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arXiv 2024年

作者： López-Fernández, Daniel Gordillo, Aldo Pérez, Jennifer Tovar, Edmundo Computer Science Department Universidad Politécnica de Madrid Madrid Spain Computer Languages and Systems and Software Engineering Department Universidad Politécnica de Madrid Madrid Spain

Contribution: This article analyzes learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test and questionnaire) with the purpose of comparing the effectiveness of Game-Based Learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software design. Findings: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students’ perceptions were more positive when a face-to-face format was used. Copyright © 2024, The Authors. All rights reserved.

关键词： Motivation

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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Data spaces as enablers for sustainability

Data spaces as enablers for sustainability

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Hoppe, Christoph Schmelzer, Robert Möller, Frederik Schoormann, Thorsten Fraunhofer Institute for Software and Systems Engineering Industrial Manufacturing Speicherstraße 6 Dortmund44147 Germany Technische Universität Chemnitz Business Informatics Straße der Nationen 62 Chemnitz09111 Germany Technische Universität Braunschweig Institute for Business Informatics Rebenring 58a Braunschweig38106 Germany Universität Hildesheim Department for Information Systems Samelsonplatz Hildesheim31141 Germany

ISBN: (纸本)9783885797319

One of our society's most fundamental challenges is promoting sustainable development. Data sharing across organizations is one way to spur innovation and address the Sustainable Development Goals (SDGs) through optimizing resource utilization, fostering circular supply chains, and producing accurate information about CO2 emissions. However, organizations often hesitate to share data given a range of concerns, including the fear of data misappropriation or the lack of control over what others do with their data. Data spaces as a novel artifact seek to tackle these issues by providing an integrated data management approach upholding data sovereignty. These spaces can boost sustainable development by being a secure and trusted digital infrastructure for organizations to share data for sustainable purposes. To disclose how this digital solution fosters sustainability, we present a set of 65 potentials of data spaces along with the dimensions of ecological, economic, and social sustainability. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Sustainable development

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Strategies for Semi-Automated Registration of Historic Aerial Photographs Utilizing Street and Roof Segmentations as Durable Landmarks 34

Strategies for Semi-Automated Registration of Historic Aeria...

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34th European Modeling and Simulation Symposium, EMSS 2022

作者： Zwettler, Gerald A. Ono, Yuta Stradner, Michael Praschl, Christoph Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Graduate School of Software and Information Science Iwate Prefectural University Japan Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria

Historical and current aerial photographs are only of great value if the geolocation or address of the photographed areas is also available. In Western Europe, especially Austria, Germany and Czech Republic, there is a market for the sale of aerial photographs of one’s own private residential building. Automated geolocation is a feasible way to enable the sales agents to assign the addresses for the sale more quickly. In the course of this research work, a process chain is modeled that allows the assignment of aerial photographs to residential addresses using machine vision. After model-based rectifying the aerial images to compensate for perspective distortions, larger image blocks get assembled using image stitching. The assignment to a 2D reference map, such as satellite imagery via Google Maps, is done by applying a U-Net CNN after extracting durable image features such as roads or buildings. The mapping of aerial imagery to two-dimensional cartography is either automated via registration approaches or based on manually placed corresponding landmarks and homography. Test runs on imagery between the years 1969 and 2020 show that the labor-intensive process of geolocation of aerial imagery can be solved by the proposed process model in a hybrid way. © 2022 The Authors.

关键词： Antennas

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Towards an Automated Process for Adaptive Modelling of Orthoses and Shoe Insoles in Additive Manufacturing 12

Towards an Automated Process for Adaptive Modelling of Ortho...

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12th International Workshop on Innovative Simulation for Health Care, IWISH 2023

作者： Zwettler, Gerald A. Trixner, Martin Schartmüller, Clemens Bauernfeind, Sophie Stockinger, Thomas Praschl, Christoph Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Granitweg 1 Kirchschlag bei Linz4202 Austria Pulvermühlstrasse 3 Linz4040 Austria

ISBN: (纸本)9788885741966

Although orthopedics is becoming increasingly important as a medical domain, especially in emerging countries, the level of automation is still marginal and hardly any Industry 4.0 paradigms have been implemented. In this scientific work, solution concepts for holistic process automation in orthopedics are introduced so that prosthetic covers and orthoses for different body regions can be automated by using AI and evaluated with sensor networks. In this process, body scan models are adapted to the conditions of the anatomy or prosthesis models, so that stability as well as fitting accuracy are given in comparison with the other half of the body. Automation in the field of orthopedics leads not only to a significant reduction in costs but can also help to close the research gap regarding objectifiability of results. The first partial aspects have already been successfully implemented for leg prostheses, arm prostheses and shoe insoles with the aid of machine learning processes and physical models for elastic form fitting. As soon as the overall process has been realized, the applicability will be validated in the following year of the project by means of clinical studies and evaluated by utilizing sensor networks for pressure and temperature measurements. © 2023 The Authors.

关键词： Sensor networks

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M3bunet:Mobile Mean Max Unet for Pancreas Segmentation on Ct-Scans

SSRN

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SSRN 2024年

作者： Juwita, Juwita Hassan, Ghulam Mubashar Akhtar, Naveed Datta, Amitava Department of Computer Science and Software Engineering The University of Western Australia Australia School of Computing and Information Systems The University of Melbourne Australia Department of Informatics Syiah Kuala University Banda Aceh Indonesia

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on NIH and MSD pancreas datasets. Our approach demonstrates performance improvement, achieving an average DSC value of up to 89.53±1.82 and IOU score of up to 81.16±0.03 for the NIH, and 88.60 ±1.48 DSC and 79.90±2.19 IOU for the MSD. © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans

arXiv

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arXiv 2024年

Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks. Currently, manual CT scan segmentation by radiologists is prevalent, especially for organs like the pancreas, which requires a high level of domain expertise for reliable segmentation due to factors like small organ size, occlusion, and varying shapes. When resorting to automated pancreas segmentation, these factors translate to limited reliable labeled data to train effective segmentation models. Consequently, the performance of contemporary pancreas segmentation models is still not within acceptable ranges. To improve that, we propose M3BUNet, a fusion of MobileNet and U-Net neural networks, equipped with a novel Mean-Max (MM) attention that operates in two stages to gradually segment pancreas CT images from coarse to fine with mask guidance for object detection. This approach empowers the network to surpass segmentation performance achieved by similar network architectures and achieve results that are on par with complex state-of-the-art methods, all while maintaining a low parameter count. Additionally, we introduce external contour segmentation as a preprocessing step for the coarse stage to assist in the segmentation process through image standardization. For the fine segmentation stage, we found that applying a wavelet decomposition filter to create multi-input images enhances pancreas segmentation performance. We extensively evaluate our approach on the widely known NIH pancreas dataset and MSD pancreas dataset. Our approach demonstrates a considerable performance improvement, achieving an average Dice Similarity Coefficient (DSC) value of up to 89.53%±1.82 and an Intersection Over Union (IOU) score of up to 81.16±0.03% for the NIH pancreas dataset, and 88.60%±1.48 DSC and 79.90%±2.19 IOU for the MSD Pancreas dataset. © 2024, CC BY-NC-SA.

关键词： Computerized tomography

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AI-Driven Cybersecurity and Functional Framework for Remote Laboratories: A software engineering Perspective

AI-Driven Cybersecurity and Functional Framework for Remote ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories Computer architecture Threat assessment Real-time systems Resource management Reliability Computer security Protection Standards software engineering

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Towards an Automated System for Reverse Geocoding of Aerial Photographs 30

Towards an Automated System for Reverse Geocoding of Aerial ...

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30th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2022

作者： Praschl, Christoph Stradner, Michael Ono, Yuta Zwettler, Gerald Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Austria Graduate School of Software and Information Science Iwate Prefectural University Japan Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Austria

ISBN: (纸本)9788086943336

Aerial photographs of buildings are often used as memorabilia sold by trading companies. Such photographs come with an issue regarding the address of the shown buildings, since the recording location of the camera may be known, but shows a spatial distance to the actual subject of the image. In addition to that, also this recording location is often not known in detail but only roughly in the form of the flight route/area. To address this problem, a methodology for reverse geocoding is proposed, allowing to identify the position of buildings that are photographed from aerial vehicles. This is done using a process for extending recording locations and a second process based on the registration of invariant features within aerial shots compared to maps. © 2022 University of West Bohemia. All rights reserved.

关键词： Aerial photography

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Runtime Verification of Autonomous systems Utilizing Digital Twins as a Service

Runtime Verification of Autonomous Systems Utilizing Digital...

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Autonomic Computing and Self-Organizing systems Companion (ACSOS-C), IEEE International Conference on

作者： Morten Haahr Kristensen Alberto Bonizzi Cláudio Gomes Simon Thrane Hansen Carlos Isasa Hannes Iven Eduard Kamburjan Peter Gorm Larsen Martin Leucker Prasad Talasila Valdemar Trøjgård Tang Stefano Tonetta Lars B. Vosteen Thomas Wright Department of Electrical and Computer Engineering Aarhus University Denmark Fondazione Bruno Kessler Italy Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Institute for Software Engineering and Programming Languages Universität zu Lübeck Germany Department of Informatics University of Oslo Norway

ISBN: (数字)9798350389760

ISBN: (纸本)9798350389777

Autonomous systems (AS) enable systems to adapt to drastic and unprecedented environmental changes, a capability that can be enhanced through the utilization of Digital Twins (DTs). However, the additional capabilities of AS come at the cost of explainability, as the expanding adaptation space complicates the reasoning about the system's behavior. For certain types of systems, it is crucial to ensure that specific properties are upheld despite the system's autonomous behavior. To facilitate the monitoring of these properties, we propose the use of Runtime Verification (RV). This tutorial demonstrates the integration of RV tools into the Digital Twins as a Service (DTaaS) platform to monitor and verify the behavior of AS in real-time. By exploring various methods to incorporate RV tools within a DT context, the tutorial aims to advance the application of RV technologies in autonomic computing and self-adaptive system design. Specifically, we demonstrate how the behavior of a self-configuring DT can be verified utilizing RV. This is accomplished through the DTaaS platform, which supports seamless deployment of DT-based AS.

关键词： Runtime Costs Autonomous systems Tutorials Real-time systems Cognition Digital twins Distributed computing Monitoring System analysis and design

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