Non-consensual Intimate Media (NCIM) refers to the distribution of sexual or intimate content without consent. NCIM is common and causes significant emotional, financial, and reputational harm. We developed Hands-Off, an interaction technique for messaging applications that deters non-consensual screenshots. Hands-Off requires recipients to perform a hand gesture in the air, above the device, to unlock media—which makes simultaneous screenshotting difficult. A lab study shows that Hands-Off gestures are easy to perform and reduce non-consensual screenshots by 67%. We conclude by generalizing this approach and introduce the idea of Feminist Interaction Techniques (FIT), interaction techniques that encode feminist values and speak to societal problems, and reflect on FIT’s opportunities and limitations.
https://doi.org/10.1145/3654777.3676380
This study investigates the effect of Lift-off Distance (LoD) on a computer mouse, which refers to the height at which a mouse sensor stops tracking when lifted off the surface. Although a low LoD is generally preferred to avoid unintentional cursor movement in mouse lifting (=clutching), especially in first-person shooter games, it may reduce tracking stability. We conducted a psychophysical experiment to measure the perceptible differences between LoD levels and quantitatively measured the unintentional cursor movement error and tracking stability at four levels of LoD while users performed mouse lifting. The results showed a trade-off between movement error and tracking stability at varying levels of LoD. Our findings offer valuable information on optimal LoD settings, which could serve as a guide for choosing a proper mouse device for enthusiastic gamers.
https://doi.org/10.1145/3654777.3676442
Mouse movement data contain rich information about users, performed tasks, and user interfaces, but separating the respective components remains challenging and unexplored. As a first step to address this challenge, we propose DisMouse – the first method to disentangle user-specific and user-independent information and stochastic variations from mouse movement data. At the core of our method is an autoencoder trained in a semi-supervised fashion, consisting of a self-supervised denoising diffusion process and a supervised contrastive user identification module. Through evaluations on three datasets, we show that DisMouse 1) captures complementary information of mouse input, hence providing an interpretable framework for modelling mouse movements, 2) can be used to produce refined features, thus enabling various applications such as personalised and variable mouse data generation, and 3) generalises across different datasets. Taken together, our results underline the significant potential of disentangled representation learning for explainable, controllable, and generalised mouse behaviour modelling.
https://doi.org/10.1145/3654777.3676411
Blind users rely on keyboards and assistive technologies like screen readers to interact with user interface (UI) elements. In modern applications with complex UI hierarchies, navigating to different UI elements poses a significant accessibility challenge. Users must listen to screen reader audio descriptions and press relevant keyboard keys one at a time. This paper introduces Wheeler, a novel three-wheeled, mouse-shaped stationary input device, to address this issue. Informed by participatory sessions, Wheeler enables blind users to navigate up to three hierarchical levels in an app independently using three wheels instead of navigating just one level at a time using a keyboard. The three wheels also offer versatility, allowing users to repurpose them for other tasks, such as 2D cursor manipulation. A study with 12 blind users indicates a significant reduction (40%) in navigation time compared to using a keyboard. Further, a diary study with our blind co-author highlights Wheeler's additional benefits, such as accessing UI elements with partial metadata and facilitating mixed-ability collaboration.
https://doi.org/10.1145/3654777.3676396