TY - CHAP A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Winkens, Marvin A1 - Zündorf, Albert T1 - Catalog integration of heterogeneous and volatile product data T2 - DATA 2020: Data Management Technologies and Applications N2 - The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations. Y1 - 2021 SN - 978-3-030-83013-7 U6 - https://doi.org/10.1007/978-3-030-83014-4_7 N1 - International Conference on Data Management Technologies and Applications, DATA 2020, 7-9 July SP - 134 EP - 153 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 - Pohle-Fröhlich, Regina A1 - Dalitz, Christoph A1 - Richter, Charlotte A1 - Hahnen, Tobias A1 - Stäudle, Benjamin A1 - Albracht, Kirsten T1 - Estimation of muscle fascicle orientation in ultrasonic images T2 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 N2 - We compare four different algorithms for automatically estimating the muscle fascicle angle from ultrasonic images: the vesselness filter, the Radon transform, the projection profile method and the gray level cooccurence matrix (GLCM). The algorithm results are compared to ground truth data generated by three different experts on 425 image frames from two videos recorded during different types of motion. The best agreement with the ground truth data was achieved by a combination of pre-processing with a vesselness filter and measuring the angle with the projection profile method. The robustness of the estimation is increased by applying the algorithms to subregions with high gradients and performing a LOESS fit through these estimates. Y1 - 2020 SN - 978-989-758-402-2 U6 - https://doi.org/10.5220/0008933900790086 N1 - 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISAPP 2020, Valletta, Malta SP - 79 EP - 86 PB - SciTePress CY - Setúbal, Portugal ER - TY - CHAP A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Winkens, Marvin A1 - Zündorf, Albert T1 - Catalog integration of low-quality product data by attribute label ranking T2 - Proceedings of the 9th International Conference on Data Science, Technology and Applications DATA - Volume 1 N2 - The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach. Y1 - 2020 SN - 978-989-758-440-4 U6 - https://doi.org/10.5220/0009831000900101 N1 - 9th International Conference on Data Science, Technologies and Applications (DATA 2020), 7 - 9 July 2020, online SP - 90 EP - 101 PB - SciTePress CY - Setúbal, Portugal ER - TY - CHAP A1 - Iomdina, Elena N. A1 - Kiseleva, Anna A. A1 - Kotliar, Konstantin A1 - Luzhnov, Petr V. T1 - Quantification of Choroidal Blood Flow Using the OCT-A System Based on Voxel Scan Processing T2 - Proceedings of the International Conference on Biomedical Innovations and Applications- BIA 2020 N2 - The paper presents a method for the quantitative assessment of choroidal blood flow using an OCT-A system. The developed technique for processing of OCT-A scans is divided into two stages. At the first stage, the identification of the boundaries in the selected portion was performed. At the second stage, each pixel mark on the selected layer was represented as a volume unit, a voxel, which characterizes the region of moving blood. Three geometric shapes were considered to represent the voxel. On the example of one OCT-A scan, this work presents a quantitative assessment of the blood flow index. A possible modification of two-stage algorithm based on voxel scan processing is presented. Y1 - 2020 SN - 978-1-7281-7073-2 U6 - https://doi.org/10.1109/BIA50171.2020.9244511 N1 - International Conference on Biomedical Innovations and Applications, Varna, Bulgaria, September 24 - 27, 2020 SP - 41 EP - 44 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Sildatke, Michael A1 - Karwanni, Hendrik A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - Automated Software Quality Monitoring in Research Collaboration Projects T2 - ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops N2 - In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem. Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ. Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible. In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production. Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data. Y1 - 2020 U6 - https://doi.org/10.1145/3387940.3391478 N1 - ICSE '20: 42nd International Conference on Software Engineering, Seoul, Republic of Korea, 27 June 2020 - 19 July 2020 SP - 603 EP - 610 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Schmidts, Oliver A1 - Kraft, Bodo A1 - Siebigteroth, Ines A1 - Zündorf, Albert T1 - Schema Matching with Frequent Changes on Semi-Structured Input Files: A Machine Learning Approach on Biological Product Data T2 - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS Y1 - 2019 SN - 978-989-758-372-8 U6 - https://doi.org/10.5220/0007723602080215 SP - 208 EP - 215 ER - TY - CHAP A1 - Eschler, Eric A1 - Wozniak, Felix A1 - Richter, Christoph A1 - Drechsler, Klaus T1 - Materialanalyse an lokal verstärkten Triaxialgeflechten T2 - Leichtbau in Forschung und industrieller Anwendung von der Nano- bis zur Makroebene, LLC, Landshuter Leichtbau-Colloquium, 9 Y1 - 2019 SN - 978-3-9818439-2-7 SP - 120 EP - 131 PB - Leichtbau Cluster CY - Landshut ER - TY - CHAP A1 - Hingley, Peter A1 - Dikta, Gerhard T1 - Finding a well performing box-jenkins forecasting model for annualised patent filings counts T2 - International Symposium on Forecasting, Thessaloniki, Greece, June 2019 Y1 - 2019 ER - TY - CHAP A1 - Siebigteroth, Ines A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - A Study on Improving Corpus Creation by Pair Annotation T2 - Proceedings of the Poster Session of the 2nd Conference on Language, Data and Knowledge (LDK-PS 2019) Y1 - 2019 SN - 1613-0073 SP - 40 EP - 44 ER -