Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (1121)
- TÜBİTAK (210)
- Wright State University (197)
- Technological University Dublin (192)
- University of New Mexico (165)
-
- Old Dominion University (143)
- Claremont Colleges (126)
- Missouri University of Science and Technology (125)
- University of Nebraska - Lincoln (125)
- San Jose State University (119)
- City University of New York (CUNY) (117)
- Brigham Young University (105)
- Neutrosophic Systems with Applications (92)
- University of Texas at El Paso (88)
- University of Northern Iowa (78)
- University of South Florida (78)
- Chulalongkorn University (73)
- Dartmouth College (71)
- MBZUAI (70)
- Kennesaw State University (66)
- Zayed University (62)
- Portland State University (61)
- Chapman University (60)
- New Jersey Institute of Technology (57)
- Boise State University (56)
- Edith Cowan University (56)
- University of Arkansas, Fayetteville (55)
- University of Nevada, Las Vegas (53)
- Purdue University (51)
- University of Texas Rio Grande Valley (51)
- Keyword
-
- Machine learning (211)
- Natural language processing (211)
- Artificial intelligence (157)
- Natural Language Processing (155)
- Machine Learning (147)
-
- Deep learning (132)
- Artificial Intelligence (101)
- Large language models (88)
- Computer Science (84)
- Social media (83)
- NLP (76)
- Twitter (70)
- Sentiment analysis (69)
- Mathematics (68)
- Deep Learning (67)
- Large Language Models (62)
- Text mining (59)
- Neural networks (58)
- Computational linguistics (54)
- Computer science (53)
- Department of Computer Science and Engineering (53)
- Education (49)
- Fuzzy logic (49)
- Generative AI (49)
- AI (48)
- Data mining (47)
- Linguistics (43)
- Classification (41)
- Social Media (39)
- Information retrieval (38)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (1037)
- Theses and Dissertations (224)
- Turkish Journal of Electrical Engineering and Computer Sciences (199)
- Branch Mathematics and Statistics Faculty and Staff Publications (150)
- Master's Projects (99)
-
- Conference papers (96)
- Neutrosophic Systems with Applications (92)
- Kno.e.sis Publications (84)
- Dissertations (83)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (70)
- Computer Science Faculty Research & Creative Works (66)
- Electronic Theses and Dissertations (65)
- Journal of Humanistic Mathematics (63)
- All Works (62)
- Browse all Theses and Dissertations (62)
- Computer Science Faculty Publications (59)
- Faculty Publications (59)
- USF Tampa Graduate Theses and Dissertations (55)
- Publications and Research (51)
- Iowa Academy of Science Documents (50)
- Computer Science Faculty Publications and Presentations (45)
- Departmental Technical Reports (CS) (44)
- Dissertations, Theses, and Capstone Projects (44)
- Articles (42)
- Faculty, Staff and Student Publications (42)
- Open Access Theses & Dissertations (42)
- Journal of Informatics and Web Engineering (41)
- SMU Data Science Review (41)
- Walden Dissertations and Doctoral Studies (39)
- Computer Science and Engineering Faculty Publications (38)
- Publication Type
- File Type
Articles 2491 - 2520 of 6325
Full-Text Articles in Entire DC Network
Script Combination For Enhanced Story Understanding And Story Generation Systems, Megan Mckenzie, Alexis Kilayko, Jamie C. Macbeth, Scott Carter, Katharine Sieck, Matthew Klenk
Script Combination For Enhanced Story Understanding And Story Generation Systems, Megan Mckenzie, Alexis Kilayko, Jamie C. Macbeth, Scott Carter, Katharine Sieck, Matthew Klenk
Computer Science: Faculty Publications
Scripts, knowledge structures defining sequences of events in stereotypical social situations, were traditionally used to simulate the ways in which people can infer unstated details in understanding a story. In this paper, we describe the MUltiple SCRipt AcTivator (MUSCRAT), and the Script Combination Applier Mechanism (SCAM), significant enhancements of Cullingford’s Script Applier Mechanism which accomplish two novel aims. One system, MUSCRAT, is able to activate more than one script and use them during a story understanding process. The second system, SCAM, uses scripts for story generation, using script variables as “terminals” for combining two or more scripts together to create …
Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu
Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu
Research Collection School Of Computing and Information Systems
User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of …
Equipment Rental Companies Leaders’ Customer Retention Strategies, Katrell Shalaine Mcneil
Equipment Rental Companies Leaders’ Customer Retention Strategies, Katrell Shalaine Mcneil
Walden Dissertations and Doctoral Studies
Equipment rental business leaders that employ inadequate customer retention strategies negatively impact organizations’ performance and profitability. Improved customer retention strategies might improve key customer retention and increase market share. Grounded in the customer retention management theory, the purpose of this qualitative multiple case study was to explore strategies rental leaders use to retain customers. Participants were 10 equipment rental leaders from the Southwest Region of the United States that successfully retained their key customers through effective customer retention strategies. Data were collected using semistructured interviews and a review of materials from organizational websites regarding customer order fulfillment and technology. Through …
Methodology Of Augmented Reality Chinese Language Articulatory Pronunciation Practice: Game And Study Design, Daria Sinyagovskaya
Methodology Of Augmented Reality Chinese Language Articulatory Pronunciation Practice: Game And Study Design, Daria Sinyagovskaya
Electronic Theses and Dissertations, 2020-2023
Learning a language can be hard. Learning a language that contains tones to convey meaning is even harder. This dissertation presents a novel methodology for creating a language practice using augmented reality that has never been used before. The design of a new app in AR and non-AR versions can evaluate the same practice methodology. This methodology was applied to new software and was examined in regard to the importance of this software. Although the study results are inconclusive, progress has been made in answering research questions on the effectiveness of AR versus non-AR and the reliability of peer assessment. …
Analyzing Behavioral Adaptation To Covid-19 And Return To Pre-Pandemic Baselines In A Cohort Of College Seniors, Vlado Vojdanovski
Analyzing Behavioral Adaptation To Covid-19 And Return To Pre-Pandemic Baselines In A Cohort Of College Seniors, Vlado Vojdanovski
Computer Science Senior Theses
As the critical phase of the COVID-19 pandemic seems to be winding down, it is important to analyze the adjustment to COVID-19 and return to normalcy of various populations. In this study we focus on the behavioral adjustments exhibited by a cohort of N=114 college seniors. To infer COVID-19 adjustment we compare the 2021 year (second year of COVID-19) to the 2020 year (first year of COVID-19) and 2019 (prepandemic baseline year). We begin with a broad analysis between the second and first covid year, finding that the second year of COVID-19 shows significant returns to pre-pandemic baselines on multiple …
Spoken Language Interaction With Robots: Recommendations For Future Research, Casey Kennington
Spoken Language Interaction With Robots: Recommendations For Future Research, Casey Kennington
Computer Science Faculty Publications and Presentations
With robotics rapidly advancing, more effective human–robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. In this article, based on the report of an interdisciplinary workshop convened by the National Science Foundation, we identify key scientific and engineering advances needed to enable effective spoken language interaction with robotics. We make 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods …
Understanding Intention For Machine Theory Of Mind: A Position Paper, Casey Kennington
Understanding Intention For Machine Theory Of Mind: A Position Paper, Casey Kennington
Computer Science Faculty Publications and Presentations
Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention—choice with commitment—following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I …
Incremental Unit Networks For Distributed, Symbolic Multimodal Processing And Representation, Mir Tahsin Imtiaz, Casey Kennington
Incremental Unit Networks For Distributed, Symbolic Multimodal Processing And Representation, Mir Tahsin Imtiaz, Casey Kennington
Computer Science Faculty Publications and Presentations
Incremental dialogue processing has been an important topic in spoken dialogue systems research, but the broader research community that makes use of language interaction (e.g., chatbots, conversational AI, spoken interaction with robots) have not adopted incremental processing despite research showing that humans perceive incremental dialogue as more natural. In this paper, we extend prior work that identifies the requirements for making spoken interaction with a system natural with the goal that our framework will be generalizable to many domains where speech is the primary method of communication. The Incremental Unit framework offers a model of incremental processing that has been …
Symbol And Communicative Grounding Through Object Permanence With A Mobile Robot, Josue Torres-Fonseca, Catherine Henry, Casey Kennington
Symbol And Communicative Grounding Through Object Permanence With A Mobile Robot, Josue Torres-Fonseca, Catherine Henry, Casey Kennington
Computer Science Faculty Publications and Presentations
Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration as it relates to symbol and communicative grounding in spoken dialogue systems. In this paper, we leverage SLAM as a module for tracking object permanence and use a robot platform to move around a scene where it discovers objects and learns how they are denoted. We evaluated by comparing our system’s effectiveness at learning words from human dialogue partners both with and without object permanence. …
Religious Violence And Twitter: Networks Of Knowledge, Empathy And Fascination, Samah Senbel, Carly Seigel, Emily Bryan
Religious Violence And Twitter: Networks Of Knowledge, Empathy And Fascination, Samah Senbel, Carly Seigel, Emily Bryan
School of Computer Science & Engineering Faculty Publications
Twitter analysis through data mining, text analysis, and visualization, coupled with the application of actor-network-theory, reveals a coalition of heterogenous religious affiliations around grief and fascination. While religious violence has always existed, the prevalence of social media has led to an increase in the magnitude of discussions around the topic. This paper examines the different reactions on Twitter to violence targeting three religious communities: the 2015 Charleston Church shooting, the 2018 Pittsburgh Synagogue shooting, and the 2019 Christchurch Mosque shootings. The attacks were all perpetrated by white nationalists with firearms. By analyzing large Twitter datasets in response to the attacks, …
An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous
An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous
Dissertations
Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …
Sentiment Without Sentiment Analysis: Using The Recommendation Outcome Of Steam Game Reviews As Sentiment Predictor, Anqi Zhang
Electronic Theses and Dissertations
This paper presents and explores a novel way to determine the sentiment of a Steam game review based on the predicted recommendation of the review, testing different regression models on a combination of Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) features. A dataset of Steam game reviews extracted from the Programming games genre consisting of 21 games along with other significant features such as the number of helpful likes on the recommendation, number of hours played, and others. Based on the features, they are grouped into three datasets: 1) either having keyword features only, 2) keyword features …
Cats Are Fuzzy Pets: A Corpus And Analysis Of Potentially Euphemistic Terms, Martha Gavidia, Patrick Lee, Anna Feldman, Jing Peng
Cats Are Fuzzy Pets: A Corpus And Analysis Of Potentially Euphemistic Terms, Martha Gavidia, Patrick Lee, Anna Feldman, Jing Peng
Department of Computer Science Faculty Scholarship and Creative Works
Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nonetheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, …
Probing With Noise: Unpicking The Warp And Weft Of Taxonomic And Thematic Meaning Representations In Static And Contextual Embeddings, Filip Klubička
Probing With Noise: Unpicking The Warp And Weft Of Taxonomic And Thematic Meaning Representations In Static And Contextual Embeddings, Filip Klubička
Doctoral
The semantic relatedness of words has two key dimensions: it can be based on taxonomic information or thematic, co-occurrence-based information. These are captured by different language resources—taxonomies and natural corpora—from which we can build different computational meaning representations that are able to reflect these relationships. Vector representations are arguably the most popular meaning representations in NLP, encoding information in a shared multidimensional semantic space and allowing for distances between points to reflect relatedness between items that populate the space. Improving our understanding of how different types of linguistic information are encoded in vector space can provide valuable insights to the …
Collected Papers (On Neutrosophics Theory And Applications), Volume Xii, Florentin Smarandache
Collected Papers (On Neutrosophics Theory And Applications), Volume Xii, Florentin Smarandache
Branch Mathematics and Statistics Faculty and Staff Publications
This twelfth volume of Collected Papers includes 86 papers comprising 976 pages on Neutrosophic Theory and Applications, published between 2013-2021 in the international journal and book series NSS by the author alone or in collaboration with the following 112 co-authors (alphabetically ordered) from 21 countries.
Novel Natural Language Processing Models For Medical Terms And Symptoms Detection In Twitter, Farahnaz Golrooy Motlagh
Novel Natural Language Processing Models For Medical Terms And Symptoms Detection In Twitter, Farahnaz Golrooy Motlagh
Browse all Theses and Dissertations
This dissertation focuses on disambiguation of language use on Twitter about drug use, consumption types of drugs, drug legalization, ontology-enhanced approaches, and prediction analysis of data-driven by developing novel NLP models. Three technical aims comprise this work: (a) leveraging pattern recognition techniques to improve the quality and quantity of crawled Twitter posts related to drug abuse; (b) using an expert-curated, domain-specific DsOn ontology model that improve knowledge extraction in the form of drug-to-symptom and drug-to-side effect relations; and (c) modeling the prediction of public perception of the drug’s legalization and the sentiment analysis of drug consumption on Twitter. We collected …
Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew
Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew
Research Collection School Of Computing and Information Systems
Rapid technological advances in recent years drastically transformed our world. Amidst modern technological inventions such as smart phones, smart watches and smart home devices, consumers of electronic digital devices experience greatly improved automation, productivity, and efficiency in conducting routine daily tasks, information searching, shopping as well as finding entertainment. In the last few years, the global smart speaker market has undergone significant growth. As technology continues to advance and smart speakers are equipped with innovative features, the adoption of smart speakers will increase and so will consumer expectations. This research paper presents an aspect-specific sentiment analysis of consumer reviews of …
Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan
Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Cross-modal recipe retrieval has recently been explored for food recognition and understanding. Text-rich recipe provides not only visual content information (e.g., ingredients, dish presentation) but also procedure of food preparation (cutting and cooking styles). The paired data is leveraged to train deep models to retrieve recipes for food images. Most recipes on the Web include sample pictures as the references. The paired multimedia data is not noise-free, due to errors such as pairing of images containing partially prepared dishes with recipes. The content of recipes and food images are not always consistent due to free-style writing and preparation of food …
Explicating Consumer Adoption Of Wearable Technologies: A Case Of Smartwatches From The Asean Perspective, Veerisa Chotiyaputta, Donghee Shin
Explicating Consumer Adoption Of Wearable Technologies: A Case Of Smartwatches From The Asean Perspective, Veerisa Chotiyaputta, Donghee Shin
All Works
This research aims to determine the key antecedent factors in consumers' adoption of and their intention to recommend smartwatch wearable technology. The proposed research model combines the current technology acceptance and innovation diffusion theories with perceived aesthetic and perceived privacy risk to explain individuals' smartwatch adoption and subsequent recommendation to other people. Based on a sample of 299 completed individual online surveys, the research employed partial least squares (a variance-based analysis method) for the model and hypotheses testing. The results showed some similarities as well as differences from the previous literature. The study found that performance expectancy, habit, and perceived …
Building An Understanding Of Human Activities In First Person Video Using Fuzzy Inference, Bradley A. Schneider
Building An Understanding Of Human Activities In First Person Video Using Fuzzy Inference, Bradley A. Schneider
Browse all Theses and Dissertations
Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by …
Semantics-Driven Abstractive Document Summarization, Amanuel Alambo
Semantics-Driven Abstractive Document Summarization, Amanuel Alambo
Browse all Theses and Dissertations
The evolution of the Web over the last three decades has led to a deluge of scientific and news articles on the Internet. Harnessing these publications in different fields of study is critical to effective end user information consumption. Similarly, in the domain of healthcare, one of the key challenges with the adoption of Electronic Health Records (EHRs) for clinical practice has been the tremendous amount of clinical notes generated that can be summarized without which clinical decision making and communication will be inefficient and costly. In spite of the rapid advances in information retrieval and deep learning techniques towards …
Deep Understanding Of Technical Documents : Automated Generation Of Pseudocode From Digital Diagrams & Analysis/Synthesis Of Mathematical Formulas, Nikolaos Gkorgkolis
Deep Understanding Of Technical Documents : Automated Generation Of Pseudocode From Digital Diagrams & Analysis/Synthesis Of Mathematical Formulas, Nikolaos Gkorgkolis
Browse all Theses and Dissertations
The technical document is an entity that consists of several essential and interconnected parts, often referred to as modalities. Despite the extensive attention that certain parts have already received, per say the textual information, there are several aspects that severely under researched. Two such modalities are the utility of diagram images and the deep automated understanding of mathematical formulas. Inspired by existing holistic approaches to the deep understanding of technical documents, we develop a novel formal scheme for the modelling of digital diagram images. This extends to a generative framework that allows for the creation of artificial images and their …
The Amorphous Nature Of Hackers: An Exploratory Study, Kento Yasuhara, Daniel Walnycky, Ibrahim Baggili, Ahmed Alhishwan
The Amorphous Nature Of Hackers: An Exploratory Study, Kento Yasuhara, Daniel Walnycky, Ibrahim Baggili, Ahmed Alhishwan
Annual ADFSL Conference on Digital Forensics, Security and Law
In this work, we aim to better understand outsider perspectives of the hacker community through a series of situation based survey questions. By doing this, we hope to gain insight into the overall reputation of hackers from participants in a wide range of technical and non-technical backgrounds. This is important to digital forensics since convicted hackers will be tried by people, each with their own perception of who hackers are. Do cyber crimes and national security issues negatively affect people’s perceptions of hackers? Does hacktivism and information warfare positively affect people’s perception of hackers? Do individual personality factors affect one’s …
Land Is Life: Settler Colonial Governance Of National Parks And Hunting In Taiwan, Wei-Chieh Jarita Chen
Land Is Life: Settler Colonial Governance Of National Parks And Hunting In Taiwan, Wei-Chieh Jarita Chen
Environmental Studies Honors Projects
This thesis situates Taiwan as a settler colonial state by examining the discourse around the governance of national parks and the criminalization of Indigenous hunting. Placed in the context of historical patterns of land dispossession and cultural genocide, these two issues represent the ongoing process of settler colonialism and the reproduction of settler colonial relations through environmentalism. I focus on the narratives around three case studies: the controversial and ultimately unsuccessful campaign for the Maqaw National Park, the Tumpu Daingaz buluo’s struggle with the Yushan National Park, and the Tama Talum Indigenous hunting constitutional reinterpretation case. I argue that settler …
Deep Learning For Video-Grounded Dialogue Systems, Hung Le
Deep Learning For Video-Grounded Dialogue Systems, Hung Le
Dissertations and Theses Collection (Open Access)
In recent years, we have witnessed significant progress in building systems with artificial intelligence. However, despite advancements in machine learning and deep learning, we are still far from achieving autonomous agents that can perceive multi-dimensional information from the surrounding world and converse with humans in natural language. Towards this goal, this thesis is dedicated to building intelligent systems in the task of video-grounded dialogues. Specifically, in a video-grounded dialogue, a system is required to hold a multi-turn conversation with humans about the content of a video. Given an input video, a dialogue history, and a question about the video, the …
Adversarial Attacks On Speech Separation Systems, Kendrick Trinh
Adversarial Attacks On Speech Separation Systems, Kendrick Trinh
Master's Projects
Speech separation is a special form of blind source separation in which the objective is to decouple two or more sources such that they are distinct. The need for such an ability grows as speech activated device usage increases in our every day life. These systems, however, are susceptible to malicious actors. In this work, we repurpose proven adversarial attacks and leverage them against a combination speech separation and speech recognition system. The attack adds adversarial noise to a mixture of two voices such that the two outputs of the speech separation system are similarly transcribed by the speech recognition …
Using Graph Evolutionary To Retrieve Related Tweets, Patta Yovithaya
Using Graph Evolutionary To Retrieve Related Tweets, Patta Yovithaya
Chulalongkorn University Theses and Dissertations (Chula ETD)
Due to its popularity and daily active users, social media has become powerful and influential in the last decade. With the nature of a micro-blogging platform, instant messages and the latest short posts are sent throughout the network on Twitter. Therefore, most users utilize Twitter to update breaking news or the latest events. Since a huge volume of tweet messages have been published on Twitter, event evolution has also rapidly developed into related events within similar topics. In this study, we present a novel method to retrieve tweets that relate to a given query term. Not only perfectly matched tweets, …
Real Time Map Matching For Low Frequency Gps Data Based On Machine Learning Technology, Ornicha Sinthopvaragul
Real Time Map Matching For Low Frequency Gps Data Based On Machine Learning Technology, Ornicha Sinthopvaragul
Chulalongkorn University Theses and Dissertations (Chula ETD)
GPS accuracy can be compromised in urban areas due to multipath issues, leading to low accuracy in GPS location. Therefore, map matching has been introduced as a method to reduce the GPS location error. However, current map matching methods require high-frequency GPS data, and may not perform well with low-frequency data. To address this issue, this study explores the use of machine learning technology and classification methods to adjust the low-frequency GPS location. The model developed in this research employs features such as speed, heading, and location of the previous GPS point for machine learning training, ultimately leading to more …
Parallels Of Quantum Mechanics And Mahāyāna Buddhist Philosophy: An Argument For Relational Quantum Mechanics, Axel I. Palapa
Parallels Of Quantum Mechanics And Mahāyāna Buddhist Philosophy: An Argument For Relational Quantum Mechanics, Axel I. Palapa
CMC Senior Theses
Western orthodoxy philosophy is based on the principle of noncontradiction and thus, the philosophy of science is as well. The most prominent interpretations of quantum mechanics, since its inception, have followed this principle. In this paper, two quantum phenomena, the Observer Problem (measurement problem) and quantum entanglement will be analyzed from a Mahayana Buddhism ontological perspective. I will analyze the mathematical and philosophical arguments proposed by Graham Priest and Jay Garfield, based on dialethism, pertaining to Nagarjuna and the Net of Indra. Demonstrating the parallels and adaptability of the arguments to further the philosophical groundwork for Carlo Reveille’s Relational Quantum …
A Study On Developing Novel Methods For Relation Extraction, Darshini Mahendran
A Study On Developing Novel Methods For Relation Extraction, Darshini Mahendran
Theses and Dissertations
Relation Extraction (RE) is a task of Natural Language Processing (NLP) to detect and classify the relations between two entities. Relation extraction in the biomedical and scientific literature domain is challenging as text can contain multiple pairs of entities in the same instance. During the course of this research, we developed an RE framework (RelEx), which consists of five main RE paradigms: rule-based, machine learning-based, Convolutional Neural Network (CNN)-based, Bidirectional Encoder Representations from Transformers (BERT)-based, and Graph Convolutional Networks (GCNs)-based approaches. RelEx's rule-based approach uses co-location information of the entities to determine whether a relation exists between a selected entity …