Abstract

The purpose of this study is to examine the differences between different types of news stories. Given the huge impact of social networks, online content plays an important role in forming or changing the opinions of people. Unlike traditional journalism where only certain news organizations can publish content, online journalism has given chance even for individuals to publish. This has its own advantages like individual empowerment but has given a chance to a lot of malicious entities to spread misinformation for their own benefit. As reported by many organizations in recent history, this even has influence on major events like the outcome of elections. Therefore, it is of great importance now, to have some sort of automated classification of news stories. In this work, we propose a deep hierarchical attention neural architecture combining sentiment and semantic embeddings for more accurate fine grained classification of news stories. Experimental results show that the sentiment embedding along with semantic information outperform several state-of-the art methods in this task.

Abstract

Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. In this work, we propose a model which extends variational autoencoders by exploiting the rich information present in the past preference history. We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network. At each time-step of the RNN, the sequence is fed through a series of fully-connected layers, the output of which models the probability distribution of the most likely future preferences. We show that handling temporal information is crucial for improving the accuracy of the VAE: In fact, our model beats the current state-of-the-art by valuable margins because of its ability to capture temporal dependencies among the user-consumption sequence using the recurrent encoder still keeping the fundamentals of variational autoencoders intact.

Abstract

Ensemble POS taggers are a good choice to integrate and leverage benefits of various types of POS taggers. This can help the large number (6500+) of resource-poor languages which do not have much annotated training data by providing ways to integrate semi-supervised/unsupervised taggers with supervised taggers. In this paper we present our experiments of developing ensemble POS taggers using a decision tree. We integrate a semi-supervised data mining approach that uses context based lists (CBLs) for POS tagging with supervised (1) Support Vector Machine based POS tagger, called SVMTool and (2) Conditional Random Field based POS tagger. The results are enhanced semi-supervised ensemble POS taggers which outperform the base methods. In these POS taggers, we use a decision tree to decide when to rely on the output of supervised tagger, and when to rely on the semi-supervised CBL method. The CBL based tagger uses rich contextual information which helps in tagging both existing and unseen words and uses no domain knowledge while supervised taggers give good performance for words present in the training model and can include domain based features. Hence, these algorithms have complementary strengths and in our ensemble we are able to combine these strengths. Enhanced performance of our new POS taggers over the base methods suggests that integrating these methods combines the qualities of these in the new tagger which enhances the performance. Therefore, these new semi-supervised ensemble taggers are more suitable for resource-poor languages.

Abstract

Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that frst mapped musical features in auditory cortices and then decoded novel musical pieces. We then probed the infuence of stimuli duration (number of time points) and spatial extent (number of voxels) on decoding accuracy. Our approach revealed a linear increase in accuracy with duration and a point of optimal model performance for the spatial extent. We further showed that Shannon entropy is a driving factor, boosting accuracy up to 95% for music with highest information content. These fndings provide key insights for future decoding and reconstruction algorithms and open new venues for possible clinical applications.

Abstract

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. New method: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined.

Abstract

The main challenge in studying cognition & designing effective tasks for children with learning disorders is creating personalized & adaptive tasks in line with the current abilities & mood of the child. The current study confronts this challenge by testing a new paradigm to access the current state of mind and adapting the tasks based on the current mood & abilities of the child. Children were given chess puzzles with various levels of difficulty (from just identifying the pieces, legal moves and eventually even capturing pieces with depth=1). while the children were performing the tasks the pupil-metric data (for cognitive load), facial expressions and the head pose were used to gauge the current-state and adapt the puzzles accordingly. Further development of dynamic feedback and providing rewards for looking at the right squares are also underway. custom software with off the shelf web-cameras were used as the current solutions in the market are prohibitively expensive for testing on large scale.

Abstract

Affective education is an formal curriculum designed to help children better understand their feelings and respond to challenging situations,thereby, transforming themselves and the world around them.Emotions impact the learning ability at multiple levels(Attention,Memory and decision making etc.).Though they have been advancements in terms of the content(Rich multimedia-based lessons etc.) for effective learning,proportionate advancements in have not taken place in the affective learning domain-for example "How to adapt the learning based in the current situation?".Can we mitigate the adverse effects of emotions?This problem of learning is especially compounded for university students where each student has flood of information to absorb & assimilate and is constantly under under stress,furthermore,Personalized & Real time/continual mentoring by the teachers to students is not practical.We achieve this by capturing the real time gestures and facial expressions(based on universal facial expressions of 7 emotions The Task(Chess Puzzles) given to validate the effectiveness of Methods show significant Improvement on sample size of 80 students

Abstract

A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fxed SC architecture. Recent modeling attempts point to the possibility of a single difusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-difusion-kernel model (SDK) and propose a multi-scale difusion scheme. Our multiscale model is formulated as a reaction-difusion system giving rise to spatio-temporal patterns on a fxed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine difusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specifc FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specifc reorganization in the brain structure and function.

Abstract

Extreme learning machine (ELM) is a new algorithm for training single-hidden layer feedforward neural networks which provides good performance as well as fast learning speed. ELM tends to produce good generalization performance with large number of hidden neurons as the input weights and hidden neurons biases are randomly initialized and remain unchanged during the learning process, and the output weights are analytically determined. In this paper, two swarm intelligence based metaheuristic techniques, viz. Artificial Bee Colony (ABC) and Invasive Weed Optimization (IWO) are proposed for tuning the input weights and hidden biases. The proposed approaches are called ABC-ELM and IWO-ELM in which the input weights and hidden biases are selected using ABC and IWO respectively and the output weights are computed using the Moore-Penrose (MP) generalized inverse. The proposed approaches are tested on different benchmark classification data sets and simulations show that the proposed approaches obtain good generalization performance in comparison to the other techniques available in the literature.

Abstract

Bug report filing is a major part of software maintenance. Due to extensive number of bugs filed everyday in large software projects and the asynchronous nature of bug report filing ecosystem, duplicate bug reports are filed. Capturing and tagging duplicate bug reports is crucial in order to avoid assignment of the same bug to different developers. Efforts have been made in the past to detect duplicate bug reports by using topic modelling [2], discriminative methods [5], meta-attributes [6], etc. Recently, Yang et al.[8] proposed an approach to combine word embeddings, TF-IDF and meta-attributes to compute bug similarity between two bug reports.