69 research outputs found
Understanding ACT-R - an Outsider's Perspective
The ACT-R theory of cognition developed by John Anderson and colleagues
endeavors to explain how humans recall chunks of information and how they solve
problems. ACT-R also serves as a theoretical basis for "cognitive tutors",
i.e., automatic tutoring systems that help students learn mathematics, computer
programming, and other subjects. The official ACT-R definition is distributed
across a large body of literature spanning many articles and monographs, and
hence it is difficult for an "outsider" to learn the most important aspects of
the theory. This paper aims to provide a tutorial to the core components of the
ACT-R theory
Discriminately Decreasing Discriminability with Learned Image Filters
In machine learning and computer vision, input images are often filtered to
increase data discriminability. In some situations, however, one may wish to
purposely decrease discriminability of one classification task (a "distractor"
task), while simultaneously preserving information relevant to another (the
task-of-interest): For example, it may be important to mask the identity of
persons contained in face images before submitting them to a crowdsourcing site
(e.g., Mechanical Turk) when labeling them for certain facial attributes.
Another example is inter-dataset generalization: when training on a dataset
with a particular covariance structure among multiple attributes, it may be
useful to suppress one attribute while preserving another so that a trained
classifier does not learn spurious correlations between attributes. In this
paper we present an algorithm that finds optimal filters to give high
discriminability to one task while simultaneously giving low discriminability
to a distractor task. We present results showing the effectiveness of the
proposed technique on both simulated data and natural face images
Can Who-Edits-What Predict Edit Survival?
As the number of contributors to online peer-production systems grows, it
becomes increasingly important to predict whether the edits that users make
will eventually be beneficial to the project. Existing solutions either rely on
a user reputation system or consist of a highly specialized predictor that is
tailored to a specific peer-production system. In this work, we explore a
different point in the solution space that goes beyond user reputation but does
not involve any content-based feature of the edits. We view each edit as a game
between the editor and the component of the project. We posit that the
probability that an edit is accepted is a function of the editor's skill, of
the difficulty of editing the component and of a user-component interaction
term. Our model is broadly applicable, as it only requires observing data about
who makes an edit, what the edit affects and whether the edit survives or not.
We apply our model on Wikipedia and the Linux kernel, two examples of
large-scale peer-production systems, and we seek to understand whether it can
effectively predict edit survival: in both cases, we provide a positive answer.
Our approach significantly outperforms those based solely on user reputation
and bridges the gap with specialized predictors that use content-based
features. It is simple to implement, computationally inexpensive, and in
addition it enables us to discover interesting structure in the data.Comment: Accepted at KDD 201
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