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Proceedings of the 27th European Conference on Pattern Languages of Programs

作者： Kristian Toldy Waheedullah Sulaiman Khail Valentino Vranić Institute of Informatics / Faculty of Informatics and Information Technologies / Information Systems and Software Engineering Slovak University of Technology in Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien und Energie Gmbh Germany

ISBN: (纸本)9781450395946

People are more and more in a position to work remotely. They face the same problems of establishing and running remote work environments over and over again. In order to help them a bit, six organizational patterns of remote work environments are presented in this paper: Unofficial Meetings, Remote Mentor, Unified Communication Channels, Standard Operating Procedures, Information Hub, and Cut Meetings. The patterns come from a firsthand remote work experience obtained in a company focusing on quality assurance through a crowd testing methodology where software companies get real users to test their newest products.

关键词： organizational patterns agile distributed teams communication meeting remote work

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A Fused Machine Learning Approach for Intrusion Detection System

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Computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Investigating Efficacy of Perplexity in Detecting LLM-Generated Code

arXiv

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arXiv 2024年

作者： Xu, Jinwei Zhang, He Yang, Yanjin Cheng, Zeru Lyu, Jun Liu, Bohan Zhou, Xin Yang, Lanxin Bacchelli, Alberto Chiam, Yin Kia Chiew, Thiam Kian State Key Laboratory of Novel Software Technology Software Institute Nanjing University Nanjing210008 China Department of Informatics University of Zürich Zürich8006 Switzerland Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Large language model-generated code (LLMgCode) has become increasingly prevalent in software development. Many studies report that LLMgCode has more quality and security issues than human-authored code (HaCode). It is common for LLMgCode to mix with HaCode in a code change, while the change is signed by only human developers, without being carefully checked. Many automated methods have been proposed to detect LLMgCode from HaCode, in which the perplexity-based method (PERPLEXITY for short) is the state-of-the-art method. However, the efficacy evaluation of PERPLEXITY has focused on the detection accuracy. In this article, we are interested in whether PERPLEXITY is good enough in a wider range of realistic evaluation settings. To this end, we devise a large-scale dataset that includes 11,664 HaCode snippets and 13,164 LLMgCode snippets, and based on that, we carry out a family of experiments to compare PERPLEXITY against feature-based and pre-training-based methods from three perspectives: (1) detection accuracy in terms of programming language, degree of difficulty, and scale of solution, (2) generalization capability, and (3) inference efficiency. The experimental results show that PERPLEXITY has the best generalization capability while it has low accuracy and efficiency in most cases. Based on the experimental results and detection mechanism of PERPLEXITY, we discuss implications into both the strengths and limitations of PERPLEXITY, e.g., PERPLEXITY is unsuitable for high-level programming languages while it has good interpretability. As the first large-scale investigation on detecting LLMgCode from HaCode, this article provides a wide range of evidence for future improvement. Copyright © 2024, The Authors. All rights reserved.

关键词： High level languages

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A Mathematical Model for Capacity Loss Optimization of Electrical Vehicles Iron-Phosphate-Based Supercharged Batteries Using Bees Algorithm 23

A Mathematical Model for Capacity Loss Optimization of Elect...

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23rd International Middle East Power Systems Conference, MEPCON 2022

作者： Mourad, D. Yousef, A. Sammany, M. Shawa, Z. Steef, A. Atalla, A. University of PSAU Faculty of Eng Department of Electr. Eng Al-Kharj Saudi Arabia University of Helwan Faculty of Tech and Indl. Edu Department of Electr. Tech Cairo Egypt Matareya College of Technology Department of Electrical Cairo Egypt Heliopolis University for Sustainable Development Faculty of Pharmacy Department of Pharmacy Practice Cairo Egypt University of Aleppo Faculty of Information Engineering Department of Software Engineering Aleppo Syria Faculty of Informatics Engineering Idlib Syria Ain Shams University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9781665463638

The recent orientation towards using Electrical Vehicles (EVs), as an alternative to fossil-fuelled- powered vehicles, led to increasing the interest in producing super charged batteries, which is the critical component of EVs and the key of its development and rapid spread. Iron-Phosphate-Based Supercharged Battery (IP-BSBs) has proved its efficiency as a competitor to lead and lithium batteries. Now, it became necessary to increase its efficiency, by the optimum design, to appropriately fit its correspondent vehicle. However, conventional calibration models used to obtain the optimal design parameters often lead to a dramatic waste of time, effort, and resources (cost), without any guarantee to reach the optimal solution. In this paper, a mathematical model is proposed to optimize the capacity loss of IP-BSBs under real manufacturing conditions. The proposed model was solved using meta-heuristic search algorithm represented by Bees Algorithm (BA). Simulation results have shown the precision of our model and the possibility of obtaining an optimal design of IP-BSBs compared to its counterparts of widely existing types. © 2022 IEEE.

关键词： Lithium batteries

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Kco: Balancing Class Distribution in Just-in-Time software Defect Prediction Using Kernel Crossover Oversampling

SSRN

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SSRN 2023年

作者： Ismail, Ahmad Muhaimin Hamid, Siti Hafizah Ab Sani, Asmiza Abdul Daud, Nur Nasuha Mohd Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics University Malaysia Terengganu Kuala Terengganu Terengganu21030 Malaysia

ContextThe performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced *** address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and cross-over interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and *** first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by cross-interpolating different data templates within the selected data *** to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline *** experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results. © 2023, The Authors. All rights reserved.

关键词： Interpolation

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ARIMA vs LSTM on NASDAQ stock exchange data 26

ARIMA vs LSTM on NASDAQ stock exchange data

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Kobiela, Dariusz Krefta, Dawid Król, Weronika Weichbroth, Pawel Gdansk University of Technology Gabriela Narutowicza 11/12 Gdansk80-233 Poland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdansk80-233 Poland

This study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using the relative metric mean square error (MSE) and mean absolute percentage error (MAPE). Selected metrics are typically used in regression problems. The performed analysis shows which model achieves better results by comparing the chosen metrics in different models. It is concluded that the ARIMA model performs better than the LSTM model in terms of using just one feature - historical price values - and predicting more than one time period, using the p, q parameters in the range from 0 to 2, Adam optimizer, tanh activation function, and 2xLSTM layer architecture. The longer the data window period, the better ARIMA performs, and the worse LSTM performs. The comparison of the models was made by comparing the values of the MAPE error. When predicting 30 days, ARIMA is about 3.4 times better than LSTM. When predicting an averaged 3 months, ARIMA is about 1.8 times better than LSTM. When predicting an averaged 9 months, ARIMA is about 2.1 times better than LSTM. © 2022 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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ETHACA - A Container-Based Cyber Range for Research and Education 29

ETHACA - A Container-Based Cyber Range for Research and Educ...

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29th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Chouliaras, Nestoras Kantzavelou, Ioanna Pantziou, Grammati Maglaras, Leandros Chouliara-Sidera, Panagiota Adamakos, Antonis Chalatsakos, Panagiotis University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom University of Thessaly Department of Computer Science and Biomedical Infromatics Lamia Greece Senior Software Engineer at *** Athens Greece Open University of Cyprus Faculty of Pure and Applied Sciences Nicosia Cyprus

ISBN: (纸本)9798350377644

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences. © 2024 IEEE.

关键词： Cyber attacks

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A QoE evaluation of augmented reality for the informational phase of procedure assistance

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Quality and User Experience 2023年第1期8卷 1-13页

作者： Hynes, Eoghan Flynn, Ronan Lee, Brian Murray, Niall Faculty of Engineering and Informatics Technological University of the Shannon: Midlands Midwest Athlone Co. Westmeath Ireland Software Research Institute Technological University of the Shannon: Midlands Midwest Athlone Co. Westmeath Ireland

Augmented reality (AR) is an emerging technology that has significant potential as a solution for novel procedure assistance in mass customisation. Procedure assistance is a series of steps or instructions required to aid a person to complete a task. The informational phase of a procedure is the period when a user is trying to understand instructions, in advance of implementing them. With AR as a potential solution to communicate these steps, it is important to understand the factors that influence user acceptability and experience. In this context, this paper reports the results of a Quality of Experience (QoE) evaluation of two approaches for informational phase assistance: AR procedure assistance and paper-based procedure assistance (control group). Each approach presented a procedure to solve a Rubik’s Cube® in the minimum number of steps. As part of the evaluation methodology of these procedure assistance methods, different metrics were captured. These included the user’s physiological ratings, facial expression features and self-reported measures in terms of affect, task load and QoE. The results show that AR-based assistance yielded significantly reduced procedure completion times and increased success rates compared to paper-based instructions. Several correlations were discovered between physiological and self-reported measures. For example, frustration and mental task load components were seen to correlate to both electrodermal and interbeat interval ratings. The findings from this work will stimulate experimentation and theoretical discussion on the use of physiological ratings and facial expressions as indicators of task load and QoE.

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Efficient Classification of Live Sensor Data on Low-Energy IoT Devices with Simple Machine Learning Methods 19thth

Efficient Classification of Live Sensor Data on Low-Energy...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Hanreich, Martin Krauss, Oliver Zwettler, Gerald Research and Development Research group AIST University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Media Technology and Design University of Applied Sciences Upper Austria Hagenberg4232 Austria Faculty for Informatics Communications and Media Department of Software Engineering University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031829598

This paper addresses the implementation of time series classification on Internet of Things (IoT) devices with minimal resource usage while maintaining required accuracy levels. Although IoT devices have grown more powerful, optimizing resource usage remains essential to reduce costs and extend battery life. This work explores the practical aspects of developing and deploying time series classification models on IoT devices, emphasizing non-neural network models due to their lower resource demands. A detection system utilizing multiple predictions and a voting-based mechanism is constructed to enhance prediction stability. The input data comprises multivariate sensor streams of the same length. All computations are performed on the device, leveraging edge computing principles without external server dependency. This approach demonstrates how to balance accuracy and resource efficiency in IoT-based time series classification, offering a practical framework for similar applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy-Efficiency IoT-Devices Machine Learning Time-Series-Classification

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A Systematic Literature Review on Implementing Non-functional Requirements in Agile software Development: Issues and Facilitating Practices 5th

A Systematic Literature Review on Implementing Non-functiona...

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5th International Conference on Lean and Agile software Development, LASD 2021

作者： Jarzębowicz, Aleksander Weichbroth, Pawel Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering 11/12 Gabriela Narutowicza Street Gdańsk80-233 Poland

ISBN: (纸本)9783030670832

Agile software Development methods have become a widespread approach used by the software industry. Non-functional requirements (NFRs) are often reported to be a problematic issue for such methods. We aimed to identify (within the context of Agile projects): (1) the issues (challenges and problems) reported as affecting the implementation of NFRs;and (2) practices that facilitate the successful implementation of NFRs. We conducted a systematic literature review and processed its results to obtain a comprehensive summary. We were able to present two lists, dedicated to issues and practices, respectively. Most items from both lists, but not all, are related to the requirements engineering area. We found out that the issues reported are mostly related to the common themes of: NFR documentation techniques, NFR traceability, elicitation and communication activities. The facilitating practices mostly cover similar topics and the recommendation is to start focusing on NFRs early in the project. © 2021, Springer Nature Switzerland AG.

关键词： Requirements engineering

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