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Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App
MindScape aims to study the benefits of integrating time series behavioral patterns (e. g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being.
From User Surveys to Telemetry-Driven Agents: Exploring the Potential of Personalized Productivity Solutions
We present a comprehensive, user-centric approach to understand preferences in AI-based productivity agents and develop personalized solutions tailored to users' needs.
Affective Conversational Agents: Understanding Expectations and Personal Influences
The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains.
Personal Productivity and Well-being -- Chapter 2 of the 2021 New Future of Work Report
We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work.
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization
We show that person-independent models yield significantly lower performance (55% average F1 and accuracy across 40 subjects) than person-dependent models (60. 3%), leading to a generalization gap of 5. 3%.
Designing Style Matching Conversational Agents
Advances in machine intelligence have enabled conversational interfaces that have the potential to radically change the way humans interact with machines.
