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Open Data Workshop
(2023)
ChatGPT – Freund oder Feind?
(2023)
ChatGPT 4.0 – friend or foe?
(2023)
ChatGPT 4.0 – friend or foe?
(2023)
ChatGPT – friend or foe?
(2023)
ChatGPT – Freund oder Feind?
(2023)
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.
The healthcare sector is growing in importance as people continue to age and pandemics complicate the boundary conditions of such systems. The number of innovative approaches to solve singular tasks and problems in this area is only slowly increasing. This is particularly evident when looking at medical technology planning, medical training and process simulation. In this paper a concept for versatile digital improvements to these problems by using state of the art development methods of Virtual Reality (VR) and Augmented Reality (AR) are presented. The programming and design of the software is done with the help of Unity Engine, which provides an open interface for docking with the developed framework for future work. The solutions were tested under domain specific environments and have shown good results and positive feedback.
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.
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.
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
We compare results of simulations of solar facular-like conditions performed using the numerical codes MURaM and STAGGER. Both simulation sets have a similar setup, including the initial condition of ≈200 G vertical magnetic flux. After interpolating the output physical quantities to constant optical depth, we compare them and test them against inversion results from solar observations. From the snapshots, we compute the monochromatic continuum in the visible and infrared, and the full Stokes vector of the Fe i spectral line pair around 6301–6302 Å. We compare the predicted spectral lines (at the simulation resolution and after smearing to the HINODE SP/SOT resolution) in terms of their main parameters for the Stokes I line profiles, and of their area and amplitude asymmetry for the Stokes V profiles. The codes produce magnetoconvection with similar appearance and distribution in temperature and velocity. The results also closely match the values from recent relevant solar observations. Although the overall distribution of the magnetic field is similar in both radiation-magnetohydrodynamic (RMHD) simulation sets, a detailed analysis reveals substantial disagreement in the field orientation, which we attribute to the differing boundary conditions. The resulting differences in the synthetic spectra disappear after spatial smearing to the resolution of the observations. We conclude that the two sets of simulations provide robust models of solar faculae. Nevertheless, we also find differences that call for caution when using results from RMHD simulations to interpret solar observational data.
Short-term Incentives of Research Evaluations: Evidence from the UK Research Excellence Framework
(2023)
AI Engineering @ FHTW
(2022)
Vortrag im Zuge des Security Monats in Form des FHTW Security Potpourri 2022
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.
Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24%
and 96.10% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08% and 94.31% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38% and 93.28%
mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications. View Full-Text
Keywords: deep learning; machine learning; plant leaf morphometrics; explainable AI
Abstract: The biological investigation of a population’s shape diversity using digital images is typi-
cally reliant on geometrical morphometrics, which is an approach based on user-defined landmarks.
In contrast to this traditional approach, the progress in deep learning has led to numerous applications
ranging from specimen identification to object detection. Typically, these models tend to become black
boxes, which limits the usage of recent deep learning models for biological applications. However, the
progress in explainable artificial intelligence tries to overcome this limitation. This study compares
the explanatory power of unsupervised machine learning models to traditional landmark-based
approaches for population structure investigation. We apply convolutional autoencoders as well
as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent
structure using consensus clustering. The explanatory factors of the machine learning models were
extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are
formulated to test the unambiguity of population diversity unveiled by the machine learning models.
The findings show that it is possible to obtain biologically meaningful results relying on unsupervised
machine learning. Furthermore we show that the machine learning models unveil latent structures
close to the true population clusters. We found that 80% of the true population clusters relying on
the convolutional autoencoder are significantly different to the remaining clusters. Similarly, 60% of
the true population clusters relying on the Gaussian process latent variable model are significantly
different. We conclude that the machine learning models outperform generalized Procrustes analysis,
where 16% of the population cluster was found to be significantly different. However, the applied
machine learning models still have limited biological explainability. We recommend further in-depth
investigations to unveil the explanatory factors in the used model.
Keywords: generalized procrustes analysis; machine learning; convolutional autoencoder; Gaussian
process latent variable models
Background: Most clinical studies report the symptoms experienced by those infected with coronavirus disease 2019 (COVID-19) via patients already hospitalized. Here we analyzed the symptoms experienced outside of a hospital setting.
Methods: The Vienna Social Fund (FSW; Vienna, Austria), the Public Health Services of the City of Vienna (MA15) and the private company Symptoma collaborated to implement Vienna's official online COVID-19 symptom checker. Users answered 12 yes/no questions about symptoms to assess their risk for COVID-19. They could also specify their age and sex, and whether they had contact with someone who tested positive for COVID-19. Depending on the assessed risk of COVID-19 positivity, a SARS-CoV‑2 nucleic acid amplification test (NAAT) was performed. In this publication, we analyzed which factors (symptoms, sex or age) are associated with COVID-19 positivity. We also trained a classifier to correctly predict COVID-19 positivity from the collected data.
Results: Between 2 November 2020 and 18 November 2021, 9133 people experiencing COVID-19-like symptoms were assessed as high risk by the chatbot and were subsequently tested by a NAAT. Symptoms significantly associated with a positive COVID-19 test were malaise, fatigue, headache, cough, fever, dysgeusia and hyposmia. Our classifier could successfully predict COVID-19 positivity with an area under the curve (AUC) of 0.74.
Conclusion: This study provides reliable COVID-19 symptom statistics based on the general population verified by NAATs.
Keywords: Chatbot; Machine learning; Self-reported; Symptom assessment; Symptom checker.
Cross-disciplinary collaborations have become an increasingly important part of science. They are seen as key if we are to find solutions to pressing, global-scale societal challenges, including green technologies, sustainable food production, and drug development. The synergistic and skillful combining of different disciplines can achieve insight beyond current borders and thereby generate novel solutions to complex problems. The combination of methods and data from different fields can achieve more than the sum of the individual parts could do alone.
Initiating and successfully maintaining cross-disciplinary collaborations can be challenging but highly rewarding. In this talk I will focus on the specific challenges associated with cross-disciplinary research, from the perspective of the theoretician in particular. Based on “10 simple rules” (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004214) I will describe the key benefits, as well as some possible pitfalls, arising from collaborations between scientists with very different backgrounds.
Rethinking in Education
(2010)
Teaching Security at the UAS Technikum Wien – an interdisciplinary approach in higher education
(2016)
The dynamics of proteins are crucial for their function. However, commonly used techniques for studying protein structures are limited in monitoring time-resolved dynamics at high resolution. Combining electric fields with existing techniques to study gas-phase proteins, such as single particle imaging using free-electron lasers and gas-phase small angle X-ray scattering, has the potential to open up a new era in time-resolved studies of gas-phase protein dynamics. Using molecular dynamics simulations, we identify well-defined unfolding pathways of a protein, induced by experimentally achievable external electric fields. Our simulations show that strong electric fields in conjunction with short-pulsed X-ray sources such as free-electron lasers can be a new path for imaging dynamics of gas-phase proteins at high spatial and temporal resolution.
Open Data Projects
(2021)
Usability and user experience are the most critical success factors for software and technical products. Today's users want user-friendly products and are no longer willing to accept a poor user experience.
Therefore, all persons involved in developing software or technical products need basic knowledge of usability and UX. This book prepares you for the Certified Professional for Usability Engineering, User Experience Design Foundation Level (CPUE-FL) exam of the user Experience Quality Certification Center (UXQCC) and ensures exactly this knowledge.
In an exciting yet easy-to-understand way, you will learn the basics of successful usability and UX design. Theory and numerous examples from the authors' practice illustrate the content and create an exciting learning experience.
Open Data Workshop
(2020)
One of the challenges facing single particle imaging with ultrafast X-ray pulses is the structural heterogeneity of the sample to be imaged. For the method to succeed with weakly scattering samples, the diffracted images from a large number of individual proteins need to be averaged. The more the individual proteins differ in structure, the lower the achievable resolution in the final reconstructed image. We use molecular dynamics to simulate two globular proteins in vacuum, fully desolvated as well as with two different solvation layers, at various temperatures. We calculate the diffraction patterns based on the simulations and evaluate the noise in the averaged patterns arising from the structural differences and the surrounding water. Our simulations show that the presence of a minimal water coverage with an average 3 Å thickness will stabilize the protein, reducing the noise associated with structural heterogeneity, whereas additional water will generate more background noise.