65,400 research outputs found
A Classification Model for Sensing Human Trust in Machines Using EEG and GSR
Today, intelligent machines \emph{interact and collaborate} with humans in a
way that demands a greater level of trust between human and machine. A first
step towards building intelligent machines that are capable of building and
maintaining trust with humans is the design of a sensor that will enable
machines to estimate human trust level in real-time. In this paper, two
approaches for developing classifier-based empirical trust sensor models are
presented that specifically use electroencephalography (EEG) and galvanic skin
response (GSR) measurements. Human subject data collected from 45 participants
is used for feature extraction, feature selection, classifier training, and
model validation. The first approach considers a general set of
psychophysiological features across all participants as the input variables and
trains a classifier-based model for each participant, resulting in a trust
sensor model based on the general feature set (i.e., a "general trust sensor
model"). The second approach considers a customized feature set for each
individual and trains a classifier-based model using that feature set,
resulting in improved mean accuracy but at the expense of an increase in
training time. This work represents the first use of real-time
psychophysiological measurements for the development of a human trust sensor.
Implications of the work, in the context of trust management algorithm design
for intelligent machines, are also discussed.Comment: 20 page
Automatic emotional state detection using facial expression dynamic in videos
In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states.
The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems
Dropout Model Evaluation in MOOCs
The field of learning analytics needs to adopt a more rigorous approach for
predictive model evaluation that matches the complex practice of
model-building. In this work, we present a procedure to statistically test
hypotheses about model performance which goes beyond the state-of-the-practice
in the community to analyze both algorithms and feature extraction methods from
raw data. We apply this method to a series of algorithms and feature sets
derived from a large sample of Massive Open Online Courses (MOOCs). While a
complete comparison of all potential modeling approaches is beyond the scope of
this paper, we show that this approach reveals a large gap in dropout
prediction performance between forum-, assignment-, and clickstream-based
feature extraction methods, where the latter is significantly better than the
former two, which are in turn indistinguishable from one another. This work has
methodological implications for evaluating predictive or AI-based models of
student success, and practical implications for the design and targeting of
at-risk student models and interventions
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
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