Hanne, Thomas

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Hanne
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Thomas
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Hanne, Thomas

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2010 - 201958
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Anfangsdatum: 2010 × Enddatum: 2019 ×

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Gerade angezeigt 1 - 10 von 58
  • Vorschaubild
    Publikation
    A credit rating model in a fuzzy inference system environment
    (MDPI, 2019) Yazdi, Amir Karbassi; Hanne, Thomas; Wang, Yong J.; Wee, Hui-Ming [in: Algorithms]
    One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    A 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
  • Vorschaubild nicht verfügbar
    Publikation
    Sentiment analysis for a swiss gig platform company
    (2019) Pustulka, Elzbieta; Hanne, Thomas
    We work with a Swiss Gig Platform Company to identify innovative solutions which could strengthen its position as a market leader in Switzerland and Europe. The company mediates between employers and employees in short term work contracts via a platform system. We first looked at the business processes and saw that some process parts were not being controlled by the company, which is now being remedied. Second, we analyzed the job reviews which the employers and employees write, and implemented a prototype which can detect negative statements automatically, even if the review is positive overall. We worked with a dataset of 963 job reviews from employers and employees, in German, French and English. The reviews have a star rating (1 to 4 stars), with some discrepancies between the star rating and the text. We scored the reviews manually as negative or other, as negative reviews are important for business improvement. We tested several machine learning methods and a hybrid method from Lexalytics.
    06 - Präsentation
  • Vorschaubild nicht verfügbar
    Publikation
    An 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
  • Vorschaubild nicht verfügbar
    Publikation
    Optimization of multi-robot sumo fight simulation by a genetic algorithm to identify dominant robot capabilities
    (2019) Lehner, Joël Enrico; Dornberger, Rolf; Simic, Radovan; Hanne, Thomas [in: 2019 IEEE Congress on Evolutionary Computation (CEC 2019). Proceedings]
    This paper analyzes the multirobot sumo fight simulation. This simulation is based on a computational model of several sumo fighters, which physically interact while trying to move the opponent out of the arena (lost fight). The problem is optimized using a genetic algorithm (GA), where the capabilities of not only one particular robot but of all robots simultaneously are improved. In this particular problem setup, the problem definition changes depending on the optimization path, because all robots also get better, competing against each other. The influence of different operators of the GA is investigated and compared. This paper raises the questions, which genetically controlled capabilities (e.g. size, speed) are dominant over time and how they can be identified by a sensitivity analysis using a GA. The results shed light on which parameters are dominant. This experiment typically opens up interesting fields of further research, especially about how to address optimization problems, where the optimization process influences the search space and how to eliminate the factor of randomness.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    Gig 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
  • Vorschaubild
    Publikation
    Multilingual 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
  • Vorschaubild nicht verfügbar
    Publikation
    Sketch-based user authentication with a novel string edit distance model
    (IEEE, 2018) Riesen, Kaspar; Hanne, Thomas; Schmidt, Roman [in: IEEE Transactions on Systems, Man, and Cybernetics: Systems]
    The vast majority of user authentication in digital applications is based on alphanumeric passwords. Yet, due to severe problems that might arise with this approach, various efforts have been made in the last decade to replace this authentication paradigm. One candidate for the prospective paradigm shift might be found in the field of graphical passwords. The present paper introduces a novel framework for user authentication based on freehand sketches. The basic idea is that during the registration phase a user draws an arbitrary sketch in a specific drawing canvas (rather than typing a password). Registered users can then be authenticated whenever they are able to reproduce their personal sketch with sufficient precision. The major challenge of such a system is twofold. First, it has to provide a certain degree of error-tolerance such that the authentication of genuine users can be smoothly accomplished. Second, the system should detect even subtle forgeries and reject possible intruders.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Comparison of the behavior of swarm robots with their computer simulations applying target-searching algorithms
    (Engineering and Technology, 2018) Zhong, Jia; Dornberger, Rolf; Hanne, Thomas [in: International Journal of Mechanical Engineering and Robotics Research]
    This paper investigates the functionality and quality of the implementation of a search and target surrounding swarm robotic algorithm using physical swarm robots named Kilobots. The implementation was developed and tested in the simulator V-REP, then transferred onto the actually running Kilobots: Ten Kilobots were used for the experiment, where one Kilobot acts as the target and nine Kilobots act as the searchers. The algorithm allows the searchers to swarm out to find the target while avoiding collisions with other searchers, to orbit around other searchers, which are closer to the target, and finally to surround the target once it is found. The results of the implementation using the physical Kilobots are compared with the results of two adjusted computer simulations. Differences between the simulations and the real robot implementation are investigated: Discrepancies regarding the locomotion and the communication capabilities are identified and discussed.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Gaussian guided self-adaptive wolf search algorithm
    (MDPI, 2018) Song, Qun; Fong, Simon; Deb, Suash; Hanne, Thomas [in: Entropy]
    Nowadays, swarm intelligence algorithms are becoming increasingly popular for solving many optimization problems. The Wolf Search Algorithm (WSA) is a contemporary semi-swarm intelligence algorithm designed to solve complex optimization problems and demonstrated its capability especially for large-scale problems. However, it still inherits a common weakness for other swarm intelligence algorithms: that its performance is heavily dependent on the chosen values of the control parameters. In 2016, we published the Self-Adaptive Wolf Search Algorithm (SAWSA), which offers a simple solution to the adaption problem. As a very simple schema, the original SAWSA adaption is based on random guesses, which is unstable and naive. In this paper, based on the SAWSA, we investigate the WSA search behaviour more deeply. A new parameter-guided updater, the Gaussian-guided parameter control mechanism based on information entropy theory, is proposed as an enhancement of the SAWSA. The heuristic updating function is improved. Simulation experiments for the new method denoted as the Gaussian-Guided Self-Adaptive Wolf Search Algorithm (GSAWSA) validate the increased performance of the improved version of WSA in comparison to its standard version and other prevalent swarm algorithms.
    01A - Beitrag in wissenschaftlicher Zeitschrift