Search Results for author:
Found 6 papers, 1 papers with code
FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer
The success of a specific neural network architecture is closely tied to the dataset and task it tackles; there is no one-size-fits-all solution.
CLAN: A Contrastive Learning based Novelty Detection Framework for Human Activity Recognition
It is tailored to challenges with human activity characteristics, including the significance of temporal and frequency features, complex activity dynamics, shared features across activities, and sensor modality variations.
DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition
The dataset, DOO-RE, includes data streams from various ambient sensor types such as Sound and Projector.
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space
In this paper, we focus on a group activity by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme which extracts causality patterns from pervasive sensor event sequences generated by a group of users to support as good recognition accuracy as the state-of-the-art graphical model.
Beyond COVID-19: Network science and sustainable exit strategies
On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc.
Physics and Society
Automated Contact Tracing: a game of big numbers in the time of COVID-19
In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2.
