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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Khattab, Mohammad Ismail Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

During a software development process, a customer and the development team need to communicate and understand each other. Poor communication between a customer and the development team is one of the most common challenges in the software development process, which leads to software failures and further increases the budget. In our practical experience and from the literature, we found that lack of customer involvement, no common communication language, complex business processes, end-user resistance toward system development, lack of trust, lack of leadership, and and end-user capacity are the main challenges toward the development team understanding. We bring six newly discovered patterns that address these problems and improve the understanding between a customer and the development team during the software development process. We also bring some of the known organizational patterns that also mitigate these problems into relation to the newly discovered patterns. To create a pattern language from these patterns, we illustrated the sequences of the patterns so that the development team and customer could understand each other. © 2022 ACM.

关键词： software design

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Toward Organizational Pattern Ontology 27

Toward Organizational Pattern Ontology

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Waseeb, Shakirullah Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Slovakia

ISBN: (纸本)9781450395946

Organizational patterns of agile software development are proven practices for dealing organizational principles. Finding and selecting the right pattern is difficult. One way to select a pattern is to follow the sequence and compositions given in a pattern language. However, in general, pattern languages do not always reveal every reliable connection. Patterns are described in informal and unstructured text, making it difficult to understand their connections. This work attempts to provide a conceptual ontology model for organizational patterns-describing the collection of concepts (terms) and their relations. This contribution can be an attempt to express, expose, and share semantic knowledge between patterns using ontology. The resulting ontology can be used on top of the organizational patterns repositories to help retrieve patterns based on their logical connections. © 2022 ACM.

关键词： Semantics

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Do Data Scientists Dream About Their Skills' Assessment? - Transforming a Competition Platform Into an Assessment Platform

Do Data Scientists Dream About Their Skills' Assessment? - T...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Slȩzak, Dominik Janusz, Andrzej Swiechowski, MacIej Chądzynska-Krasowska, Agnieszka Kaminski, Jacek Qed Software Poland University of Warsaw Institute of Informatics Poland Polish-Japanese Academy of Information Technology Poland Qut School of Information Systems Australia

ISBN: (纸本)9798350362480

We present a platform for automatic assessment of technical data science skills (hard skills) and competencies that help to apply those technical skills in practice (soft skills). The platform serves so-called assessment platform tasks that resemble data-focused tasks typical for online data science competitions. Actually, these tasks are designed based on international conference competitions that we have been organizing on our competition platform ***. The main idea relies on our observation that the given person's behavior during a competition (dynamics of submitting solutions, activity on competition forum, etc.) can be correlated with his/her soft competencies. Accordingly, our goal is to translate the specifics of international conference competitions into a framework that investigates the behaviors of data science job candidates, employees who want to improve their work in data science projects, students who wish to build their future professional careers on AI solutions, etc. We claim that such behaviors - if properly measured based on solving our assessment platform tasks - can effectively indicate both hard and soft types of competencies. © 2024 IEEE.

关键词： Data Science

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A New RISC-V CPU for Safety-Critical systems 12

A New RISC-V CPU for Safety-Critical Systems

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava842 16 Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Bratislava842 16 Slovakia

ISBN: (纸本)9798350322910

In this paper, we propose a novel hardening technique against Single Event Effects, which enables high-frequency operation and does not cause large power consumption and area overhead. The protection is based on redundancy, separation of the pipeline into two main sections, and is fully applicable at Register Transfer Level. It provides fast detection of faults, simple recovery by a flush of the pipeline, and allows a large prediction unit to be unprotected. We present the protection scheme at the core with the RISC-V instruction set. Simulations confirm that the protection can handle the injected faults. Synthesis shows that protection lowers the maximum frequency by only 3.9%. The area increase is only 108% and can be even lower. Total power consumption increased by 119%. © 2023 IEEE.

关键词： Pipelines

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Predicting Students Performance Using Data Mining Approach (Case Study: IT Del)

Predicting Students Performance Using Data Mining Approach (...

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2023 IEEE International Conference on Data and software engineering, ICoDSE 2023

作者： Bonar Sirait, Jaime Christ Togatorop, Parmonangan R. Tambunan, Paulus M.L.P Yanti Marpaung, Yemima Febe Situmeang, Samuel I.G Simanjuntak, Humasak T.A Faculty of Informatics and Electrical Engineering Institute of Del Technology Department of Information Systems Indonesia Faculty of Informatics and Electrical Engineering Institute of Del Technology Lecturer in Department of Information Systems Indonesia

ISBN: (纸本)9798350381382

Predicting student performance stands as a critical task to comprehensively assess their academic achievements, whether they classified as the category of good, sufficient, or bad. The outcome of a student's final exam can be influenced by a multitude of factors, among which the quality of the learning process and teaching methodology hold considerable significance. Recognizing the intricate interplay of these factors, it becomes evident that the integration of a satisfaction survey becomes imperative. In the realm of this research, we harness the potential embedded within both student satisfaction survey data and their ultimate academic grades. Overcoming existing challenges by an imbalanced dataset, we employ a holistic approach by utilizing the Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) to rectify the skewed distribution of data. To further enhance prediction capabilities, we introduced an innovative technique known as Deep Feature Synthesis. This advanced approach generating 36 novel features. The culmination of our research underscores the superiority of the random forest algorithm, especially when enriched by the harmonious merging of Deep Feature Synthesis and SVM-SMOTE through the strategic implementation of Random Search. Notably, our model achieves an impressive accuracy rate of 69%, complemented by 47% precision, 52% recall, and 47% F1-score. copy;2023 IEEE. © 2023 IEEE.

关键词： Support vector machines

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Utilizing Organizational Patterns to Build High-Performing Remote Teams 28

Utilizing Organizational Patterns to Build High-Performing R...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Toldy, Kristián Khail, Waheedullah Sulaiman Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien Energie Gmbh Germany

ISBN: (纸本)9798400700408

In recent years, remote work has become a more popular option for companies across various industries. While remote work provides numerous benefits, such as flexibility and increased work-life balance, it also presents unique challenges that can hinder team collaboration, productivity, and overall success. In order to overcome these obstacles, it is essential to adopt effective organizational patterns that can help build high-performing remote teams. Building a high-performing remote team requires a set of strategies with some key focus points to be kept in mind such as communication, trust, goals, collaboration, and performance. For all of these, there have been an extensive research and publication of observed patterns to which we have also contributed with a new set of these four patterns: Use Common Language, Discuss Publicly, Lights, Camera, Action!, and Praise in Public, Correct in Private. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Computer programming

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Decentralized control in active distribution grids via supervised and reinforcement learning

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Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

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International Journal of systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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