Sexual interaction in digital contexts and its implications for sexual health: a conceptual analysis. - In: Frontiers in psychology, ISSN 1664-1078, Bd. 12 (2021), 769732, S. 1-18
Based on its prevalence, there is an urgent need to better understand the mechanisms, opportunities and risks of sexual interaction in digital contexts (SIDC) that are related with sexual arousal. While there is a growing body of literature on SIDC, there is also a lack of conceptual clarity and classification. Therefore, based on a conceptual analysis, we propose to distinguish between sexual interaction (1) through, (2) via, and (3) with digital technologies. (1) Sexual interactions through digital technologies are face-to-face sexual interactions that (a) have been started digitally (e.g., people initiating face-to-face sexual encounters through adult dating apps) or (b) are accompanied by digital technology (e.g., couples augmenting their face-to-face sexual encounters through filming themselves during the act and publishing the amateur pornography online). (2) Sexual interactions via digital technology are technology-mediated interpersonal sexual interactions (e.g., via text chat: cybersex; via smartphone: sexting; via webcam: webcam sex/camming). (3) Sexual interactions with digital technology occur when the technology itself has the role of an interaction partner (e.g., sexual interaction with a sex robot or with a media persona in pornography). The three types of SIDC and their respective subtypes are explained and backed up with empirical studies that are grouped according to two major mediators: consent and commerce. Regarding the causes and consequences of the three types of SIDC we suggest a classification that entails biological, psychological, social, economic, and technological factors. Regarding implications of SIDC we suggest to focus on both opportunities and risks for sexual health. The proposed conceptual framework of SIDC is meant to inform future research.
https://doi.org/10.3389/fpsyg.2021.769732
Digitale Gender-Diversity-Bildungsangebote an der TU Ilmenau: eine Zwischenbilanz nach zehn Jahren. - In: Freiburger Zeitschrift für GeschlechterStudien, ISSN 2196-4459, Bd. 27 (2021), 1, S. 167-172
https://doi.org/10.3224/fzg.v27i1.18
Die "normative Macht" der Plattformen - Gegenstand der zukünftigen Digitalregulierung? : Erfassung und mögliche Grenzen der regulierenden und verhaltenssteuernden Macht von Unternehmen im Digitalen. - In: MMR, ISSN 2698-7988, Bd. 24 (2021), 11, S. 857-861
Sensor simulation for monocular depth estimation using deep neural networks. - In: 2021 International Conference on Cyberworlds, (2021), S. 9-16
Depth estimation is one of the basic building blocks for scene understanding. In the case of monocular depth estimation using neural networks, many such approaches are highly hardware dependent because they result in a task- and environment-specific optimizing problem. Most DNN methods use commonly available datasets which leads to overfitting on particular sensor properties. Finding a generalized model with the consideration of different hardware properties of sensors and platforms is challenging if not impossible. For this reason, it is desirable to adapt existing and well-trained models into a new domain in order to let them simulate different depth sensors without the need for large datasets and time-consuming learning. Therefore, a small dataset has been created with the Structure Sensor for evaluating the transferable structural characteristic between neural networks. Finally, two input feature representations for the neural networks are considered to mimic the depth sensor including its artifacts including holes. The results show that a simple domain adaptation technique and a small dataset are adequate to simulate and adapt to a specific domain from a target domain. Therefore, the network is able to accurately predict depth maps as if they were created by a specific depth sensor. This also includes unique artifacts of the sensor, thereby allowing for a plausible simulation of specific depth sensing hardware which is beneficial for areas like prototyping in the context of Augmented Reality.
https://doi.org/10.1109/CW52790.2021.00010