6,486 research outputs found
GEO-BLEU: Similarity Measure for Geospatial Sequences
In recent geospatial research, the importance of modeling large-scale human
mobility data and predicting trajectories is rising, in parallel with progress
in text generation using large-scale corpora in natural language processing.
Whereas there are already plenty of feasible approaches applicable to
geospatial sequence modeling itself, there seems to be room to improve with
regard to evaluation, specifically about measuring the similarity between
generated and reference trajectories. In this work, we propose a novel
similarity measure, GEO-BLEU, which can be especially useful in the context of
geospatial sequence modeling and generation. As the name suggests, this work is
based on BLEU, one of the most popular measures used in machine translation
research, while introducing spatial proximity to the idea of n-gram. We compare
this measure with an established baseline, dynamic time warping, applying it to
actual generated geospatial sequences. Using crowdsourced annotated data on the
similarity between geospatial sequences collected from over 12,000 cases, we
quantitatively and qualitatively show the proposed method's superiority
Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures
Hand and arm gesture recognition using the radio frequency (RF) sensing
modality proves valuable in manmachine interface and smart environment. In this
paper, we use curve matching techniques for measuring the similarity of the
maximum instantaneous Doppler frequencies corresponding to different arm
gestures. In particular, we apply both Frechet and dynamic time warping (DTW)
distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, take
into account both the location and the order of the points for rendering two
curves similar or dissimilar. It is shown that improved arm gesture
classification can be achieved by using the DTW method, in lieu of L2 and L1
distances, under the nearest neighbor (NN) classifier.Comment: 6 pages, 7 figures, 2020 IEEE radar conference. arXiv admin note:
substantial text overlap with arXiv:1910.1117
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity
Nowadays, events usually burst and are propagated online through multiple
modern media like social networks and search engines. There exists various
research discussing the event dissemination trends on individual medium, while
few studies focus on event popularity analysis from a cross-platform
perspective. Challenges come from the vast diversity of events and media,
limited access to aligned datasets across different media and a great deal of
noise in the datasets. In this paper, we design DancingLines, an innovative
scheme that captures and quantitatively analyzes event popularity between
pairwise text media. It contains two models: TF-SW, a semantic-aware popularity
quantification model, based on an integrated weight coefficient leveraging
Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series
alignment model matching different event phases adapted from Dynamic Time
Warping. We also propose three metrics to interpret event popularity trends
between pairwise social platforms. Experimental results on eighteen real-world
event datasets from an influential social network and a popular search engine
validate the effectiveness and applicability of our scheme. DancingLines is
demonstrated to possess broad application potentials for discovering the
knowledge of various aspects related to events and different media
Novel modeling of task versus rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics
Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a measure with which to cluster or compare similarity between features but in a conceptually different way. We have used DTW to provide a more interpretable spectral description of the data, compared to standard approaches such as the Fourier and related transforms. The DTW approach and standard discrete Fourier transform (DFT) are assessed against benchmark measures of neural dynamics. These include EEG microstates, EEG avalanches, and the sum squared error (SSE) from a multilayer perceptron (MLP) prediction of the EEG time series, and simultaneously acquired FMRI BOLD signal. We explored the relationships between these variables of interest in an EEG-FMRI dataset acquired during a standard cognitive task, which allowed us to explore how DTW differentially performs in different task settings. We found that despite strong correlations between DTW and DFT-spectra, DTW was a better predictor for almost every measure of brain dynamics. Using these DTW measures, we show that predictability is almost always higher in task than in rest states, which is consistent to other theoretical and empirical findings, providing additional evidence for the utility of the DTW approach
- …