TY - CHAP A1 - Kohl, Philipp A1 - Schmidts, Oliver A1 - Klöser, Lars A1 - Werth, Henri A1 - Kraft, Bodo A1 - Zündorf, Albert T1 - STAMP 4 NLP – an agile framework for rapid quality-driven NLP applications development T2 - Quality of Information and Communications Technology. QUATIC 2021 N2 - The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments. KW - Machine learning KW - Process model KW - Natural language processing Y1 - 2021 SN - 978-3-030-85346-4 SN - 978-3-030-85347-1 U6 - https://doi.org/10.1007/978-3-030-85347-1_12 N1 - International Conference on the Quality of Information and Communications Technology, QUATIC 2021, 8-11 September, Algarve, Portugal SP - 156 EP - 166 PB - Springer CY - Cham ER - TY - CHAP A1 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Bialonski, Stephan T1 - FHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning T2 - Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments : 17th Conference on Natural Language Processing KONVENS 2021 Y1 - 2021 U6 - https://doi.org/10.48415/2021/fhw5-x128 N1 - KONVENS (Konferenz zur Verarbeitung natürlicher Sprache/Conference on Natural Language Processing) 2021, 6. - 9. September 2021, Düsseldorf SP - 105 EP - 111 PB - Heinrich Heine University CY - Düsseldorf ER - TY - CHAP A1 - Olderog, M. A1 - Mohr, P. A1 - Beging, Stefan A1 - Tsoumpas, C. A1 - Ziemons, Karl T1 - Simulation study on the role of tissue-scattered events in improving sensitivity for a compact time of flight compton positron emission tomograph T2 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) N2 - In positron emission tomography improving time, energy and spatial detector resolutions and using Compton kinematics introduces the possibility to reconstruct a radioactivity distribution image from scatter coincidences, thereby enhancing image quality. The number of single scattered coincidences alone is in the same order of magnitude as true coincidences. In this work, a compact Compton camera module based on monolithic scintillation material is investigated as a detector ring module. The detector interactions are simulated with Monte Carlo package GATE. The scattering angle inside the tissue is derived from the energy of the scattered photon, which results in a set of possible scattering trajectories or broken line of response. The Compton kinematics collimation reduces the number of solutions. Additionally, the time of flight information helps localize the position of the annihilation. One of the questions of this investigation is related to how the energy, spatial and temporal resolutions help confine the possible annihilation volume. A comparison of currently technically feasible detector resolutions (under laboratory conditions) demonstrates the influence on this annihilation volume and shows that energy and coincidence time resolution have a significant impact. An enhancement of the latter from 400 ps to 100 ps leads to a smaller annihilation volume of around 50%, while a change of the energy resolution in the absorber layer from 12% to 4.5% results in a reduction of 60%. The inclusion of single tissue-scattered data has the potential to increase the sensitivity of a scanner by a factor of 2 to 3 times. The concept can be further optimized and extended for multiple scatter coincidences and subsequently validated by a reconstruction algorithm. Y1 - 2021 SN - 978-1-7281-7693-2 U6 - https://doi.org/10.1109/NSS/MIC42677.2020.9507901 N1 - 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 31 Oct.-7 Nov. 2020, Boston, MA, USA PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Merkens, Torsten A1 - Hebel, Christoph T1 - Sharing mobility concepts – flexible, sustainable, smart T2 - Proceedings of the 1st UNITED – Southeast Asia Automotive Interest Group (SAIG) International Conference KW - Sharing mobility KW - electro mobility KW - business models KW - mobility behaviour Y1 - 2021 SN - 978-3-902103-94-9 N1 - 1st UNITED-SAIG International Conference, 21-22 APR 2021, Chulalongkorn University, Thailand SP - 43 EP - 44 ER - TY - CHAP A1 - Tran, Ngoc Trinh A1 - Staat, Manfred T1 - FEM shakedown analysis of Kirchhoff-Love plates under uncertainty of strength T2 - Proceedings of UNCECOMP 2021 N2 - A new formulation to calculate the shakedown limit load of Kirchhoff plates under stochastic conditions of strength is developed. Direct structural reliability design by chance con-strained programming is based on the prescribed failure probabilities, which is an effective approach of stochastic programming if it can be formulated as an equivalent deterministic optimization problem. We restrict uncertainty to strength, the loading is still deterministic. A new formulation is derived in case of random strength with lognormal distribution. Upper bound and lower bound shakedown load factors are calculated simultaneously by a dual algorithm. Y1 - 2021 SN - 978-618-85072-6-5 U6 - https://doi.org/10.7712/120221.8041.19047 N1 - UNCECOMP 2021, 4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering, streamed from Athens, Greece, 28–30 June 2021. SP - 323 EP - 338 ER - TY - CHAP A1 - Hüning, Felix A1 - Stüttgen, Marcel T1 - Work in Progress: Interdisciplinary projects in times of COVID-19 crisis – challenges, risks and chances T2 - 2021 IEEE Global Engineering Education Conference (EDUCON) N2 - Project work and inter disciplinarity are integral parts of today's engineering work. It is therefore important to incorporate these aspects into the curriculum of academic studies of engineering. At the faculty of Electrical Engineering and Information Technology an interdisciplinary project is part of the bachelor program to address these topics. Since the summer term 2020 most courses changed to online mode during the Covid-19 crisis including the interdisciplinary projects. This online mode introduces additional challenges to the execution of the projects, both for the students as well as for the lecture. The challenges, but also the risks and chances of this kind of project courses are subject of this paper, based on five different interdisciplinary projects Y1 - 2021 U6 - https://doi.org/10.1109/EDUCON46332.2021.9454006 N1 - 2021 IEEE Global Engineering Education Conference (EDUCON), 21-23 April 2021, Vienna, Austria SP - 1175 EP - 1179 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Hoegen, Anne von A1 - Doncker, Rik W. De A1 - Bragard, Michael A1 - Hoegen, Svenja von T1 - Problem-based learning in automation engineering: performing a remote laboratory aession aerving various educational attainments T2 - 2021 IEEE Global Engineering Education Conference (EDUCON) N2 - During the Covid-19 pandemic, vocational colleges, universities of applied science and technical universities often had to cancel laboratory sessions requiring students’ attendance. These above of all are of decisive importance in order to give learners an understanding of theory through practical work.This paper is a contribution to the implementation of distance learning for laboratory work applicable for several upper secondary educational facilities. Its aim is to provide a paradigm for hybrid teaching to analyze and control a non-linear system depicted by a tank model. For this reason, we redesign a full series of laboratory sessions on the basis of various challenges. Thus, it is suitable to serve different reference levels of the European Qualifications Framework (EQF).We present problem-based learning through online platforms to compensate the lack of a laboratory learning environment. With a task deduced from their future profession, we give students the opportunity to develop own solutions in self-defined time intervals. A requirements specification provides the framework conditions in terms of time and content for students having to deal with the challenges of the project in a self-organized manner with regard to inhomogeneous previous knowledge. If the concept of Complete Action is introduced in classes before, they will automatically apply it while executing the project.The goal is to combine students’ scientific understanding with a procedural knowledge. We suggest a series of remote laboratory sessions that combine a problem formulation from the subject area of Measurement, Control and Automation Technology with a project assignment that is common in industry by providing extracts from a requirements specification. Y1 - 2021 U6 - https://doi.org/10.1109/EDUCON46332.2021.9453925 N1 - 2021 IEEE Global Engineering Education Conference (EDUCON), 21-23 April 2021, Vienna, Austria SP - 1605 EP - 1614 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Ferrein, Alexander A1 - Meeßen, Marcus A1 - Limpert, Nicolas A1 - Schiffer, Stefan ED - Lepuschitz, Wilfried T1 - Compiling ROS schooling curricula via contentual taxonomies T2 - Robotics in Education N2 - The Robot Operating System (ROS) is the current de-facto standard in robot middlewares. The steadily increasing size of the user base results in a greater demand for training as well. User groups range from students in academia to industry professionals with a broad spectrum of developers in between. To deliver high quality training and education to any of these audiences, educators need to tailor individual curricula for any such training. In this paper, we present an approach to ease compiling curricula for ROS trainings based on a taxonomy of the teaching contents. The instructor can select a set of dedicated learning units and the system will automatically compile the teaching material based on the dependencies of the units selected and a set of parameters for a particular training. We walk through an example training to illustrate our work. Y1 - 2021 SN - 978-3-030-67411-3 U6 - https://doi.org/10.1007/978-3-030-67411-3_5 N1 - RiE: International Conference on Robotics in Education (RiE); Advances in Intelligent Systems and Computing book series (AISC, volume 1316) SP - 49 EP - 60 PB - Springer CY - Cham ER - TY - CHAP A1 - El Moussaoui, Noureddine A1 - Kassmi, Khalil A1 - Alexopoulos, Spiros A1 - Schwarzer, Klemens A1 - Chayeb, Hamid A1 - Bachiri, Najib T1 - Simulation studies on a new innovative design of a hybrid solar distiller MSDH alimented with a thermal and photovoltaic energy T2 - Materialstoday: Proceedings N2 - In this paper, we present the structure, the simulation the operation of a multi-stage, hybrid solar desalination system (MSDH), powered by thermal and photovoltaic (PV) (MSDH) energy. The MSDH system consists of a lower basin, eight horizontal stages, a field of four flat thermal collectors with a total area of 8.4 m2, 3 Kw PV panels and solar batteries. During the day the system is heated by thermal energy, and at night by heating resistors, powered by solar batteries. These batteries are charged by the photovoltaic panels during the day. More specifically, during the day and at night, we analyse the temperature of the stages and the production of distilled water according to the solar irradiation intensity and the electric heating power, supplied by the solar batteries. The simulations were carried out in the meteorological conditions of the winter month (February 2020), presenting intensities of irradiance and ambient temperature reaching 824 W/m2 and 23 °C respectively. The results obtained show that during the day the system is heated by the thermal collectors, the temperature of the stages and the quantity of water produced reach 80 °C and 30 Kg respectively. At night, from 6p.m. the system is heated by the electric energy stored in the batteries, the temperature of the stages and the quantity of water produced reach respectively 90 °C and 104 Kg for an electric heating power of 2 Kw. Moreover, when the electric power varies from 1 Kw to 3 Kw the quantity of water produced varies from 92 Kg to 134 Kg. The analysis of these results and their comparison with conventional solar thermal desalination systems shows a clear improvement both in the heating of the stages, by 10%, and in the quantity of water produced by a factor of 3. Y1 - 2021 U6 - https://doi.org/10.1016/j.matpr.2021.03.115 SN - 2214-7853 N1 - The Fourth edition of the International Conference on Materials & Environmental Science (ICMES 2020), virtual conference, November 18-28, 2020, Morocco VL - 45 IS - 8 SP - 7653 EP - 7660 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Schulte, Maximilian A1 - Eggert, Mathias T1 - Predicting hourly bitcoin prices based on long short-term memory neural networks T2 - Proceedings of the International Conference on Wirtschaftsinformatik (WI) 2021 N2 - Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %. Y1 - 2021 N1 - 16th International Conference on Wirtschaftsinformatik, March 2021, Essen, Germany ER -