136,739 research outputs found
Modeling Interdependent and Periodic Real-World Action Sequences
Mobile health applications, including those that track activities such as
exercise, sleep, and diet, are becoming widely used. Accurately predicting
human actions is essential for targeted recommendations that could improve our
health and for personalization of these applications. However, making such
predictions is extremely difficult due to the complexities of human behavior,
which consists of a large number of potential actions that vary over time,
depend on each other, and are periodic. Previous work has not jointly modeled
these dynamics and has largely focused on item consumption patterns instead of
broader types of behaviors such as eating, commuting or exercising. In this
work, we develop a novel statistical model for Time-varying, Interdependent,
and Periodic Action Sequences. Our approach is based on personalized,
multivariate temporal point processes that model time-varying action
propensities through a mixture of Gaussian intensities. Our model captures
short-term and long-term periodic interdependencies between actions through
Hawkes process-based self-excitations. We evaluate our approach on two activity
logging datasets comprising 12 million actions taken by 20 thousand users over
17 months. We demonstrate that our approach allows us to make successful
predictions of future user actions and their timing. Specifically, our model
improves predictions of actions, and their timing, over existing methods across
multiple datasets by up to 156%, and up to 37%, respectively. Performance
improvements are particularly large for relatively rare and periodic actions
such as walking and biking, improving over baselines by up to 256%. This
demonstrates that explicit modeling of dependencies and periodicities in
real-world behavior enables successful predictions of future actions, with
implications for modeling human behavior, app personalization, and targeting of
health interventions.Comment: Accepted at WWW 201
Spectral convergence in tapping and physiological fluctuations: coupling and independence of 1/f noise in the central and autonomic nervous systems.
When humans perform a response task or timing task repeatedly, fluctuations in measures of timing from one action to the next exhibit long-range correlations known as 1/f noise. The origins of 1/f noise in timing have been debated for over 20 years, with one common explanation serving as a default: humans are composed of physiological processes throughout the brain and body that operate over a wide range of timescales, and these processes combine to be expressed as a general source of 1/f noise. To test this explanation, the present study investigated the coupling vs. independence of 1/f noise in timing deviations, key-press durations, pupil dilations, and heartbeat intervals while tapping to an audiovisual metronome. All four dependent measures exhibited clear 1/f noise, regardless of whether tapping was synchronized or syncopated. 1/f spectra for timing deviations were found to match those for key-press durations on an individual basis, and 1/f spectra for pupil dilations matched those in heartbeat intervals. Results indicate a complex, multiscale relationship among 1/f noises arising from common sources, such as those arising from timing functions vs. those arising from autonomic nervous system (ANS) functions. Results also provide further evidence against the default hypothesis that 1/f noise in human timing is just the additive combination of processes throughout the brain and body. Our findings are better accommodated by theories of complexity matching that begin to formalize multiscale coordination as a foundation of human behavior
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved
utilizing reliable trajectory prediction methods. More precise situational
awareness, in turn, results in notably better performance of critical safety
applications, such as Forward Collision Warning (FCW), as well as comfort
applications like Cooperative Adaptive Cruise Control (CACC). Therefore,
vehicle trajectory prediction problem needs to be deeply investigated in order
to come up with an end to end framework with enough precision required by the
safety applications' controllers. This problem has been tackled in the
literature using different methods. However, machine learning, which is a
promising and emerging field with remarkable potential for time series
prediction, has not been explored enough for this purpose. In this paper, a
two-layer neural network-based system is developed which predicts the future
values of vehicle parameters, such as velocity, acceleration, and yaw rate, in
the first layer and then predicts the two-dimensional, i.e. longitudinal and
lateral, trajectory points based on the first layer's outputs. The performance
of the proposed framework has been evaluated in realistic cut-in scenarios from
Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable
improvement in the prediction accuracy in comparison with the kinematics model
which is the dominant employed model by the automotive industry. Both ideal and
nonideal communication circumstances have been investigated for our system
evaluation. For non-ideal case, an estimation step is included in the framework
before the parameter prediction block to handle the drawbacks of packet drops
or sensor failures and reconstruct the time series of vehicle parameters at a
desirable frequency
Recommended from our members
A decision support environment for behavioral synthesis
We present a specification of a general environment for behavioral synthesis centered around the user/designer as the primary motivator for decisions in design development. At each stage of the design process, the user can perform transformations on the design description through graphical user interfaces. Quality measures, physical estimates, and design hints are given to the user at each stage
An Empirical Investigation of Gaming Responses to Performance Incentives
This paper studies a particular kind of gaming responses to explicit incentives in a large government organization. The gaming responses we consider occur when agents strategically report their performance outcomes to maximize their awards. An important contribution of this work is to examine whether this behavior diverts resources (e.g. agents' time) from productive activities or whether it simply reflects an accounting phenomenon. We evaluate the efficiency impact of the behavior we identify and find that it has a negative impact on the true goal of the organization.
Machine Understanding of Human Behavior
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
- …