Please login to be able to save your searches and receive alerts for new content matching your search criteria.
This work aims to explore how a user’s understanding of a creative AI’s decision-making affects their experience when collaborating with it, through the inclusion of Explainable AI features in an interactive generative music system. We have created ...
While anomaly detection stands among the most important and valuable problems across many scientific domains, anomaly detection research often focuses on AI methods that can lack the nuance and interpretability so critical to conducting scientific ...
Smart home environments are designed to provide services that help improve the quality of life for the occupant via a variety of sensors and actuators installed throughout the space. Many automated actions taken by a smart home are governed by the output ...
Intuitive human-robot collaboration requires adaptive modalities for humans and robots to communicate and learn from each other. For diverse teams of humans and robots to naturally collaborate on novel tasks, robots must be able to model roles for ...
We examine the problem of smoothed online optimization, where a decision maker must sequentially choose points in a normed vector space to minimize the sum of per-round, non-convex hitting costs and the costs of switching decisions between rounds. The ...
The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training time of Deep Neural Networks (DNNs). This article presents a systematic ...
Tsetlin Machine (TM) is a new machine learning algorithm that encodes propositional logic into learning automata---a set of logical expressions composed of boolean input features---to recognise patterns. The simplicity, efficiency, and accuracy of this ...
With the proliferation of high bandwidth cameras and AR/VR devices, and their increasing use in situation awareness applications, edge computing is gaining prominence to meet the throughput requirements of such applications. This work focuses on camera ...
We report goals, paper submissions, keynotes, and organizations of this UserNLP workshop. User-centered NLP can fill these gaps by explicitly considering stylistic variations across individuals or groups of individuals and focusing on user-level ...
A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is an expensive task, unsupervised and semi-supervised learning techniques ...
Current state-of-the-art object detection neural networks, such as YOLO and SSD, are trained and developed on server-class GPUs. These neural networks do not scale down well to resource-constrained devices, with both accuracy and precision taking a ...
With the widespread proliferation of (miniaturized) sensing facilities and the massive growth and popularity of the field of machine learning (ML) research, new frontiers in automated sensor data analysis have been explored that lead to paradigm shifts ...
Green Security Games (GSGs) have been successfully used in the protection of valuable resources such as fisheries, forests, and wildlife. Real-world deployment involves both resource allocation and subsequent coordinated patrolling with communication in ...
High-performing teams learn effective communication strategies to judiciously share information and reduce the cost of communication overhead. Within multi-agent reinforcement learning, synthesizing effective policies requires reasoning about when to ...
Generative deep learning algorithms have progressed to a point where it is difficult to tell the difference between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, ...
Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework ...
Research in sensor based human activity recognition (HAR) has been a core concern of the mobile and ubiquitous computing community. Sophisticated systems have been developed with the main view on applications of HAR methods in research settings. This ...
We present a method of training character manipulation of amorphous materials such as those often used in cooking. Common examples of amorphous materials include granular materials (salt, uncooked rice), fluids (honey), and visco-plastic materials (...
There is a growing interest in deploying complex deep neural networks (DNN) in autonomous systems to extract task-specific information from real-time sensor data and drive critical tasks. The perturbations in sensor data due to noise or environmental ...
Current research in human-agent interaction primarily focuses on short term interaction and rarely addresses day to day use. We propose a prototype system based on a genetic algorithm that places long term interaction as the core design goal. The goal ...