TY - GEN A1 - Hrastansky, Thomas T1 - Phishing: Targeting YouTube Content Creators N2 - Vortrag im Zuge des Security Monats in Form des FHTW Security Potpourri 2022 KW - Phishing KW - IT-Security KW - YouTube Y1 - ER - TY - JOUR A1 - Groen-Xu, Moqi A1 - Boes, Gregor A1 - Teixeira, Pedro A. A1 - Voigt, Thomas A1 - Knapp, Bernhard T1 - Short-term Incentives of Research Evaluations: Evidence from the UK Research Excellence Framework JF - Research Policy KW - research funding systems KW - evaluation effects KW - economics of science KW - incentives KW - university Y1 - VL - Vol. 52 IS - Issue 6 ER - TY - GEN A1 - Knapp, Bernhard T1 - AI Engineering @ FHTW KW - Secure Services KW - eHealth & Mobility Y1 - ER - TY - GEN A1 - Knapp, Bernhard T1 - Keynote Lecture: Der Weg eines Bioinformatikers der ersten Stunde KW - Secure Services KW - eHealth & Mobility Y1 - ER - TY - GEN A1 - Knapp, Bernhard T1 - From academia to industry and back KW - Secure Services KW - eHealth & Mobility Y1 - ER - TY - CHAP A1 - Mehnen, Lars A1 - Pohn, Birgit A1 - Blaickner, Matthias A1 - Mandl, Thomas A1 - Dregely, Isabel T1 - Teaching & Learning Analytics for Data-Based Optimization of Teaching and Learning Processes in Courses with Blended Learning T2 - 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2022 N2 - Learning Management Systems (LMS), such as Moodle, enable the rapid progress of digitisation in teaching, which is no longer only taking place in the lecture hall, but increasingly “online” and asynchronously. New didactic concepts (blended learning, “flipped classroom”) consist of alternating self-learning and face-to-face phases, with the former taking place in the LMS, i.e. online. However, no analysis has yet been carried out as to how students act with the material in the self-learning phase, or the teachers are not provided with any information about the learning progress of the students during the self-learning phase. In this paper, concepts of learning and teaching analytics are presented to answer these questions and to integrate the measures derived from them into the teaching processes in a sustainable manner. KW - teaching analytics KW - learning analytics KW - learning management systems KW - artificial intelligence KW - Learning management systems Y1 - 2022 SN - 978-953-290-117-7 PB - IEEE ER - TY - CHAP A1 - Wagner, Fabian A1 - Jank, Miran A1 - Balz, Andrea A1 - Forjan, Mathias A1 - Urbauer, Philipp T1 - Immersive Spatial Planning in Healthcare: Developing a Pipeline to Automatically Convert Computer Aided DesignData to Virtual Reality T2 - dHealth 2023, 17th Annual Conference on Health Informatics meets Digital Health N2 - Equipping rooms used for medical purposes, like e.g., intensive care units, is an expensive and time-consuming task. In order to avoid extensive subsequent adjustments due to inappropriate layout visualization or geometric conditions difficult to identify in 2D plans, it is of utmost importance to provide an optimal planning environment to future users such as physicians and nurses. In this paper we present the concept of a fully automatized pipeline, which is designed to visualize computer aided design (CAD) data using virtual reality (VR). The immersive VR experience results in improvement of efficiency in the decision- making process during the planning phase due to better spatial imagination. The pipeline was successfully tested with CAD data from existing Intensive Care Units. The results indicate that the pipeline can be a valuable tool in the field of spatial planning in healthcare, due to simple usage and fast conversion of CAD data. The next step will be the development of a plugin for CAD tools to allow for interactions with the CAD models in Virtual Reality, which is not yet possible without manual intervention KW - Virtual Reality KW - Spacial Planning KW - Automation Y1 - U6 - http://dx.doi.org/https://doi.org/10.3233/shti230019 VL - 301 SP - 96 EP - 101 ER - TY - JOUR A1 - Marc, Zobel A1 - Knapp, Bernhard A1 - Nateqi, Jama A1 - Martin, Alistair T1 - Correlating global trends in COVID-19 cases with online symptom checker self-assessments JF - PLOS ONE N2 - Background Online symptom checkers are digital health solutions that provide a differential diagnosis based on a user’s symptoms. During the coronavirus disease 2019 (COVID-19) pandemic, symptom checkers have become increasingly important due to physical distance constraints and reduced access to in-person medical consultations. Furthermore, various symptom checkers specialised in the assessment of COVID-19 infection have been produced. Objectives Assess the correlation between COVID-19 risk assessments from an online symptom checker and current trends in COVID-19 infections. Analyse whether those correlations are reflective of various country-wise quality of life measures. Lastly, determine whether the trends found in symptom checker assessments predict or lag relative to those of the COVID-19 infections. Materials and methods In this study, we compile the outcomes of COVID-19 risk assessments provided by the symptom checker Symptoma (www.symptoma.com) in 18 countries with suitably large user bases. We analyse this dataset’s spatial and temporal features compared to the number of newly confirmed COVID-19 cases published by the respective countries. Results We find an average correlation of 0.342 between the number of Symptoma users assessed to have a high risk of a COVID-19 infection and the official COVID-19 infection numbers. Further, we show a significant relationship between that correlation and the self-reported health of a country. Lastly, we find that the symptom checker is, on average, ahead (median +3 days) of the official infection numbers for most countries. Conclusion We show that online symptom checkers can capture the national-level trends in coronavirus infections. As such, they provide a valuable and unique information source in policymaking against pandemics, unrestricted by conventional resources. KW - Online symptom checkers KW - coronavirus disease Y1 - U6 - http://dx.doi.org/https://doi.org/10.1371/journal.pone.0281709 VL - 18 IS - 2 ER - TY - JOUR A1 - Kierspel, Thomas A1 - Kadek, Alan A1 - Barran, Perdita A1 - Bellina, Bruno A1 - Bijedic, Adi A1 - Brodmerkel, Maxim N. A1 - Commandeur, Jan A1 - Caleman, Carl A1 - Damjanovic, Tomislav A1 - Dawod, Ibrahim A1 - De Santis, Emiliano A1 - Lekkas, Alexandros A1 - Lorenzen, Kristina A1 - López Morillo, Luis A1 - Mandl, Thomas A1 - Marklund, Erik G. A1 - Papanastasiou, Dimitris A1 - Ramakers, Lennart A. I. A1 - Schweikhard, Lutz A1 - Simke, Florian A1 - Sinelnikova, Anna A1 - Smyrnakis, Athanasios A1 - Timneanu, Nicusor A1 - Uetrecht, Charlotte T1 - Coherent diffractive imaging of proteins and viral capsids: simulating MS SPIDOC JF - Analytical and Bioanalytical Chemistry N2 - MS SPIDOC is a novel sample delivery system designed for single (isolated) particle imaging at X-ray Free-Electron Lasers that is adaptable towards most large-scale facility beamlines. Biological samples can range from small proteins to MDa particles. Following nano-electrospray ionization, ionic samples can be m/z-filtered and structurally separated before being oriented at the interaction zone. Here, we present the simulation package developed alongside this prototype. The first part describes how the front-to-end ion trajectory simulations have been conducted. Highlighted is a quadrant lens; a simple but efficient device that steers the ion beam within the vicinity of the strong DC orientation field in the interaction zone to ensure spatial overlap with the X-rays. The second part focuses on protein orientation and discusses its potential with respect to diffractive imaging methods. Last, coherent diffractive imaging of prototypical T = 1 and T = 3 norovirus capsids is shown. We use realistic experimental parameters from the SPB/SFX instrument at the European XFEL to demonstrate that low- resolution diffractive imaging data (q < 0.3 nm −1 ) can be collected with only a few X-ray pulses. Such low-resolution data are sufficient to distinguish between both symmetries of the capsids, allowing to probe low abundant species in a beam if MS SPIDOC is used as sample delivery. KW - SPI KW - X-ray KW - Native MS KW - Protein complex structure KW - Simulation Y1 - U6 - http://dx.doi.org/https://doi.org/10.1007/s00216-023-04658-y VL - 2023 IS - 415 SP - 4209 EP - 4220 ER - TY - CHAP A1 - Steiner, Fabian A1 - Lueger, Bernhard A1 - Wallisch, Bernhard A1 - Polzer, Thomas T1 - Automated Evaluation System for Microcontroller Assignments T2 - 2023 IEEE Global Engineering Education Conference (EDUCON), Kuwait N2 - As IoT systems have increased the number of deployed embedded devices drastically and most of these devices are used in safety or security critical environments, the education of embedded software engineers is more important than ever. A critical part of their education is the development of their intuition for secure and safe software. In this paper 1 1 This research was funded by the city of Vienna (MA-23 call 21, project no. 9). we present an evaluation system used to generate fast and accurate feedback for student submission in, but not limited to, embedded software development courses. The system can be used as a first feedback loop to outline to the students where problems exist in their code and give them the opportunity to analyze and correct their errors. These extra steps ensure that the students can and will be notified early about their mistakes and can search for correct solutions, supporting the student's learning process. We present the implementation of the system and analyze its deployment in a microcontroller software development lecture. This analysis was done by means of surveys of the students and lecturers as well as a statistical analysis of the student submissions. The results show that the students made use of this extra features and even would prefer to have this feedback in other software development lectures as well. KW - Student-Centered Learning Environments KW - Virtual and Remote Labs and Classrooms Y1 - SN - 979-8-3503-9943-1 U6 - http://dx.doi.org/https://doi.org/10.1109/EDUCON54358.2023.10125112 SN - 2165-9567 ER -