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RESEARCH ARTICLE Multi-Scale Compositionality: Identifying the Compositional Structures of Social Dynamics Using Deep Learning
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"... Objective Social media exhibit rich yet distinct temporal dynamics which cover a wide range of differ-ent scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signature ..."
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Objective Social media exhibit rich yet distinct temporal dynamics which cover a wide range of differ-ent scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signatures interact and form higher-level meanings. Method In this paper, we propose the Recursive Convolutional Bayesian Model (RCBM) to address both of these fundamental questions. The key idea behind our approach consists of con-structing a deep-learning framework using specialized convolution operators that are de-signed to exploit the inherent heterogeneity of social dynamics. RCBM’s runtime and convergence properties are guaranteed by formal analyses. Results Experimental results show that the proposed method outperforms the state-of-the-art ap-proaches both in terms of solution quality and computational efficiency. Indeed, by applying the proposed method on two social network datasets, Twitter and Yelp, we are able to iden-tify the compositional structures that can accurately characterize the complex social dynam-ics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. Fi-nally, our analysis offers new insights on understanding and engineering social media dy-namics, with direct applications to opinion spreading and online content promotion.
Scalable Clustering of Time Series with U-Shapelets
"... A recently introduced primitive for time series data mining, unsupervised shapelets (u-shapelets), has demonstrated significant potential for time series clustering. In contrast to approaches that consider the entire time series to compute pairwise similarities, the u-shapelets technique allows cons ..."
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A recently introduced primitive for time series data mining, unsupervised shapelets (u-shapelets), has demonstrated significant potential for time series clustering. In contrast to approaches that consider the entire time series to compute pairwise similarities, the u-shapelets technique allows considering only relevant subsequences of time series. Moreover, u-shapelets allow us to bypass the apparent chicken-and-egg paradox of defining relevant with reference to the clustering itself. U-shapelets have several advantages over rival methods. First, they are defined even when the time series are of different lengths; for example, they allow clustering datasets containing a mixture of single heartbeats and multi-beat ECG recordings. Second, u-shapelets mitigate sensitivity to irrelevant data such as noise, spikes, dropouts, etc. Finally, u-shapelets demonstrated ability to provide additional insights into the data. Unfortunately, the state-of-the-art algorithms for u-shapelets search are intractable and so their advantages have only been demonstrated on tiny datasets. We propose a simple approach to speed up a u-shapelet discovery by two orders of magnitude, without any significant loss in clustering quality. 1
Noname manuscript No. (will be inserted by the editor) Fast Classification of Univariate and Multivariate Time series Through Shapelets Discovery
"... Abstract Time-series classification is an important problem for the data min-ing community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive for the target variable. Shapelets are discovered by ..."
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Abstract Time-series classification is an important problem for the data min-ing community due to the wide range of application domains involving time-series data. A recent paradigm, called shapelets, represents patterns that are highly predictive for the target variable. Shapelets are discovered by measur-ing the prediction accuracy of a set of potential (shapelet) candidates. The candidates typically consist of all the segments of a dataset, therefore, the dis-covery of shapelets is computationally expensive. This paper proposes a novel method that avoids measuring the prediction accuracy of similar candidates in Euclidean distance space, through an online clustering/pruning technique. In addition, our algorithm incorporates a supervised shapelet selection that filters out only those candidates that improve classification accuracy. Empirical evi-dence on 45 univariate datasets from the UCR collection demonstrate that our method is 3-4 orders of magnitudes faster than the fastest existing shapelet-discovery method, while providing better prediction accuracy. In addition, we extended our method to multivariate time-series data. Runtime results over 4 real-life multivariate datasets indicate that our method can classify MB-scale data in a matter of seconds and GB-scale data in a matter of minutes. The achievements do not compromise quality, on the contrary, our method is even superior to the multivariate baseline in terms of classification accuracy.
Computer Sciences
"... Abstract—In the functional Magnetic Resonance Imaging (fMRI) data analysis, detecting the activated voxels is a challenging research problem where the existing methods have shown some limits. We propose a new method wherein brain mapping is done based on Dempster-Shafer theory of evidence (DS) that ..."
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Abstract—In the functional Magnetic Resonance Imaging (fMRI) data analysis, detecting the activated voxels is a challenging research problem where the existing methods have shown some limits. We propose a new method wherein brain mapping is done based on Dempster-Shafer theory of evidence (DS) that is a useful method in uncertain representation analysis. Dempster-Shafer allows finding the activated regions by checking the activated voxels in fMRI data. The activated brain areas related to a given stimulus are detected by using a belief measure as a metric for evaluating activated voxels. To test the performance of the proposed method, artificial and real auditory data have been employed. The comparison of the introduced method with the t-test and GLM method has clearly shown that the proposed method can provide a higher correct detection of activated voxels. Keywords—Dempster-Shafer theory; fMRI; GLM; t-test; HRF; OTSU method I.
