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