50 research outputs found
Classification of Short-Texts Generated During Disasters: Traditional and Deep learning Approach
Micro-blogging sites provide a wealth of resources during disaster events in the form of short texts.
Correct classification of those short texts into various actionable classes can be of great help in
shaping the means to rescue people in disaster-a�ected places. The process of classification of short
texts poses a challenging problem because the texts are usually short and very noisy and Inding good
features that can distinguish these texts into di�erent classes is time consuming, tedious and often
requires a lot of domain knowledge. In this thesis, we explore various non-deep learning and deep
learning methods and propose a deep learning based model to classify tweets into difierent actionable
classes such as resource need and availability, activities of various NGO etc. The proposed model
requires no domain knowledge and can be used in any disaster scenario with little to no modification.
Keywords: Text classification, Topic Modelling, LDA, Word-embeddings, LSTM, Deep Learnin
Two Dimensional Clustering of Gamma-Ray Bursts using durations and hardness
Gamma-Ray Bursts (GRBs) have been conventionally bifurcated into two distinct
categories: ``short'' and ``long'' with durations less than and greater than
two seconds respectively. However, there is a lot of literature (although with
conflicting results) regarding the existence of a third intermediate class. To
investigate this issue, we extend a recent study (arXiv:1612.08235) on
classification of GRBs to two dimensions by incorporating the GRB hardness in
addition to the observed durations. We carry out this unified analysis on GRB
datasets from four detectors, viz. BATSE, RHESSI, Swift (observed and intrinsic
frame), and Fermi-GBM. We consider the duration and hardness features in
log-scale for each of these datasets and determine the best-fit parameters
using Gaussian Mixture Model. This is followed by information theoretic
criterion (AIC and BIC) to determine if a three-component fit is favored
compared to a two-component one or vice-versa. For BATSE, we find that both AIC
and BIC show preference for three components with decisive significance. For
Fermi and RHESSI, both AIC and BIC show preference for two components, although
the significance is marginal from AIC, but decisive using BIC. For Swift
dataset in both the observed and rest frame, we find that three components are
favored according to AIC with decisive significance, and two are preferred with
BIC with marginal to strong significance.Comment: 13 pages, 10 figures. This is an extension of arXiv:1612.08235 to
two-dimension
A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems
In order to sustain the user-base for a web service, it is important to know the return time of a
user to the service. In this work, we propose a point process model which captures the temporal
dynamics of the user activities associated with a web service. The time at which the user returns to
the service is predicted, given a set of historical data. We propose to use a Bayesian non-parametric
model, log Gaussian Cox process (LGCP), which allows the latent intensity function generating the
return times to be learnt non-parametrically from the data. It also allows us to encode prior domain
knowledge such as periodicity in users return time using Gaussian process kernels. Further, we cap-
ture the similarities among the users in their return time by using a multi-task learning approach
in the LGCP framework. We compare the performance of LGCP with different kernels on a real-
world last.fm data and show their superior performance over standard radial basis function kernel
and baseline models. We also found LGCP with multitask learning kernel to provide an improved
predictive performance by capturing the user similarity
A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems
In order to sustain the user-base for a web service, it is important
to know the return time of a user to the service. We propose a
Bayesian point process, log Gaussian Cox process (LGCP), to model
and predict return time of users. It allows encoding the prior do-
main knowledge and non-parametric estimation of latent intensity
functions capturing user behaviour. We capture the similarities
among the users in their return time by using a multi-task learning
approach. We show the effectiveness of the proposed approaches
on predicting the return time of users to last.fm music service
Continuous Depth Recurrent Neural Differential Equations
Recurrent neural networks (RNNs) have brought a lot of advancements in
sequence labeling tasks and sequence data. However, their effectiveness is
limited when the observations in the sequence are irregularly sampled, where
the observations arrive at irregular time intervals. To address this,
continuous time variants of the RNNs were introduced based on neural ordinary
differential equations (NODE). They learn a better representation of the data
using the continuous transformation of hidden states over time, taking into
account the time interval between the observations. However, they are still
limited in their capability as they use the discrete transformations and a
fixed discrete number of layers (depth) over an input in the sequence to
produce the output observation. We intend to address this limitation by
proposing RNNs based on differential equations which model continuous
transformations over both depth and time to predict an output for a given input
in the sequence. Specifically, we propose continuous depth recurrent neural
differential equations (CDR-NDE) which generalizes RNN models by continuously
evolving the hidden states in both the temporal and depth dimensions. CDR-NDE
considers two separate differential equations over each of these dimensions and
models the evolution in the temporal and depth directions alternatively. We
also propose the CDR-NDE-heat model based on partial differential equations
which treats the computation of hidden states as solving a heat equation over
time. We demonstrate the effectiveness of the proposed models by comparing
against the state-of-the-art RNN models on real world sequence labeling
problems and data
Accelerating Hawkes Process for Modelling Event History Data
Hawkes Processes are probabilistic models use-
ful for modelling the occurrences of events over
time. They exhibit mutual excitation property,
where a past event influences future events. This
has been successful in modelling the evolution
of memes and user behaviour in social net-
works. In the Hawkes process, the occurrences
of events are determined by an underlying inten-
sity function which considers the influence from
past events. The intensity function models the
mutual-exciting nature by adding up the influ-
ence from past events. The calculation of the in-
tensity function for every new event requires time
proportional to the number of past events. When
the number of events is high, the repeated in-
tensity function calculation will become expen-
sive. We develop a faster approach which takes
only constant time complexity to calculate the in-
tensity function for every new event in a mutu-
ally exciting Hawkes process. This is achieved
by developing a recursive formulation for mutu-
ally exciting Hawkes process and maintaining an
additional data structure which takes a constant
space. We found considerable improvement in
runtime performance of the Hawkes process ap-
plied to the sequential stance classification task
on synthetic and real world datasets
HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process
The prevalence of location-based social networks (LBSNs) has eased the
understanding of human mobility patterns. Knowledge of human dynamics can aid
in various ways like urban planning, managing traffic congestion, personalized
recommendation etc. These dynamics are influenced by factors like social
impact, periodicity in mobility, spatial proximity, influence among users and
semantic categories etc., which makes location modelling a critical task.
However, categories which act as semantic characterization of the location,
might be missing for some check-ins and can adversely affect modelling the
mobility dynamics of users. At the same time, mobility patterns provide a cue
on the missing semantic category. In this paper, we simultaneously address the
problem of semantic annotation of locations and location adoption dynamics of
users. We propose our model HAP-SAP, a latent spatio-temporal multivariate
Hawkes process, which considers latent semantic category influences, and
temporal and spatial mobility patterns of users. The model parameters and
latent semantic categories are inferred using expectation-maximization
algorithm, which uses Gibbs sampling to obtain posterior distribution over
latent semantic categories. The inferred semantic categories can supplement our
model on predicting the next check-in events by users. Our experiments on real
datasets demonstrate the effectiveness of the proposed model for the semantic
annotation and location adoption modelling tasks.Comment: 11 page
Longitudinal Modeling of Social Media with Hawkes Process based on Users and Networks
Online social networks provide a platform for
sharing information at an unprecedented scale. Users generate
information which propagates across the network resulting in
information cascades. In this paper, we study the evolution of
information cascades in Twitter using a point process model
of user activity. We develop several Hawkes process models
considering various properties including conversational structure,
users’ connections and general features of users including the
textual information, and show how they are helpful in modeling
the social network activity. We consider low-rank embeddings
of users and user features, and learn the features helpful in
identifying the influence and susceptibility of users. Evaluation
on Twitter data sets associated with civil unrest shows that
incorporating richer properties improves the performance in
predicting future activity of users and memes