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

Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy automated real time detection of stampede prone areas. Then, we use robotic agents to aid the crowd management police in controlling the crowd in these stampede prone areas. While doing so, we aim for minimum interference by robotic agents in our environment. Thereby not disturbing the ambiance and aesthetics of the place. We evaluate the effectiveness of our model in dealing with difficult scenarios like emergency evacuation and presence of localized congestion. Lastly, we simulate a multi agent system based on our model and use it to illustrate the utility of robotic agents for detecting and reducing congestion.

Abstract

Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real-life behaviour and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of a framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. Our framework defines metrics to evaluate congestion avoidance strategies. To show utility of the framework, we present experimental studies of few strategies on a public transport system.

Abstract

A queue is required when a service provider is not able to handle jobs arriving over the time. In a highly flexible and dynamic environment, some jobs might demand for faster execution at run-time especially when the resources are limited and the jobs are competing for acquiring resources. A user might demand for speed up (reduced wait time) for some of the jobs present in the queue at run time. In such cases, it is required to accelerate (directly sending the job to the server) urgent jobs (requesting for speed up) ahead of other jobs present in the queue for an earlier completion of urgent jobs. Under the assumption of no additional resources, such acceleration of jobs would result in slowing down of other jobs present in the queue. In this paper, we formulate the problem of Speed Up Scheduling without acquiring any additional resources for the scheduling of on-line speed up requests posed by a user at run-time and present algorithms for the same. We apply the idea of Speed Up Scheduling to two different domains-Web Scheduling and CPU Scheduling. We demonstrate our results with a simulation based model using trace driven workload and synthetic datasets to show the usefulness of Speed Up scheduling. Speed Up provides a new way of addressing urgent jobs, provides a different evaluation criteria for comparing scheduling algorithms and has practical applications.

Abstract

Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens research area to build a framework for traffic management that can capture essential parameters of real-life behavior and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of such framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. To show the utility of the framework, we present experimental studies of few strategies on a public transport system.

Abstract

Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy automated real time detection of stampede prone areas. Further, we use robotic agents for aiding the crowd management police in controlling the crowd in these stampede prone areas. Lastly, we simulate a multi agent system based on our model and use it to illustrate the utility and viability of robotic agents for detecting and reducing congestion.

Abstract

We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The results illustrate that our model for disease term classification finds all disease term classes with an average F-score of 0.966.

Abstract

The problem of predicting the 3D native conformation of a polypeptide given the primary sequence is of fundamental importance in contemporary biology. Here we present a novel Genetic algorithm method to obtain the optimal geometry of a pentapeptide. The pentapeptide moiety is found in wide set of configurations in protein databases. A genetic algorithm utilizes optimization procedure similar to natural genetic evolution. Unlike other genetic algorithms, which are constrained to operate on bit strings, the algorithm used here operates on real values. A random population of conformations is generated taking into account the constraints of Ramachandran plot. This population advances through successive generations in which the solutions evolve via genetic operators (Mutate, Variate and Crossover); in such a way that it gives both better survival chance to fitter soluctions and a larger diversity within the population. More attention will be given to favorable local structures while unfavorable local structures will be rapidly abandoned on the basis of their fitness functions (potential energy), which are calculated using the GROMOS-96 force field of Spdbv version-3.7. The SCWRL3.0 program developed at Dunbrack lab is used for placing side chains to a fixed backbone coordinates. This algorithm proved to be very efficient for the penta-peptide Met-Enkephalin, a natural Opioid polypeptide produced in the brain. The algorithm converged only in 41 minutes and in 23 generations keeping the population size fixed (20 individual per population). The converged structure is energetically as stable as the native conformation of the protein. Its shows a RMSD of 5.9 from the original native conformation.