25,385 research outputs found
Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach
As pre-diagnostic technologies are becoming increasingly accessible, using
them to improve the quality of care available to dementia patients and their
caregivers is of increasing interest. Specifically, we aim to develop a tool
for non-invasively assessing task performance in a simple gaming application.
To address this, we have developed Caregiver Assessment using Smart Gaming
Technology (CAST), a mobile application that personalizes a traditional word
scramble game. Its core functionality uses a Fuzzy Inference System (FIS)
optimized via a Genetic Algorithm (GA) to provide customized performance
measures for each user of the system. With CAST, we match the relative level of
difficulty of play using the individual's ability to solve the word scramble
tasks. We provide an analysis of the preliminary results for determining task
difficulty, with respect to our current participant cohort.Comment: 7 pages, 1 figures, 6 table
A Survey of Brain Inspired Technologies for Engineering
Cognitive engineering is a multi-disciplinary field and hence it is difficult
to find a review article consolidating the leading developments in the field.
The in-credible pace at which technology is advancing pushes the boundaries of
what is achievable in cognitive engineering. There are also differing
approaches to cognitive engineering brought about from the multi-disciplinary
nature of the field and the vastness of possible applications. Thus research
communities require more frequent reviews to keep up to date with the latest
trends. In this paper we shall dis-cuss some of the approaches to cognitive
engineering holistically to clarify the reasoning behind the different
approaches and to highlight their strengths and weaknesses. We shall then show
how developments from seemingly disjointed views could be integrated to achieve
the same goal of creating cognitive machines. By reviewing the major
contributions in the different fields and showing the potential for a combined
approach, this work intends to assist the research community in devising more
unified methods and techniques for developing cognitive machines
Links between the personalities, styles and performance in computer programming
There are repetitive patterns in strategies of manipulating source code. For
example, modifying source code before acquiring knowledge of how a code works
is a depth-first style and reading and understanding before modifying source
code is a breadth-first style. To the extent we know there is no study on the
influence of personality on them. The objective of this study is to understand
the influence of personality on programming styles. We did a correlational
study with 65 programmers at the University of Stuttgart. Academic achievement,
programming experience, attitude towards programming and five personality
factors were measured via self-assessed survey. The programming styles were
asked in the survey or mined from the software repositories. Performance in
programming was composed of bug-proneness of programmers which was mined from
software repositories, the grades they got in a software project course and
their estimate of their own programming ability. We did statistical analysis
and found that Openness to Experience has a positive association with
breadth-first style and Conscientiousness has a positive association with
depth-first style. We also found that in addition to having more programming
experience and better academic achievement, the styles of working depth-first
and saving coarse-grained revisions improve performance in programming.Comment: 27 pages, 6 figure
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
In modern computer science education, massive open online courses (MOOCs) log
thousands of hours of data about how students solve coding challenges. Being so
rich in data, these platforms have garnered the interest of the machine
learning community, with many new algorithms attempting to autonomously provide
feedback to help future students learn. But what about those first hundred
thousand students? In most educational contexts (i.e. classrooms), assignments
do not have enough historical data for supervised learning. In this paper, we
introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero
shot" feedback challenge. We are able to provide autonomous feedback for the
first students working on an introductory programming assignment with accuracy
that substantially outperforms data-hungry algorithms and approaches human
level fidelity. Rubric sampling requires minimal teacher effort, can associate
feedback with specific parts of a student's solution and can articulate a
student's misconceptions in the language of the instructor. Deep learning
inference enables rubric sampling to further improve as more assignment
specific student data is acquired. We demonstrate our results on a novel
dataset from Code.org, the world's largest programming education platform.Comment: To appear at AAAI 2019; 9 page
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for
semantically parsing documents in specific domains. Basically, OONP reads a
document and parses it into a predesigned object-oriented data structure
(referred to as ontology in this paper) that reflects the domain-specific
semantics of the document. An OONP parser models semantic parsing as a decision
process: a neural net-based Reader sequentially goes through the document, and
during the process it builds and updates an intermediate ontology to summarize
its partial understanding of the text it covers. OONP supports a rich family of
operations (both symbolic and differentiable) for composing the ontology, and a
big variety of forms (both symbolic and differentiable) for representing the
state and the document. An OONP parser can be trained with supervision of
different forms and strength, including supervised learning (SL) ,
reinforcement learning (RL) and hybrid of the two. Our experiments on both
synthetic and real-world document parsing tasks have shown that OONP can learn
to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201
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