Abstract

A method, non-transitory computer readable medium, and apparatus for receiving data from a cloud database. One or more queries requesting data from the cloud database are received. The one or more queries are converted from a row-store database query into a column-store database query. An optimal join plan is identified for the one or more queries using a cost based optimizer based on metadata for one or more relations in the cloud database. The optimal join plan is executed using a cloud application programming interface.

Abstract

In a traditional ER model, once we specify a subclass or superclass relationship, any changes to that relationship are treated as schema evolution. Further, ER models are rigid in the sense that once a relationship type is specified across a set of entity types, an instance of relationship type occur when one instance of all participating entity types are specified. Therefore, it is difficult to introduce in a simplified manner all relationship types across subsets of given set of entity types. In this paper, we provide mechanisms to model in our extended ER model: (i) specification of dynamic relationship types across subsets of instances of entity types, (ii) a simplified specification of relationships across subsets of given set of entity types, and (iii) mapping our extended ER model to relational database schema. We also show through an e-contract example the utility of our extended ER model.

Abstract

The subset sum problem, also referred as SSP, is a NP-Hard computational problem. SSP has its applications in broad domains like cryptography, number theory, operation research and complexity theory. The most famous algorithm for solving SSP is Backtracking Algorithm which has exponential time complexity. Therefore, our goal is to design and develop better alternate enumeration techniques for faster generation of SSP solutions. Given the set of first n natural numbers which is denoted by Xn and a target sum S, we propose various alternate enumeration techniques which find all the subsets of Xn that add up to sum S. In this paper, we present the mathematics behind this exponential problem. We analyze the distribution of power set of Xn and present formulas which show definite patterns and relations among these subsets. We introduce three major distributions for power set of Xn: Sum Distribution, Length-Sum Distribution and Element Distribution. These distributions are prepossessing procedures for various alternate enumeration techniques for solving SSP. We propose novel algorithms: Subset Generation using Sum Distribution, Subset Generation using Length-Sum Distribution, Basic Bucket Algorithm, Maximum and Minimum Frequency Driven Bucket Algorithms and Local Search using Maximal and Minimal Subsets for enumerating SSP.

Abstract

We propose an unsupervised approach to extract all competing algorithms present in a given scholarly article. The algorithm names are treated as named entities and natural language processing techniques are used to extract them.All extracted entity names are linked with their respective original papers in the reference section by our novel entity-citation linking algorithm. Then the seen citation pairs are ranked based on the number of comparison related cue-words present in the entity-citation context. We manually annotated a small subset of DBLP Computer Science conference papers and report both qualitative and quantitative results of our algorithm on it

Abstract

With the advent of statistical modeling in sports, predicting the outcome of a game has been established as a fundamental problem.Cricket is one of the most popular team games in the world. With this article, we embark on predicting the outcome of a One Day International (ODI) cricket match using a supervised learning approach from a team composition perspective. Our work suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player’s batting and bowling performances, forming the basis of our approach. We use career statistics as well as the recent performances of a player to model him. Player independent factors have also been considered in order to predict the outcome of a match. We show that the k-Nearest Neighbor (kNN) algorithm yields better results as compared to other classifiers

Abstract

In this paper we investigate the problem of user authentication based on keystroke timing pattern. We propose a simple, robust and non parameterized nearest neighbor regression based feature ranking algorithm for anomaly detection. Our approach successfully handle drawbacks like outlier detection, scale variation and prevents over fitting.Apart from using existing keystroke timing features from the dataset liked well time and flight time, other features namely big ram time and inversion ratio time are engineered as well. The efficiency and effectiveness ofour method is demonstrated through extensive comparisons with others tate-of-the-art techniques using CMU keystroke dynamics bench markage equal error rate(EER) than other proposed techniques. We achieved an average equal error rate of0.051for the user authentication task.

Abstract

Shallow discourse parsing enables us to study discourse as a coherent piece of information rather than a sequence of clauses, sentences and paragraphs. In this paper, we identify arguments of explicit discourse relations in Hindi. This is the first such work carried out for Hindi. Building upon previous work carried out on discourse connective identification in Hindi, we propose a hybrid pipeline which makes use of both sub-tree extraction and linear tagging approaches. We report state-ofthe-art performance for this task.

Abstract

The current web APIs are end-user centric as they mostly focus on the end results. In this paper, we break this paradigm for one class of scientific workflow problems—machine translation, by designing an API that caters not only to the end users but also allows researchers to find bugs in their systems by exposing the ability to programmatically manipulate the results. Moreover, it follows an easy to replicate workflow based mechanism, which is built on the concept of microservices

Abstract

In Computational Linguistics, Hindi and Urdu are not viewed as a monolithic entity and have received separate attention with respect to their text processing. From part-of-speech tagging to machine translation, models are separately trained for both Hindi and Urdu despite the fact that they represent the same language. The reasons mainly are their divergent literary vocabularies and separate orthographies, and probably also their political status and the social perception that they are two separate languages. In this article, we propose a simple but efficient approach to bridge the lexical and orthographic differences between Hindi and Urdu texts. With respect to text processing, addressing the differences between the Hindi and Urdu texts would be beneficial in the following ways: (a) instead of training separate models, their individual resources can be augmented to train single, unified models for better generalization, and (b) their individual text processing applications can be used interchangeably under varied resource conditions. To remove the script barrier, we learn accurate statistical transliteration models which use sentence-level decoding to resolve word ambiguity. Similarly, we learn cross-register word embeddings from the harmonized Hindi and Urdu corpora to nullify their lexical divergences. As a proof of the concept, we evaluate our approach on the Hindi and Urdu dependency parsing under two scenarios: (a) resource sharing, and (b) resource augmentation. We demonstrate that a neural network-based dependency parser trained on augmented, harmonized Hindi and Urdu resources performs significantly better than the parsing models trained separately on the individual resources. We also show that we can achieve near state-of-the-art results when the parsers are used interchangeably.

Abstract

We present a comparative error analysis of two parsers - MALT and MST on Telugu Dependency Treebank data. MALT and MST are currently two of the most dominant data-driven dependency parsers. We discuss the performances of both the parsers in relation to Telugu language. we also talk in detail about both the algorithmic issues of the parsers as well as the language specific constraints of Telugu.The purpose is, to better understand how to help the parsers deal with complex structures, make sense of implicit language specific cues and build a more informed Treebank.