Aktuelle Projekte

Many universities use e-learning platforms like Moodle and Olat. At the University of Hamburg, in the department of informatics, the platforms are frequently used to accompany the lessons with tests. These tests make correcting easy, as the platform allows automatic checking of the answers by matching them with a stored solution (OpenOlat, Benutzerhandbuch, Tests). This speeds up marking tests, but the lecturer has difficulties seeing the reasons for the student's performance. While, for example, Olat provides some form of test statistics (OpenOlat, eTesting), these statistics are on a rudimentary level and do not provide detailed analytic possibilities. To enable enhancing their course, lecturers should have the possibility to create their personal analytics and explore the available data in depth.

To optimize their courses better, it should be easy for lecturers to analyze their course data. The given statistics by the platforms are not enough to understand the course to adapt the following courses or units. For this, the complete course must be analyzed and not the result of individual students. Multiple tools to analyze the data exist but configuring these to work together as a complete system is tedious, time-consuming, and requires knowledge of all the tools used. Lecturers do not have the time and often do not have the knowledge to set up such a system.

This is accompanied by the fact that most of the available data is not in an easily analyzable format and can be spread apart across multiple files or databases. As each course and lecturer have different requirements, creating new metrics has to be possible without deep knowledge of the tools and the data structures.

Participants: Matthias Feldmann

The goal of this work is to evaluate different methods for detecting the fill level of a beverage refrigerator. In addition, the system to be designed should be able to track which person has taken how many drinks.

To do this, it must first be possible to measure the fill level of the refrigerator. The sensors must be accurate enough to measure the level down to a single bottle. It should also be possible to detect whether a person has just placed something else in the refrigerator (for example, an empty bottle or a brick) in order to bypass the system.

In addition, the refrigerator must be lockable so that it can be tracked which person is removing drinks. This locking mechanism should be secure (barrier to tampering sufficiently high), simple and, if possible, not obstruct the view of the drinks. For this purpose, a system must be designed that opens the refrigerator after authentication by a person.

In order for this solution to be used as easily as possible, the design must not be too complicated and not too expensive. In addition, the maintenance effort of the system should be as low as possible.

Participants: Jakob Fischer, Markus Behrens

Hoping robotics will assist people in the future with house chores, I will look at doing laundry. One aspect is sorting clothes either by color or kind. The task can be split into the following parts: First finding the clothes, second picking them up, third deciding on what kind of clothing it is and last putting it into the right basket. To fulfill these steps, a robot needs to interact autonomously with non-rigid objects in a known environment.

Robots can do unfavorable tasks for humans. When doing laundry, it usually needs to be sorted by color, material, and type. A robot that can sort laundry is a necessary step for having an autonomous setup that does the complete laundry chore.

The project in itself can be broken down into several parts. Each can be well defined on its own: Grasping clothes from a heap or basket, moving it in front of a camera, applying a classifier, and finally placing it in a predefined place accordingly. It is a good introduction to working with a robot and corresponding software.

During the project a Universal Robotics UR-5 or an Agile Robotics Diana 7 is used as hardware with ROS and MoveIT to move the robot. To process image date OpenCV is used as well as Open3D for the depth information.

Participants: Valerie Bartel

Electricity grids have to employ expensive infrastructure to remain stable at peak consumption times. The peak usage is only required for a short time each day.

Currently most devices achieve tasks by running immediately. More intelligent scheduling using demand-side management enables a more effective usage of the grid. But few algorithms look at the problem with the context of the devices being in a network. We plan to implement a demand-side management optimization algorithm which locally decides on a limited number of suggestions based on input suggestions received from other devices in a network. We will compare different scenarios and test against randomized and eager baseline strategies. The project will conclude with a final presentation and a scientific paper.

Participants: Tom Schmolzi

Hamburg is a vibrant city with many events taking place throughout the year. To gain insights into these events, it is essential to observe and visualize them in a comprehensive manner. The proposed system aims to develop an automated application that can detect events related to the letzte- generation activist groups in Hamburg and extract relevant information such as the date and time of the event, location of the event, type of event, and description of the event.

This information then later can be used by different stakeholders to analyse the event history, correlate new laws with the intensity and frequency of their events, and so on. The system will use NLP techniques such as keyword matching, NER, and machine learning algorithms to identify events and extract relevant information from different sources such as social media and news outlets. The extracted information will be visualized using different techniques such as heat maps, graphs, and maps to provide a better understanding of the events related to the letzte-generation activist groups in Hamburg. The proposed system will provide a valuable tool for tracking events related to the letzte-generation activist groups in Hamburg and understanding their impact.

Participants: Shaista Shabbir

This project aims to understand code suggestions and feedback of developers reviewing pull requests on GitHub and to propose a machine learning-based solution for automating code suggestions for these pull requests. To this end, a representative data set of open-source software repositories hosted on GitHub will be created.

The mined data, which consist of the pull requests and their corresponding comments and code suggestions, will be pre-processed, and analyzed using an empirical methodology. In the next step, the gathered data will be used to train a Transformer- based machine learning model to automatically recommend code suggestions.

Participants: Aref El-Maarawi Tefur

This project focuses on the folding of a set of scenarios into one Petri net model. We assume the scenarios to be defined as causal nets, specifying one concrete run of a system.

In particular, we will look at ideas from structured occurrence nets and scenario nets to accomplish a compact folded model. This will lead to a prototype for Renew which is a tool to create and simulate various kinds of Petri net formalism.

Participants: Leon Zander