Seminar FSS 2020

Course Information:

  • Course ID: IE 704
  • Credit Points:
    • B.Sc. Wirtschaftsinformatik: 5 ECTS
    • M.Sc: Wirtschaftsinformatik: 4 ECTS
    • MMDS: 4 ECTS
    • Supervision: Dr. Christian Bartelt

Schedule:

  • Until Sunday, February 09, 2020 (23:59 CET): Please register for the kick-off meeting by sending two preferred topics and a list of your completed courses (Transcript of Records, CV optional) via mail to Nils Wilken (wilken@es.uni-mannheim.de)
  • Tuesday, February 11, 2020: As we can only offer a limited amount of places, you will be informed whether you can participate in this seminar.
  • Friday, February 14, 2020: Latest possible drop-out date without a penalty (A drop-out after this date will be graded with 5.0)
  • Friday, February 14, 2020 (13:45 – 15:15 CET): Milestone 1 – Kick-Off Meeting
  • Sunday, May 24, 2020 (23:59 CEST): Milestone 2 – Submission of final seminar paper
  • Sunday, May 31, 2020 (23:59 CEST): Milestone 3 – Submission of reviews
  • TBA, 2020: Milestone 4 - Presentation of your final seminar paper
  • Sunday, June 28, 2020 (23:59 CEST): Milestone 5 – Submission of camera-ready seminar paper and document that indicates the differences between the first submitted version and the camera-ready version of the seminar papers

Important notes

  • Missing a mile-stone will result in a final grade of 5.0.
  • The four parts final paper version, camera-ready paper version, feedback (reviews + presentation feedback), and presentation will all be graded separately, where each part counts 25% of the final grade.
  • This seminar is open for Bachelor and Master Students focusing on “Business Informatics” and “Data Science”. Master students enrolled in the “Mannheim Master in Data Science” are also highly welcome to apply for this seminar.
  • Only Master Students enrolled in the program “Business Informatics”: This seminar will be held as Module “IE 704” and is thus only applicable for the Specialization Tracks “Information Technology”, “System Design and Development” and “Data and Web Science”.

Suggested Topics:

TOPIC 1: Applications of current NLP Technologies in Requirements Engineering

Introduction: Requirements Engineering consists of multiple activities and techniques in order to define, document and maintain software artefacts. Especially the initial phase of Requirements Elicitation plays a vital role for the success of a software project, which involves different practices (often interviews and questionnaires based on natural language) to discover and gather requirements from the relevant stakeholders. Advances in Natural Language Processing (NLP) in recent years have also impacted Requirements Engineering. The goal of this seminar work is to provide a clear overview of approaches that apply various NLP technologies to support Requirements Elicitation with a focus on current approaches (e.g. state-of-the-art language models).

Goal and Objective: Overview of different state-of-the-art approaches that apply current NLP technologies in the field of Requirements Engineering and comparison between them.

Starting Paper:

  • Casagrande, E., Woldeamlak, S., Woon, W. L., Zeineldin, H. H., & Svetinovic, D. (2014). NLP-KAOS for systems goal elicitation: Smart metering system case study. IEEE Transactions on Software Engineering, 40(10), 941-956.

TOPIC 2: Explainable Artificial Intelligence (XAI)

Introduction: Currently, the explainability of AI techniques (XAI) is a widely discussed topic, both in AI research and in society. Missing or insufficient explanatory capabilities of AI-based systems are a serious limitation for the use of these techniques in many applications domains for various reasons.

Goal and Objective: In this seminar you will familiarize yourself with XAI approaches for AI techniques (like artificial neural networks) based on scientific literature as well as your own exploration.

Starting Papers:

  • D. Carvalho, E. Pereira, and J. Cardoso. Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, 8(8):832–865, 2019.
  • L. Gilpin, D. Bau, B. Yuan, A. Bajwa, M. Specter, and L. Kagal. Explaining Explanations: An Overview of Interpretability of Machine Learning. In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pages 80–89, 2018.
  • Christoph Molnar. Interpretable Machine Learning. https://christophm.github.io/interpretableml- book/. 2019

TOPIC 3: Micro-Service Composition for Open Software Systems

Introduction: Currently, more and more Application Programming Interfaces (API) are accessible using e.g. internet protocols. For example, API search engines such as RAPID API or Programmable Web allow humans to quickly find and interpret use case relevant services. However, such an interpretation is a complex problems for software systems (e.g. IoT devices or intelligent service systems ) as input parameter names or response messages are just character sequenecs. If a software platform is not hard-coded towards accessing predefined external APIs, then matching and mapping approaches can be utilized for determining required services at runtime. This can potentially boost up the amount of use cases a service system can handle with only minimal software engineering effort.

Goal and Objective: In this seminar paper, a survey about exisiting API composition approaches from a technological viewpoint is to be made. Optionally, you can also try out available matching algorithms or mapping languages by yourself.

Starting Paper:

  • Butzin, B., Golatowski, F., & Timmermann, D. (2016, September). Microservices approach for the internet of things. In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1-6). IEEE. https://ieeexplore.ieee.org/abstract/document/7733707

TOPIC 4Causal reasoning in Multi-Agent Systems

Introduction: As opposed to Data Science, Causation Theory is based on the assumption that not everything is in the data. Therefore, the difference between correlation and causation is very much emphasized, and methods of causal calculus are applied to draw conclusions from observations. In Multi-Agent Systems (MAS), understanding cause and effects of actions inherently poses an important challenge, both for agents and for any governing instance. Nevertheless, many current methods in MAS are based on data only, and equipping such systems with causal inference can possibly leverage their abilities substantially.

Goal and Objective: In this seminar, you will investigate the current state of Causation Theory in connection with Multi-Agent Systems, and you will understand the potential, the drawbacks and the challenges of this particular field of research.

Starting Papers:

  • J. Pearl: Causality. Models, Reasoning, and Inference. Cambridge University Press, 2000.
  • S. Maes, S. Meganck, B. Manderick: Identification of Causal Effects in Multi-Agent Causal Models. In: Proceedings of the International Conference on Artificial Intelligence and Applications, 2005, pp. 178-182.
  • R. Kuznets et al.: Causality and Epistemic Reasoning in Byzantine Multi-Agent Systems. In: Proceedings of the Seventeenth Conference on Theoretical Aspects of Rationality and Knowledge (TARK), 2019, pp. 293–312.

 

Nach oben

PostanschriftBesucheradresse  EmailTelefonnummer
Institute for Enterprise Systems  L15, 1-6office-ines@uni-mannheim.de  Office Management
Schloss68131 Mannheim +49 621 181-3560
68131 MannheimGermany
Germany