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检索条件"主题词=software algorithms"

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Hardware-software Co-Design of the Lightweight Authenticated Encryption Algorithm ASCON for Internet of Things

Hardware-Software Co-Design of the Lightweight Authenticated...

引用

Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Yunqiang Wu School of Electronics and Information Nanchang Institute of Technology Nanchang China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

With the rapid development of Internet of Things (IoT) technology, the popularity of smart devices poses new challenges to data security. ASCON algorithm is an ideal choice for IoT applications because of its lightweight characteristics and excellent security performance. This paper first summarizes the basic principle of ASCON algorithm and its position in NIST lightweight encryption standard, and then introduces the goal and implementation strategy of its software and hardware co-design in detail. By designing an efficient hardware IP core and optimizing the software implementation, this paper significantly improves the encryption processing speed, reduces the memory occupation and optimizes the energy efficiency. The test results show that the co-design of hardware and software not only improves the performance stability of ASCON algorithm, but also provides a more solid security guarantee for IoT devices, laying a foundation for future smart device applications.

关键词： Performance evaluation software algorithms software Hardware Energy efficiency Encryption Internet of Things Smart devices Vehicle dynamics Thermal stability

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Feature Importance in the Context of Traditional and Just-In-Time software Defect Prediction Models

Feature Importance in the Context of Traditional and Just-In...

引用

Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Susmita Haldar Luiz Fernando Capretz School of Information Technology Fanshawe College London Canada Department of Electrical and Computer Engineering Western University London Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

software defect prediction models can assist software testing initiatives by prioritizing testing error-prone modules. In recent years, in addition to the traditional defect prediction model approach of predicting defects from class, modules, etc., Just-In-Time defect prediction research, which focuses on the change history of software products is becoming prominent. For building these defect prediction models, it is important to understand which features are primary contributors to these classifiers. This study considered developing defect prediction models incorporating the traditional and the Just-In-Time approaches from the publicly available datasets including the Apache Camel project. A multi-layer Deep Learning algorithm was applied to these datasets in comparison with machine learning algorithms. The prediction models developed using the Deep Learning algorithm achieved an accuracy of 80% and 86%, with the area under receiving operator curve (AUC) scores of 66% and 78% for traditional and Just-In-Time defect prediction, respectively. Finally, the feature importance of these models was identified using a model-specific integrated gradient method and a model-agnostic Shapley Additive Explanation (SHAP) technique.

关键词： Deep learning software testing Machine learning algorithms Computational modeling software algorithms Predictive models Prediction algorithms

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Automatic Parallelization of Embedded software via Hierarchical Process Network Transformations

Automatic Parallelization of Embedded Software via Hierarchi...

引用

Forum on Specification, Verification and Design Languages, FDL

作者： Fahimeh Bahrami Rodolfo Jordão Ingo Sander George Ungureanu KTH Royal Institute of Technology Stockholm Sweden Ericsson AB Stockholm Sweden

ISBN: (数字)9798331504571

ISBN: (纸本)9798331504588

To fully utilize multi-processors, new tools are required to manage software complexity. We present a novel technique that enables automating hierarchical process network transformations to derive optimized parallel applications. Designers leverage a library of process constructors and data-parallel algorithmic skeletons, utilizing the well-defined semantics of a restricted set of operators. This carefully chosen set addresses both temporal and spatial aspects of computation, enabling the automated identification of various parallel patterns. We utilize an augmented version of a meta-modeling framework grounded in system graphs and trait hierarchies to generate an intermediate representation (IR) of the system model to simplify automatic transformations and evaluations. Our augmentation allows for capturing skeletons and hierarchical networks. By meticulously selecting the underlying framework, we alleviate the need for tool integration in our design flow. We validate our approach through a proof-of-concept implementation, where our automated tool applied 193 transformations to fully parallelize an image processing application.

关键词： Embedded systems Image processing Computational modeling software algorithms Semantics Metamodeling Skeleton

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Unveiling Hidden Anomalies: Leveraging SMAC-LSTM for Enhanced software Log Analysis

Unveiling Hidden Anomalies: Leveraging SMAC-LSTM for Enhance...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Yicheng Sun Jacky Keung Jingyu Zhang Hi Kuen Yu Wenqiang Luo Shuo Liu Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

software logs are essential records generated during the functioning of software systems, aiding in the identification of irregularities and prevention of system failures. Recently, deep learning models have garnered significant interest among researchers due to their efficacy in detecting anomalies within software logs. This research paper constructs a novel dataset, consisting of three parts: two datasets derived from our software system, along with a publicly available dataset obtained from the LogHub platform. The extensive logs within the dataset undergo preprocessing to extract meaningful features. Furthermore, this study introduces a novel model named SMAC-LSTM, designed specifically for detecting anomalies in software logs. Sequential Model-based Algorithm Configuration (SMAC) is a suitable method for hyperparameter optimization and automated deep learning. SMAC-LSTM involves determining the optimal hyperparameter values for the LSTM model using the SMAC. Additionally, SMAC-LSTM combines the temporal dependency capturing ability of Long Short-Term Memory (LSTM) with a context-dependent mechanism achieved through a Bayesian optimization algorithm based on random forests. This fusion enhances the model's ability to detect subtle anomalies in time series data, which are frequently disregarded by con-ventional LSTM models. The thorough evaluation demonstrates the superior performance of SMAC-LSTM models compared to traditional deep learning models, showcasing significant enhance-ments in precision (98.63%), and recall (92.31%), with an F1-Score of 95.36%, outperforming all other models. These results underscore the potential of SMAC-LSTM in the realm of software log anomaly detection.

关键词： Deep learning software algorithms Time series analysis software systems Feature extraction Data models Long short term memory

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software Defect Prediction Using Ensemble Technique

Software Defect Prediction Using Ensemble Technique

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International Conference on Information Management and Technology (ICIMTech)

作者： Pandu Wicaksono Panji Arisaputra Rezky Yunanda Willy Kristian Almuzhidul Mujhid Software Engineering Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

software defect prediction is the methodical process of identifying code segments that are likely to have problems. This is done by analyzing software metrics and using categorization algorithms. This work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. The models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. The models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. The test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. This study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms.

关键词： Codes Source coding software algorithms Predictive models Prediction algorithms Feature extraction Transformers software Ensemble learning software measurement

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Design of Visualization Simulation software for Collaborative Formation Control of Mobile Robot Cluster

Design of Visualization Simulation Software for Collaborativ...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Haiming Liu Jiaxiang Luo Mei Zhang An Chen School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Simulation tools are important auxiliary tools for scientific research. Design method of a practical and convenient simulation software for visual simulation of collaborative formation control for mobile robot cluster was discussed in this paper. The simulation software was designed in two main parts: human-computer interaction and visualization display of algorithm simulation. The software was implemented based on Qt development platform and some key methods of design and implementation involved were detailed. The simulation results of this software under different formation control algorithms are shown to demonstrate its practicality and flexibility in applications.

关键词： Human computer interaction Visualization Design methodology software algorithms Clustering algorithms Collaboration software Formation control Hardware Mobile robots

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Improving Fairness in Machine Learning software via Counterfactual Fairness Thinking

Improving Fairness in Machine Learning Software via Counterf...

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Zhipeng Yin Zichong Wang Wenbin Zhang Florida International University Miami Florida USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Machine Learning (ML) software is increasingly influencing decisions that impact individuals' lives. However, some of these decisions show discrimination and thus introduce algorithmic biases against certain social subgroups defined by sensitive attributes (e.g., gender or race). This has elevated software fairness bugs to an increasingly significant concern for software engineering (SE). However, most existing bias mitigation works enhance software fairness, a non-functional software property, at the cost of software performance. To this end, we proposed a novel framework, namely Group Equality Counterfactual Fairness (GECF), which aims to miti-gate sensitive attribute bias and labeling bias using counterfactual fairness while reducing the resulting performance loss based on ensemble learning. Experimental results on 6 real-world datasets show the superiority of our proposed framework from different aspects.

关键词： Machine learning algorithms Costs software algorithms Computer bugs software performance Labeling Ensemble learning

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Security Vulnerability Detection Method of Open-source software Based on Graph Attention Network

Security Vulnerability Detection Method of Open-source Softw...

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Artificial Intelligence, Computer Technology and Control Engineering (ACTCE), International Seminar on

作者： Qinghao Li Zhida Guo Deru Guo Ran Hu Hanlin Du Mingqian Xiao Huizhou Power Supply Corporation of Guangdong Power Grid Co Ltd. China Huizhou China

ISBN: (数字)9798331521714

ISBN: (纸本)9798331521721

With the popularity of open source software, software security problems have become increasingly prominent. Traditional vulnerability detection methods are no longer able to cope with today’s complex virus vulnerabilities, and the detection results often have certain limitations. Therefore, an open source software security vulnerability detection method based on graph attention network is proposed. Firstly, the graph attention network model is constructed, and the source code detection is used to identify and detect the graph attention vulnerability. In order to verify the performance of the graph attention network model in the proposed method, the paper compares the proposed model with Drebin algorithm. The results show that the graph attention network model can effectively detect the number of security vulnerabilities in open source software, and its maximum error value is 4. Compared with Drebin algorithm, the security vulnerability detection method has higher accuracy and efficiency.

关键词： Seminars Accuracy Control engineering Source coding software algorithms Neural networks Security Data mining Open source software Viruses (medical)

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An Empirical Study on Self-Admitted Technical Debt in Quantum software

An Empirical Study on Self-Admitted Technical Debt in Quantu...

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Asia-Pacific Conference on software Engineering

作者： Yuta Ishimoto Yuto Nakamura Ryota Katsube Naoto Sato Hideto Ogawa Masanari Kondo Yasutaka Kamei Naoyasu Ubayashi Kyushu University Fukuoka Japan Research & Development Group Hitachi Ltd. Kanagawa Japan Waseda University Tokyo Japan

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Quantum computers, which utilize the principles of quantum mechanics, are expected to be applied to a wide range of fields. With the advancement of quantum computer development, a lot of quantum software, which enables the operation of quantum computers, has been developed. It has a distinct nature (e.g., superposition and entanglement of qubits) compared to traditional software, leading to the unique challenges of its development. While prior studies have clarified and defined some unique challenges of quantum software, many remain unclear due to limited research. In this study, we conducted an empirical study of Self-Admitted Technical Debt (SATD) for quantum software. SATD is a type of technical debt, a problem in the code that the developer is aware of. Hence, we conjecture that analyzing SATDs can reveal the unique challenges developers face when developing quantum software. We manually coded 202 comments from the Python® files of the 61 open-source quantum software on GitHub®. The 202 comments correspond to a 95% confidence level with a 5% confidence interval, as in previous studies. The results showed that 88 comments (45.6% of all SATD comments) were quantum-specific SATDs (QSATDs), which require knowledge of quantum computation to repay. Furthermore, we propose a taxonomy for QSATDs. This taxonomy, which consists of four main categories and eight subcategories, classifies QSATDs in terms of quantum-specific aspects such as circuit implementation, backend, and algorithms. Our empirical results are beneficial for quantum software developers, helping them understand implementation areas that require attention. For researchers, our results promote further research, including the exploration of challenges in QSATD repayment.

关键词： Computers Quantum computing Codes Taxonomy software algorithms Qubit Quantum mechanics software Quantum circuit software engineering

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Characterizing software Architectural Metrics for Continuous Compliance in the Automotive Domain

Characterizing Software Architectural Metrics for Continuous...

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Working IEEE/IFIP Conference on software Architecture

作者： Domenico Amalfitano Marco De Luca Anna Rita Fasolino Patrizio Pelliccione Tiziano Santilli DIETI University of Naples Federico II Naples Italy Gran Sasso Science Institute (GSSI) L'Aquila Italy

ISBN: (数字)9798350359169

ISBN: (纸本)9798350359176

The software of critical systems, such as automotive, is increasingly required to change and evolve after production. In the automotive domain, this is a consequence of self-driving and connected cars, which continuously collect data from the field that is then exploited to produce safer and more advanced and reliable versions of the used algorithms or AI modules. Consequently, there exists a need for techniques and tools to facilitate incremental and Continuous Compliance with safety and security standards. This paper focuses on software architectural metrics that can be used for Continuous Compliance in the automotive domain. Our initial stride involved a literature review to find metrics capable of assessing software architectures. Subsequently, in collaboration with architecture, safety, and security experts in the automotive domain, we proposed a framework defining the characteristics these metrics must possess for continuous evaluation of software architectural compliance. The framework was used to characterize 48 metrics gathered from the literature review and to associate them with a score expressing their suitability to be used in software architecture Continuous Compliance processes.

关键词： Measurement software architecture Bibliographies software algorithms Production software Safety

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