1,279 research outputs found
A Call to Arms: Revisiting Database Design
Good database design is crucial to obtain a sound, consistent database, and -
in turn - good database design methodologies are the best way to achieve the
right design. These methodologies are taught to most Computer Science
undergraduates, as part of any Introduction to Database class. They can be
considered part of the "canon", and indeed, the overall approach to database
design has been unchanged for years. Moreover, none of the major database
research assessments identify database design as a strategic research
direction.
Should we conclude that database design is a solved problem?
Our thesis is that database design remains a critical unsolved problem.
Hence, it should be the subject of more research. Our starting point is the
observation that traditional database design is not used in practice - and if
it were used it would result in designs that are not well adapted to current
environments. In short, database design has failed to keep up with the times.
In this paper, we put forth arguments to support our viewpoint, analyze the
root causes of this situation and suggest some avenues of research.Comment: Removed spurious column break. Nothing else was change
Development of an Optimization-Based Atomistic-to-Continuum Coupling Method
Atomistic-to-Continuum (AtC) coupling methods are a novel means of computing
the properties of a discrete crystal structure, such as those containing
defects, that combine the accuracy of an atomistic (fully discrete) model with
the efficiency of a continuum model. In this note we extend the
optimization-based AtC, formulated in arXiv:1304.4976 for linear,
one-dimensional problems to multi-dimensional settings and arbitrary
interatomic potentials. We conjecture optimal error estimates for the
multidimensional AtC, outline an implementation procedure, and provide
numerical results to corroborate the conjecture for a 1D Lennard-Jones system
with next-nearest neighbor interactions.Comment: 12 pages, 3 figure
An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises
Technology-assisted systems to monitor and assess rehabilitation
exercises have an opportunity of enhancing rehabilitation practices
by automatically collecting patient’s quantitative performance data.
However, even if a complex algorithm (e.g. Neural Network) is
applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex
algorithm is limited to be a black-box system that cannot provide
explanations on its predictions. To address these challenges, this
paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable
artificial intelligence (AI) technique for quantitative assessment of
stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with
therapists and the dataset of three upper-limb stroke rehabilitation
exercises from 15 post-stroke and 11 healthy subjects using a Kinect
sensor. Experimental results show that a hybrid model can achieve
comparable performance with a ML model using Neural Network,
but also provide explanations on a model prediction with a RB
model. The results indicate the potential of a hybrid model as an
explainable AI technique to support the interpretation of a model
and fine-tune a model with user-specific rules for personalization.info:eu-repo/semantics/publishedVersio
Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapy
A robotic exercise coaching system requires the
capability of automatically assessing a patient’s exercise to in teract with a patient and generate corrective feedback. However,
even if patients have various physical conditions, most prior
work on robotic exercise coaching systems has utilized generic,
pre-defined feedback.
This paper presents an interactive approach that combines
machine learning and rule-based models to automatically assess
a patient’s rehabilitation exercise and tunes with patient’s
data to generate personalized corrective feedback. To generate
feedback when an erroneous motion occurs, our approach
applies an ensemble voting method that leverages predictions
from multiple frames for frame-level assessment. According to
the evaluation with the dataset of three stroke rehabilitation
exercises from 15 post-stroke subjects, our interactive approach
with an ensemble voting method supports more accurate frame level assessment (p < 0.01), but also can be tuned with held-out
user’s unaffected motions to significantly improve the perfor mance of assessment from 0.7447 to 0.8235 average F1-scores
over all exercises (p < 0.01). This paper discusses the value of
an interactive approach with an ensemble voting method for
personalized interaction of a robotic exercise coaching system.info:eu-repo/semantics/publishedVersio
Finding the optimal time window for increased classification accuracy during motor imagery
Motor imagery classification using electroencephalography is based on feature extraction over a length of
time, and different configurations of settings can alter the performance of a classifier. Nevertheless, there
is a lack of standardized settings for motor imagery classification. This work analyzes the effect of age on
motor imagery training performance for two common spatial pattern-based classifier pipelines and various
configurations of timing parameters, such as epochs, windows, and offsets. Results showed significant (p
≤ 0.01) inverse correlations between performance and feature quantity, as well as between performance and
epoch/window ratio.info:eu-repo/semantics/publishedVersio
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