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.

This work repurposes a Voice PickUp Unit (VPU) to capture SAWs on surfaces (including smooth surfaces, odd geometries, and fabrics) over long distances and in noisy environments, and demonstrates utility in both interactive and activity recognition applications.

Videos of the Waymo and Tesla FSD self-driving cars show how these systems fail to both yield for others, as well as failing to go when yielded to, illustrating both the complexity of designing for road interaction, but also how the space of physical machine/human social interactions more broadly can be designed for.

The results show that an infinite-horizon SOFC model is able to reproduce key characteristics of redirected reaches and highlight the benefits of SOFC as a tool for simulating, evaluating, and gaining insights about redirection techniques.

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

It is shown that the effects of AI-based credibility indicators on the detection and spread of misinformation are larger as compared to when social influence is absent, when these indicators are provided to people before they form their own judgements about the news.

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.