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S17 - SKILL 2021 - Studierendenkonferenz Informatik

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    Bicycle Detection from Top View Perspective in Surveillance System using Convolutional Neural Network
    (SKILL 2021, 2021) Ramkumar, Sanal Darshid
    Bicycle detection and tracking from top view perspective using deep learning is a highly active research area for video surveillance and automatic ticket generation in Advanced Public Transportation System (APTS). People detection using conventional cameras has received massive attention for video surveillance inside public transportation systems but inattentive towards bicycle detection. Experimentation is performed on You Only Look Once (YOLO), Faster Regional-Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD). Due to the sparse availability of dataset for this work, a customized dataset was recorded in the Media Computing lab, Junior Professorship of Media Computing, TU Chemnitz, Germany. The customized dataset was recorded using a wide-angle smart stereo sensor (S2000, Intenta GmbH) mounted in bird’s eye perspective. Furthermore, two additional datasets were recorded using a mobile camera representing indoor and outdoor bicycle parking area. This paper provides best case solution for bicycle detection from a top view perspective.
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    Multiple Sequence Alignment using Deep Reinforcement Learning
    (SKILL 2021, 2021) Joeres, Roman
    Multiple sequence alignment (MSA) is one of the primal problems in biology and bioinformatics. The question of how to align multiple sequences correctly is crucial for many other fields of research, e.g., gaining information about the evolutionary distance of two or more sequences and therefore about their corresponding species, finding protein targets for drugs, or finding a drug for a certain target protein. Reinforcement learning (RL), and especially deep reinforcement learning (DRL), has become popular in recent years. To name just a few, DRL has shown major success in complex games such as Atari Games, Chess, and Go. We model the problem of aligning multiple sequences as a Markov decision process (MDP) and examine the performance of different (D)RL algorithms compared to state-of-the-art tools.
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    Anomaly Detection in Motion Timeseries using the Bosch XDK and Dynamic Time Warping
    (SKILL 2021, 2021) Mejía, Julián Rico; Isaías, Oscar Aguilar Aguila; Paschapur, Priyanka
    This paper presents the development of an anomaly detector for robotic movements using the dynamic time warping (DTW) algorithm and its implementation in Matlab. Data was collected by mounting the Bosch Cross-Domain Development Kit (XDK) sensor on a collaborative robot arm (Cobot), aiming at industrial applications in need for motion anomaly detection during repetitive tasks. The paper discusses practical issues like parameter tuning as well as algorithmic variants such as de­coupling accelerometer and gyroscope data.
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    The Impact of Domain Knowledge on Applying Machine Learning Methods to Exoplanet Detection
    (SKILL 2021, 2021) Nguyen, The-Gia Leo
    Exoplanets do not emit electromagnetic waves which makes it challenging to detect them. Based on transit photometry, we trained a neural network on NASA Kepler space telescope data to detect exoplanets based on light intensity curves. We showcase, that with a well designed data pipeline, a small neural network is sufficient to achieve state-of-the-art performance, saving both computation time and hardware cost. The strongest improvement in performance could only be achieved by adding domain specific processing steps to the data pipeline. Domain knowledge was essential in selecting the appropriate machine learning concepts that are beneficial to solving the problem and have a higher impact on the performance than the actual classification method itself. We encourage to consider the data pipeline as an additional component, besides the classification model, that can potentially improve the overall performance.
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    Künstliche Intelligenz im Requirements Engineering
    (SKILL 2021, 2021) Breuninger, Judith; Kücher, Franziska; Misic, Natali
    Dem Einsatz von Künstlicher Intelligenz (KI) im Requirements Engineering (RE) wird ein hohes Potenzial zugeschrieben. Der Stand der Forschung gestaltet sich jedoch unübersichtlich. Im Rahmen einer Systematischen Literaturrecherche werden 27 wissenschaftliche Publikationen aus drei Datenbanken identifiziert und analysiert. Anschließend werden diese in die RE-Phasen der Anforderungserhebung, -analyse, -spezifikation und -validierung eingeordnet und zusammengefasst. Die Ergebnisse zeigen, dass KI in den vier Phasen eingesetzt wird, allerdings ist die Anwendung unterschiedlich stark ausgeprägt. Weitere tiefgehende Forschungsarbeit, insbesondere zum Einsatz von KI in der Anforderungsvalidierung, ist notwendig. Die vorliegende Arbeit stellt dafür einen wesentlichen Ausgangspunkt dar, indem sie einen strukturierten Überblick der verschiedenen KI-Methoden zum Einsatz im RE aufzeigt und diskutiert.
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    IT-Qualitätsmanagement im Rahmen des Informationsmanagements
    (SKILL 2021, 2021) Zenth, Benjamin; Malik, Majeed
    Die Anzahl an zu verarbeitenden Unternehmensdaten steigt stetig an, in diesem Kontext stellt das Informationsmanagement eine zentrale Disziplin dar. Dem IT-Qualitätsmanagement kommt hierbei eine wichtige strategische Rolle zu, da es die Qualitätssicherung der einzelnen Teilbereiche des Informationsmanagements zum Ziel hat. Obwohl dieses folglich eine zentrale Managementaufgabe darstellt, fehlt es einer aktuellen Betrachtung zum Stand der Wissenschaft. Mit dem vorliegenden Beitrag schließen wir diese Forschungslücke und zeigen den aktuellen Stand der Wissenschaft zum IT-Qualitätsmanagement im Rahmen des Informationsmanagements und decken möglichen Forschungsbedarf auf. Hierfür wurden 38 Teilthemen zum IT-Qualitätsmanagement im Rahmen des Informationsmanagements definiert und je Teilthema eine Literaturanalyse im Zeitraum 2016 bis 2021 durchgeführt. Dabei wurde je Teilthema der aktuelle Stand der Wissenschaft aufgezeigt und eine Einordnung hinsichtlich des zukünftigen Forschungsbedarfs vorgenommen. Hierbei konnte in einem Teilthema ein hoher, in 20 ein mittlerer und in 16 ein niedriger Forschungsbedarf identifiziert werden.
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    Comparison of material models in modern physically based rendering pipelines
    (SKILL 2021, 2021) Bittner, Franca
    The appearance of materials results from a complex interaction of light, material properties and the geometric shape of an object. In computer graphics, various models were developed to describe these correlations. Modern rendering pipelines commonly adapt the philosophy of PBR. This study examines if the reproduction of materials differs across modern PBR tools, and compares the intuitiveness of material design, the quality and range of reproducible materials. A sequential rendering framework was developed to evaluate the visual influences of four selected parameters on material appearance. The rendered images are qualitatively compared based on material charts, scanline plots and difference images. The examined rendering tools mostly yield similar results, with the main differences caused by disparate rendering methods. Still, subtle variations between the tools are noticable, indicating the individual strengths and flaws of each renderer in terms of intuitiveness and physical accuracy.
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    Data-based Transparency and Leadership in Small and Medium-sized Enterprises
    (SKILL 2021, 2021) Mayer, Carmen
    Based on the increasing usage of Information Systems (IS), the amount of employee-specific data in companies is rising. As this data is more often used for leadership, referred to as data-based leadership, the question about complete transparency and its consequences in companies needs consideration. This work therefore, aims to analyze the amount of gathered employee-specific data, the resulting data-based leadership, and the exercised control and transparency in small and medium enterprises (SME) which have limited experience in using digital leadership approaches. The applied case study provides qualitative insights into these aspects. This case study is based on five selected SMEs from different industries. With my study, I enhance control theory and derive practical recommendations for a sustainable handling of employee-data for leadership.
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    SKILL 2021 - Studierendenkonferenz Informatik - Komplettband
    (SKILL 2021, 2021) Geselllschaft für Informatik e.V.
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    Designing an ethical technology project with the help of Data Feminism
    (SKILL 2021, 2021) Gleißner, Lea-Kathrin; Bui, Magdalena; Kühn, Fey; Nenninger, Amelie
    Algorithms and new technologies help people in several life situations, but society pays a high price for their advantages. Several scandals occurred recently, showing that algorithms are neither neutral nor fair – quite the contrary: They discriminate people as humans do. One approach to create less biased data science projects is the “Data Feminism” method, presented by Catherine D’Ignazio and Lauren F. Klein in their book of the same title. This paper evaluates how feasible the method can be implemented in student projects based on the experiences four Leipzig students made by trying to implement the method into their project ‘Questioning Street Names Leipzig’. The paper focusses on three main concepts: subjective viewpoints and context, crediting all forms of labour, and building and linking communities through public tagging events, thus opening the academic question for some citizen science help. The project utilizes open data and open data sources such as Wikidata and OpenStreetMap. The authors of “Data Feminism” want to encourage students, as well as academic professionals, to think about their bias in their data and to use the data feminism approach to reduce the impact of them and create more ethical computer science projects.