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14th IEEE Global Engineering Education Conference, EDUCON 2023

作者： Rothe, Irene Kless, Andre Bonn-Rhein-Sieg University of Applied Sciences Dept. of Electrical Engineering Mechanical Engineering and Technical Journalism Sankt Augustin Germany Bonn-Rhein-Sieg University of Applied Sciences Dept. of Computer Science Sankt Augustin Germany

ISBN: (纸本)9798350399431

A multipurpose software tool for building web apps and software-based Open Education Resources, called 'Digital Makerspace' (DMS), was developed in the last 8 years at the applied university and is now ready for use. The tool is useful for creating and designing almost all thinkable material and tools for online usage by web users, for example providing digital learning material and digital interactive exercises. The tool is free for use for everyone and is supposed to be self-explaining. In this paper, the DMS is introduced and as an example multi-purpose learning material for students are shown which were designed via the DMS. © 2023 IEEE.

关键词： E-learning

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Amortized Variational Peak Fitting For Spectroscopic Data 33

Amortized Variational Peak Fitting For Spectroscopic Data

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Hansen, David Frich Alstrom, Tommy Sonne Schmidt, Mikkel N. Technical University of Denmark Richard Petersens Dept. of Applied Mathematics and Computer Science Plads 324 Kgs. Lyngby2800 Denmark

ISBN: (纸本)9798350324112

Spectroscopic analysis relies on identifying and understanding the spectral peaks that represent unique characteristics of an analyte. In high-speed real-time settings, current peak fitting techniques, particularly Bayesian methods involving MCMC or variational approximation, can be prohibitively expensive. We propose an unsupervised method using a convolutional neural network to estimate the number of peaks and their parameters along with posterior uncertainty, by amortizing variational inference in a classical parametric peak model. In a simulated data study, our method reliably determines the number of peaks, precisely estimates parameters similar to direct variational inference, and accurately captures uncertainty comparable to MCMC methods. Our novel, fast, and precise method for Bayesian spectral analysis opens new possibilities in real-time spectral data processing for high-speed monitoring and control. © 2023 IEEE.

关键词： Spectrum analysis

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Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality Technology and Tangible Objects 12

Tangispeak: A Digital Game Based Language Learning System by...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Ongoro, Catherine Akoth Fanjiang, Yong-Yi Hung, Chi-Huang Lin, Bing-Jun Lee, Mu-En Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350340181

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills. © 2023 IEEE.

关键词： Augmented reality

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Model and Procedure for Assessing the Qualification of a Software Developer 23

Model and Procedure for Assessing the Qualification of a Sof...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Gavriliev, Erchimen I. Avdeenko, Tatiana V. Novosibirsk State Technical University Dept. of Theoretical and Applied Computer Science Novosibirsk Russia

ISBN: (纸本)9781665498043

Developers' qualifications and their professional development are the important aspects of IT project's success. Software developers are interested in advancing their careers and improving their skills in order to maintain their level of professional value both for organizations and for the labor market. However, the evaluation of developers' qualifications is frequently based on the subjective opinion of managers, which can negatively affect the further professional development of employees. Quantitative metrics become particularly relevant in the assessment, because they can reduce the level of subjectivity. Therefore, the objective of this work is to develop data model for assessing the qualifications of software developers. The proposed model is based on data from version control system, knowledge management system and task management system. In order to collect information from these systems, as well as to carry out calculations, a decision support system based on the *** software platform was developed. The designed model and system were implemented in a software development company. For the assessment of relations between selected metrics and developers' qualifications a multiple regression analysis was carried, where the employee's qualification rating assessed by the manager was the dependent variable. As a result, 2 models were prepared to assess the responsibility and the level of technical skills. Together, these models will provide a more comprehensive picture of developer's qualification. © 2022 IEEE.

关键词： Managers

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Topic Modelling using Latent Dirichlet Allocation (LDA) and Analysis of Students Sentiments 20

Topic Modelling using Latent Dirichlet Allocation (LDA) and ...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Ishmael, Ontiretse Kiely, Etain Quigley, Cormac McGinty, Donal Atlantic Technological University Dept. Computer Science & Applied Physics Galway Ireland Atlantic Technological University Dept. Department of Analytical Biopharmaceutical Galway Ireland Atlantic Technological University Dept. Computing Services Galway Ireland

ISBN: (纸本)9798350300505

Open-ended question responses are an unstructured data source that contains extremely useful information. The idea of obtaining information using open-ended questions is particularly popular and frequently employed in various schools and digital platforms. In the field of education, universities collect and generate vast amounts of data daily, left unused or not analysed. This demonstrates the need to develop different models to promote the use of text-based data analysis techniques. In this study, surveys with open-ended questions were used to draw out analytical insights from student views on automated personalised feedback assessments. Opened-ended questions present questions that require answers that use natural language. The human language data described in text documents is made possible by the use of various Natural Language Processing (NLP) approaches. These approaches include the Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) that analyse open ended questions and surveys data. In this study, LDA is used for modelling topics to analyse students' sentiments on the feedback assessment given to them regarding their performance on the module. Providing automated personalised feedback to large student groups has many challenges, such as the type of communication delivery mode and the language used for feedback about their progress. This study offers an application of NLP in an education environment to provide meaningful and useful student feedback. The paper presents the steps, algorithm and findings so that practitioners can apply this model to their education setting. The findings reveal 87% of students have positive sentiments, 7% are neutral, 5% are confused by the personalised feedback forms. This informs practitioners about the value of the feedback but also areas for future work when offering feedback to students. © 2023 IEEE.

关键词： Students

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A Framework for the Systematic Assessment of Anomaly Detectors in Time-Sensitive Automotive Networks

A Framework for the Systematic Assessment of Anomaly Detecto...

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IEEE Vehicular Networking Conference (VNC)

作者： Philipp Meyer Timo Häckel Teresa Lübeck Franz Korf Thomas C. Schmidt Dept. Computer Science Hamburg University of Applied Sciences Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and - as versatile platforms to host Network Anomaly Detection Systems (NADSs) - for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs.

关键词： Systematics Protocols Detectors Real-time systems Vectors Topology Security

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Exploring the Effect of Age and Sex on Subject-Independent EEG-Based Emotion Recognition Methods

Exploring the Effect of Age and Sex on Subject-Independent E...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Camilo E. Valderrama Anshul Sheoran Qian Liu Dept. of Applied Computer Science University of Winnipeg Winnipeg Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

In the field of emotion recognition, there are two approaches used to develop predictive models that recognize emotions from EEG signals: subject-dependent and subject-independent. Subject-independent models, although more practical, tend to yield a lower performance due to the high variability of EEG signals between individuals. Recent studies have indicated that incorporating prior demographic information about individuals can improve the accuracy of subject-independent approaches. Some studies have supported this claim by showing that including individuals’ sex can boost model accuracy. However, until now, no one has used interpretable models to measure to what extent demographics can enhance subject-independent approaches. In this work, we follow this direction by using a logistic regression model to correlate the output of a deep learning model with subjects’ age and sex, thereby evaluating whether these factors impact emotion prediction. Our analysis indicates that the ‘sex’ variable significantly influenced the predictions of the deep learning model in three out of five emotions, whereas ‘age’ does not have any effect. These findings suggest that sex is a factor that needs to be considered when designing EEG-based emotion recognition models, which could lead to more robust subject-independent models with potential applications in areas such as healthcare, education, and marketing.

关键词： Deep learning Emotion recognition Logistic regression Accuracy Stacking Education Medical services

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A Framework for the Systematic Assessment of Anomaly Detectors in Time-Sensitive Automotive Networks

arXiv

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arXiv 2024年

作者： Meyer, Philipp Häckel, Timo Lübeck, Teresa Korf, Franz Schmidt, Thomas C. Dept. Computer Science Hamburg University of Applied Sciences Germany

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and – as versatile platforms to host Network Anomaly Detection Systems (NADSs) – for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Zoom-DF: A Dataset for Video Conferencing Deepfake 1

Zoom-DF: A Dataset for Video Conferencing Deepfake

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1st ACM Workshop on Security Implications of Deepfakes and Cheapfakes, WDC 2022, co-located with ACM AsiaCCS 2022

作者： Park, Geon-Woo Park, Eun-Ju Woo, Simon S. Computer Science & Engineering Dept. Sunkyunkwan University Suwon Korea Republic of Applied Data Science Dept. Sunkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781450391788

With the rapid growth of deep learning methods, AI technologies for generating deepfake videos also have been significantly advanced. Nowadays, the manipulated videos such as deepfakes are so sophisticated that one cannot easily differentiate between real and fake, and one can create such videos with little effort. However, such technologies can be likely to be abused by people with malicious intents. To address this issue, approaches and efforts to detect deepfakes have been researched significantly. However, the performances of the detectors in general depends on the quantity and quality of the training data. In this paper, we introduce a new deepfake dataset, Zoom-DF, which can be injected during the remote meeting and video conferencing, to create a sequence of fake participant images. While most deepfake datasets focus on the face area, our dataset primarily targets for the remote meeting, and manipulates movements of the participants. We evaluate existing deepfake detectors on our new Zoom-DF dataset and present the performance results. © 2022 ACM.

关键词： Video conferencing

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Preparing for Super-Reactivity: Early Fault-Detection in the Development of Exceedingly Complex Reactive Systems

arXiv

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arXiv 2024年

作者： Harel, David Marron, Assaf Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

We introduce the term Super-Reactive Systems to refer to reactive systems whose construction and behavior are complex, constantly changing and evolving, and heavily interwoven with other systems and the physical world. Finding hidden faults in such systems early in planning and development is critical for human safety, the environment, society and the economy. However, the complexity of the system and its interactions and the absence of adequate technical details pose a great obstacle. We propose an architecture for models and tools to overcome such barriers and enable simulation, systematic analysis, and fault detection and handling, early in the development of super-reactive systems. The approach is facilitated by the inference and abstraction capabilities and the power and knowledge afforded by large language models and associated AI tools. It is based on: (i) deferred, just-in-time interpretation of model elements that are stored in natural language form, and (ii) early capture of tacit interdependencies among seemingly orthogonal requirements. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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