Abstract

Predicting the outcome of a match has always been at the center of sports analytics. Indian Premier League (IPL), a professional Twenty20 (T20) cricket league in India, has established itself as one of the biggest tournaments in cricket history. In this paper, we propose a model to predict the winner at the end of each over in the second innings of an IPL cricket match. Our methodology not only incorporates the dynamically updating game context as the game progresses, but also includes the relative strength between the two teams playing the match. Estimating the relative strength between two teams involves modeling the individual participating players’ potentials. To model a player, we use his career as well as recent performance statistics. Using the various dynamic features, we evaluate several supervised learning algorithms to predict the winner of the match. Finally, using the Random Forest Classifier (RFC), we have achieved an accuracy of 65.79% - 84.15% over the course of second innings, with an overall accuracy of 75.68%.

Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.

Abstract

Extracting structured information from unstructured text is a critical problem. Over the past few years, various clustering algorithms have been proposed to solve this problem. In addition, various algorithms based on probabilistic topic models have been developed to find the hidden thematic structure from various corpora (i.e publications, blog setc). Both types of algorithms have been transferred to the domain of scientific literature to extract structured information to solve problems like data exploration, expert detection etc. In order to remain domain-agnostic, these algorithms do not exploit the structure present in a scientific publication. Majority of researchers interpret a scientific publication as research conducted to report progress in solving some research problems.Following this interpretation, in this paper we present a different outlook to the same problem by modelling scientific publications around research problems. By associating a scientific publication with a research problem, exploring the scientific literature becomes more intuitive. In this paper, we propose an unsupervised framework to mine research problems from titles and abstracts of scientific literature. Our framework uses weighted frequent phrase mining to generate phrases and filters them to obtain high-quality phrases.These high-quality phrases are then used to segment the scientific publication into meaningful semantic units. After segmenting publications, we apply a number of heuristics to score the phrases and sentences to identify the research problems. In a post-processing step we use a neighborhood based algorithm to merge different representations of the same problems. Experiments conducted on parts of DBLP dataset show promising results.

Abstract

Cricket is one of the most popular team sports in the world.Players have multiple roles in a game of cricket, predominantly as bats-men and bowlers. Over the generations, statistics such as batting and bowling averages, and strike and economy rates have been used to judge the performance of individual players. These measures, however, do not take into consideration the context of the game in which a player per-formed. Furthermore, these types of statistics are incapable of comparing the performance of players across different roles. In this paper, we present an approach to quantitatively assess the performances of individual players in single match of One Day International (ODI) cricket. For this, we have developed a new measure, called the Work Index, which represents the amount of work that is yet to be done by a team to achieve its tar-get. Our approach incorporates game situations and the team strength sto measure the player contributions. This not only helps us in evaluating the individual performances, but also enables us to compare players within and across various roles on a common scale. Using the player performances in a match, we predict the player of the match award for the ODI matches played between 2006 and 2016. We have achieved an accuracy of 86.80% for the top-3 positions, which is superior to base linemodels and previous works, to the best of our knowledge.

Abstract

We present a regression-based feature learning algorithm that generates new features from a set of available features (raw data points). 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.Our experimental evaluation on 20 datasets taken from UC Irvine and Gene Expression, across different domains, provides evidence that the features learnt through our model can improve the overall prediction accuracy, substantially, over the original feature space across 8 different classifiers without any domain knowledge.

Abstract

The problem of ordering documents in a large collection into a sequence that is efficient for learning (both human and machine) is of high practical significance,but has not yet been well-formulated. We formulate this problem as mining a maximally informative simple sequence of documents. The mined sequence should be maximally informative in the sense that the reader learns quickly by reading only a few documents, and its should be simple so that the reader is not over whelmed while trying to learn the content. The task can be posed as: Given that a reader wishes to read (at most)k documents, which documents should be selected from the repository and in what order, so as to provide maximum information. We present the WikiSeq algorithm for this purpose. We also design a metric based on information-gain to help objectively evaluate WikiSeq, and conduct experiments to compare with indicative baselines. Finally, we provide case-studies to subjectively illustrate WikiSeq’s merits.

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.