43 research outputs found
A Survey of Brain Computer Interface Using Non-Invasive Methods
Research on Brain-Computer Interface (BCI) began in the 1970s and has
increased in volume and diversified significantly since then. Today BCI is
widely used for applications like assistive devices for physically challenged
users, mental state monitoring, input devices for hands-free applications,
marketing, education, security, games and entertainment. This article explores
the advantages and disadvantages of invasive and non-invasive BCI technologies
and focuses on use cases of several non-invasive technologies, namely
electroencephalogram (EEG), functional Magnetic Resonance Imaging (fMRI), Near
Infrared Spectroscopy (NIRs) and hybrid systems
Parikh One-Counter Automata
Counting abilities in finite automata are traditionally provided by two orthogonal extensions: adding a single counter that can be tested for zeroness at any point, or adding ?-valued counters that are tested for equality only at the end of runs. In this paper, finite automata extended with both types of counters are introduced. They are called Parikh One-Counter Automata (POCA): the "Parikh" part referring to the evaluation of counters at the end of runs, and the "One-Counter" part to the single counter that can be tested during runs.
Their expressiveness, in the deterministic and nondeterministic variants, is investigated; it is shown in particular that there are deterministic POCA languages that cannot be expressed without nondeterminism in the original models. The natural decision problems are also studied; strikingly, most of them are no harder than in the original models. A parametric version of nonemptiness is also considered
A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing
Emotions are an essential part of human behavior that can impact thinking,
decision-making, and communication skills. Thus, the ability to accurately
monitor and identify emotions can be useful in many human-centered applications
such as behavioral training, tracking emotional well-being, and development of
human-computer interfaces. The correlation between patterns in physiological
data and affective states has allowed for the utilization of deep learning
techniques which can accurately detect the affective states of a person.
However, the generalisability of existing models is often limited by the
subject-dependent noise in the physiological data due to variations in a
subject's reactions to stimuli. Hence, we propose a novel cost function that
employs Optimal Transport Theory, specifically Wasserstein Distance, to scale
the importance of subject-dependent data such that higher importance is
assigned to patterns in data that are common across all participants while
decreasing the importance of patterns that result from subject-dependent noise.
The performance of the proposed cost function is demonstrated through an
autoencoder with a multi-class classifier attached to the latent space and
trained simultaneously to detect different affective states. An autoencoder
with a state-of-the-art loss function i.e., Mean Squared Error, is used as a
baseline for comparison with our model across four different commonly used
datasets. Centroid and minimum distance between different classes are used as a
metrics to indicate the separation between different classes in the latent
space. An average increase of 14.75% and 17.75% (from benchmark to proposed
loss function) was found for minimum and centroid euclidean distance
respectively over all datasets.Comment: 9 page
NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
Although a lot of research has been done on utilising Online Social Media
during disasters, there exists no system for a specific task that is critical
in a post-disaster scenario -- identifying resource-needs and
resource-availabilities in the disaster-affected region, coupled with their
subsequent matching. To this end, we present NARMADA, a semi-automated platform
which leverages the crowd-sourced information from social media posts for
assisting post-disaster relief coordination efforts. The system employs Natural
Language Processing and Information Retrieval techniques for identifying
resource-needs and resource-availabilities from microblogs, extracting
resources from the posts, and also matching the needs to suitable
availabilities. The system is thus capable of facilitating the judicious
management of resources during post-disaster relief operations.Comment: ACL 2020 Workshop on Natural Language Processing for Social Media
(SocialNLP