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7th International Conference on informatics and Computing, ICIC 2022

作者： Hidayati, Anita Ismail, Iklima Ermis Yuly, Ade Rahma Edison, Henry Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Blekinge Institute of Technology Department of Software Engineering Sweden

ISBN: (纸本)9798350345711

software development companies commonly use Global software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.

关键词： software design

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Bringing bare soil detection on-board Intuition-1 through exploiting data-level digital twins 74

Bringing bare soil detection on-board Intuition-1 through ex...

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74th International Astronautical Congress, IAC 2023

作者： Wijata, Agata M. Lakota, Tomasz Cwiek, Marcin Ruszczak, Bogdan Gumiela, Michal Tulczyjew, Lukasz Longépé, Nicolas Nalepa, Jakub KP Labs Gliwice Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Department of Informatics Opole University of Technology Opole Poland Department of Algorithmics and Software Silesian University of Technology Gliwice Poland Frascati Italy

The nature of activity in the agricultural sector has changed over the years due to the growth of the population and the increase in its demand for food, fiber and fuel. The limited availability of agricultural land requires targeted management of resource production and leads to the increasing adoption of precision agriculture approaches. This requires the assessment of soil quality, its hydration, fertilizer content, and the impact of seasonality of ecosystems, which translate into crop yields. Satellite imaging brings enormous scalability in this context, if a satellite captured multi- or hyperspectral imagery (MSI and HSI) that allows us to perform non-invasive in-orbit soil analysis. This helps also reduce the amount of data to transfer back to Earth - instead of sending large-size HSI, we can transmit the extracted soil parameters only. Precise detection of the agricultural area corresponding to the soil in such imagery is important due to the possibility of reducing the amount of data for further processing, to extract various soil parameters, such as potassium, phosphorus pentoxide, magnesium or pH. The detection of the bare soil areas in the MSI and HSI data can be based on the investigation of various vegetation indices (which can later be used to estimate soil parameters or crop yields as well). The use of a multitude of available bands enables soil detection using sensors with different spectral ranges. However, such bands may not be available in real sensors that will fly on-board a satellite. In this paper, we present our approach toward building an algorithm for bare soil detection from HSI which will be captured on-board Intuition-1 - a satellite mission designed to observe the Earth using a hyperspectral instrument and an on-board computing unit capable of processing data using artificial intelligence. We will discuss our approach toward simulating on-board acquisition conditions that we expect to meet once the satellite is in orbit - we exploit dat

关键词： bare soil detection hyperspectral image Intuition-1 multispectral image on-board processing segmentation

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Development of an Intelligent Racking System for early Detection of Abnormal Conditions

Development of an Intelligent Racking System for early Detec...

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International Conference on Radioelektronika

作者： Martin Dobrovolný Jan Fikejz Jiøí Roleček Dept. of Electrical Engineering Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic Dept. of Software Technologies Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic

This paper deals with the design and application of intelligent sensors for monitoring the condition of racking systems. The article briefly describes the current state of the available solutions, which are, however, unsatisfactory for customers. The paper presents the concept and architecture of the proposed solution and the communication links between the components. The solution is based mainly on accelerometer sensors and an evaluation system. Part of the project preparations included the preliminary measurement of accelerometric accelerations during impacts to racking systems. The research and application work will be carried out within the MIT OP TAK project “SmartRack - Research and development of intelligent racking systems for early detection of nonstandard conditions”, which will be solved at the faculty of Electrical engineering and Computer Science of the University of Pardubice in the years 2023-2025.

关键词： Electrical engineering Computer science Accelerometers Surveillance software algorithms Sensor systems Stability analysis

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Options Evaluator With an Artificial Intelligence-Based Volatility Model

Options Evaluator With an Artificial Intelligence-Based Vola...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Árpád Rigó Balázs Tusor John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

The subject of this paper is an options modeling system, which aims to provide the most accurate profit forecast possible for options portfolios in a comprehensible form, as software on the market will misrepresent this in the absence of accurate implied volatility data, which can put trading success at risk. The software determines future implied volatility from 1-year historical options on VXX stock with 6 samples per trading day using statistics and a deep neural network (Long short-term memory LSTM). Using this statistical approach and the trained volatility model, the system calculates the profit/loss curve, thus providing a more accurate picture of the possible future outcomes of a given portfolio.

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Constellation Recognition on Digital Images

Constellation Recognition on Digital Images

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Zsuzsanna Molnár Dániel Kiss John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

In this paper, we present an optimization-based approach for star constellation recognition. The main components of the proposed procedure are the processing of digital images and the minimization of the error in point cloud matching by parameter optimization. In the optimization phase, the Nelder– Mead algorithm, the simulated annealing algorithm, and an evolutionary algorithm were used. Also, the behavior of these methods is evaluated and compared in the paper based on their average error values for different test images. Results on our test dataset showed that the Nelder–Mead simplex algorithm was performing the best in solving the recognition task.

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An Explainable Counterfeit and Genuine Ethiopian Banknote Classification Using Deep Learning

SSRN

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SSRN 2024年

作者： Woldehana, Yared Dereje Yimer, Mohammed Abebe School of Computing and Informatics Dilla University Dilla Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Counterfeiting is a serious crime with serious impact around the world and in Ethiopia in particular. The national bank of Ethiopia has implemented various countermeasures to combat counterfeiting. The most successful counterfeit banknote detectors in use today are cash counters, which are hardware-based systems that use optical and magnetic sensors to detect and validate banknotes. This technology entails a high cost and low availability for the public and small businesses, where we find the biggest cash circulation outside of banks. Existing countermeasures are insufficient to address this critical issue. Advancements in technology, such as digital printing and sophisticated scanning equipment, have made it easier for counterfeiters to mislead their victims by producing banknotes very similar to genuine ones. Only a handful of studies have been conducted on the current Ethiopian banknotes. This study presents an explainable deep learning-based model for the classification of genuine and counterfeit Ethiopian banknotes. The study used transfer learning with SHAP (Shapley Additive explanations) and TF-EXPLAIN (TensorFlow Explain) explainable artificial intelligence frameworks for a better understanding of the classification prediction behind the models. Experimental results show that DENSE121 achieved the best accuracy of 99.87%, and InceptionV3 achieved a very similar result of 99.50%. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Parameter Optimization of a Cellular Automaton Model in Distributed Environment

Parameter Optimization of a Cellular Automaton Model in Dist...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Attila Jancsi Dániel Kiss John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Software Engineering Institute John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

We present a distributed software environment to search for optimal parameters of the Cellular Potts Model using data from cell culture experiments. The software architecture follows a master-worker pattern. The worker nodes are executing simulations of the model with given parameter sets and send the computed objective function values back to the master. The master node implements an optimization method, a genetic algorithm in our case, and a task scheduler to distribute the generated simulation tasks among the workers through the network. Results showed that an optimal combination of parameters can be found for the model, and the efficiency scales linearly with the number of workers.

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Analysis Comparison of Hard and Soft Voting Ensemble Model for Sentiment Analysis on IMDB Movie Reviews

Analysis Comparison of Hard and Soft Voting Ensemble Model f...

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International Conference on informatics and Computing (ICIC)

作者： Nur Ghaniaviyanto Ramadhan Gita Fadila Fitriana Data Science School of Computing Telkom University Bandung Indonesia Software Engineering Faculty of Informatics Telkom University Purwokerto Purwokerto Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

In the context of movie reviews, sentiment analysis plays a crucial role as these reviews often capture the emotions and opinions of viewers regarding the film. With the growing number of reviews across platforms like social media and movie review sites, it becomes feasible to automatically classify them into positive or negative sentiments. However, manually analyzing a dataset of 50,000 reviews is impractical. Therefore, this study focuses on conducting sentiment analysis on the IMDB movie review dataset, consisting of 50,000 entries, using an ensemble voting model. The machine learning models applied in this ensemble include Random Forest (RF) and Adaptive Boosting (AdaBoost). Two test scenarios were employed: the first compared the soft and hard voting approaches, while the second examined the impact of different training-test data splits. The findings indicate that the soft voting ensemble (SVE) model with an 80%-20% training-testing split achieved an accuracy of 88%, an F1-score of 88%, and an AUC of 0.94, which is 4% higher than the hard voting model.

关键词： Radio frequency Sentiment analysis Analytical models Adaptation models Accuracy Reviews Social networking (online) Motion pictures Ensemble learning Random forests

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A Multivocal Review of MLOps Practices, Challenges and Open Issues

arXiv

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arXiv 2024年

作者： Eken, Beyza Pallewatta, Samodha Tran, Nguyen Khoi Tosun, Ayse Babar, Muhammad Ali Faculty of Computer and Information Sciences Sakarya University Turkey CREST - The Centre for Research on Engineering Software Technology The University of Adelaide Australia Faculty of Computer and Informatics Engineering Istanbul Technical University Turkey

With the increasing trend of Machine Learning (ML) enabled software applications, the paradigm of ML Operations (MLOps) has gained tremendous attention of researchers and practitioners. MLOps encompasses the practices and technologies for streamlining the resources and monitoring needs of operationalizing ML models. software development practitioners need access to the detailed and easily understandable knowledge of MLOps workflows, practices, challenges and solutions to effectively and efficiently support the adoption of MLOps. Whilst the academic and industry literature on the MLOps has been growing rapidly, there have been relatively a few attempts at systematically synthesizing and analyzing the vast amount of existing literature of MLOps for improving ease of access and understanding. We conducted a Multivocal Literature Review (MLR) of 150 relevant academic studies and 48 gray literature to provide a comprehensive body of knowledge on MLOps. Through this MLR, we identified the emerging MLOps practices, adoption challenges and solutions related to various areas, including development and operation of complex pipelines, managing production at scale, managing artifacts, and ensuring quality, security, governance, and ethical aspects. We also report the socio-technical aspect of MLOps relating to diverse roles involved and collaboration practices across them through the MLOps lifecycle. We assert that this MLR provides valuable insights to researchers and practitioners seeking to navigate the rapidly evolving landscape of MLOps. We also identify the open issues that need to be addressed in order to advance the current state-of-the-art of MLOps. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Adaptive Learning and Virtual Reality based Conceptual Framework for Dental Training

Adaptive Learning and Virtual Reality based Conceptual Frame...

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International Conference on Computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Artifical Intellegence Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt Department of Software Enginerring Faculty of Informatics and Computer Sceince The British University in Egypt Cairo Egypt

Recently, there has been increasing interest in utilizing virtual reality (VR) technology in dental education and student training. VR based training can provide students with sufficient training and hands-on experience before entering the clinical setting, which in turn helps avoid risks ranging from small errors to severe consequences. However, previous research in this area is limited and none of the previous work proposed a complete, scalable framework for dental students’ education and training. This paper proposes an improved virtual environment for dental training, through an adaptive learning approach, integrated with the VR environment, which is - to the best of our knowledge - the first framework proposed for this purpose. The framework consists of eight modules that aim to provide students with a holistic dental training experience, equipping them with the essential skills and confidence required for their future clinical practice. Furthermore, it aims to improve patient care and safety. In order to guarantee the effectiveness and usability of the proposed framework, a thorough evaluation will be conducted, encompassing both a system perspective, which involves assessing software metrics, as well as evaluating its usability.

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