Abstract

We present Paper2vec, a novel neural network embedding based approach for creating scientific paper representations which make use of both textual and graph-based information. An academic citation network can be viewed as a graph where individual nodes contain rich textual information. With the current trend of open-access to most scientific literature, we presume that this full text of a scientific article contain vital source of information which aids in various recommendation and prediction tasks concerning this domain. To this end, we propose an approach, Paper2vec, which comprises of information from both the modalities and results in a rich representation for scientific papers. Over the recent past representation learning techniques have been studied extensively using neural networks. However, they are modeled independently for text and graph data. Paper2vec leverages recent research in the broader field of unsupervised feature learning from both graphs and text documents. We demonstrate the efficacy of our representations on three real world academic datasets in two tasks - node classification and link prediction where Paper2vec is able to outperform state-of-the-art by a considerable margin.

Abstract

In this work we demonstrate a method to detect controversy on news issues. This is done by performing an analysis of people’s reaction on social media to news articles reporting these issues. Detecting controversial news topics on web is a relevant problem today. It helps to identify the issues upon which people have divided opinion and is specially useful on topics such as a presidential election, government reforms,climate change etc. We use sentiment analysis and word matching to accomplish this task. We show the application of our method for detecting controversial topics during the US Presidential elections 2016

Abstract

n recent years, the importance of feature engineer-ing has been confirmed by the exceptional performance of deep learning techniques, that automate this task for some applications.For others, feature engineering requires substantial manual effort in designing and selecting features and is often tedious and non-scalable. We present AutoLearn, a regression-based feature learning algorithm. Being data-driven, it requires no domain knowledge and is hence generic. Such a representation is learnt by mining pairwise feature associations, identifying the linear or non-linear relationship between each pair, applying regression and selecting those relationships that are stable and improve the prediction performance. Our experimental evaluation on 18 UCIrvine and 7 Gene expression datasets, across different domains,provides evidence that the features learnt through our model can improve the overall prediction accuracy by 13.28%, compared to original feature space and 5.87% over other top performing models, across 8 different classifiers without using any domain knowledge.

Abstract

Computational Core for Plant Metabolomics (CCPM) is a web-based collaborative platform for researchers in the field of metabolomics to store, analyze and share their data. Metabolomics is a newly emerging field of ‘omics’ research concerned with the characterization of large numbers of metabolites using chromatography in conjunction with mass spectrometry and NMR. There is abundant volume and variety in the data, and unpredictable velocity. An interdisciplinary engagement such as this faces significant non-technical challenges solvable using a balanced approach to software management in a university setting to create an environment promoting collaborative contributions. In this paper we report on our experiences, challenges and methods in delivering a usable solution. CCPM provides a secure data repository with advanced toolsfor analysis including preprocessing, pretreatment, data filtration, statistical analysis, and pathway analysis functions; and also visualization,integration and sharing of data. As all users are not equally IT-savvy,it is essential that the user interface is robust, friendly and interactive where the user can submit and control various tasks running simultane-ously without stopping/interfering with other tasks. In each stage of its pipeline architecture, users are also allowed to upload external data that has been partially processed till the previous stage in other platforms.Use of open source softwares for development makes the maintenance and development of our modules easier than others which depend on proprietary softwares.

Abstract

In this paper, an approach to detect the sentiment of a song based on its multi-modality natures (text and audio) is presented. The textual lyric features are extracted from the bag of words. By using these features, Doc2Vec will generate a single vector for each song. Support Vector Machine (SVM), Naive Bayes (NB) and a combination of both these classifiers are developed to classify the sentiment using the textual lyric features. Audio features are used as an add-on to the lyrical ones which include prosody features, temporal features, spectral features, tempo and chroma features. Gaussian Mixture Models (GMM), SVM and a combination of both these classifiers are developed to classify the sentiment using audio features. GMM are known for capturing the distribution in the features and SVM are known for discriminating the features. Hence these models are combined to improve the performance of sentiment analysis. Performance is further improved by combining the text and audio feature domains. These text and audio features are extracted at the beginning, ending and for the whole song. From our experimental results, it is observed that the first 30 seconds (s) of a song gives better performance for detecting the sentiment of the song rather than the last 30s or from the whole song.

Abstract

Recent developments in deep learning methods have greatly influenced the performances of speech recognition systems. In a Hidden Markov model-Deep neural network (HMM-DNN) based speech recognition system, DNNs have been employed to model senones (context dependent states of HMM),where HMMs capture the temporal relations among senones. Due to the use of more deeper networks significant improvement in the performances has been observed and developing deep learning methods to train more deeper architectures has gained a lot of scientific interest. Optimizing a deeper network is more complex ask than to optimize a less deeper network, but recently residual network have exhibited a capability to train a very deep neural network architectures and are not prone to vanishing/exploding gradient problems. In this work, the effectiveness of residual networks have been explored for of speech recognition. Along with the depth of the residual network, the criticality of width of the residual network has also been studied. It has been observe dthat at higher depth, width of the networks is also a crucial parameter for attaining significant improvements. A 14-hoursubset of WSJ corpus is used for training the speech recognition systems, it has been observed that the residual networks have shown much ease in convergence even with a depth much higher than the deep neural network. In this work, using residual networks an absolute reduction of 0.4 in WER error rates (8%reduction in the relative error) is attained compared to the best performing deep neural network.

Abstract

Significant developments in deep learning methods have been achieved with the capability to train more deeper networks. The performance of speech recognition system has been greatly improved by the use of deep learning techniques. Most of the developments in deep learning are associated with the development of new activation functions and the corresponding initializations. The development of Rectified linear units (ReLU)has revolutionized the use of supervised deep learning methods for speech recognition. Recently there has been a great deal of research interest in the development of activation functionsLeaky-ReLU (LReLU), Parametric-ReLU (PReLU), Exponentia lLinear units (ELU) and Parametric-ELU (PELU). This work isaimed at studying the influence of various activation functions onspeech recognition system. In this work, a hidden Markov model-Deep neural network (HMM-DNN) based speech recognitionis used, where deep neural networks with different activationfunctions have been employed to obtain the emission probabilitiesof hidden Markov model. In this work, two datasets i.e., TIMITand WSJ are employed to study the behavior of various speechrecognition systems with different sized datasets. During thestudy, it is observed that the performance of ReLU-networksis superior compared to the other networks for the smaller sizeddataset (i.e., TIMIT dataset). For the datasets of sufficientlylarger size (i.e., WSJ) performance of ELU-networks is superiorto the other networks.

Abstract

Sampark is machine translation system providing translations among nine pairs of Indian languages. Machine translation system is a class of natural language processing applications that is far more complex and highly compute intensive in nature. As the load on the deployed system increases, optimization becomes a challenge. Caching is one of the available options to improve the performance of a software system with increasing load which exhibit the characteristics of locality of reference. Sampark MT system being a natural language processing application exhibits this characteristic.Memcachedis a well-known, simple, in-memory caching solution that has been applied to improve the performance of several distributed web applications in the past. This paper describes how memcached has been applied to improve the performance of Sampark machine translation service which is deployed on a large cluster of machines. By applying distributed caching to MT system the performance of the system has improved upto 40%

Abstract

In this paper, deep neural networks (DNN) are applied for acoustic scene classification task provided by DCASE2017 challenge. We perform experiment on a dataset consisting of 15 types of acoustic scenes with a given total development data and evolution data of task1. We propose an DNN architecture for utterance level classification. Evaluation of models were performed on given evolution data of task1 for 4 folds using development data. In this approach MFCC and IMFCC feature vectors are used to train DNN model and their DNN scores were combined to test the system. On the official development data set of the task1 challenge, an accuracy of 81.28% is achieved.

Abstract

This paper investigates the application of unsupervised acoustic unit discovery for topic identification (topic ID) of spoken audio documents. The acoustic unit discovery method is based on a non-parametric Bayesian phone-loop model that segments a speech utterance into phone-like categories. The discovered phone-like (acoustic) units are further fed into the conventional topic ID framework. Using multilingual bottleneck features for the acoustic unit discovery, we show that the proposed method outperforms other systems that are based on cross-lingual phoneme recognizer.