406 research outputs found
Technique for Debugging Incomplete Constructor Anomaly in Object-Oriented System
No full textThe Object-oriented (OO) programming is an evolutionary approach to software
engineering which encompasses the entire software life cycle. With the use of objectoriented
approach, programming becomes modularize but at the same time testing
becomes complex and program will be difficult to debug, which in turn results in a
huge loss of resources. To overcome the difficulty of debugging, a technique named
program slicing has been introduced. It is a technique to extract the desired parts of
program using some slicing criterion. It has been introduced to aid the developers in
the area of debugging and program comprehension by reducing the complexity of the
program.
There are many dependence graphs like Control Dependence Graph, Data
Dependence Graph, Program Dependence Graph, System Dependence Graph, etc.
that can be used for program representation in the process of program slicing. There
are many faults like State definition anomaly, Anomalous behavior construction,
Incomplete constructor, etc. that are related to Inheritance and Polymorphism features
of object-oriented system. The process of debugging the faults present in the Object-
Oriented code can be made approachable by the use of Program Slicing with
dependence graphs.Computer Science and Engineering Department
ME-Software Engineerin
Ideas for rent: an overview of markets for technology
No full textThis article surveys some of the recent literature on technology markets, and summarizes its main issues and insights. We structure our analysis in three parts: the supply and demand of technology; the factors that condition the formation and growth of technology markets; industry structure and dynamic issues. In addition, we summarize some of the studies that have tried to document the size and growth of these markets. We find that the literature has focused mainly on the supply of technology, but several other aspects of these markets remain under-studied, including the demand for external technology, the role of uncertainty in technology markets, and the dynamic interaction between industry structure and the market for technology. Understanding these will illuminate whether markets for technology will continue to grow or remained confined to pockets of the economy. Copyright 2010 The Author 2010. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved., Oxford University Press.
Metrics for analytics and visualization of big data with applications to activity recognition
No full textActivity recognition systems detect the hidden actions of an agent from sensor measurements made on the agents' actions and the environmental conditions. For such systems, metrics are important for both performance evaluation and visualization purposes. In this thesis, such metrics are developed and illustrated. For human activity recognition datasets, a reporting structure is described to visualize the metrics in a systematic manner. The other contribution of this thesis is to describe a visualization tool for estimating the orientation (attitude) of a rigid body from streaming motion sensor (accelerometer and gyroscope) data. A feedback particle filter (FPF) is implemented algorithmically to solve the estimation problem.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01The student, Rohan Arora, accepted the attached license on 2016-04-25 at 10:47.The student, Rohan Arora, submitted this Thesis for approval on 2016-04-25 at 10:48.This Thesis was approved for publication on 2016-04-27 at 15:05.DSpace SAF Submission Ingestion Package generated from Vireo submission #9459 on 2016-07-07 at 14:17:57Made available in DSpace on 2016-07-07T21:18:02Z (GMT). No. of bitstreams: 2
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Previous issue date: 2016-04-27Embargo set by: Seth Robbins for item 93308
Lift date: 2018-07-07T21:18:16Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 93308 on 2018-07-08T09:15:30Z
The Influence of the Board Characteristics on the Environmental, Social, and Governance Performance of the NSE Listed Companies
No full textThis article examines how the size of the board, the presence of women on the board, and the critical mass of women on the board affect the environmental, social, and governance (ESG) performance of companies listed on the NSE. This cross-sectional analysis relies on secondary data from the CRISIL database and examines a sample of 973 companies listed on the NSE. The Credit Rating Information Services of India Limited (CRISIL) assigns an ESG score to assess Indian firms’ ESG performance. The authors collected data on female representation and board size from the yearly reports of the firms and their respective websites. The findings indicate that Indian corporations prioritize social and governance issues more significantly than environmental concerns. The results indicate that the board characteristics have a varied impact on the ESG performance of the companies listed on the NS
THE INFLUENCE OF THE BOARD CHARACTERISTICS ON THE ENVIRONMENTAL, SOCIAL, AND GOVERNANCE PERFORMANCE OF THE NSE-LISTED COMPANIES
No full textThis article examines how the size of the board, the presence of women on the board, and the critical mass of women on the board affect the environmental, social, and governance (ESG) performance of companies listed on the NSE. This study is a cross-sectional analysis that relies on secondary data from the CRISIL database and examines a sample of 973 companies listed on the NSE. The Credit Rating Information Services of India Limited (CRISIL) assigns an ESG score to assess Indian firms\u27 ESG performance. We collected data on female representation and board size from the yearly reports of the firms and their respective websites. Our investigation findings indicate that Indian corporations prioritize social and governance issues more significantly than environmental concerns. The results indicate that the board characteristics have a varied impact on the ESG performance of the companies listed on the NSE. 
Chaotic grey wolf optimization algorithm for constrained optimization problems
No full textAbstract
The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems.
Highlights Chaos has been introduced to the GWO to develop Chaotic GWO for global optimization. Ten chaotic maps have been investigated to tune the key parameter ‘a’, of GWO. Effectiveness of the algorithm is tested on many constrained benchmark functions. Results show CGWO's better performance over other nature-inspired optimization methods. The proposed CGWO is also used for some engineering design applications.</jats:p
First generation Asian immigrants and mental health treatment
No full textAny first generation immigrant has a hard time assimilating to life in a new country, and this holds true for the Asian population and their mental health (Arora et al., 2020). This project focused on what impacts mental health of first generation Asian immigrants.Research presentationFaculty Mentor: Dr. Kathy Andrese
Towards automated classification of fine-art painting style: a comparative study
No full textThis thesis presents a comparative study of different classification methodologies for the task of fine-art genre classification. The problem of painting classification involves classifying new unknown paintings among different art genres. Two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models while the second level touches the features aspect of the paintings and compares Semantic-level features vs low-level and intermediate-level features present in the painting. Three models are studied and compared, namely - 1) A Discriminative model using a Bag-of-Words (BoW) approach; 2) A Generative model using BoW; 3) Discriminative model using Semantic-level features. Various experiments and techniques like Bag of Words model, Topic models and Classeme features are employed to get insights into potential of these automatic classification techniques for painting styles.M.S.Includes bibliographical referencesby Ravneet Singh Aror
Micro-power Pulsed-Doppler Radar Clutter and Displacement Source Classification Dataset
No full textThis is the official dataset for the ACM BuildSys 2019 publication One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification.
The training code for MSC-RNN can be found at https://github.com/dhruboroy29/MSCRNN
Kindly cite this work as:
@article{roy2019one,
title={One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification},
author={Roy, Dhrubojyoti and Srivastava, Sangeeta and Kusupati, Aditya and Jain, Pranshu and Varma, Manik and Arora, Anish},
journal={arXiv preprint arXiv:1909.03082},
year={2019}
}
</pre
Correction to: Visual acuity correlates with multimodal imaging-based categories of central serous chorioretinopathy (Eye, (2021), 10.1038/s41433-021-01788-4)
No full textIn this article the author name Ramesh Venkatesh was incorrectly written as Ramesh Vankatesh. The original article has been corrected
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