Hanne, Thomas
Lade...
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Hanne
Vorname
Thomas
Name
Hanne, Thomas
8 Ergebnisse
Suchergebnisse
Gerade angezeigt 1 - 8 von 8
- PublikationA logistics serious game(2021) Pustulka, Elzbieta; Güler, Attila; Hanne, Thomas [in: GSGS'21. 6th International Conference on Gamification & Serious Game]Switzerland is a logistics hub which needs many trained professionals. As logistics does not have a strong public image, the profession does not attract enough young people. A logistics game could help recruit more candidates at the apprenticeship and university level and help in teaching. We have prototyped a logistics game and found out that it raises interest in logistics and successfully teaches about cargo ships. The game test showed that the game is visually appealing but the competitive aspect may interfere with learning.04B - Beitrag Konferenzschrift
- PublikationFLIE: form labeling for information extraction(2021) Pustulka, Elzbieta; Hanne, Thomas; Gachnang, Phillip; Biafora, Pasquale; Arai, Kohei; Kapoor, Supriya; Bhatia, Rahul [in: Proceedings of the Future Technologies Conference (FTC) 2020]Information extraction (IE) from forms remains an unsolved problem, with some exceptions, like bills. Forms are complex and the templates are often unstable, due to the injection of advertising, extra conditions, or document merging. Our scenario deals with insurance forms used by brokers in Switzerland. Here, each combination of insurer, insurance type and language results in a new document layout, leading to a few hundred document types. To help brokers extract data from policies, we developed a new labeling method, called FLIE (form labeling for information extraction). FLIE first assigns a document to a cluster, grouping by language, insurer, and insurance type. It then labels the layout. To produce training data, the user annotates a sample document by hand, adding attribute names, i.e. provides a mapping. FLIE applies machine learning to propagate the mapping and extracts information. Our results are based on 24 Swiss policies in German: UVG (mandatory accident insurance), KTG (sick pay insurance), and UVGZ (optional accident insurance). Our solution has an accuracy of around 84-89%. It is currently being extended to other policy types and languages.04B - Beitrag Konferenzschrift
- PublikationA game teaching population based optimization using teaching-learning-based optimization(2019) Pustulka, Elzbieta; Hanne, Thomas; Richard, Wetzel; Egemen, Kaba; Benjamin, Adriaensen; Stefan, Eggenschwiler; Adriaensen, Benjamin [in: GSGS'19. 4th Gamification & Serious Game Symposium]We want to lower the entry barrier to optimization courses. To that aim, we deployed a game prototype and tested it with students who had no previous optimization experience. We found out that the prototype led to an increased student motivation, an intuitive understanding of the principles of optimization, and a strong interaction in a team. We will build on this experience to develop further games for classroom use.04B - Beitrag Konferenzschrift
- PublikationAn experiment with an optimization game(2019) Pustulka, Elzbieta; Hanne, Thomas; Adriaensen, Benjamin; Eggenschwiler, Stefan; Kaba, Egemen; Wetzel, Richard; Blashki, Katherine; Xiao, Yingcai [in: IADIS International Conference Interfaces and Human Computer Interaction 2019 (part of MCCSIS 2019)]We aim to improve the teaching of the principles of optimization, including computational intelligence (CI), to a mixed audience of business and computer science students. Our students do not always have sufficient programming or mathematics experience and may be put off by the expected difficulty of the course. In this context we are testing the potential of games in teaching. We deployed a game prototype (design probe) and found out that the prototype led to increased student motivation, intuitive understanding of the principles of optimization, and strong interaction in a team. Ultimately, with the future work we sketch out, this novel approach could improve the learning and understanding of optimization algorithms and CI in general, contributing to the future of Explainable AI (XAI).04B - Beitrag Konferenzschrift
- PublikationGig Work Business Process Improvement(27.08.2018) Pustulka, Elzbieta; Telesko, Rainer; Hanne, Thomas; Wong, Ka Chun [in: 6th International Symposium on Computational and Business Intelligence (ISCBI 2018)]We collaborate with a gig work platform company (GPC) in Switzerland. The project aims to improve the business by influencing process management within the GPC, providing automated matching of jobs to workers, improving worker acquisition and worker commitment, and particularly focusing on the prevention of no shows. One expects to achieve financial, organizational and efficiency gains. As research tools we use a combination of text mining and sentiment analysis, Business Process Modeling and Notation (BPMN), interviews with workers and employers, and the design of sociotechnical improvements to the process, including platform improvements and prototypes. Here, we focus on the successful combination of BPMN modelling with sentiment analysis in the identification of problems and generation of ideas for future modifications to the business processes.04B - Beitrag Konferenzschrift
- PublikationMultilingual Sentiment Analysis for a Swiss Gig(27.08.2018) Pustulka, Elzbieta; Hanne, Thomas; Blumer, Eliane; Frieder, Manuel; Wong, Ka Chun [in: 6th International Symposium on Computational and Business Intelligence (ISCBI 2018)]We are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.04B - Beitrag Konferenzschrift
- PublikationGig work business process improvement(2018) Pustulka, Elzbieta; Telesko, Rainer; Hanne, Thomas [in: ISCBI 2018. 6th International Symposium on Computational and Business Intelligence. Proceedings]We collaborate with a gig work platform company (GPC) in Switzerland. The project aims to improve the business by influencing process management within the GPC, providing automated matching of jobs to workers, improving worker acquisition and worker commitment, and particularly focusing on the prevention of no shows. One expects to achieve financial, organizational and efficiency gains. As research tools we use a combination of text mining and sentiment analysis, Business Process Modeling and Notation (BPMN), interviews with workers and employers, and the design of sociotechnical improvements to the process, including platform improvements and prototypes. Here, we focus on the successful combination of BPMN modelling with sentiment analysis in the identification of problems and generation of ideas for future modifications to the business processes.04B - Beitrag Konferenzschrift
- PublikationMultilingual sentiment analysis for a Swiss gig(2018) Pustulka, Elzbieta; Hanne, Thomas; Blumer, Eliane; Frieder, Manuel [in: ISCBI 2018. 6th International Symposium on Computational and Business Intelligence. Proceedings]We are developing a multilingual sentiment analysis solution for a Swiss human resource company working in the gig sector. To examine the feasibility of using machine learning in this context, we carried out three sentiment assignment experiments. As test data we use 963 hand annotated comments made by workers and their employers. Our baseline, machine learning (ML) on Twitter, had an accuracy of 0.77 with the Matthews correlation coefficient (MCC) of 0.32. A hybrid solution, Semantria from Lexalytics, had an accuracy of 0.8 with MCC of 0.42, while a tenfold cross-validation on the gig data yielded the accuracy of 0.87, F1 score 0.91, and MCC 0.65. Our solution did not require language assignment or stemming and used standard ML software. This shows that with more training data and some feature engineering, an industrial strength solution to this problem should be possible.04B - Beitrag Konferenzschrift