“The real purpose of research is not to solve a problem, but to understand it.”
– David Deutsch
Redefinition of Art in an Age of Digitalization
Benedikt Geiger – Self-Initiated Research, 2021
The increasing presence of advanced technologies in the creative domain has sparked ongoing debate about the definition of art. This research explores whether the concept of art requires redefinition in an age of digitalization, where machines are increasingly capable of generating creative output. A notable example is Mario Klingemann’s Memories of Passersby I, an autonomous AI system that continuously produces original portraits and has received international recognition and awards.
Building on this context, the work examines how emerging technologies—specifically Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR)—are reshaping both the creation and perception of art. Through historical and theoretical analysis, the study outlines the evolution of artistic tools and methods and discusses the implications of machine-assisted creativity.
Based on a structured review of key concepts and contemporary examples, the paper evaluates the opportunities and limitations of digital techniques in artistic contexts and critically reflects on whether a redefinition of art is necessary in the digital era.
Collaborative Multi-User Virtual Reality Systems in Engineering Education
Benedikt Geiger – University of Koblenz, 2020
Research Work Supervised by Dr. Nils Höhner
Engineering education is an ever-changing area, which currently is facing challenges of distributed collaborative learning due to globalization and consequences of the Covid-19 pandemic. The need arises for solution methods which facilitate didactic courses for multiple, geographically distributed trainees, without neglecting requirements of interactivity, collaboration, and active learning. The rapid development of multi-user Virtual Reality systems appears to offer a promising approach for addressing these issues in engineering education. Virtual Reality is a technology which immerses users in virtual environments, and which has proven to facilitate and enhance learning processes, knowledge transfer, motivation, and active and collaborative learning. Due to the technology’s novelty, there is no general guideline for developing educational multi-user Virtual Reality systems in engineering, yet. This work addresses this concern by approaching a process pipeline for the implementation in this area. This guideline is based on requirements of general and engineering education, Virtual Reality technology, and the combination of different key findings of related works. This thesis is relevant for engineering, computer science students, and teachers.
Towards Engineering Security- and Privacy-aware Augmented Reality Systems
Benedikt Geiger – University of Koblenz, 2020
Seminar Work Supervised by Dr. Qusai Ramadan
Augmented Reality is a technology that utilizes individual data to enhance the user’s environment with virtual information. These systems require continuous access to sensor data to execute processes. As a result, large quantities of user information are inevitably gathered, leading to the following privacy issues: sensitive information about users and bystanders can be accessed and potentially misused. Furthermore, users could be exposed to inappropriate content. Due to the technology’s novelty, there is currently no general concept for protecting user privacy while preserving full system functionality. This work addresses this concern by proposing SecAR, a general architecture for security- and privacy-aware Augmented Reality systems. SecAR is based on selected techniques that maintain functionality during different stages of the Augmented Reality information pipeline while safeguarding user privacy. The benefits and flaws of these related works are subsequently discussed and combined. As a result, a concept that preserves security and privacy throughout all stages of the Augmented Reality information pipeline is elaborated and presented.
Offensive Language Finding
Benedikt Geiger, Manjunath Shankar, Ishan Sharma, and Anurekha Rethinakumar – University of Koblenz, 2020
Research Project Supervised by Dr. Oul Han
In our project Offensive Language Finding at the University of Koblenz, we developed a Bi-LSTM-based neural network to detect offensive language on Twitter. Using a combined dataset of over 120,000 tweets, we achieved an accuracy of approximately 86.8%. Our system distinguished whether tweets were offensive and whether they targeted individuals or groups, confirming that individuals are more frequently targeted. This research highlights the importance of automated offensive language detection to promote safer communication in social media.