2,457 research outputs found
Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures
Event detection is a critical feature in data-driven systems as it assists
with the identification of nominal and anomalous behavior. Event detection is
increasingly relevant in robotics as robots operate with greater autonomy in
increasingly unstructured environments. In this work, we present an accurate,
robust, fast, and versatile measure for skill and anomaly identification. A
theoretical proof establishes the link between the derivative of the
log-likelihood of the HMM filtered belief state and the latest emission
probabilities. The key insight is the inverse relationship in which gradient
analysis is used for skill and anomaly identification. Our measure showed
better performance across all metrics than related state-of-the art works. The
result is broadly applicable to domains that use HMMs for event detection.Comment: 8 pages, 7 figures, double col, ieee conference forma
Conflict and Computation on Wikipedia: a Finite-State Machine Analysis of Editor Interactions
What is the boundary between a vigorous argument and a breakdown of
relations? What drives a group of individuals across it? Taking Wikipedia as a
test case, we use a hidden Markov model to approximate the computational
structure and social grammar of more than a decade of cooperation and conflict
among its editors. Across a wide range of pages, we discover a bursty war/peace
structure where the systems can become trapped, sometimes for months, in a
computational subspace associated with significantly higher levels of
conflict-tracking "revert" actions. Distinct patterns of behavior characterize
the lower-conflict subspace, including tit-for-tat reversion. While a fraction
of the transitions between these subspaces are associated with top-down actions
taken by administrators, the effects are weak. Surprisingly, we find no
statistical signal that transitions are associated with the appearance of
particularly anti-social users, and only weak association with significant news
events outside the system. These findings are consistent with transitions being
driven by decentralized processes with no clear locus of control. Models of
belief revision in the presence of a common resource for information-sharing
predict the existence of two distinct phases: a disordered high-conflict phase,
and a frozen phase with spontaneously-broken symmetry. The bistability we
observe empirically may be a consequence of editor turn-over, which drives the
system to a critical point between them.Comment: 23 pages, 3 figures. Matches published version. Code for HMM fitting
available at http://bit.ly/sfihmm ; time series and derived finite state
machines at bit.ly/wiki_hm
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