou Hong Zhu School of Mathematics and Physics Shanghai Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing East China Jiao Tong University Shanghai China School of Computer Science Fudan University Shanghai China

The trustworthiness of algorithms is one of the important parts of that of softwares. We introduce a trust model and define trustworthy degree for the approximation algorithms by a probability approach. Relative prope... 详细信息

关键词： Approximation algorithms Approximation methods software algorithms Algorithm design and analysis software Polynomials Optimization

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Investigation of correlation between state of link channel and algorithms of applied software

Investigation of correlation between state of link channel a...

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International Conference on CAD Systems in Microelectronics (CADSM)

作者： Anna Babich Murad Ali A. Kharkiv National University of RadioElectronics Kharkiv UKRAINE CAD Department Kharkiv National University of RadioElectronics 14 Lenin ave. 61166 UKRAINE

The method of correlation analysis of link channel quality and applied software (AS) effectiveness is considered. The goal of this method is to separate task of increasing of AS effectiveness from task of monitoring and diagnosing of computer networks, because these tasks should be solved separately and independently. The practical realization of the proposed method and the results of its applying are given.

关键词： Correlation software Time factors software algorithms Computer networks Data communication Servers

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software Fault Detection algorithms Using Artificial Intelligence: A Review and Classification

Software Fault Detection Algorithms Using Artificial Intelli...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Lungisani Ndlovu Bheki Cossa Caroline Makokoe Lethukuthula Khumalo Ntomfuthi Ntshangase Siphelele Myaka Nomalisa Ndhlovu Mamello Mtshali Information and Cyber Security Centre Council for Scientific and Industrial Research (CSIR) Pretoria South Africa

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

With the rise of software applications, software development has become a rapidly evolving field, propelled by technological advancements and the increasing demand for innovative solutions to sustain our digital era. Detecting software faults has emerged as a critical attribute in the software development cycle to ensure system reliability, quality, and user satisfaction. However, traditional fault detection methods often suffer from drawbacks such as time consumption and error proneness, particularly in the context of large and complex software systems. In response to these hurdles, artificial intelligence (AI) presents itself as a promising methodology, with the aim of improving accuracy, scalability, automation, and proactiveness in fault detection. This paper systematically reviews the literature on AI-based software fault detection algorithms. Leveraging the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a curated set of 50 peer-reviewed research publications was identified in Google Scholar and subsequently analyzed using a classification coding framework. The findings reveal that AI-based software fault detection algorithms played an important role in improving software system reliability and performance.

关键词： Fault detection Scalability software algorithms Propulsion software systems Classification algorithms software reliability Internet Artificial intelligence software development management

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Poster: Searching for High-Performing software Configurations with Metaheuristic algorithms

Poster: Searching for High-Performing Software Configuration...

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Chong Tang Kevin Sullivan Baishakhi Ray University of Virginia Charlottesville VA US

Modern systems often have complex configuration spaces. Research has shown that people often just use default settings. This practice leaves significant performance potential unrealized. In this work, we propose an approach that uses metaheuristic search algorithms to explore the configuration space of Hadoop for high-performing configurations. We present results of a set of experiments to show that our approach can find configurations that perform significantly better than defaults. We tested two metaheuristic search algorithms-coordinate descent and genetic algorithms-for three common MapReduce programs-Wordcount, Sort, and Terasort-for a total of six experiments. Our results suggest that metaheuristic search can find configurations cost-effectively that perform significantly better than baseline default configurations.

关键词： Genetic algorithms software engineering Clustering algorithms Heuristic algorithms software software algorithms Computational modeling

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A Novel Variability-Rich Scheme for software Updates of Automotive Systems

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IEEE ACCESS 2024年 12卷 79530-79548页

作者： Bazzi, Abir Shaout, Adnan Ma, Di Univ Michigan Dearborn Dept Elect & Comp Engn Dearborn MI 48128 USA Univ Michigan Dearborn Dept Comp & Informat Sci Dearborn MI 48128 USA

The automotive industry is experiencing a significant evolution from traditional hardware-defined to software-defined architecture, enabling higher levels of autonomy, connectivity, safety, and richer in-vehicle experiences. A service oriented architecture is essential for realizing software-Defined Vehicles (SDVs) and fostering new business models for OEMs. However, this architecture evolution requires new development paradigms to address the increasing complexity of software, which is essential for seamless software development, integration, and deployment from cloud or backend repositories to the vehicle. Given the complexity of vehicular software updates, particularly when dealing with highly distributed embedded ECUs, a software-centric approach is more efficient and suitable to cover different architectures and configurations, ensuring consistency across all platforms. Therefore, we propose a variability-rich scheme for software updates based on a Merkle tree approach that can cope with the complexity of the new software architecture while addressing the safety and security requirements of real-time and resource-constrained embedded systems in the vehicle. The technical analysis and experiments conducted in this paper demonstrate how the proposed scheme, which combines a digital signature and a Merkle hash tree, achieves synergistic authentication and verification of multiple software variants. Our approach offers OEMs the ability to sign a software module once and verify it across multiple ECU variants, providing a more efficient alternative to the traditional method of creating a software update package for each variant. This approach not only adds flexibility to software updates and reduces the complexity of software variant management, but also maintains the security of the vehicle, ensuring that there is no compromise in the safety of the passengers.

关键词： software Computer architecture Automotive engineering Complexity theory Industries Cloud computing software algorithms Cyber-physical systems software development management Service-oriented architecture Cyber physical systems digital signatures distributed software development Merkle tree safety-critical systems service oriented architecture software over the air updates software-defined vehicle variant management

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Efficient software Implementation of the SIKE Protocol Using a New Data Representation

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IEEE TRANSACTIONS ON COMPUTERS 2022年第3期71卷 670-683页

作者： Tian, Jing Wang, Piaoyang Liu, Zhe Lin, Jun Wang, Zhongfeng Grosschaedl, Johann Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Univ Luxembourg L-4365 Esch Sur Alzette Luxembourg

Thanks to relatively small public and secret keys, the Supersingular Isogeny Key Encapsulation (SIKE) protocol made it into the third evaluation round of the post-quantum standardization project of the National Institute of Standards and Technology (NIST). Even though a large body of research has been devoted to the efficient implementation of SIKE, its latency is still undesirably long for many real-world applications. Most existing implementations of the SIKE protocol use the Montgomery representation for the underlying field arithmetic since the corresponding reduction algorithm is considered the fastest method for performing multiple-precision modular reduction. In this paper, we propose a new data representation for supersingular isogeny-based Elliptic-Curve Cryptography (ECC), of which SIKE is a sub-class. This new representation enables significantly faster implementations of modular reduction than the Montgomery reduction, and also other finite-field arithmetic operations used in ECC can benefit from our data representation. We implemented all arithmetic operations in C using the proposed representation such that they have constant execution time and integrated them to the latest version of the SIKE software library. Using four different parameters sets, we benchmarked our design and the optimized generic implementation on a 2.6 GHz Intel Xeon E5-2690 processor. Our results show that, for the prime of SIKEp751, the proposed reduction algorithm is approximately 2.61 times faster than the currently best implementation of Montgomery reduction, and our representation also enables significantly better timings for other finite-field operations. Due to these improvements, we were able to achieve a speed-up by a factor of about 1.65, 2.03, 1.61, and 1.48 for SIKEp751, SIKEp610, SIKEp503, and SIKEp434, respectively, compared to state-of-the-art generic implementations.

关键词： Protocols Cryptography Elliptic curves Elliptic curve cryptography software algorithms Public key NIST Supersingular Isogeny Diffie-Hellman (SIDH) key exchange Elliptic Curve Cryptography (ECC) modular reduction operation montgomery representation barrett representation Post-Quantum Cryptography (PQC)

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Performance Analysis of Datamining algorithms for software Quality Prediction

Performance Analysis of Datamining Algorithms for Software Q...

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International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom

作者： N. Gayatri Nickolas S. A.V. Reddy R. Chitra Department of Computer Applications National Institute of Technology Tiruchirappalli India

Data mining techniques are applied in building software fault prediction models for improving the software quality. Early identification of high-risk modules can assist in quality enhancement efforts to modules that are likely to have a high number of faults. Classification tree models are simple and effective as software quality prediction models, and timely predictions of defects from such models can be used to achieve high software reliability. In this paper, the performance of five data mining classifier algorithms named J48, CART, Random Forest, BFTree and Naive Bayesian classifier (NBC) are evaluated based on 10 fold cross validation test. Experimental results using KC2 NASA software metrics dataset demonstrates that decision trees are much useful for fault predictions and based on rules generated only some measurement attributes in the given set of the metrics play an important role in establishing final rules and for improving the software quality by giving correct predictions. Thus we can suggest that these attributes are sufficient for future classification process. To evaluate the performance of the above algorithms Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Receiver Operating Characteristic (ROC) and Accuracy measures are applied.

关键词： Performance analysis software algorithms software quality Predictive models Data mining Fault diagnosis Classification tree analysis software reliability Bayesian methods Niobium compounds

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