Digital Commons Network^™

Effectiveness Of In-Person Versus Online Negotiation Teaching For Practitioners, Patricia Oehlschläger, Michael A. Merz

Faculty Research, Scholarly, and Creative Activity

Most negotiation courses have been taught in person. However, online education has become more prevalent over the past decade due to its flexibility, cost and time efficiency, and new digital technologies designed to compensate for the lack of personal contact. The global pandemic has accelerated this trend, raising the question of whether negotiation courses taught online are as effective as those taught in person. The few studies that have examined the effect of teaching modality on student performance were limited to undergraduate and graduate student samples and the results have been mixed. To contribute to this discussion, we conducted two …

Multi-Label Text Classification With Transfer Learning, Likhitha Yelamanchili

Master's Projects

Multi-label text categorization is a crucial task in Natural Language Processing, where each text instance can be simultaneously assigned to numerous labels. This project's goal is to assess how well several deep learning models perform on a real-world dataset for multi-label text classification. We employed data augmentation techniques like Synonym Substitution and Random Word Substitution to address the problem of data imbalance. We conducted experiments on a toxic comment classification dataset to evaluate the effectiveness of several deep learning models including Bi-LSTM, GRU, and Bi-GRU, as well as fine- tuned pre-trained BERT models. Many metrics, including log loss, recall@k, and …

Detecting Botnets Using Hidden Markov Model, Profile Hidden Markov Model And Network Flow Analysis, Rucha Mannikar

Master's Projects

Botnet is a network of infected computer systems called bots managed remotely by an attacker using bot controllers. Using distributed systems, botnets can be used for large-scale cyber attacks to execute unauthorized actions on the targeted system like phishing, distributed denial of service (DDoS), data theft, and crashing of servers. Common internet protocols used by normal systems for regular communication like hypertext transfer (HTTP) and internet relay chat (IRC) are also used by botnets. Thus, distinguishing botnet activity from normal activity can be challenging. To address this issue, this project proposes an approach to detect botnets using peculiar traits in …

Hate Speech Detection In Hindi, Pranjali Prakash Bansod

Master's Projects

Social media is a great place to share one’s thoughts and to express oneself. Very often the same social media platforms become a means for spewing hatred.The large amount of data being shared on these platforms make it difficult to moderate the content shared by users. In a diverse country like India hate is present on social media in all regional languages, making it even more difficult to detect hate because of a lack of enough data to train deep/ machine learning models to make them understand regional languages.This work is our attempt at tackling hate speech in Hindi. We …

Personalized Tweet Recommendation Using Users’ Image Preferences, Shashwat Avinash Kadam

Master's Projects

In the era of information explosion, the vast amount of data on social media platforms can overwhelm users. Not only does this information explosion contain irrelevant content, but also intentionally fabricated articles and images. As a result, personalized recommendation systems have become increasingly important to help users navigate and make sense of this data. We propose a novel technique to use users’ image preferences to recommend tweets. We extract vital information by analyzing images liked by users and use it to recommend tweets from Twitter. As many images online have no descriptive metadata associated with them, in this framework, we …

Image Captioning Using Reinforcement Learning, Venkat Teja Golamaru

Master's Projects

Image captioning is a crucial technology with numerous applications, including enhancing accessibility for the visually impaired, developing automated image indexing and retrieval systems, and enriching social media experiences. However, accurately describing the content of an image in natural language remains a challenge, particularly in low-resource settings where data and computational power are limited. The most advanced image captioning architectures currently use encoder-decoder structures that incorporate a sequential recurrent prediction model. This study adopts a typical Convolutional Neural Network (CNN) encoder Recurrent Neural Network (RNN) decoder structure for image captioning, but it has framed the problem as a sequential decision-making task. …

Proof-Of-Stake For Spartangold, Nimesh Ashok Doolani

Master's Projects

Consensus protocols are critical for any blockchain technology, and Proof-of- Stake (PoS) protocols have gained popularity due to their advantages over Proof-of- Work (PoW) protocols in terms of scalability and efficiency. However, existing PoS mechanisms, such as delegated and bonded PoS, suffer from security and usability issues. Pure PoS (PPoS) protocols provide a stronger decentralization and offer a potential solution to these problems. Algorand, a well-known cryptocurrency, employs a PPoS protocol that utilizes a new Byzantine Agreement (BA) mechanism for consensus and Verifiable Random Functions (VRFs) to securely scale the protocol to accommodate many participants, making it possible to handle …

Application Of Knowledge Graph Techniques On Textbooks, Yutong Yao

Master's Projects

Textbooks are written and organized in a way that facilitates learning and understanding. Sections like glossary terms at the end of a textbook provide guidance on the topic of interest. However, it takes manual effort to create the index terms in the glossary that highlight the key referenced terminologies and related terms. Knowledge graphs, which have been used to represent and even reason over data and knowledge, can potentially capture textbook’s important terms, concepts, and their relations. Popular since the initial introduction by Google Knowledge Graphs (KGs), they combine graph and data to capture and model enormous amounts of relational …

The Bias Report: An Automated News Aggregator For Political Bias Classification And News Summarization, Anant Joshi

Master's Projects

Political polarization is on the rise in the US, driven in large part by divisive news that goes viral on the Internet. Specifically, many media outlets use slanted language and publish misinformation in order to drive user traffic and engagement. Almost 80% of US citizens get their news from online sources, but there is a lack of public safeguards against biased news. A large amount of news is published online every day by media organizations, and it is impossible to manually analyze this amount of data. There is a clear need for automated, public-facing solutions in the current political climate …

Influence Maximization Based On Community Detection And Dominating Sets, Ameya Marathe

Master's Projects

An online platform where various people come together to share information and communicate is called a social network. These platforms are set apart from other means of communication mostly because you can follow and interact also with different people even some you never met, comment on their posts, and re-sharing their posts. Companies such as Amazon and Walmart use these platforms daily for marketing purposes, like spreading information regarding new products and services they offer. They carefully select a subset of users, called influencers, who are usually the ones with high influence over the rest of the users. Influencers receive …

Wikipedia Web Table Interpretation, Keyword-Based Search, And Ranking, Kartikee Dabir

Master's Projects

Information retrieval and data interpretation on the web, for the purpose of gaining knowledgeable insights, has been a widely researched topic from the onset of the world wide web or what is today popularly known as the internet. Web tables are structured tabular data present amidst unstructured, heterogenous data on the web. This makes web tables a rich source of information for a variety of tasks like data analysis, data interpretation, and information retrieval pertaining to extracting knowledge from information present on the web. Wikipedia tables which are a subset of web tables hold a huge amount of useful data, …

Nft Artifact Prediction Using Machine Learning, Rishabh Pandey

Master's Projects

NFT Prediction Systems are web applications that provide their users with valuable insights about the artifact. These insights are useful for investors and collectors to make better decisions about their purchases. This project builds upon the same concept of prediction by developing a web application to dynamically provide recommendations based on user input and training an ML model to predict their cost. Preliminary work for the prediction system involved data collection, pre-processing, analysis, and filtering of large datasets from diverse sources. The project focused on the development of a user- friendly UI to enable seamless categorization of search results generated …

Comparative Analysis Of Transformer-Based Models For Text-To-Speech Normalization, Pankti Dholakia

Master's Projects

Text-to-Speech (TTS) normalization is an essential component of natural language processing (NLP) that plays a crucial role in the production of natural-sounding synthesized speech. However, there are limitations to the TTS normalization procedure. Lengthy input sequences and variations in spoken language can present difficulties. The motivation behind this research is to address the challenges associated with TTS normalization by evaluating and comparing the performance of various models. The aim is to determine their effectiveness in handling language variations. The models include LSTM-GRU, Transformer, GCN-Transformer, GCNN-Transformer, Reformer, and a BERT language model that has been pre-trained. The research evaluates the performance …

Untraining Gender Bias: An Eye-Tracking Study, Ripujit S. Bamrah

Master's Projects

In recent years, social cognitive theory has emphasized the role of cognitive processes in shaping perceptions and behavior related to gender bias. By examining the impact of targeted training interventions, this study seeks to better understand the influence of such processes on decision-making in the context of character selection. This human-computer interaction study explores the potential of intervention-based training to untraining gender bias in character selection. With an increasing need to address gender bias in various domains, understanding the impact of gender-based training becomes crucial. According to our hypothesis, exposure to masculine characters would boost people’s preference for female- intellectualized …

Enhancing The Security Of Yioop Discussion Board, Prajna Gururaj Puranik

Master's Projects

Yioop is an open-source web portal that serves as a search engine and a discussion board, enabling users to create, join, and share content within groups. Data security is a critical concern for Yioop, as it involves storing and accessing user-generated data and generating statistical data. Yioop has an existing security mechanism in place, but continuous enhancements are needed to protect against potential vulnerabilities and cyber threats.

This project aims to strengthen the security of Yioop by implementing additional security measures that build upon the existing security mechanism. To prevent statistical attacks, this project extends differential privacy to mask the …

Yelp Restaurant Popularity Score Calculator, Sneh Bindesh Chitalia

Master's Projects

Yelp is a popular social media platform that has gained much traction over the last few years. The critical feature of Yelp is it has information about any small or large-scale business, as well as reviews received from customers. The reviews have both a 1 to 5 star rating, as well as text. For a particular business, any user can view the reviews, but the stars are what most users check because it is an easy and fast way to decide. Therefore, the star rating is a good metric to measure a particular business’s value. However, there are other attributes …

Static Taint Analysis Via Type-Checking In Typescript, Abhijn Chadalawada

Master's Projects

With the widespread use of web applications across the globe, and the ad- vancements in web technologies in recent years, these applications have grown more ubiquitous and sophisticated than ever before. Modern web applications face the constant threat of numerous web security risks given their presence on the internet and the massive influx of data from external sources. This paper presents a novel method for analyzing taint through type-checking and applies it to web applications in the context of preventing online security threats. The taint analysis technique is implemented in TypeScript using its built-in type-checking features, and then integrated into …

Steganographic Capacity Of Selected Machine Learning And Deep Learning Models, Lei Zhang

Master's Projects

As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic based attack, information would be hidden in a learning model, which might then be used to gain unauthorized access to a computer, or for other malicious purposes. In this research, we determine the steganographic capacity of various classic machine learning and deep learning models. Specifically, we determine the number of low-order bits of the trained parameters of a given model that can be altered without significantly affecting the performance of …

Leveraging Tweets For Rapid Disaster Response Using Bert-Bilstm-Cnn Model, Satya Pranavi Manthena

Master's Projects

Digital networking sites such as Twitter give a global platform for users to discuss and express their own experiences with others. People frequently use social media to share their daily experiences, local news, and activities with others. Many rescue services and agencies frequently monitor this sort of data to identify crises and limit the danger of loss of life. During a natural catastrophe, many tweets are made in reference to the tragedy, making it a hot topic on Twitter. Tweets containing natural disaster phrases but do not discuss the event itself are not informational and should be labeled as non-disaster …

The Search For Metabolic Variants In Response To Climate Change In The American Pika, Tyler Stewart Trader

Master's Projects

Climate change and rising temperatures pose a serious threat to the long-term survival of American pika (Ochotana princeps), emphasizing the interest in the adaptive capability of the pika. This project queried single nucleotide polymorphisms in a population of American pika in Yosemite National Park using Whole Genome Sequencing data, with a specific interest in metabolic variants. The sample data included temporally separated cohorts, comparing modern population data to historical data taken before rapid anthropogenic climate change. Statistically significant variants were identified under Approximate Bayesian Computation using a population decline model. Although population statistics indicated little change between the …

Pet Loss And Grieving Strategies: A Systematic Review Of Literature, Ruth E. Cervantes

Master's Projects

The impact that companion animals have on human lives has been explored in many aspects including therapy and emotional support. In some cases, these types of pets have been deemed necessary for pet owners with medical problems. However, pets are not only a medical necessity for some humans, but they are also companions, and most are included as family members. The loss of a pet often brings on intense grief that is not often acknowledged in the workforce. Bereavement leave for immediate family members is a benefit offered to employees, and as we will read in this literature review pet …

Effectiveness Of Standardized Vs. Individualized Cancer Treatment Education, Jija Issac

Master's Projects

As the number of cancer diagnoses continues to increase and is expected to keep rising, it becomes imperative to explore avenues to optimize patient education. Research has consistently demonstrated that effective education plays a crucial role in improving patient outcomes. Through my previous experiences in various outpatient oncology infusion centers, I have observed significant variations in the methods employed for delivering patient education across different facilities. In this literature review, I explored the effects of standardized chemotherapy education and individualized chemotherapy education on patients in outpatient oncology clinics. The predominant themes across these studies encompass patient awareness of anticipated symptoms, …

A Roadmap To Safer Hyaluronic Acid Injections, Mandi Lonergan

Master's Projects

Non surgical aesthetic treatments have become increasingly more popular in the United States (2020 Plastic Surgery Statistics Report 2021). Hyaluronic acid (HA) fillers are the second most popular non-surgical aesthetic treatment behind Botulinum toxin injections (The Aesthetic Society, 2021). The increased use of these fillers has led to increased rates of complications With the release of new long lasting fillers in 2014, providers began to see an increase in delayed onset nodules (DON). A DON is considered in the aesthetic field to be a nodule that occurs 30 days to years after filler injection (Convery et al., 2021). A …

Reducing Catheter-Associated Urinary Tract Infection Rates By Educating Staff On Bundle Interventions, Tirath Kaur

Master's Projects

PURPOSE: Catheter associated urinary tract infection is an undesired burden to the patients and healthcare system. This systematic review explores the published literature and determines the impact of educating nursing staff on the indication of urinary catheter, insertion of foley catheters, maintenance of urinary catheters, and periodical communication between nurses and physicians for changing foley catheters can reduce the CAUTI rates in adult patients in the acute care hospital setting. METHOD: A systemic review was conducted using CINHAL, Science Direct, PubMed, and MEDLINE within the time period of ten years from 2012 to 2022. Eight published articles are reviewed and …

The Effects Of Nursing Bedside Shift Report On Patient Safety And Satisfaction: A Systematic Review Of The Literature, Alyssa Wong

Master's Projects

Introduction: Nursing bedside shift report is a recommended strategy to promote the effective exchange of accurate patient information during the handoff process with the goal of decreasing communication errors and adverse patient events. However, adopting this handoff method into nursing practice has been challenging in the clinical setting. This systematic review aims to understand the rationale behind nursing bedside handoff by identifying the advantages and barriers to implementation and exploring how this affects patient safety and experience.
Methods: A systematic review was conducted by searching through electronic databases and Google Scholar to identify English-language peer-reviewed journal articles published between 2012 …

Sending And Receiving Internet Messages From Disconnected Areas, Abhishek Prakash Gaikwad

Master's Projects

Over 62% of the world is connected to the internet with more than 6.9 billion smartphone users. The omnipresence of technology in the form of the internet and smartphones have led to constant research in improving communication throughout the world. But even today, 37% (2.9 billion people) are not connected to the internet even though most of the people in such areas have smartphones. To solve this problem of access to internet services in disconnected areas, a software-only mobile-first approach has been proposed for disconnected data distribution infrastructure which can support different internet applications in limited connectivity. A prototype application …

Job Tailored Resume Content Generation, Sumedh Kale

Master's Projects

Generally candidates apply to multiple jobs with a single resume and do not tend to customize their resume to match the job description. This hampers their chances of getting a resume shortlisted for the job. The project aims to help such candidates build job tailored resumes that help them create a customized and targeted resume for a specific job or industry. The tool specifically targets candidates’ employment history, for resume content generation. We then use natural language processing

(NLP) techniques to extract and organize this data into a structured format for the dataset. We experiment with multiple variations of the …

Implicit Racial Bias: A Human Computer Interaction Study Using Eye Tracker, Wenfan Zhang

Master's Projects

Contemporary Human-Computer Interaction (HCI) research has an increasing emphasis on reducing ethnicity bias. The study presents a new method to explore and reduce biases using detailed experiments. The experimental procedure involves presenting participants with images of ethnically diverse characters across three conditions. The study's results significantly illuminate ethnicity bias in character selection dynamics. Participants exposed to targeted training interventions displayed a significant shift in preferences for characters engaged in intellectual activities. Notably, this shift was influenced by the ethnicity of the characters involved. Interestingly, the eye-tracking data unveiled distinct patterns of cognitive load, characterized by slower response times and greater …

Unlearning Hidden Bias Between Refugees : An Initial Empirical Investigation, Akshay Sunil Gurnaney

Master's Projects

The challenges that refugees in various regions encounter are common knowledge. One such challenge is a bias among refugees on ethnocentric grounds. In particular, there are various articles that have pointed out the struggles faced by Syrian refugees in countries like Europe as a result of implicit bias. In fact, the media coverage of Syrian crises and the government responses to the same shed negative light on the refugees themselves. On the contrary, the media coverage of Ukrainian crises is very different with lesser restrictions from the governments.

This research attempts to identify the extent of implicit bias between Ukrainian …

Interest-Based Recommendation System Using Gmail Topic Modelling, Pranav Ghaskadbi

Master's Projects

Emails are a fundamental part of modern communication. Much of communicative discourse in modern society occurs over email, resulting in personal collections for each mail user which are rich in latent user’s interests. Conventional recommendation systems require historical data of user activity and interactions to derive user interests. The absence of activity and interaction data poses an interesting challenge for generating relevant recommendations for users. We were motivated to investigate approaches to identify user interests in the absence of historical data to generate personalized content recommendations. There is opportunity to derive user interests from email data, which can be used …