Vocal training is difficult because the muscles that control pitch, resonance, and phonation are internal and invisible to learners. This paper investigates how Electromyography (EMG) and ultrasonic imaging (UI) can make these muscles observable for training purposes. We report three studies. First, we analyze the EMG and UI data from 16 singers (beginners, experienced & professionals), revealing differences among three vocal groups of the muscle control proficiency. Second, we use the collected data to create a system that visualizes an expert’s muscle activity as reference. This system is tested in a user study with 12 novices, showing that EMG highlighted muscle activation nuances, while UI provided insights into vocal cord length and dynamics. Third, to compare our approach to traditional methods (audio analysis and coach instructions), we conducted a focus group study with 15 experienced singers. Our results suggest that EMG is promising for improving vocal skill development and enhancing feedback systems. We conclude the paper with a detailed comparison of the analyzed modalities (EMG, UI and traditional methods), resulting in recommendations to improve vocal muscle training systems.

Collecting user data is ubiquitous in the modern web, because it enables different kinds of services like personalised recommendations. Specific data shared by the users can be analysed further to infer additional information – e.g., gender, age, appearance, or preferences. Such inferences are challenging to predict for users when deciding on whether or not to share data. In this paper, we explore privacy avatars as a way to visualise collected and inferred data to users by a picture. For this, we conducted semi-structured interviews (N=20) where participants were shown privacy avatars based on products they bought in a fictitious online-shopping scenario and additional privacy avatars with a varied level of detail and information. Our results show that participants were shocked by avatars that accurately depicted parts of their visual appearance and perceived inaccurate avatars as privacy-preserving. However, participants preferred information they considered sensitive (e.g., gender) to be accurate creating a tension between privacy and accuracy of shared or inferred information. We conclude with a discussion on how such privacy avatars could be generated and used in practice to offer intuitive and informative privacy interfaces.

Obfuscating people’s faces using synthetically generated faces, i.e., DeepFakes, has been shown to be effective at privacy preservation. While recent work showed that DeepFake obfuscation is well perceived by viewers, the perspectives of a) the owner of the obfuscated photo, and b) the person that is being obfuscated, remain unclear. This paper reports on the results of a user study where participants uploaded their own group photos, in which they appear, and applied obfuscation techniques to both themselves and others in the image. The obfuscation methods included DeepFakes and four traditional techniques: blurring, pixelating, masking, and avatars. Our findings show that both photo owners and obfuscated individuals perceive DeepFake obfuscation as significantly more effective in protecting privacy compared to the traditional methods, and was found to integrate well with the environment.

To increase open-source software supply chain security, protecting the development environment of contributors against attacks is crucial. For example, contributors must protect authentication credentials for software repositories, code-signing keys, and their systems from malware.Previous incidents illustrated that open-source contributors struggle with protecting their development environment. In contrast to companies, open-source software projects cannot easily enforce security guidelines for development environments. Instead, contributors’ security setups are likely heterogeneous regarding chosen technologies and strategies.To the best of our knowledge, we perform the first in-depth qualitative investigation of the security of open-source software contributors’ individual security setups, their motivation, decision-making, and sentiments, and the potential impact on open-source software supply chain security. Therefore, we conduct 20 semi-structured interviews with a diverse set of experienced contributors to critical open-source software projects.Overall, we find that contributors have a generally high affinity for security. However, security practices are rarely discussed in the community or enforced by projects. Furthermore, we see a strong influence of social mechanisms, such as trust, respect, or politeness, further impeding the sharing of security knowledge and best practices.We conclude our work with a discussion of the impact of our findings on open-source software and supply chain security, and make recommendations for the open-source software community.