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Found 19 papers, 3 papers with code
Hitting your MARQ: Multimodal ARgument Quality Assessment in Long Debate Video
The combination of gestures, intonations, and textual content plays a key role in argument delivery.
PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition
PULSAR is trained and evaluated on data collected from 382 participants (183 self-reported as PD patients).
PARK: Parkinson's Analysis with Remote Kinetic-tasks
We present a web-based framework to screen for Parkinson's disease (PD) by allowing users to perform neurological tests in their homes.
SAPIEN: Affective Virtual Agents Powered by Large Language Models
In this demo paper, we introduce SAPIEN, a platform for high-fidelity virtual agents driven by large language models that can hold open domain conversations with users in 13 different languages, and display emotions through facial expressions and voice.
Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework
Parkinson's disease (PD) diagnosis remains challenging due to lacking a reliable biomarker and limited access to clinical care.
Using AI to Measure Parkinson's Disease Severity at Home
We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD).
TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models
Our approach, TextMI, significantly reduces model complexity, adds interpretability to the model's decision, and can be applied for a diverse set of tasks while achieving superior (multimodal sarcasm detection) or near SOTA (multimodal sentiment analysis and multimodal humor detection) performance.
NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters
Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world.
A Survey on Open Information Extraction from Rule-based Model to Large Language Model
Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text, unrestricted by relation type or domain.
A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient
A schema-guided approach to dialogue management has been shown in recent work to be effective in creating robust customizable virtual agents capable of acting as friendly peers or task assistants.
Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos
In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severity-assessment) using computer vision.
DBATES: DataBase of Audio features, Text, and visual Expressions in competitive debate Speeches
In this work, we present a database of multimodal communication features extracted from debate speeches in the 2019 North American Universities Debate Championships (NAUDC).
A Mental Trespass? Unveiling Truth, Exposing Thoughts and Threatening Civil Liberties with Non-Invasive AI Lie Detection
In order to rectify these shortcomings, we introduce the legal concept of mental trespass and use this concept as the basis for proposed regulation.
Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality of Public Speeches
Our work ties together a novel metric for public speeches in both verbal and non-verbal domain with the computational power of a neural network to design a fair prediction system for speakers.
Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online
One of the symptoms of Parkinson's disease (PD) is hypomimia or reduced facial expressions.
Detecting Individuals with Depressive Disorder fromPersonal Google Search and YouTube History Logs
Depressive disorder is one of the most prevalent mental illnesses among the global population.
The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study
The goal of this study is to examine, among college students, the relationship between deteriorating mental health conditions and changes in user behaviors when engaging with Google Search and YouTube during COVID-19.
Detecting Parkinson's Disease From an Online Speech-task
In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD).
Integrating Multimodal Information in Large Pretrained Transformers
It does so by generating a shift to internal representation of BERT and XLNet; a shift that is conditioned on the visual and acoustic modalities.
