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The Role of Soft Skills in engineering Education

The Role of Soft Skills in Engineering Education

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Frontiers in Education (FIE) Conference

作者： Marcin Fojcik Martyna Katarzyna Fojcik Adam Ziebinski Bjarte Pollen Anne-Lena Kampen Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway Department of Humanities and Education Volda University Collage Volda Norway Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The rapid but uneven education development worldwide is creating many challenges. Changing conditions and rules in engineering fields such as programming, automation, and electronics are not always followed by changes in education. Employers expect potential candidates to be current with the latest domain competencies and soft skills. The idea is to hire candidates with both knowledge (theoretical and practical) and experience in reflection and collaboration skills. Universities can not always keep up with the development of technology and the adaptation of curricula because change happens so quickly. Therefore, upgrades are needed in engineering education. Focusing education solely on measurable knowledge and results is a simple but often insufficient approach to presenting the entire curriculum within the time frame of a course. Our study of about 150 engineering students in Poland and Norway included the role of students' experiences in hands-on activities, especially labs. The research aimed to determine what students expect, what influences their results, and what complications may arise. The study results show that students often struggle with time management, prioritizing, and understanding practical tasks. In addition, there is a difference between students with and without practical work experience. Our research showed that both groups of students did not achieve all their intended educational goals for various reasons. Therefore, adapting the current teaching system to the student's needs and requirements is important.

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Effective Prediction of Energy Consumption in Automated Guided Vehicles with Recurrent and Convolutional Neural Networks

Effective Prediction of Energy Consumption in Automated Guid...

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IEEE International Conference on Big Data

作者： Pawel Benecki Daniel Kostrzewa Piotr Grzesik Bohdan Shubyn Jia-Hao Syu Jerry Chun-Wei Lin Vaidy Sunderam Dariusz Mrozek Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Computer Science Emory University Atlanta GA USA

Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement values with actual observations. Thus, it is crucial to predict telemetry signals properly. In this paper, we research the prediction of energy consumption using state-of-the-art Artificial Neural Networks architectures (SCINet) compared with other Recurrent Neural Network (RNN) approaches on the data streams acquired from CoBotAGV. We especially focus on the possibility of applying feature weighting. We show that it can improve prediction capabilities. We also investigate resource utilization in terms of time to fit the embedded AGV environment.

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Sectoral Cybersecurity Skills Gap: The Case of Maritime Cybersecurity Certification Training

Sectoral Cybersecurity Skills Gap: The Case of Maritime Cybe...

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IEEE International Conference on engineering, Technology and Innovation

作者： Pinelopi Kyranoudi Nineta Polemi Paresh Rathod Ricardo Gregorio Lugo Sotiris Ioannidis Paulinus Ofem Department of Informatics University of Piraeus Piraeus Greece Department of Informatics University of Piraeus Greece & trustilio BV The Netherlands Cybersecurity Thematic RDI Laurea University of Applied Sciences Espoo Finland Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia School of Electrical and Computer Engineering Technical University of Crete Chania Greece

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Cybersecurity is crucial for businesses as they face a shortage of specialists globally. This paper explores the growing concern of the skills gap in both the private and public sectors, leading to initiatives by the EU, national governments, academia, and industry to address the European skills shortage. This work identifies skill gaps in knowledge areas for the critical sectors including a maritime sector with a particular emphasis on certifications. The focus on certifications stems from the maritime industry's reliance on non-EU certification providers. This paper focuses on providing solutions by bridging the gap in Maritime Cybersecurity, proposing a syllabus of a cybersecurity certification practical training that may serve as a module curricula in a professional training programme.

关键词： Industries Technological innovation Government Europe computer security Certification Faces Business

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors Medical services Feature extraction Regulation

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First steps towards modeling the interaction of cardiovascular agents and smooth muscle activation in arterial walls

First steps towards modeling the interaction of cardiovascul...

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92nd Annual Meeting of the International Association of applied Mathematics and Mechanics, GAMM 2022

作者： Nurani Ramesh, S. Uhlmann, K. Saßmannshausen, L. Rheinbach, O. Klawonn, A. Heinlein, A. Balzani, D. Civil and Environmental Engineering Ruhr University Bochum Universitätsstraße 150 Bochum44801 Germany University of Cologne Department of Mathematics and Computer Science Cologne Germany Technische Universität Bergakademie Freiberg Department of Mathematics and Informatics Freiberg Germany Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

Numerical simulation of the response of healthy and pathological arteries to cardiovascular agents can provide valuable information to the physician in the treatment of diseases such as hypertension, atherosclerosis, and the Marfan syndrome. Here, we provide a first step towards a computational framework to model the effects of antihypertensive agents on the mechanical response of arterial walls. A material model is developed by extending an existing formulation for wall tissue to incorporate the effects of calcium-ion channel blockers. The resulting coupled deformation-diffusion problem is then solved using the finite element method. Simulation results with drug activity show that, indeed, an increased lumen diameter due to reduced contraction is obtained. Additionally, a decrease in the rate of arterial contraction is observed, which is also consistent with expected behavior. Finally, we compare results for an implicit or explicit treatment of the the deformation-diffusion coupling, and we observe that both coupling schemes yield comparable results for a wide range of time step sizes. © 2023 The Authors. Proceedings in applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Diseases

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A Review of Android and iOS Operating System Security

A Review of Android and iOS Operating System Security

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2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022

作者： Khan, Shahnawaz Yusuf, Ammar Haider, Mohammad Thirunavukkarasu, K. Nand, Parma Imam Rahmani, Mohammad Khalid Faculty of Engineering Design and Information and Communications Technology Bahrain Polytechnic Isa Town Bahrain Applied Science University College of Arts and Science Bahrain Saudi Electronic University College of Computing and Informatics Dammam Saudi Arabia Galgotias University Department of Computer Science and Engineering Greater Noida India Sharda University School of Engineering and Technology Greater Noida India Saudi Electronic University College of Computing and Informatics Riyadh11673 Saudi Arabia

ISBN: (数字)9781665469197

ISBN: (纸本)9781665469197

Mobile devices are an inseparable part of our lives. They have made it possible to access all the information and services anywhere at any time. Almost all of the organizations try to provide a mobile device-based solution to its users. However, this convenience has arisen the risk of losing personal information and has increased the threat to security. It has been observed recently that some of the mobile device manufacturers and mobile apps developers have lost the private information of their users to hackers. It has risen a great concern among mobile device users about their personal information. Android and iOS are the major operating systems for mobile devices and share over 99% of the mobile device market. This research aims to conduct a comparative analysis of the security of the components in the Android and iOS operating systems. It analyses the security from several perspectives such as memory randomization, application sandboxing, isolation, encryption, built-in antivirus, and data storage. From the analysis, it is evident that iOS is more secure than Android operating system. However, this security comes with a cost of losing the freedom. © 2022 IEEE.

关键词： Android (operating system)

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An application of some machine learning methods for biological data modeling 15

An application of some machine learning methods for biologic...

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15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria

The development of fast and reliable methods for predicting the biological activity of the substances in computational biology is of a great importance. This improves the development of some new compounds while keeping costs low. Among many scientists, an attractive target for docking experiments is the Delta-opioid receptor (DOR) and delta-opioid ligands (DOL). Their biological efficacy can be measured by various techniques, which could facilitate the establishment of the relationship between the structure of the compounds and their biological effect. The relationship between the results of the computer experiments and the biological activity of these compounds is modelled by using machine learning regressors. The primary goal of this study is to determine the most appropriate neural network for modelling the relationship between in vitro and in silico results for DOR and delta-opioid ligands. © Published under licence by IOP Publishing Ltd.

关键词： Bioactivity

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