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 introduces a semi-automatic system leveraging vector charts to extract the underlying data easily and accurately, and successfully recovered all data, even when hidden, with a 78% lower relative error.

This paper analyzes how the CHI literature might have changed in research ethics, openness, and transparency by analyzing samples of 118 CHI 2017 and 127 CHI 2022 papers—randomly drawn and stratified across conference sessions.

This work conducts user-centred design activities to build three high-fidelity AAC prototypes with different communities with CCNs, specialists and stakeholders, and conducts two qualitative focus groups using convergent and divergent co-design methods with people with the language impairment aphasia.

It is concluded that GPT-3 can, in this context, yield believable accounts of HCI experiences and the low cost and high speed of LLM data generation should be useful in ideating and piloting new experiments, although any findings must obviously always be validated with real data.

DataParticles is a block-based story editor that leverages the latent connections between text, data, and visualizations to help creators flexibly prototype, explore, and iterate on a story narrative and its corresponding visualizations.

The need to expand an existing taxonomy of harm to include emotional load and social injustice is proposed and recommendations for designers interested in dismantling the deceptive infrastructure of safety technologies are offered.

This work presents a technique to render tactile feedback to the palmar side of the hand while keeping it unobstructed and preserving manual dexterity during interactions with physical objects, and enables new applications such as tactile notifications during dexterous activities or VR experiences that rely heavily on physical props.

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.