Abstract

The shortage of experimental platforms needed for learning IT systems in a hands-on way is one reason for the lack of quality education in the area of IT. For most students who have access to a PC, their ability to experiment on that machine is limited by the risk of possible irrecoverable damage their experimentation may cause to the equipment. We report the development of a 'Virtual Laboratory for Systems and Software' that teaches the basics of Linux. The goal of this laboratory is to give the students a platform through which they can learn the essentials of linux. The student interacts with a virtual instance of a linux machine through the browser on his machine to learn basic tasks of linux system administration. The student's machine could be running any operating system (not necessarily linux). This obviates the need to have a linux machine on the part of the student. The lab consists of a suite of experiments designed as web applications which are based on IT Workshop courses at undergraduate level in universities.

Abstract

Distributed and concurrent development of virtual labs across multiple institutes and teams presents several challenges, both organizational and technical in nature. We present models for managing the process of virtual lab development, deployment and distribution. Close to twenty labs in computer science and chemical sciences are currently following our model. The architecture is designed to ultimately encourage and support a community ecosystem where teachers or students from any college could contribute to lab development, and a college could choose to subscribe to a set of virtual labs. Our deployment model, yet to be fully implemented, has as its back end a flexible virtualization architecture for running experiments and and a package based distribution model that encourages community participation in the use of virtual labs.

Abstract

As education and technology merge, the opportunity for teaching and learning expand even more. However, the juxtaposition of the latest technologies has also raised concern for academic institutions as to which technologies are most effective in terms of cost, reach, richness, and, most importantly, learning. In this paper, we present a novel development framework, which we call as DISCOVIR, for asynchronous virtual lab development. Our aim is to simplify the process of developing the content for a virtual lab and to make the resulting labs more consistent for the learners (students). We incorporate the popular Model View Controller architecture, in building the user interface for the virtual lab. It allows the lab developers to focus completely on the quality of the content while the look and the feel for the resultant lab is provided in the form of customizable themes. We define an HTML5 based semantic structure for writing the lab content. It ensures long term sustainability of the lab and supports semantic search capabilities. Our model makes no prior assumptions or requisites in terms of expertise and tools required to use it in practice. A lab developer is free to use any html editor on any operating system to build his lab. It requires no costly software installations and runs locally without a web server support. Our proposed model is currently being used by 16 of the 28 virtual labs from IIIT, Hyderabad.

Abstract

We define the class of primal maps using the concepts of mapcode theory and show that a map is primal if and only if it is partial recursive.

Abstract

A set X together with a finite collection of self-maps on X is called a multiple discrete flow. We develop the theory of fair convergence for multiple flows and show how the UNITY programs of Chandy and Misra can be reformulated in terms of this theory.

Abstract

Prediction of electric load is very important issue for modern day power system engineers and a very good day ahead prediction of electric load is required for efficient performance of various Energy Management System (EMS) functions such as unit commitment, economic dispatch, fuel scheduling, and unit maintenance. A fuzzy based approach for day ahead prediction of electric load using Mahalanobis distance has been chosen in this work. Mahalanobis distance provides the similar characteristic days from the historical data set based on some independent variables generally of climate and time (such as temperature, day of the week, month etc.) and that are used to predict the dependent variable, i.e., day ahead electric load demand. The independent variables considered for the distance measure include the hourly humidity values, hourly temperatures values, and the day type variable.The similarity between the load on the day of prediction and that on similar characteristic days, which is evaluated using fuzzy system

Abstract

IISc Bangalore has developed a recognition engine for Tamil printed text, which has been tested on 1000 document images of pages scanned from books printed between 1950 and 2000. IIIT Hyderabad has developed a XML based annotated database for storing the 5000 images of scanned pages and the corresponding typed text in Unicode. CDAC, Noida has developed an efficient evaluation tool, which compares the OCR output text to the reference typed text (ground truth) and flashes the substitution, deletion and insertion errors in different colours on the screen, so that the design team can quickly identify the issues with the OCR and make corrective steps for improving the performance. IIT Delhi has proposed and developed a novel scheme for segmenting only the text regions from document images containing pictures. The OCRs uses Karhunen-Louve transform (KLT) as features and a support vector machine (SVM) classifier with RBF kernel in a discriminative directed acyclic graph (DDAG) configuration. They assume an uncompressed input image of the document page, scanned at a minimum of 300 dpi with 256 gray levels (not binary or two-level). Tamil OCR currently gives over 94% recognition accuracy at the Unicode level, evaluated on over 1000 printed pages, some of them also containing old Tamil letters.

Abstract

ndexing image data for content-based image search is an important area in Computer Vision. The state of theart uses the 128-dimensional SIFT as low level descriptors. Indexing even a moderate collection involves severalmillions of such vectors. The search performance depends on the quality of indexing and there is often a need tointeractively tune the process for better accuracy. In this paper, we propose a a visualization-based tool to tunethe indexing process for images and videos. We use a feature selection approach to improve the clustering of SIFTvectors. Users can visualize the quality of clusters and interactively control the importance of individual or groupsof feature dimensions easily. The results of the process can be visualized quickly and the process can be repeated.The user can use a filter or a wrapper model in our tool. We use input sampling, GPU-based processing, and visualtools to analyze correlations to provide interactivity. We present results of tuning the indexing for a few standarddatasets. A few tuning iterations result in an improvement of over 4% in the final classification performance, whichis significant.

Abstract

Graphics processing units provide a large compu-tational power at a very low price which position them as anubiquitous accelerator. General purpose programming on thegraphics processing units (GPGPU) is best suited for regulardata parallel algorithms. They are not directly amenable foralgorithms which have irregular data access patterns suchas list ranking, and finding the connected components of agraph, and the like. In this work, we present a GPU-optimizedimplementation for finding the connected components of agiven graph. Our implementation tries to minimize the impactof irregularity, both at the data level and functional level.Our implementation achieves a speed up of 9 to 12 timesover the best sequential CPU implementation. For instance, ourimplementation finds connected components of a graph of 10million nodes and 60 million edges in about 500 millisecondson a GPU, given a random edge list. We also draw interestingobservations on why PRAM algorithms, such as the Shiloach-Vishkin algorithm may not be a good fit for the GPU and howthey should be modified.

Abstract

Genetic algorithms are effective in solving many optimiza-tion tasks. However, the long execution time associated withit prevents its use in many domains. In this paper, we pro-pose a new approach for parallel implementation of geneticalgorithm on graphics processing units (GPUs) using CUDAprogramming model. We exploit the parallelism within achromosome in addition to the parallelism across multiplechromosomes. The use of one thread per chromosome byprevious efforts does not utilize the GPU resources effec-tively. Our approach uses multiple threads per chromosome,thereby exploiting the massively multithreaded GPU moreeffectively. This results in good utilization of GPU resourceseven at small population sizes while maintaining impressivespeed up for large population sizes. Our approach is mod-eled after the GAlib library and is adaptable to a varietyof problems. We obtain a speedup of over 1500 over theCPU on problems involving a million chromosomes. Prob-lems of such magnitude are not ordinarily attempted due tothe prohibitive computation times.