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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3233 条记录，以下是371-380 订阅

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Analyzing a Semantics-Aware Bug Seeding Tool's Efficacy: A qualitative study with the SemSeed tool 23

Analyzing a Semantics-Aware Bug Seeding Tool's Efficacy: A q...

引用

37th Brazilian Symposium on software engineering, SBES 2023, held in conjunction with the Brazilian conference on software: Theory and Practice, CBSoft 2023

作者： Martins, ViníCius Ramalho, Camila T. Marques, Lucas Cordeiro Pereira, Juliana Alves Garcia, Alessandro Lucena, Carlos Feijó, Bruno Furtado, Antonio L. Rio de Janeiro Brazil

ISBN: (纸本)9798400707872

software developers can benefit from machine learning solutions to predict bugs. machine learning solutions usually require a lot of data to train a model in order to achieve reliable results. In this context, developers use bug-seeding approaches to generate synthetic bugs, which should be similar to human-made bugs. A recent state-of-the-art tool, called SemSeed, uses a semantics-aware bug seeding approach in order to hopefully achieve more realistic bugs. In this study, we report on the investigation of SemSeed's efficacy. We create a survey that shows developers a bug and asks whether it is a Real or Synthetic bug. We collected and analyzed the answers from 47 developers, and we show that SemSeed can be very accurate in seeding realistic bugs. © 2023 ACM.

关键词： machine learning

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machine learning in the Quantum Era 2

Machine Learning in the Quantum Era

引用

2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Khurana, Sachin Nene, Manisha J SoCE&MS Defence Institute of Advanced Technology Pune India

ISBN: (纸本)9798350308846

machine learning (ML) has been extensively utilized in various scientific and engineering domains. But the inherent constraints and computational complexity that arise in classical machine learning are particularly evident when dealing with large-scale, high-dimensional datasets or when attempting to solve intricate optimisation problems. Quantum systems exhibit unconventional patterns that are typically not effectively generated by traditional systems and can also tackle high dimensional and large size datasets efficiently. Therefore, it is believed that quantum computing systems may outperform traditional computing devices in machine learning tasks. Quantum machine learning (QML) has emerged as a rapidly developing field that combines the concepts of quantum mechanics and machine learning to solve complex problems across various disciplines. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. However, the major challenges encountered are hardware limitations, noise, and development of quantum algorithms. The objective of this paper is to provide an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML. As data continues to grow in size and complexity, together with the emergence of nonlinear and high-dimensional real-world situations, the computing benefits offered by QML have the potential to facilitate significant advancements in the fields of healthcare, finance, robotics, logistics, communication, and cyber security. This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning. © 2023 IEEE.

关键词： Algorithms machine learning Quantum Computing

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The Assessment of the Design Risk for ORAN

The Assessment of the Design Risk for ORAN

引用

2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Shiang-Jiun Chang, YaHan Raksasook, Nattapol Wu, Wei-Liang National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology International Electrical Engineering and Computer Science Taipei Taiwan University of Taipei Department of Mathematics Taiwan

ISBN: (纸本)9798350324174

The Open Radio Access Network (O-RAN) uses a virtualization architecture to disaggregate components and connect them through open interfaces, and also uses software defined networking (SDN) technology for decoupling. The O-RAN architecture is an innovative telecommunication architecture, which also brings some potential problems for security, including but not limited to the followings: new architecture (disaggregation, Real Time RAN intelligent controllers (Near-RT RIC), virtualization), the open interfaces (O1, A1, and E2, etc.), artificial intelligent and machine learning (AIML) workflow, and standardization issues. Facing the new structure, in addition to the original risks, new risks also need to be considered. "Secure in design"is imperative to mitigate the information security risks. This study will look at the risk assessment of O-RAN from the perspective of testing and do security analysis. © 2023 IEEE.

关键词： machine learning

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Yet Another Model! A Study on Model's Similarities for Defect and Code Smells 1

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26th international conference on Fundamental Approaches to software engineering (FASE) Held as Part of the European Joint conferences on Theory and Practice of software (ETAPS)

作者： Santos, Geanderson Santana, Amanda Vale, Gustavo Figueiredo, Eduardo Univ Fed Minas Gerais Belo Horizonte MG Brazil Saarland Univ Saarbrucken Germany

ISBN: (数字)9783031308260

ISBN: (纸本)9783031308253;9783031308260

software defect and code smell prediction help developers identify problems in the code and fix them before they degrade the quality or the user experience. The prediction of software defects and code smells is challenging, since it involves many factors inherent to the development process. Many studies propose machine learning models for defects and code smells. However, we have not found studies that explore and compare these machine learning models, nor that focus on the explainability of the models. This analysis allows us to verify which features and quality attributes influence software defects and code smells. Hence, developers can use this information to predict if a class may be faulty or smelly through the evaluation of a few features and quality attributes. In this study, we fill this gap by comparing machine learning models for predicting defects and seven code smells. We trained in a dataset composed of 19,024 classes and 70 software features that range from different quality attributes extracted from 14 Java open-source projects. We then ensemble five machine learning models and employed explainability concepts to explore the redundancies in the models using the top-10 software features and quality attributes that are known to contribute to the defects and code smell predictions. Furthermore, we conclude that although the quality attributes vary among the models, the complexity, documentation, and size are the most relevant. More specifically, Nesting Level Else-If is the only software feature relevant to all models.

关键词： Defect Prediction Code Smells Detection Explainable machine learning Quality Attributes

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Using machine learning for Efficient Smishing Detection

Using Machine Learning for Efficient Smishing Detection

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2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Sheng-Shan Sun, Chin-Yu Pai, Tun-Wen National Taipei University of Technology Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

The prevalence of mobile phones and messaging apps has made smishing attacks a growing concern. Attackers use smishing to deceive individuals into revealing sensitive information, downloading malicious software, or performing other harmful actions. These disregarded responses cause significant financial loss to individuals or organizations. We proposed using a machine learning model to analyze message contents and detect phishing URLs. Our experiments collected and manually annotated text messages from mobile phones, and both natural language processing techniques and machine-learning algorithms were applied to the dataset simultaneously. The results show that our proposed method achieved an F1 score of 94%, showing high effectiveness and efficiency in accurately detecting Smishing attacks. © 2023 IEEE.

关键词： Cybersecurity

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Graphic Model Features of Distribution Network Electrical engineering Based on machine learning Algorithm 1

Graphic Model Features of Distribution Network Electrical En...

引用

1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Weichao, Wang Li, Xiang Haisheng, Hong Guang-zhou Power Supply Bureau Guangdong Power Grid Co. Ltd Guang-dong China

ISBN: (纸本)9798350323795

With the rapid development of modern society, the power load is also growing rapidly. Whether in terms of power supply quality or power supply service, users have various demands for power. As the direct service object of electric energy, power users' outage situation and power consumption feeling will largely reflect the power supply problem in the substation area. It is of great significance to improve the quality of power supply and the satisfaction of power customers if we can combine the demands of users with the operation of distribution transformers and explore the power laws therein. The feature of electrical engineering graphic model of distribution network based on machine learning algorithm is a software that helps to create and manage distribution network with real-time data. It provides graphic models for electrical engineering students to learn different types of networks. The software can be used to design, analyze and simulate various distribution networks, such as bus, star topology, ring topology, etc. It also provides the operation of these networks under different operating conditions, such as voltage drop caused by resistance and capacitance values. © 2023 IEEE.

关键词： Electric loads

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A Multi-label Classification Method for Positive and Unlabeled Dataset. 4

A Multi-label Classification Method for Positive and Unlabel...

引用

4th Asia Service Sciences and software engineering conference, ASSE 2023

作者： Takahashi, Hikaru Qiangfu, Zhao University of Aizu Computer Science and Engineering Department Major in Computer Information System Japan University of Aizu Computer Science and Engineering Department Japan

ISBN: (纸本)9798400708534

Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classification problem. However, previously proposed methods assume a strict condition. In this paper, we first investigate if the condition can be satisfied in real world situations and then propose a normalized weighted method which can relax the difficultly of PU learning. © 2023 ACM.

关键词： machine learning

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Enhancing Trojan Detection using machine learning: Comparative Analysis of Classifier Performance on Embedded Hardware 9

Enhancing Trojan Detection using Machine Learning: Comparati...

引用

9th international conference on Signal Processing and Communication, ICSC 2023

作者： Parmar, Roshani Gajjar, Nagendra Dept of Electronics and Communication Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

ISBN: (纸本)9798350383201

In today's connected world, malware attacks, especially Trojan attacks are rapidly rising. A Trojan is a form of malware that takes on a legitimate piece of software but is malicious and uses the victim's device to carry out unauthorized tasks. Researchers have created several detecting approaches utilizing machine learning to defend against Trojan assaults. This work compares and assesses the effectiveness of six alternative Trojan detection classifiers using machine learning. The following six classifiers will be utilized: Naive Bayes, Support Vector machine, Decision Tree, Random Forest, Neural Network, and Logistic Regression. Each classifier will be trained and tested using benign and Trojan program samples from a publicly accessible Dataset. This paper will assess the classifiers based on the accuracy, precision, recall, and F1 score. The experimentation on the Nvidia Jetson Nano and Raspberry pi hardware platforms to see if the suggested technique can be implemented in resource-constrained settings. The research analyzes different classifiers' abilities to identify Trojans in machine learning models. The results offer helpful insights for safeguarding machine learning systems in edge computing environments by demonstrating the potency of the suggested technique in identifying Trojans on both Nvidia Jetson Nano and Raspberry Pi. © 2023 IEEE.

关键词： Malware

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A Priori Lane Selection Strategy for Reinforcement learning of Dynamic Expressway Tolling

A Priori Lane Selection Strategy for Reinforcement Learning ...

引用

2023 international conference on Pattern Recognition, machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Zhang, Xi Wang, Wei Chen, Jing Yunnan University School of Software Kunming China Yunnan University School of Information Science and Engineering Kunming China

ISBN: (纸本)9798350346596

Dynamic tolling of toll roads is a way to dynamically adjust the toll rates according to the changing road traffic conditions in order to alleviate traffic congestion and improve commuting efficiency. Aiming at the dynamic toll collection problem of Chinese expressway, we design a reinforcement learning simulation environment for China's expressway network and propose a reinforcement learning dynamic toll model based on a priori lane selection strategy that adapts to the characteristics of the network and travelers' travel habits. Experiments show that the reinforcement learning-based dynamic tolling can increase the total revenue by more than 10% compared with the fixed- rate tolling scheme and keep the congestion rate at a low level. In addition, the ablation experiments demonstrate that the priori knowledge-based lane selection model can better weigh the "total revenue", "system throughput"and "total system travel time"of the optimized road network under the joint optimization objective © 2023 IEEE.

关键词： Reinforcement learning

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Proceedings - 2023 IEEE/ACM 11th international conference on Formal Methods in software engineering, FormaliSE 2023

Proceedings - 2023 IEEE/ACM 11th International Conference on...

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11th IEEE/ACM international conference on Formal Methods in software engineering, FormaliSE 2023

ISBN: (纸本)9798350312638

The proceedings contain 14 papers. The topics discussed include: goal controller synthesis for self-adaptive systems;verifying binary neural networks on continuous input space using star reachability;explainable human-machine teaming using model checking and interpretable machine learning;contract-based specification refinement and repair for mission planning;patch specifications via product programs;an empirical study assessing software modeling in alloy;mutant equivalence as monotonicity in parametric timed games;differential testing of a verification framework for compiler optimizations (case study);formalizing path explosion for recursive functions via asymptotic path complexity;a Dafny-based approach to thread-local information flow analysis;transparent actor model;using scylindric algebra to support local variables in rely/guarantee concurrency;a formal approach to the verification of protection systems in low-voltage distribution grids;and a verified UAV flight plan generator.

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