Digital Commons Network^™

Information Needs And Seeking Behaviour Of Nursing Students Of Bhubaneswar, Odisha During Covid-19 Pandemic Outbreak, Payel Saha, Pushpanjali Jena

Library Philosophy and Practice (e-journal)

In December, the outbreak of a new Corona virus disease called as Covid-19 in China triggered the infection and deaths of many including medical personnel. The disease is extremely infectious since in extreme cases it can be fatal and there are no specific medications. Being the front line workers this poses a huge risk to the lives and welfare of nurses and has a significant effect on their emotional reactions and coping with strategies. This study would also explore emotional reactions and coping with styles for nursing students. A famous parameter that motivated people to practice protective behaviour either directly …

Bibliometric Analysis Of Library Philosophy And Practice From 1998 To 2020: Focus On The Top 200 Most Cited Documents, Waseem Hassan, Amina Ara

Library Philosophy and Practice (e-journal)

Background:

We performed the bibliometric analysis of Library Practice and Philosophy (LPP) from 1998 to 2020.

Methodology:

In April 2021, the data was collected from Scopus, one of the largest databases in the world.

Results:

From 1998 to 2020, LPP published 3364 research documents with 6169 citations. The highest documents (n=988) were published in 2019, while the highest citations (n=1469) were noted in 2020. As per Scopus data, the overall h-index was twenty-one (n=21). In all publications, 4428 authors, 4238 institutes and 56 countries contributed. The number of publications, citations, and citations per documents (CPD), for all authors, and institutes …

Tolkien: Scholar And Modern Game Pioneer, Alicia Breinke

ART 108: Introduction to Games Studies

History can be a necessity, or necessary evil for some people when we want to comprehend real-time issues or trends. Gaming is a trend that applies to this since we often seem to be drawn in by the excitement of the graphics, music, and storylines, yet it seems like people seldomly try to uncover their origins. At the same time, though, a game’s historic foundation is essential to understand since it can help us gain a greater appreciation for these experiences. Role play games are an exceptional example of this since many renowned ones have external influences. J.R.R. Tolkien’s “The …

Hidden Markov Model-Based Clustering For Malware Classification, Shamli Singh

Master's Projects

Automated techniques to classify malware samples into their respective families are critical in cybersecurity. Previously research applied ��-means clustering to scores generated by hidden Markov models (HMM) as a means of dealing with the malware classification problem. In this research, we follow a somewhat similar approach, but instead of using HMMs to generate scores, we directly cluster the HMMs themselves. We obtain good results on a challenging malware dataset.

Assessment Of The Concept Of Strategic Planning In Nigerian Librarianship, Tope Florence Dahunsi Phd., Gbenga Odunayo Adetunla

Library Philosophy and Practice (e-journal)

This paper examined the concept of strategic planning in librarianship. This article becomes pertinent because few studies have examined academic libraries' planning documents to see how they are prioritizing among the competing issues and challenges facing them in the last two decades. This paper addresses the gap in the literature, theory and practice. The paper argued that effective strategic planning involves understanding the library domain, assessing relevant resource base and creating a shared view of the future, by asking fundamental questions such as what do libraries need to do to support excellence in service delivery and remain competitive in …

Keystroke Dynamics For User Authentication With Fixed And Free Text, Jianwei Li

Master's Projects

YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state of the art machine learning techniques and a variety of textual features.

Cyberbullying Classification Based On Social Network Analysis, Anqi Wang

Master's Projects

With the popularity of social media platforms such as Facebook, Twitter, and Instagram, people widely share their opinions and comments over the Internet. Exten- sive use of social media has also caused a lot of problems. A representative problem is Cyberbullying, which is a serious social problem, mostly among teenagers. Cyber- bullying occurs when a social media user posts aggressive words or phrases to harass other users, and that leads to negatively affects on their mental and social well-being. Additionally, it may ruin the reputation of that media. We are considering the problem of detecting posts that are aggressive. Moreover, …

Wildfire Risk Prediction For A Smart City, Rekha Rani

Master's Projects

Wildfires are uncontrolled fires that may lead to the destruction of biodiversity, soil fertility, and human resources. There is a need for timely detection and prediction of wildfires to minimize their disastrous effects. In this research, we propose a wildfire prediction model that relies on multi-criteria decision making (MCDM) to explicitly evaluates multiple conflicting criteria in decision making and weave the wildfire risks into the city’s resiliency plan. We incorporate fuzzy set theory to handle imprecision and uncertainties. In the process, we create a new data set that includes California cities’ weather, vegetation, topography, and population density records. The model …

Image-Based Real Estate Appraisal Using Cnns And Ensemble Learning, Prathamesh Dnyanesh Kumkar

Master's Projects

Real Estate Appraisal is performed to evaluate properties during a range of activities like buying, selling, mortgaging, or insuring. Traditionally, this process is done by real estate brokers who consider factors like the location of a house, its area, the number of bedrooms and bathrooms, along with other amenities to assess the property. This approach is quite subjective since different brokers may arrive at a different quote for the same property depending on their analysis. The development in machine learning algorithms has given rise to several Automated Valuation Models (AVMs) to estimate real estate prices. Real estate websites use such …

Fake Malware Classification With Cnn Via Image Conversion: A Game Theory Approach, Yash Sahasrabuddhe

Master's Projects

Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and study classification techniques using the ever-evolving field of machine learning. The goal of this research project is to identify similarities between malware families and to improve on classification of malwares within different malware families by implementing Convolutional Neural Networks (CNNs) on …

Malware Classification With Bert, Joel Lawrence Alvares

Master's Projects

Malware Classification is used to distinguish unique types of malware from each other.

This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can …

Presentation Attack Detection In Facial Biometric Authentication, Hardik Kumar

Master's Projects

Biometric systems are referred to those structures that enable recognizing an individual, or specifically a characteristic, using biometric data and mathematical algorithms. These are known to be widely employed in various organizations and companies, mostly as authentication systems. Biometric authentic systems are usually much more secure than a classic one, however they also have some loopholes. Presentation attacks indicate those attacks which spoof the biometric systems or sensors. The presentation attacks covered in this project are: photo attacks and deepfake attacks. In the case of photo attacks, it is observed that interactive action check like Eye Blinking proves efficient in …

Keystroke Dynamics Based On Machine Learning, Han-Chih Chang

Master's Projects

The development of active and passive biometric authentication and identification technology plays an increasingly important role in cybersecurity. Biometrics that utilize features derived from keystroke dynamics have been studied in this context. Keystroke dynamics can be used to analyze the way that a user types by monitoring various keyboard inputs. Previous work has considered the feasibility of user authentication and classification based on keystroke features. In this research, we analyze a wide variety of machine learning and deep learning models based on keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. …

Fake Malware Opcodes Generation Using Hmm And Different Gan Algorithms, Harshit Trehan

Master's Projects

Malware, or malicious software, is a program that is intended to harm systems. In the past decade, the number of malware attacks have grown and, more importantly, evolved. Many researchers have successfully integrated cutting edge Machine Learning techniques to combat this ever present and growing threat to cyber and information security. One big challenge faced by many researchers is the lack of enough data to train machine learning models and specifically deep neural networks properly. Generative modelling has proven to be very efficient at generating synthesized data that can match the actual data distribution.

In this project, we aim to …

Machine Learning To Detect Malware Evolution, Lolitha Sresta Tupadha

Master's Projects

Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appro-priate countermeasures can be undertaken. In this research, we perform a variety of experiments to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine learning and can be fully automated—in particular, no reverse engineering or other labor-intensive manual analysis is required. Specifically, we consider analysis based on hidden Markov models and various word embedding techniques, among other machine learning based …

Malware Analysis With Auxiliary-Classifier Gan, Rakesh Nagaraju

Master's Projects

A generative adversarial network (GAN) is a powerful machine learning concept where both a generative and discriminative model are trained simultaneously. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using GANs, and we also consider the effectiveness of GANs for malware classification. Specifically, we consider auxiliary classifier GAN (AC-GAN), which enables us to work with multiclass data. We find that AC-GAN generates malware images that cannot be reliably distinguished from real malware images. In addition, we find that the detection capabilities of AC-GAN …

Data Augmentation With Malware As Images, Aditi Walia

Master's Projects

Machine learning and deep learning techniques for malware detection and classifi- cation play an important role in the mitigation of cybersecurity threats. However, such techniques are often limited by a lack of data. Previous research has shown promising classification results by treating malware executables as images. In this research, we consider data augmentation using noise addition, geometric transforma- tions, and Auxiliary Classifier Generative Adversarial Networks (AC-GAN) for data augmentation of malware images. We train convolution neural networks (CNN) to verify that our generated images accurately model the original malware samples.

Higher-Order Link Prediction Using Node And Subgraph Embeddings, Kalpnil Anjan

Master's Projects

Social media, academia collaborations, e-commerce websites, biological structures, and other real-world networks are modeled as graphs to represent their entities and relationships in an abstract way. Such graphs are becoming more complex and informative, and by analyzing them we can solve various problems and find hidden insights. Some applications include predicting relationships and potential links between nodes, classifying nodes, and finding the most influential nodes in the graph, etc.

A large amount of research is being done in the field of predicting links between two nodes. However, predicting a future relationship among three or more nodes in a graph is …

Overlapping Community Detection In Social Networks, Akshar Panchal

Master's Projects

Social networking sites are important to connect with the world virtually. As the number of users accessing these sites increase, the data and information keeps on increasing. There are communities and groups which are formed virtually based on different factors. We can visualize these communities as networks of users or nodes and the relationships or connections between them as edges. This helps in evaluating and analyzing different factors that influence community formation in such a dense network. Community detection helps in revealing certain characteristics which makes these groups in the network unique and different from one another. We can use …

Fake News Analysis And Graph Classification On A Covid-19 Twitter Dataset, Kriti Gupta

Master's Projects

Earlier researches have showed that the spread of fake news through social media can have a huge impact to society and also to individuals in an extremely negative way. In this work we aim to study the spread of fake news compared to real news in a social network. We do that by performing classical social network analysis to discover various characteristics, and formulate the problem as a binary classification, where we have graphs modeling the spread of fake and real news. For our experiments we rely on how news are propagated through a popular social media services such as …

Classifying Illegal Advertisements On The Darknet Using Nlp, Karan Shashin Shah

Master's Projects

The Darknet has become a place to conduct various illegal activities like child labor, contract murder, drug selling while staying anonymous. Traditionally, international and government agencies try to control these activities, but most of those actions are manual and time-consuming. Recently, various researchers developed Machine Learning (ML) approaches trying to aid in the process of detecting illegal activities. The above problem can benefit by using different Natural Language Processing (NLP) techniques. More specifically, researchers have used various classical topic modeling techniques like bag of words, N-grams, Term Frequency, Term Frequency Inverse Document Frequency (TF-IDF) to represent features and train machine …

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang

Master's Projects

Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, account- ing for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor that is also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and staining normalization algorithms. A wide variety of classic machine learning and leading deep learning models are then applied to classify tissue images as either benign or cancerous. For classic techniques, the highest classification accuracy we attain is 85%, while our …

Successful Long-Distance Breeding Range Expansion Of A Top Marine Predator, Robert William Henry, Scott A. Shaffer, Michelle Antolos, María Félix-Lizárraga, David G. Foley, Elliott L. Hazen, Yann Tremblay, Daniel P. Costa, Bernie R. Tershy, Donald A. Croll

Faculty Research, Scholarly, and Creative Activity

Little is known about the effects of large-scale breeding range expansions on the ecology of top marine predators. We examined the effects of a recent range expansion on the breeding and foraging ecology of Laysan albatrosses (Phoebastria immutabilis). Laysan albatrosses expanded from historical breeding colonies in the Central Pacific Ocean to the Eastern Pacific Ocean around central Baja California, Mexico, leading to a 4,000-km shift from colonies located adjacent to the productive transition zone in the Central Pacific to colonies embedded within the eastern boundary current upwelling system of the Eastern Pacific California Current. We use electronic tagging and remote …

E-Shodhsindhu Problems And Barriers Experienced By Librarians Of Higher Educational Institutions, Amit Kumar

Library Philosophy and Practice (e-journal)

Abstract

Purpose – The paper intends to explore e-shodhsindhu problems and barriers experienced by librarians of higher educational institutions.

Design/methodology/approach – To accomplish the objectives of the study, librarians viewpoint was collected through a well-structured questionnaire consisted several question keeping in mind the objectives of study, followed by personal interview and discussion among the consortium (e-ShodhSindhu consortium) member libraries under study. In order to strengthen the consortium practice, the opinion or suggestions provided by librarians were recorded carefully and clubbed in discussion part.

Findings – The paper discuss about the concept of consortia, its objectives and influential factors followed by …

Management Of Hypothyroidism Research Publications: A Bibliometric Analysis, Abhilashbabu Babu R, Umadevi L. N Dr

Library Philosophy and Practice (e-journal)

Endocrine and metabolic diseases are among the the most common contemporary human afflictions particularly in the United States and other developing countries like India and Srilanka. Hypothyroidism is due to the under activity of thyroid gland and results from its failure to secrete sufficient hormones into the blood stream. Hypothyroidism is a condition that reflects decreased concentrations of thyroid hormones due to any cause. This paper attempts to analyse management of hypothyroidism as reflected in publication output covered by Web of Science online database during 2018-2020 USA advanced with 156 (23.5 %) Articles and it occupies the first place in …

Teaching Through A Pandemic: Classes Of Khallikote University Go On Air And Online, Raj Kishor Kampa Dr

Library Philosophy and Practice (e-journal)

As the Covid-19 pandemic has disrupted the education sector and the long lockdown has closed schools, colleges and universities in India since March 2020, the paper tries to explore the possibilities of adopting synchronous and asynchronous learning methods to teach the students of Khallikote University Berhampur India during the lockdown period. Moreover, paper discusses how the open source learning management system (LMS) Moodle can be leveraged as a tool in delivering effective asynchronous learning in higher educational institutions. This paper describes how Elearning@KUB platform for Khallikote University is designed, developed and implemented using Moodle, wherein students can access to study …

Assessment Of Challenges And Prospects Of E-Marketing In The Development Of Nigerian Universities’ Education Lecturers In South-South, Nigeria, Patience Ewomaogene Okoro

Library Philosophy and Practice (e-journal)

This research paper examined the role of e-Marketing for sustainable growth and development in Nigeria. It determined e-Marketing strategies, especially online marketing strategies for sustainable growth and development in Nigeria. The simple random sampling method was used to draw the sample for the study. The total sample used was 600 entrepreneurs of which 586 copies of the questionnaires were retrieved. The instrument for data collection for this study was a questionnaire with a four-point Likert-scale format of Strongly Agreed 4 points (SA), Agreed 3 points (A), Disagreed 2 points(D), and Strongly Disagreed 1 point (SD). Data collected were analyzed using …

Sound In Video Games: How Sound Is An Important Aspect Of The Virtual Experience, James Boen

ART 108: Introduction to Games Studies

This paper will take the form of an analysis, with video games as the medium/text that will be analysed. Although analysis is typically reserved for poems, books, short stories, or plays, video games are simply a form of conveying ideas and a form of text that is representative of the 21st century. Video games is a rare medium that has an interactive element, which can alter/enhance the experience an audience member can have, even if there were the same audio/visual components in a film or play. In most forms of media with an audio component, the analysis is done by …

Hidden Silicon-Vacancy Centers In Diamond, Christopher L. Smallwood, Ronald Ulbricht, Matthew W. Day, Tim Schröder, Kelsey M. Bates, Travis M. Autry, Geoffrey Diederich, Edward Bielejec, Mark E. Siemens, Steven T. Cundiff

Faculty Research, Scholarly, and Creative Activity

We characterize a high-density sample of negatively charged silicon-vacancy (SiV-) centers in diamond using collinear optical multidimensional coherent spectroscopy. By comparing the results of complementary signal detection schemes, we identify a hidden population of SiV- centers that is not typically observed in photoluminescence and which exhibits significant spectral inhomogeneity and extended electronic T2 times. The phenomenon is likely caused by strain, indicating a potential mechanism for controlling electric coherence in color-center-based quantum devices.

A Hybrid Gaze Pointer With Voice Control, Indhuja Ravi

Master's Projects

Accessibility in technology has been a challenge since the beginning of the 1800s. Starting with building typewriters for the blind by Pellegrino Turri to the on-screen keyboard built by Microsoft, there have been several advancements towards assistive technologies. The basic tools necessary for anyone to operate a computer are to be able to navigate the device, input information, and perceive the output. All these three categories have been undergoing tremendous advancements over the years. Especially, with the internet boom, it has now become a necessity to point onto a computer screen. This has somewhat attracted research into this particular area. …