17,448 research outputs found
Deep learning investigation for chess player attention prediction using eye-tracking and game data
This article reports on an investigation of the use of convolutional neural
networks to predict the visual attention of chess players. The visual attention
model described in this article has been created to generate saliency maps that
capture hierarchical and spatial features of chessboard, in order to predict
the probability fixation for individual pixels Using a skip-layer architecture
of an autoencoder, with a unified decoder, we are able to use multiscale
features to predict saliency of part of the board at different scales, showing
multiple relations between pieces. We have used scan path and fixation data
from players engaged in solving chess problems, to compute 6600 saliency maps
associated to the corresponding chess piece configurations. This corpus is
completed with synthetically generated data from actual games gathered from an
online chess platform. Experiments realized using both scan-paths from chess
players and the CAT2000 saliency dataset of natural images, highlights several
results. Deep features, pretrained on natural images, were found to be helpful
in training visual attention prediction for chess. The proposed neural network
architecture is able to generate meaningful saliency maps on unseen chess
configurations with good scores on standard metrics. This work provides a
baseline for future work on visual attention prediction in similar contexts
Benefit-Cost Analysis for Transportation Planning and Public Policy: Towards Multimodal Demand Modeling
This report examines existing methods of benefit-cost analysis (BCA) in two areas, transportation policy and transportation planning, and suggests ways of modifying these methods to account for travel within a multimodal system. Although the planning and policy contexts differ substantially, this report shows how important multimodal impacts can be incorporated into both by using basic econometric techniques and even simpler rule-of-thumb methods. Case studies in transportation planning focus on the California Department of Transportation (Caltrans), but benchmark California’s competencies by exploring methods used by other states and local governments. The report concludes with a list and discussion of recommendations for improving transportation planning models and methods. These will have immediate use to decision makers at Caltrans and other state DOTs as they consider directions for developing new planning capabilities. This project also identifies areas, and lays groundwork, for future research. Finally, by fitting the planning models into the broader context of transportation policy, this report will serve as a resource for students and others who wish to better understand BCA and its use in practice
Multimodal Classification of Urban Micro-Events
In this paper we seek methods to effectively detect urban micro-events. Urban
micro-events are events which occur in cities, have limited geographical
coverage and typically affect only a small group of citizens. Because of their
scale these are difficult to identify in most data sources. However, by using
citizen sensing to gather data, detecting them becomes feasible. The data
gathered by citizen sensing is often multimodal and, as a consequence, the
information required to detect urban micro-events is distributed over multiple
modalities. This makes it essential to have a classifier capable of combining
them. In this paper we explore several methods of creating such a classifier,
including early, late, hybrid fusion and representation learning using
multimodal graphs. We evaluate performance on a real world dataset obtained
from a live citizen reporting system. We show that a multimodal approach yields
higher performance than unimodal alternatives. Furthermore, we demonstrate that
our hybrid combination of early and late fusion with multimodal embeddings
performs best in classification of urban micro-events
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
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