Abstract

In this paper, we consider a set of articles or reports by journalists or others, wherein they predict or promise something about future. The problem we approach is determining the credibility of the authors based on the predictions coming out to be true. The two specific problems we address are extracting the predictions from the articles and annotating with various prediction attributes. And then we determine the truth of these predictions, using Wikipedia as a credible source to extract relevant facts which can ascertain the validity of the predictions. We proposed and built an end to end system for automated predictions validation(APV) by extracting future speculations and predictions from news articles and social media. We considered 28 news articles and extracted 97 predictions from these articles and the range of credibility scores(Fscores) for these articles are (0.57-0.71).

Abstract

We are given an environment with some objects (a city block area) and mobile agents moving in the environment. An agent (hider) can hide behind an object to be not seen by other agents (seekers) through their line of sight (visibility). The aim of hiders is not to be caught for the longest time, and the aim of the seekers is catch all of them in the shortest period of time. We formulate the problem by using visibility based map abstractions. Agents plan their moves by utilizing multi-armed bandits UCB reward update model. We evaluate our abstractions and strategies by simulating the game under various different scenarios.

Abstract

We propose an interactive visual analytics system for exploring spatio-temporal data in VAST 2017 Mini-Challenge-1. As part of this challenge, we are expected to determine repeating, seasonal and unusual cars' movements in Lekagul park dataset. We use varied visualizations such as heatmap, sequential sunburst and line plots. Further, we have developed a web deployable ad-hoc system for displaying spatio-temporal information on geographical map

Abstract

Networks have become ubiquitous in many real world appli-cations and to cluster similar networks is an important problem. There are various properties of graphs such as clustering coefficient (CC), den-sity, arboricity, etc. We introduce a measure, Clique Conversion Coeffi-cient (CCC), which captures the clique forming tendency of nodes in anundirected graph. CCC could either be used as a weighted average ofthe values in a vector or as the vector itself. Our experiments show thatCCC provides additional information about a graph in comparison torelated measures like CC and density. We cluster the real world graphsusing a combination of the features CCC, CC, and density and show thatwithout CCC as one of the features, graphs with similar clique formingtendencies are not clustered together. The clustering with the use ofCCC would have applications in the areas of Social Network Analysis,Protein-Protein Interaction Analysis, etc., where cliques have an impor-tant role. We perform the clustering of ego networks of the YOUTUBEnetwork using values in CCC vector as features. The quality of the clus-tering is analyzed by contrasting the frequent subgraphs in each cluster.The results highlight the utility of CCC in clustering subgraphs of alarge graph

Abstract

Being the ever-growing online encyclopedia, Wikipedia requires a keen investigation about which articles are to be included for it to maintain its indispensability. To prevent unnecessary articles from being included, official guidelines of Wikipedia demand these named entities to meet" notability" standards for their article inclusion. In this paper, we evaluate named entities for their notability by using reliability and entity salience features. Evaluations of our system provide evidence for the viability of our solution as an alternative to the manual decisions made by the reviewers for inclusion of an article using the notability rules.

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

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

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

We investigate the automatic generation of Wikipedia articles as an alternative to its manual creation. We propose a framework for creating a Wikipedia article for a named entity which not only looks similar to other Wikipedia articles in its category but also aggregates the diverse aspects related to that named entity from the Web. In particular, a semi-supervised method is used for determining the headings and identifying the content for each heading in the Wikipedia article generated. Evaluations show that articles created by our system for categories like actors are more reliable and informative compared to those generated by previous approaches of Wikipedia article automation.

Abstract

Robots playing games that humans are adept in is a challenge. We studied robotic agents playing Chain Catch game as a Multi-Agent System (MAS). Our game starts with a traditional Catch game similar to Pursuit evasion, and further extends it to form a growing chain of predator agents to chase remaining preys. Hence Chain Catch is a combination of two challenges-pursuit domain and robotic chain formation. These are games that require team of robotic agents to cooperate among themselves and to compete with other group of agents through quick decision making. In this paper, we present a Chain Catch simulator that allows us to incorporate game rules, design strategies and simulate the game play. We developed cost model driven strategies for each of Escapee, Catcher and Chain. Our results show that Sliding slope strategy is the best strategy for Escapees whereas Tagging method is the best method for chain s movement in Chain Catch. We also use production quality robots to implement the game play in a physical environment and analyze game strategies on real robots. Our real robots implementation in different scenarios shows that game strategies work as expected and a complete chain formation takes place successfully in each game.