5,908 research outputs found
Joint Topic-Semantic-aware Social Recommendation for Online Voting
Online voting is an emerging feature in social networks, in which users can
express their attitudes toward various issues and show their unique interest.
Online voting imposes new challenges on recommendation, because the propagation
of votings heavily depends on the structure of social networks as well as the
content of votings. In this paper, we investigate how to utilize these two
factors in a comprehensive manner when doing voting recommendation. First, due
to the fact that existing text mining methods such as topic model and semantic
model cannot well process the content of votings that is typically short and
ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to
learn word and document representation by jointly considering their topics and
semantics. Then we propose our Joint Topic-Semantic-aware social Matrix
Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates
similarity among users and votings by combining their TEWE representation and
structural information of social networks, and preserves this
topic-semantic-social similarity during matrix factorization. To evaluate the
performance of TEWE representation and JTS-MF model, we conduct extensive
experiments on real online voting dataset. The results prove the efficacy of
our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge
Management (CIKM 2017
ProLanGO: Protein Function Prediction Using Neural~Machine Translation Based on a Recurrent Neural Network
With the development of next generation sequencing techniques, it is fast and
cheap to determine protein sequences but relatively slow and expensive to
extract useful information from protein sequences because of limitations of
traditional biological experimental techniques. Protein function prediction has
been a long standing challenge to fill the gap between the huge amount of
protein sequences and the known function. In this paper, we propose a novel
method to convert the protein function problem into a language translation
problem by the new proposed protein sequence language "ProLan" to the protein
function language "GOLan", and build a neural machine translation model based
on recurrent neural networks to translate "ProLan" language to "GOLan"
language. We blindly tested our method by attending the latest third Critical
Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the
performance of our methods on selected proteins whose function was released
after CAFA competition. The good performance on the training and testing
datasets demonstrates that our new proposed method is a promising direction for
protein function prediction. In summary, we first time propose a method which
converts the protein function prediction problem to a language translation
problem and applies a neural machine translation model for protein function
prediction.Comment: 13 pages, 5 figure
Field-of-View IoU for Object Detection in 360{\deg} Images
360{\deg} cameras have gained popularity over the last few years. In this
paper, we propose two fundamental techniques -- Field-of-View IoU (FoV-IoU) and
360Augmentation for object detection in 360{\deg} images. Although most object
detection neural networks designed for the perspective images are applicable to
360{\deg} images in equirectangular projection (ERP) format, their performance
deteriorates owing to the distortion in ERP images. Our method can be readily
integrated with existing perspective object detectors and significantly
improves the performance. The FoV-IoU computes the intersection-over-union of
two Field-of-View bounding boxes in a spherical image which could be used for
training, inference, and evaluation while 360Augmentation is a data
augmentation technique specific to 360{\deg} object detection task which
randomly rotates a spherical image and solves the bias due to the
sphere-to-plane projection. We conduct extensive experiments on the 360indoor
dataset with different types of perspective object detectors and show the
consistent effectiveness of our method
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