Search Results for author:
Found 17 papers, 8 papers with code
Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art
Our research hypothesizes that visual art contains a vast unexplored space of conceptual combinations, constrained not by inherent incompatibility, but by cognitive limitations imposed by artists' cultural, temporal, geographical and social contexts.
Empirical evidence of Large Language Model's influence on human spoken communication
Artificial Intelligence (AI) agents now interact with billions of humans in natural language, thanks to advances in Large Language Models (LLMs) like ChatGPT.
PrISM-Observer: Intervention Agent to Help Users Perform Everyday Procedures Sensed using a Smartwatch
Then, we implemented a real-time agent system using a smartwatch and conducted a user study in a cooking task scenario.
Coaching Copilot: Blended Form of an LLM-Powered Chatbot and a Human Coach to Effectively Support Self-Reflection for Leadership Growth
Chatbots' role in fostering self-reflection is now widely recognized, especially in inducing users' behavior change.
Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videos
Multimodal scene search of conversations is essential for unlocking valuable insights into social dynamics and enhancing our communication.
Evaluating Large Language Models' Ability Using a Psychiatric Screening Tool Based on Metaphor and Sarcasm Scenarios
Metaphors and sarcasm are precious fruits of our highly evolved social communication skills.
IteraTTA: An interface for exploring both text prompts and audio priors in generating music with text-to-audio models
However, compared to the image domain, gaining a clear understanding of the space of possible music audios is difficult because users cannot listen to the variations of the generated audios simultaneously.
CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models
The prompt can function by drawing their interest and lowering the hurdle for resumption even when the generated continuation is insufficient to substitute their work, while recent human-AI collaboration research aiming at work substitution depends on a stable high accuracy.
SLOPT: Bandit Optimization Framework for Mutation-Based Fuzzing
Fuzzing can be optimized when targeting specific programs, and given that, some studies have employed online optimization methods to do it automatically, i. e., tuning fuzzers for any given program in a program-agnostic manner.
Human-AI communication for human-human communication: Applying interpretable unsupervised anomaly detection to executive coaching
In this paper, we discuss the potential of applying unsupervised anomaly detection in constructing AI-based interactive systems that deal with highly contextual situations, i. e., human-human communication, in collaboration with domain experts.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Self-Supervised Contrastive Learning for Singing Voices
To acquire robust representations in an unsupervised manner, regular self-supervised contrastive learning trains neural networks to make the feature representation of a sample close to those of its computationally transformed versions.
AI for human assessment: What do professional assessors need?
Their comments revealed the effectiveness of introducing unsupervised anomaly detection to enhance their feeling of confidence and objectivity of the assessment along with potential use scenarios of such AI-based systems in human assessment.
Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics
To enable this, we introduce a framework that utilizes an existing pretrained model for style transfer to calculate a perceptual style distance to the reference sample and uses black-box optimization to find the parameters that minimize this distance.
Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation
Explicitly alerting users is not always an optimal intervention, especially when they are not motivated to obey.
Generate (non-software) Bugs to Fool Classifiers
We first show the feasibility of this approach in an attack against an image classifier by employing generative adversarial networks that produce image patches that have the appearance of a natural object to fool the target model.
Robust Audio Adversarial Example for a Physical Attack
We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world.
