Digital Commons Network^™

Learning Analytics Through Machine Learning And Natural Language Processing, Bokai Yang

Theses and Dissertations

The increase of computing power and the ability to log students’ data with the help of the computer-assisted learning systems has led to an increased interest in developing and applying computer science techniques for analyzing learning data. To understand and investigate how learning-generated data can be used to improve student success, data mining techniques have been applied to several educational tasks. This dissertation investigates three important tasks in various domains of educational data mining: learners’ behavior analysis, essay structure analysis and feedback providing, and learners’ dropout prediction. The first project applied latent semantic analysis and machine learning approaches to investigate …

Beyond News Values On Twitter: Predicting Factors That Drive User Engagement In News, Zhiyan Zhong

Dartmouth College Master’s Theses

When deciding on what news stories to cover, traditional journalism determines news values by following several elements of newsworthiness, such as impact, timeliness, and prominence. However, these guidelines do not always seem to correspond with the success of content on social media. As people are increasingly turning to social media for news, our research aims to understand and predict factors that drive user engagement for news on social media. In this study, we analyze news content published on Twitter, and examine a diverse set of characteristics like metrics retrieved from the Twitter API and semantics by natural language processing, including …

Discerning Conversational Context In Online Health Communities For Personalized Digital Behavior Change Solutions Using Pragmatics To Reveal Intent In Social Media (Prism) Framework, Tavleen Singh, Kirk Roberts, Trevor Cohen, Nathan Cobb, Amy Franklin, Sahiti Myneni

Faculty, Staff and Student Publications

BACKGROUND: Online health communities (OHCs) have emerged as prominent platforms for behavior modification, and the digitization of online peer interactions has afforded researchers with unique opportunities to model multilevel mechanisms that drive behavior change. Existing studies, however, have been limited by a lack of methods that allow the capture of conversational context and socio-behavioral dynamics at scale, as manifested in these digital platforms.

OBJECTIVE: We develop, evaluate, and apply a novel methodological framework, Pragmatics to Reveal Intent in Social Media (PRISM), to facilitate granular characterization of peer interactions by combining multidimensional facets of human communication.

METHODS: We developed and applied …

Application Of The Recitation Method To Improve The Competence Of Medical Laboratory Technology Students In The Immunology Field, Patricia Gita Naully, Perdina Nursidika

Jurnal Pendidikan Sains

An accredited mid-level MLT (Medical Laboratory Technology) must have the competence to perform medical laboratory examinations from pre-analytic, analytic, and post-analytic stages in several fields, one of which is immunology. Many D3 MLT students are facing obstacles to passed the immunology course. This obstacle is not only experienced by students of D3 MLT but also by medical students, pharmacy students, and similar majors. The purpose of this research is to determine the effect of recitation method in the form of animation video making and immunology based device on the competence of Diploma 3 (D3) MLT students. This research is a …

Ten Years After Imagenet: A 360° Perspective On Artificial Intelligence, Sanjay Chawla, Preslav Nakov, Ahmed Ali, Wendy Hall, Issa Khalil, Xiaosong Ma, Husrev Taha Sencar, Ingmar Weber, Michael Wooldridge, Ting Yu

Natural Language Processing Faculty Publications

It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved - provided we have enough high-quality labelled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modelling has come to the fore. The rise of attention networks, self-supervised learning, generative modelling and graph neural networks has widened the application space of AI. Deep learning has also propelled the return of reinforcement learning as a core building block of autonomous …

Multi-Scale Hybridized Topic Modeling: A Pipeline For Analyzing Unstructured Text Datasets Via Topic Modeling, Keyi Cheng, Stefan Inzer, Adrian Leung, Xiaoxian Shen, Deanna Needell, Todd Presner, Michael Perlmutter, Michael R. Lindstrom, Joyce Chew

School of Mathematical & Statistical Sciences Faculty Publications

We propose a multi-scale hybridized topic modeling method to find hidden topics from transcribed interviews more accurately and more efficiently than traditional topic modeling methods. Our multiscale hybridized topic modeling method (MSHTM) approaches data at different scales and performs topic modeling in a hierarchical way utilizing first a classical method, Nonnegative Matrix Factorization, and then a transformer-based method, BERTopic. It harnesses the strengths of both NMF and BERTopic. Our method can help researchers and the public better extract and interpret the interview information. Additionally, it provides insights for new indexing systems based on the topic level. We then deploy our …

Cardiac Arrhythmia Disease Classifier Model Based On A Fuzzy Fusion Approach, Fatma Taher, Hamoud Alshammari, Lobna Osman, Mohamed Elhoseny, Abdulaziz Shehab, Eman Elayat

All Works

Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia. Initially, at the preprocessing steps, …

A Comprehensive Review On Deep Learning-Based Generative Linguistic Steganography, Dr Khaled Nagaty, Israa Lotfy Lotfy, Abeer Hamdy Dr.

Computer Science

. The recent development of deep learning has made a significant breakthrough in linguistic generative steganography. The text has become one of the most intensely used communication carriers on the Internet, making steganography an efficient carrier for concealing secret messages. Text steganography has long been used to protect the privacy and confidentiality of data via public transmission. Steganography utilizes a carrier to embed the data to generate a secret unnoticed and less attractive message. Different techniques have been used to improve the security of the generated text and quality of the steganographic text, such as the Markov model, Recurrent Neural …

Neutrosophic Model To Examine The Challenges Faced By Manufacturing Businesses In Adopting Green Supply Chain Practices And To Provide Potential Solutions, Zenat Mohamed, Mahmoud M. Ismail, Amal F. Abd El-Gawad

Neutrosophic Systems with Applications

Several obstacles stand in the way of companies trying to adopt green supply-chain practices. The purpose of this research is to examine the challenges faced by the industrial industry in adopting green supply chain practices and to provide potential solutions. The information for this research was gathered via in-depth, personal conversations with manufacturing sector managers who are well-versed in green supply chain practices. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique was used in the evaluation phase to evaluate obstacles and assess efficient options for introducing green supply chain practices. The TOPSIS method is integrated …

Global Scientific Trends On Healthy Eating From 2002 To 2021: A Bibliometric And Visualized Analysis, Te Fang, Hongyi Cao, Yue Wang, Yang Gong, Zhongqing Wang

Faculty, Staff and Student Publications

Diet has been recognized as a vital risk factor for non-communicable diseases (NCDs), climate changes, and increasing population, which has been reflected by a rapidly growing body of the literature related to healthy eating. To reveal a panorama of the topics related to healthy eating, this study aimed to characterize and visualize the knowledge structure, hotspots, and trends in this field over the past two decades through bibliometric analyses. Publications related to healthy eating between 1 January 2002 and 31 December 2021 were retrieved and extracted from the Web of Science database. The characteristics of articles including publication years, journals, …

An Intelligent Decision Support Model For Optimal Selection Of Machine Tool Under Uncertainty: Recent Trends, Ibrahim M. Hezam

Neutrosophic Systems with Applications

Many scholars have been interested in the subject of machine tool selection as a result of the growing number of different machines and the continuous advancement of technology associated with these machines. The selection of an unsuitable machine tool may lead to a variety of issues, including limitations on production capacities and productivity indicators when taking into account both time and money from an industrial and practical perspective. The present strategy of selecting machine tools, known as multi-criteria decision-making (MCDM), relies on the subjective viewpoint the vast majority of the time. When selecting an appropriate machining tool, however, it is …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Addressing Barriers To Proactive Restoration Of At-Risk Sagebrush Communities: A Causal Layered Analysis, Carmen Calzado-Martinez, Mark W. Brunson, Sofia Koutzoukis, Jacobo Baggio, Kari E. Veblen

Environment and Society Faculty Publications

Restoration success in degraded rangelands often depends on a site's resilience to disturbance and resistance to invasive plants. Because it is more difficult to restore plant communities after they are dominated by invasive species, a potential approach is proactive restoration in sites at risk of crossing degradation thresholds (e.g. initiating restoration prior to invasive grass dominance). When developing a new restoration approach, it is important to consider operational feasibility, including social, budgetary, and environmental factors. Accordingly, we studied influences within land management agencies on the adoption of a specific proactive restoration approach: out-planting native grass and forb seedlings into sagebrush …

Using Probabilistic Context-Free Grammar To Create Password Guessing Models, Isabelle Hjelden

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper will discuss two versions of probabilistic context-free grammar password-guessing models. The first model focuses on using English semantics to break down passwords and identify patterns. The second model identifies repeating chunks in passwords and uses this information to create possible passwords. Then, we will show the performance of each model on leaked password databases, and finally discuss the observations made on these tests.

Hierarchical Joint Entity Recognition And Relation Extraction Of Contextual Entities In Family History Records, Daniel Segrera

Theses and Dissertations

Entity extraction is an important step in document understanding. Higher accuracy entity extraction on fine-grained entities can be achieved by combining the utility of Named Entity Recognition (NER) and Relation Extraction (RE) models. In this paper, a cascading model is proposed that implements NER and Relation extraction. This model utilizes relations between entities to infer context-dependent fine-grain named entities in text corpora. The RE module runs independent of the NER module, which reduces error accumulation from sequential steps. This process improves on the fine-grained NER F1-score of existing state-of-the-art from .4753 to .8563 on our data, albeit on a strictly …

Optimal Monohedral Tilings Of Hyperbolic Surfaces, Leonardo Digiosia, Jahangir Habib, Jack Hirsch, Lea Kenigsberg, Kevin Li, Dylanger Pittman, Jackson Petty, Christopher Xue, Weitao Zhu

Rose-Hulman Undergraduate Mathematics Journal

The hexagon is the least-perimeter tile in the Euclidean plane for any given area. On hyperbolic surfaces, this "isoperimetric" problem differs for every given area, as solutions do not scale. Cox conjectured that a regular k-gonal tile with 120-degree angles is isoperimetric. For area π/3, the regular heptagon has 120-degree angles and therefore tiles many hyperbolic surfaces. For other areas, we show the existence of many tiles but provide no conjectured optima. On closed hyperbolic surfaces, we verify via a reduction argument using cutting and pasting transformations and convex hulls that the regular 7-gon is the optimal n-gonal tile of …

Solving Turkish Math Word Problems By Sequence-To-Sequence Encoder-Decoder Models, Esi̇n Gedi̇k, Tunga Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

Solving math word problems (MWP) is a challenging task due to the semantic gap between natural language texts and mathematical equations. The main purpose of the task is to take a written math problem as input and produce a proper equation as output for solving that problem. This paper describes a sequence-to-sequence (seq2seq) neural model for automatically solving Turkish MWPs based on their semantic meanings in the text. It comprises a bidirectional encoder to comprehend the semantics of the problem by encoding the input sequence and a decoder with attention to extract the equation by tracking the semantic meanings of …

Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

With the advancement of sentiment analysis (SA) models and their incorporation into our daily lives, fairness testing on these models is crucial, since unfair decisions can cause discrimination to a large population. Nevertheless, some challenges in fairness testing include the unknown oracle, the difficulty in generating suitable test inputs, and the lack of a reliable way of fixing the issues. To fill in these gaps, BiasRV, a tool based on metamorphic testing (MT), was introduced and succeeded in uncovering fairness issues in a transformer-based model. However, the extent of unfairness in other SA models has not been thoroughly investigated. Our …

Specification-Based Autonomous Driving System Testing, Yuan Zhou, Yang Sun, Yun Tang, Yuqi Chen, Jun Sun, Christopher M. Poskitt, Yang Liu, Zijiang Yang

Research Collection School Of Computing and Information Systems

Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be done safely in very realistic and highly customizable environments. Existing testing approaches, however, fail to test simulated AVs systematically, as they focus on specific scenarios and oracles (e.g., lane following scenario with the "no collision" requirement) and lack any coverage criteria measures. In this paper, we propose AVUnit, a framework for systematically testing AV systems against customizable correctness specifications. Designed modularly to support different simulators, AVUnit consists of two new languages for specifying dynamic …

Designing Polar Textures With Ultrafast Neuromorphic Features From Atomistic Simulations, Sergey Prosandeev, Sergei Prokhorenko, Yousra Nahas, Yali Yang, Changsong Xu, Julie Grollier, Diyar Talbayev, Brahim Dkhil, L. Bellaiche

Physics Faculty Publications and Presentations

This review summarizes recent works, all using a specific atomistic approach, that predict and explain the occurrence of key features for neuromorphic computing in three archetypical dipolar materials, when they are subject to THz excitations. The main ideas behind such atomistic approach are provided, and illustration of model relaxor ferroelectrics, antiferroelectrics, and normal ferroelectrics are given, highlighting the important potential of polar materials as candidates for neuromorphic computing. Some peculiar emphases are made in this Review, such as the connection between neuromorphic features and percolation theory, local minima in energy path, topological transitions and/or anharmonic oscillator model, depending on the …

Concept-Oriented Transformers For Visual Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In the richly multimedia Web, detecting sentiment signals expressed in images would support multiple applications, e.g., measuring customer satisfaction from online reviews, analyzing trends and opinions from social media. Given an image, visual sentiment analysis aims at recognizing positive or negative sentiment, and occasionally neutral sentiment as well. A nascent yet promising direction is Transformer-based models applied to image data, whereby Vision Transformer (ViT) establishes remarkable performance on largescale vision benchmarks. In addition to investigating the fitness of ViT for visual sentiment analysis, we further incorporate concept orientation into the self-attention mechanism, which is the core component of Transformer. The …

Proactive Conversational Agents, Lizi Liao, Grace Hui Yang, Chirag Shah

Research Collection School Of Computing and Information Systems

Conversational agents, or commonly known as dialogue systems, have gained escalating popularity in recent years. Their widespread applications support conversational interactions with users and accomplishing various tasks as personal assistants. However, one key weakness in existing conversational agents is that they only learn to passively answer user queries via training on pre-collected and manually-labeled data. Such passiveness makes the interaction modeling and system-building process relatively easier, but it largely hinders the possibility of being human-like hence lowering the user engagement level. In this tutorial, we introduce and discuss methods to equip conversational agents with the ability to interact with end …

Multi-Modal Api Recommendation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo

Research Collection School Of Computing and Information Systems

Too many options can be a problem, which is the case for Application Programming Interfaces (APIs). As there are many such APIs, with many more being introduced periodically, it raises the problem of choosing which API to be recommended. Furthermore, numerous APIs are commonly used together with other complementary third-party APIs. It can be challenging for developers to understand how to use each API and to remember all the complementary APIs for the API they want to use. Therefore, an accurate API recommendation approach can improve developers' efficiency in implementing certain functionality. Several approaches have been developed to automatically recommend …

The Use Of Artificial Intelligence To Detect Students Sentiments And Emotions In Gross Anatomy Reflections, Krzysztof J. Rechowicz, Carrie A. Elzie

VMASC Publications

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary …

Presidential Vote Share And Covid-19 Vaccination Rate In Indonesia: A District-Level Cross-Sectional Ecological Study, Gede Benny Setia Wirawan, Ni Luh Zallila Gustina, Ivy Cerelia Valerie, I Gusti Ayu Indah Pradnyani Rs, Muchamad Zaenal Arifin, Pande Putu Januraga

Kesmas

Political affiliation has been reported as a determinant of COVID-19 vaccine acceptance in some countries, although few studies have examined the Asian context. This study aims to fill this gap by employing an ecological study design using Indonesian regions as data points. Political affiliation was represented by incumbent President Jokowi’s vote share in the 2019 presidential election. Potential confounders included population density, human development index, availability of hospitals and primary health care, 2019–2020 economic growth, COVID-19 mortality rate, and proportion of Muslims in the population. The final analysis included 201 out of 501 districts and cities in Indonesia. Controlling for …

Rank And Analysis Several Solutions Of Healthcare Waste To Achieve Cost Effectiveness And Sustainability Using Neutrosophic Mcdm Model, Ahmed Abdelhafeez, Hoda K. Mohamed, Nariman A. Khalil

Neutrosophic Systems with Applications

Managing healthcare waste (HCWTT) from healthcare facilities is difficult. It's high up on the list of health concerns. This growth in HCWTT has been especially visible in recent years, as the quantity of medical services available has increased. Because of the potential danger, this garbage poses to people and the planet, it must be properly disposed of. Because of ineffective waste management practices, inadequate financial resources, and a lack of adequate facilities, HCWT administration is especially crucial in developing nations. Reducing HCWT via appropriate treatment is important for the area's financial and environmental health. In order to solve single-valued neutrosophic …

Neutrosophic Critic Mcdm Methodology For Ranking Factors And Needs Of Customers In Product's Target Demographic In Virtual Reality Metaverse, Ahmed Sleem, Nehal Mostafa, Ibrahim Elhenawy

Neutrosophic Systems with Applications

Affective design has come to place a premium on customer-centric creativity, which ultimately results in the creation of a product tailored to the requirements of a certain demographic. Designing a fresh and original item that appeals to clients remains challenging, despite the abundance of literature on customer-centric creativity and impact product design. This is so because optimizing technological and aesthetical design aspects and variables for a group of consumers is highly complicated, and for the same reasons that it is hard to know a customer's choice, enable the product's functioning, etc. One of the most important parts of creating cutting-edge …

Comparing Boys’ And Girls’ Attitudes Toward Computer Science, Danielle Scott, Amiee Zou, Sharin Rawhiya Jacob, Debra Richardson, Mark Warschauer

Journal of Computer Science Integration

Women are severely underrepresented in computer science (CS) degrees and careers. While student interest is a key predictor of success, little is known about how elementary students from underserved groups, such as girls, develop their interest in CS. To address this issue, we examined the differences in attitudes between upper elementary girls and boys towards CS after participating in a yearlong, inquiry-based CS curriculum designed for diverse learners. Pre-and-post surveys on students’ attitudes towards CS (n = 108) were delivered before and after student participation in the curriculum. Results from the survey showed only two demonstrated significant differences between boys …

Applying Positional Encoding To Enhance Vision-Language Transformers, Xuehao Liu, Sarah Jane Delany, Susan Mckeever

Conference papers

Positional encoding is used in both natural language and computer vision transformers. It provides information on sequence order and relative position of input tokens (such as of words in a sentence) for higher performance. Unlike the pure language and vision transformers, vision-language transformers do not currently exploit positional encoding schemes to enrich input information. We show that capturing location information of visual features can help vision-language transformers improve their performance. We take Oscar, one of the state-of-the-art (SOTA) vision-language transformers as an example transformer for implanting positional encoding. We use image captioning as a downstream task to test performance. We …

Towards A Responsive Resilient Supply Chain Based On Industry 5.0: A Case Study In Healthcare Systems, Abduallah Gamal, Amal F. Abd El-Gawad, Mohamed Abouhawwash

Neutrosophic Systems with Applications

Executives and academics have presented the idea of Industry 5.0, which is an attempt to build upon the previous iteration, Industry 4.0, by including many important tenets such as human-centricity, resilience, and sustainability. Because of the significance of this idea, the current study provides a decision-making framework to examine a responsive supply chain dubbed responsive supply chain 5.0 for healthcare systems. This framework takes into consideration the aspects of Industry 5.0. In order to do this, at the onset, the most important connected factors and strategies are determined by consulting the relevant experts and body of published research. This problem …