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Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Yuki Fushihara Hirohisa Aman Sousuke Amasaki Tomoyuki Yokogawa Minoru Kawahara Department of Computer Science Faculty of Engineering Ehime University Ehime Japan Center for Information Technology Ehime University Ehime Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Japan

Software testing is an essential activity for developing and maintaining high-quality software. Unit testing with test code (test cases) is a fundamental testing activity, and developers can test their production code whenever they create or modify the code. However, such quality assurance relies on the correctness of the test code. If a test code had a flaw, it would mislead the developers about the hidden faults and prevent early detection of the faults. This paper focuses on "test smells," which may cause test code flaws in Python programs, and analyzes their changing trends over commit history (code changes) toward better Python test code management. Through an empirical data analysis of 100 open-source projects, the paper reports the following findings: (1) a few kinds of test smells constitute the majority of smells detected in the studied projects, and (2) most kinds of smells tend to increase over commits, i.e., many test smells are likely to have remained in test code as technical debt.

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Design and Implementation of Blockchain-Based Smart Contracts for Enhancing Ancillary Services Management in Electricity Markets 10

Design and Implementation of Blockchain-Based Smart Contract...

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10th IEEE International Conference on Power and Energy, PECon 2024

作者： bt Junaidi, Nazreen Abdullah, Md Pauzi Shaaban, Mohamed Hashim, Nurul Izzati bt Abdullah, Johari B. Fazari, Atiq Firdaus bin Department of Electrical and Electronics Engineering University of Malaysia Sarawak Sarawak Kota Samarahan94300 Malaysia Faculty of Electrical Engineering University Teknologi Malaysia Johor Bharu Johor81310 UTM Malaysia Department of Electrical Engineering National Chung Cheng University Minxiong Chiayi621301 Taiwan Faculty of Computer Science and Information Technology University of Malaysia Sarawak Kota Sarawak Samarahan94300 Malaysia

ISBN: (纸本)9798350361971

This paper presents a possible solution to the challenges of managing ancillary services on power systems, focusing on the development and execution of robust smart contracts on the Ethereum blockchain. These contracts automate essential procedures like bid submission, Market Clearing Price (MCP) calculation, and settlement, reducing manual involvement and improving operational efficiency. They ensure transparency and accountability through security mechanisms like role-based access restriction and event monitoring. The contracts were created using Solidity programming language and implemented on the Ethereum Sepolia test network. Unit tests using Foundry confirmed the functionality of each contract component, indicating their accuracy and effectiveness. The study highlights the novel features of the smart contract architecture and its potential impact on ancillary service administration. ©2024 IEEE.

关键词： Smart contract

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Intelligent Energy Management Energy Storage systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

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Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Chinnaraj Govindasamy T.R Nisha Dayana Siddhika Dilip Adkar D. Shyam Prakash A. Shilpa Gupta Sanjeev Kumar Trivedi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science Vels Institute of Science Technology and Advance Studies Chennai School of Computer Science Engineering and Applications D Y Patil International University Akurdi Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad Department of Electronics Faculty of Engineering and Technology Khwaja Moinuddin chishti Language University Lucknow

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts.

关键词： Performance evaluation Cloud computing Power demand Electricity Stability criteria Machine learning Load management

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Efficient Coordination and Synchronization of Multi-Robot systems Under Recurring Linear Temporal Logic

arXiv

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arXiv 2025年

作者： Peron, Davide Fernandez-Ayala, Victor Nan Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Department of Information Engineering University of Padova Padova35122 Italy Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work. © 2025, CC BY.

关键词： Multipurpose robots

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Emergency Vehicle Detection Using Deep ConvNet2D and computer Vision

Emergency Vehicle Detection Using Deep ConvNet2D and Compute...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Elumalaivasan Poongavanam Prathik. A Munirathinam. T Kayalvizhi. V Rajesh. R Mahavishnu. C Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Sona College of Technology Salem India School of Computer Science and Engineering and Information Science Presidency University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Shreenivasa Engineering College Dharmapuri India

A surveillance system detects emergency vehicles stuck in traffic. This system helps manage traffic because the number of vehicles on the road has been increasing daily for years, causing congestion. This project implements Deep ConvNet2D (Convolutional Network 2D) and computer Vision emergency vehicle recognition. We propose a CNN-based real-time image processing model for emergency vehicle detection. The signal control unit can be set to terminate the round robin sequence when an emergency vehicle is detected. A CNN trained on Indian ambulance images solves the problem. Tensor Flow, a Python library, was used for training. Our method detects and classifies emergency cars well. Existing systems use ANN algorithm, which is inaccurate and inefficient. The system uses Deep ConvNet2D Algorithm. The proposed real-time system is accurate. The proposed system loads and executes faster than the existing system. The system is efficient, scalable, and enhanced for complex use cases.

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Real-Time Implementation of Quadrotor UAV control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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Practical Acoustic Eavesdropping On Typed Passphrases

arXiv

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arXiv 2025年

作者： Fürst, Darren Aßmuth, Andreas Department of Electrical Engineering Media and Computer Science Ostbayerische Technische Hochschule Amberg-Weiden Amberg Germany Faculty of Computer Science and Electrical Engineering Kiel University of Applied Sciences Kiel Germany

Cloud services have become an essential infrastructure for enterprises and individuals. Access to these cloud services is typically governed by Identity and Access Management systems, where user authentication often relies on passwords. While best practices dictate the implementation of multi-factor authentication, it’s a reality that many such users remain solely protected by passwords. This reliance on passwords creates a significant vulnerability, as these credentials can be compromised through various means, including side-channel attacks. This paper exploits keyboard acoustic emanations to infer typed natural language passphrases via unsupervised learning, necessitating no previous training data. Whilst this work focuses on short passphrases, it is also applicable to longer messages, such as confidential emails, where the margin for error is much greater, than with passphrases, making the attack even more effective in such a setting. Unlike traditional attacks that require physical access to the target device, acoustic side-channel attacks can be executed within the vicinity, without the user’s knowledge, offering a worthwhile avenue for malicious actors. Our findings replicate and extend previous work, confirming that cross-correlation audio preprocessing outperforms methods like mel-frequency-cepstral coefficients and fast-fourier transforms in keystroke clustering. Moreover, we show that partial passphrase recovery through clustering and a dictionary attack can enable faster than brute-force attacks, further emphasizing the risks posed by this attack vector. Copyright © 2025, The Authors. All rights reserved.

关键词： Unsupervised learning

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Multi-Agent System Based on Stacking Technique for Rice Yield Prediction

Multi-Agent System Based on Stacking Technique for Rice Yiel...

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Electronic engineering (ICEEM), International Conference on

作者： Hayam R. Seireg Yasser M. Omar Ahmed Elmahalawy Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science Faculty of Computing and Information Technology AASTMT Cairo Egypt

Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA.

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An AOP-Based Security Verification Environment for KECCAK Hash Algorithm

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computers, Materials & Continua 2022年第11期73卷 4051-4066页

作者： Hassen Mestiri Imen Barraj Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj 11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia Electrical Engineering Department Systems Integration&Emerging Energies(SI2E)National Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

Robustness of the electronic cryptographic devices against fault injection attacks is a great concern to ensure *** to significant resource constraints,these devices are limited in their *** increasing complexity of cryptographic devices necessitates the development of a fast simulation environment capable of performing security tests against fault injection *** is a good choice for Electronic System Level(ESL)modeling since it enables models to run at a faster *** enable fault injection and detection inside a SystemC cryptographic model,however,the model’s source code must be *** altering the source code,Aspect-Oriented Programming(AOP)may be used to evaluate the robustness of cryptographic *** might replace conventional cryptanalysis methods in the real *** the ESL,we discuss a unique technique for simulating security fault attacks on cryptographic *** current study presents a fault injection/detection environment for assessing the KECCAK SystemC model’s resistance against fault injection *** approach of injecting faults into KECCAK SystemC model is accomplished via the use of weaving faults in AspectC++based on AOP programming *** confirm our technique by applying it to two scenarios using a SystemC KECCAK hash algorithm case study:The first concerns discuss the effect of the AOP on fault detection capabilities,while the second concerns discuss the effect of the AOP on simulation time and executable file *** simulation results demonstrate that this technique is fully capable of evaluating the fault injection resistance of a KECCAK *** demonstrate that AOP has a negligible effect on simulation time and executable file size.

关键词： Aspect-Oriented Programming SystemC AspectC++ cryptographic circuits KECCAK fault detection fault attacks

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Kumar, Brajesh Agrawal, Prateek Uike, Dipesh Lourens, Melanie Bhati, Indira Peter, M. Chandrakumar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India India Dr. Ambedkar Institute of management studies and research Maharashtra Nagpur India Durban University of Technology Faculty of Management Sciences South Africa Department of Management India PeriyarManiammai Institute of Science and Technology Department of Software Engineering Tamilnadu Thanjavur India

ISBN: (纸本)9798331543617

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems. © 2024 IEEE.

关键词： Predictive analytics

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