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.

The results suggest that integrity and benevolence perceptions toward Google are significantly lower among technical participants than non-technical participants, and the direction of trust affecting privacy control adoption is observed to be reversed based on the purpose of the controls.

CareCall, an open-domain chatbot that aims to support socially isolated individuals via check-up phone calls and monitoring by teleoperators, is examined, finding CareCall offered a holistic understanding of each individual while offloading the public health workload and helped mitigate loneliness and emotional burdens.

It is demonstrated that stakeholders, even without AI knowledge, can provide specific and critical feedback on an AI system’s design and deployment, if empowered to do so.

Comparing platform and roaming authentication on smartphones is presented, determining the practical strengths and weaknesses of FIDO2 as perceived by users in a mobile scenario and identifying remaining adoption barriers that prevent F IDO2 from succeeding password authentication.

CiteSee is a paper reading tool that leverages a user’s publishing, reading, and saving activities to provide personalized visual augmentations and context around citations to help users prioritize their exploration.

LipLearner leverages contrastive learning to learn efficient lipreading representations, enabling few-shot command customization with minimal user effort, and exhibits high robustness to different lighting, posture, and gesture conditions on an in-the-wild dataset.

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.