Scholar's Hub

The results indicate that a conversational “check-in” medication management assistant increased system acceptance while also potentially decreasing the likelihood of accidental over-medication, a common concern for older adults dealing with MCI.

An exploratory human-subject experiment finds that while the level of model confidence significantly affects people’s belief in model accuracy, both the model’'s stated and observed accuracy generally have a larger impact on people's willingness to follow the model's predictions as well as their self-reported levels of trust in the model.

A comprehensive inventory of the kinds of maugs that appear in math documents, and a detailed account of how authors’ tools ought to be redesigned to support efficient creation of math augmentations are contributed.

Findings showed that the TAC toolkit revised and extended designers’ knowledge of technology acceptance, fostered their appreciation, empathy and ethical values while designing for acceptance, and contributed towards shaping their future design practice.

Researchers and designers are suggested to draw inspiration from no/low-technology artifacts, use empirical research to identify contextual features that have negative impacts on users, and conduct user studies on affective sense-making.

This work investigates the gap between mobile learners’ challenges and video engineers’ considerations using mixed methods and provides design guidelines for creating mobile-friendly MOOC videos and presents and validate guidelines for designing mobile- friendly MOOCs.

It is concluded that the pinching interaction between the thumb and index finger is a promising modality also for one-dimensional input on higher scales and implications for future user interfaces that benefit from pinching as an additional and complementary interaction modality are discussed.

Three empirical studies are conducted that seek to verify the relationship between the identification of color accessibility problems by people with typical color vision using simulations and people with color blindness, in the context of mobile games

The findings demonstrate the need for careful analysis of user-moderator alignment at multiple levels and argue that future work should focus on building tools to empower communities to conduct these analyses themselves.

The findings demonstrated the potential of SUMMIT, and the corresponding design guidelines, in supporting users to acquire information from lengthy discussions in ITSs, and sheds light on key design considerations and features when exploring crowd-based and machine-learning-enabled instruments for asynchronous collaboration on complex tasks such as OSS development.

It is shown that the streamers who self-identify as LGBTQ+ and/or Black were disproportionately targeted and that hate raid messages were most commonly rooted in anti-Black racism and antisemitism.

It is explored how the two types of uncertainty—aleatoric and epistemic—can help non-expert users understand the strengths and weaknesses of a classifier in an interactive setting and the need for two-level uncertainty in Machine Teaching is questioned.

A novel data processing pipeline for hand gesture recognition that combines advanced full-wave electromagnetic modelling and inversion with machine learning is presented that enables the use of simple gesture recognition algorithms, such as template-matching recognizers, that can be trained in real time and provide competitive accuracy with only a few samples.

This work develops a perceptual model to predict temporal gaze behavior, particularly saccadic latency, as a function of the statistics of a displayed image, and implements a neurologically-inspired probabilistic model that mimics the accumulation of confidence that leads to a perceptual decision.

CLIPasso is presented, an object sketching method that can achieve different levels of abstraction, guided by geometric and semantic simplifications, and utilize the remarkable ability of CLIP (Contrastive-Language-Image-Pretraining) to distill semantic concepts from sketches and images alike.

This work presents a new approach to perform queries directly on general neural implicit functions for a wide range of existing architectures, using automatic arithmetic rules to bound the output of a network over a region and conducting a study of range analysis on neural networks.

A versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations is introduced, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of 1920×1080.

It is demonstrated that the learned periodic embedding can significantly help to improve neural motion synthesis in a number of tasks, including diverse locomotion skills, style-based move- ments, dance motion synthesis from music, synthesis of dribbling motions in football, and motion query for matching poses within large animation databases.

A study in which machine-learning models were deployed to detect receptivity in the natural environment, i.e., in free-living conditions and deployed a chatbot-based digital coach that provided physical-activity interventions and motivated participants to achieve their step goals.

The study with 12 BLV creators demonstrated that GenAssist enables and simplifies the process of image selection and generation, making visual authoring more accessible to all.

This work describes an architecture that extends a large language model to store a complete record of the agent’s experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior.

This study revealed that the Grid-Coding prototype enabled BLV programmers to navigate source code quickly, find the context of a statement easily, detect syntax errors in existing code effectively, and write new code with fewer syntax errors.

This paper presents CrossA11y, a system that helps authors efficiently detect and address visual and auditory accessibility issues in videos by using cross-modal grounding analysis and automatically measures accessibility of visual and audio segments in a video by checking for modality asymmetries.

This work presents the first known method of implementing an “incognito mode” for VR, which leverages local ε-differential privacy to quantifiably obscure sensitive user data attributes and shows a significant degradation of attacker capabilities when using this solution.

A novel design space of intuitive augmented reality (AR) magnifications where an AR head-mounted display is used for the presentation of real-time magnified camera imagery and a user study evaluating and comparing different visual presentation methods and AR interaction techniques is presented.

The results indicate several issues in terms of the description of the experimental setup, the scientific rigor of the research process, and the generalizability of results, which altogether points towards a potential replication crisis in VR locomotion research.