Digital Commons Network^™

Transmorph: A Transformer Based Morphological Disambiguator For Turkish, Hi̇lal Özer, Emi̇n Erkan Korkmaz

Turkish Journal of Electrical Engineering and Computer Sciences

The agglutinative nature of the Turkish language has a complex morphological structure, and there are generally more than one parse for a given word. Before further processing, morphological disambiguation is required to determine the correct morphological analysis of a word. Morphological disambiguation is one of the first and crucial steps in natural language processing since its success determines later analyses. In our proposed morphological disambiguation method, we used a transformer-based sequence-to-sequence neural network architecture. Transformers are commonly used in various NLP tasks, and they produce state-of-the-art results in machine translation. However, to the best of our knowledge, transformer-based encoder-decoders have …

Semantic Representation Of Road Infrastructure Information, Tianjiao Zhao

Dissertations (1934 -)

A multi-sectoral collaboration completes a successful transportation infrastructure project. The cooperation involves designers, contractors, operators, users, government agencies, and maintenance staffs. Throughout the project's life cycle, a huge amount of data is generated and stored in various sectors. Therefore, an efficient information cross-sector exchange approach is necessary. Additionally, the World Wide Web is ubiquitous and enmeshed with multiple business processes. Therefore, it is imperative to represent business information in a format that improves information exchange as well as automated processing of business data. Ideally, road data scattered across different information sources, such as design software, geographic information systems (GIS), cost …

Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result …

A Concept For Weighting Sentiment Phrase Using Deterministic Solution Of Algebraic Equations, Maryam Jalali, Morteza Zahedi, Abdolali Basiri

Turkish Journal of Electrical Engineering and Computer Sciences

Many text mining methods have used statistical information as text and language-independent procedures that are not deterministic. On the other hand, grammatical structure-based methods are limited to use in a certain language and text. We aim to suggest an algorithmic algebraic equation in a deterministic and nonprobabilistic way while maintaining the advantage of language independence. We propose a mathematical approach that transforms text and labels into a set of dumb equations. By solving the equations, each word is assigned a weight that can reflect the semantic information of that word, then we use the proposed algorithm to build a novel …

Generating Ad Creatives Using Deep Learning For Search Advertising, Kevser Nur Çoğalmiş, Ahmet Bulut

Turkish Journal of Electrical Engineering and Computer Sciences

We generated advertisement creatives programmatically using deep neural networks. A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads. We treated the ad generation task as a text summarization problem and built a sequence to sequence model. In order to assess the validity of our approach, we conducted experiments on four datasets. Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters. Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up. The choice of word embedding …

Development Of Micro-Sized Algan Deep Ultraviolet Light Emitting Diodes And Monolithic Photonic Integrated Circuits, Richard Speight Floyd Iii

Theses and Dissertations

III-Nitride materials-based visible emission LEDs have emerged as a disruptive technology in the fields of lighting,ⁱ) communications,^ii,iii,iv) and displays.^v,vi) Shorter wavelength LEDs in the DUV spectral region (210nm – 360nm) with ultra-wide bandgap (UWBG) Al_xGa_1-xN active layers are now poised to displace toxic Mercury-based light sources.^vii) Over the past decade AlGaN LEDs operating in the deep ultra-violet (DUV) spectral region (200 nm < λ_emission < 300 nm) have been deployed in novel applications including autonomous drone-based sterilization and sanitization systems, ^viii) point-of-use water purification systems, ^ix) photo-therapeutics,x) gas sensors,xi) and non-line-of-sight (NLOS) communications.^xii) Similarly, DUV light detectors using ultra-wide bandgap (UWBG) Al …

Knowledge-Infused Learning, Manas Gaur

Theses and Dissertations

In DARPA’s view of the three waves of AI, the first wave of AI, symbolic AI, focused on explicit knowledge. The second and current wave of AI is termed statistical AI. Deep learning techniques have been able to exploit large amounts of data and massive computational power to improve human levels of performance in narrowly defined tasks. Separately, knowledge graphs have emerged as a powerful tool to capture and exploit a variety of explicit knowledge to make algorithms better apprehend the content and enable the next generation of data processing, such as semantic search. After initial hesitancy about the scalability …

A Narrative Investigation Of Black Familial Capital That Supports Engineering Engagement Of Middle-School-Aged Youth, Delean Tolbert Smith, Tamecia Jones, Monica E. Cardella

Journal of Pre-College Engineering Education Research (J-PEER)

A major concern in engineering education involves ensuring that youth belonging to minoritized groups have equitable access to engineering career pathways. Related research often highlights the effect of student and school characteristics on engineering success but few studies have investigated the engineering-related assets that Black families provide. This work aims to provide counterstories that highlight the presence of Black families along the pre-college engineering pathways of three Black youth from the Midwest region of the United States. The application of a counternarrative approach centers the familial capital of Black families and serves as the analytical frame for this work. The …

Allure: A Multi-Modal Guided Environment For Helping Children Learn To Solve A Rubik's Cube With Automatic Solving And Interactive Explanations, Kausik Lakkaraju, Thahimum Hassan, Vedant Khandelwal, Prathamjeet Singh, Cassidy Bradley, Ronak Shah, Forest Agostinelli, Biplav Srivastava, Dezhi Wu

Faculty Publications

Modern artificial intelligence (AI) methods have been used to solve problems that many humans struggle to solve. This opens up new opportunities for knowledge discovery and education. We demonstrate ALLURE, a collaborative educational AI system for learning to solve the Rubik's cube that is designed to help students improve their problem solving skills. ALLURE can both find its own strategies for solving the Rubik's cube and explain those strategies to humans. In the future, ALLURE will also be able to collaborate with humans by building on user-provided strategies for solving the Rubik's cube and as well as generalize to other …

Explainable And Cooperative Autonomy Across Networks Of Distributed Systems, Peter Joseph Jorgensen

USF Tampa Graduate Theses and Dissertations

Large networks of complex systems-of-systems are commonplace and evermore present in both mundane and extraordinary facets of human existence. From the exponential growth of connectivity via the internet and other information networks, to the miniaturization of computers and sensors, to cross-domain sensor and communication networks, these networks of distributed systems-of-systems (NDSS) present incredible benefits and challenges. Autonomy is perhaps the most important and most difficult to achieve enabling technology for efficient performance of the NDSS. Giving each individual agent in a network the ability to manage its internal state in dynamic operating environments and in pursuit of multiple complex and …

Identifying Informative Coronavirus Tweets Using Recurrent Neural Network Document Embedding, Rami Yousuf

Palestine Technical University Research Journal

The coronavirus pandemic has led to the spread of tremendous fake news and misleading information through tweets. Hence, an interesting task of classifying tweets into informative and uninformative has motivated researchers to employ machine learning techniques. The state-of-the-art studies showed high dependency on transformers architecture. However, the transformers architecture suffers from the catastrophic forgetting problem where important contextual information is being forgotten by the gradients. Therefore, this paper proposes a document embedding using Recurrent Neural Network. Lastly, three classifiers of LR, SVM and MLP have been used to classify documents into Informative and Uninformative. Using the benchmark dataset of WNUT-2020 …

Misinformation And Disinformation: Detecting Fakes With The Eye And Ai, Victoria Rubin

Data and Test Instruments

How do we detect, deter, and prevent the spread of mis- and disinformationwith the human eye and AI? How does theory inform the practice, and how do theevidence-based research and best practices in lie-catching and truth-seekingprofessions—inform AI? The book looks into well-established human practicessuch as the routines and processes used in detective work, journalism, and scientificinquiry, and how they contribute toward innovative AI solutions. The book explainsthe principles, inner workings, and recent evolution of five types of state-of-the-artAI technologies suitable for curtailing the spread of mis- and disinformation:automated deception detectors, clickbait detectors, satirical fake detectors, rumordebunkers, and computational fact-checking tools.

أنماط التعلم المفضلة لدى طلبة جامعة فلسطين التقنية خضوري وأثر بعض المتغيرات عليها, Murad Awadallah, Raed Frehat

Palestine Technical University Research Journal

This research aimed at investigating about the preferred learning styles and the effect of some variables such as the university branch, gender, education program, academic year, specialization, and cumulative average. To achieve that, the two researchers used the Index of learning styles (ILS) developed by Felder and Solomon (Lit zingeret al., 2005) consisting of 44 items to evaluate learning preferences, and which was applied on purpose cluster sample of (345) students. After using the SPSS statistical analysis program, the results of the study show that the preferred learning style in descending order is (active-reflective, visual verbal, sequential-global, and sensing-intuitive). The …

Progress On Static Structures In Leaky Mode Waveguides, Manusha Korimi

Theses and Dissertations

Virtual reality (VR) head-mounted displays provide a high definition, immersive experi-ence to the viewer. However, most existing technologies have flaws like bulky design and vergence-accommodation conflict that may cause stress in the neck muscles, posture issues, nausea, motion sickness and dizziness. Similarly, augmented reality (AR) displays, which use transparent light modulators, exist, but they possess low field of view and a limited number of discrete depth planes when wide field of view and continuous depth would be ideal. The ultimate goal of my research is use leaky mode waveguide devices to create wide-view angle, transparent near eye holographic displays for …

Knowledge-Driven Drug-Use Namedentity Recognition With Distant Supervision, Goonmeet Bajaj, Ugur Kursuncu, Manas Gaur, Usha Lokala, Ayaz Hyder, Srinivasan Parthasarathy, Amit Sheth

Publications

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for …

A Spectrum Of Online Rehearsal Applications: A Potential Means For Cultural Connection, Dana Kemack Goot, W. Scott Deal

Journal of Network Music and Arts

The current array of telematic music applications offers exceptional opportunities to transcend distance and enable networking between various cultures and communities. As a means to achieve this, network music applications require assessment through their previous implementations, installation requirements, cost, hardware requirements, and usability in order to assess their potential and to decipher improvements needed as a means of cultural connectivity. An early precursor of telematic music was an innovative concert by Paul Robeson in 1957, which circumvented political restrictions and enabled activism as it occurred, despite the U.S. State Department’s revocation of Robeson’s passport due to his left-wing tendencies and …

Telematic Immersion: Performance, Technology, And Audiovisual Work In Virtual Studies (2020) By Paulo C. Chagas, Cássia Carrascoza Bomfim

Journal of Network Music and Arts

This article discusses the concept of telematic immersion developed in partnership with composer Paulo C. Chagas. Focusing on Chagas’s work Virtual Studies (2020) for flute, live electronics, and 3D video, we reflect on different aspects of artistic creativity in the telematic environment including the use of audio and video technology and the multiple connections between musicians and apparatuses. The article discusses how the parameters of chamber music in the physical environment, such as the uniqueness of the live performance, the corporality of sound, and the visual and choreographic dimension of the performance are being transformed through the virtual setting of …

Who Is Missing? Characterizing The Participation Of Different Demographic Groups In A Korean Nationwide Daily Conversation Corpus, Haewoon Kwak, Jisun An, Kunwoo Park

Research Collection School Of Computing and Information Systems

A conversation corpus is essential to build interactive AI applications. However, the demographic information of the participants in such corpora is largely underexplored mainly due to the lack of individual data in many corpora. In this work, we analyze a Korean nationwide daily conversation corpus constructed by the National Institute of Korean Language (NIKL) to characterize the participation of different demographic (age and sex) groups in the corpus.

Look At All Those Big Knobs! Online Audio Technology Discourse And Sexy Gear Fetishes, Eliot Bates, Samantha Bennett

Publications and Research

Despite a predominantly digital, 21st century music production landscape, analogue hardware professional audio technologies persist. In the discoursal throes of the leading online audio technology message forum Gearslutz, such technologies are routinely objectified, sexualized, fetishized and socialized into gear. Situated in a contemporary critical, interdisciplinary framework of fetish, masculinity and sexuality studies, this research interrogates how audio technologies manufactured and intended for music production contexts become sexy. Applying a mixed-mode methodology, including an intensive discourse, image and material-semiotic analysis of an ‘epic’ sexy gear thread, we collated extensive data about technological fetishization. Sexy gear discourse articulates themes of voyeurism, acquisition, …

Reconciliation, Restoration And Reconstruction Of A Conflict Ridden Country, Muhammad S. Riaz

Theses and Dissertations

Conflict has sadly been a constant part of history. Winning a conflict and making a lasting peace are often not the same thing. While a peace treaty ends a conflict and often dictates terms from the winners’ perspective, it may not create a lasting peace. Short of unconditional surrender, modern conflict ends with a negotiated cessation of hostilities. Such accords may have some initial reconstruction agreements, but Reconciliation, Restoration and Reconstruction (RRR) is a long term process. This study maintains that to achieve a lasting peace: 1) The culture and beliefs of the conflict nation must be continuously considered and …

Nystrom Methods For High-Order Cq Solutions Of The Wave Equation In Two Dimensions, Erli Wind-Andersen

Dissertations

An investigation of high order Convolution Quadratures (CQ) methods for the solution of the wave equation in unbounded domains in two dimensions is presented. These rely on Nystrom discretizations for the solution of the ensemble of associated Laplace domain modified Helmholtz problems. Two classes of CQ discretizations are considered: one based on linear multistep methods and the other based on Runge-Kutta methods. Both are used in conjunction with Nystrom discretizations based on Alpert and QBX quadratures of Boundary Integral Equation (BIE) formulations of the Laplace domain Helmholtz problems with complex wavenumbers. CQ in conjunction with BIE is an excellent candidate …

Middle School Students Communicating Computational Thinking: A Systemic Functional Linguistics-Case Study Of Bilingual, Collaborative Teaching/Learning Of Computer Programming In Python, Jose Antonio Lecea Yanguas

Language, Literacy, and Sociocultural Studies ETDs

This dissertation presents the first Systemic Functional Linguistics-based analysis of the teaching/learning of computational thinking through computer programming and comprehensive analysis of discourse of a whole computer programming course at any educational level. The current educational research raises questions about the nature of authentic computational

vii

thinking teaching/learning environments and how they happen moment-to-moment. In one such environment, I examined the discourse of a facilitator, three students, and their Language Arts teacher in an introductory middle school after-school course (approximately 30 hours) in spring 2017 as students created a video in Python.

Methodologically, I show how a Systemic Functional Linguistics-based …

Metasoftware: Building Blocks For Legal Technology, Houman Shadab

Seattle Journal of Technology, Environmental, & Innovation Law

This Article develops a novel concept in information technology called “metasoftware.” It then applies the concept of metasoftware to developing legal technology.

Metasoftware enables users to create the software of their choosing and stands in sharp contrast to traditional, functional. Functional software is the default type of software that is currently produced and includes word processing, email, social networking, enterprise resource management, online marketplaces, and video game software. Metasoftware, by contrast, is not functional. Metasoftware presents the user with a blank slate upon which to build functional software.

I argue that software is metasoftware to that extent that (1) it …

A Music-Therapy Robotic Platform For Children With Autism: A Pilot Study, Huanghao Feng, Mohammad H. Mahoor, Francesca Dino

Electrical and Computer Engineering: Faculty Scholarship

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills including motor control, turn-taking, and emotion recognition. Innovative technology, such as socially assistive robots, has shown to be a viable method for Autism therapy. This paper presents a novel robot-based music-therapy platform for modeling and improving the social responses and behaviors of children with ASD. Our autonomous social interactive system consists of three modules. Module one provides an autonomous initiative positioning system for the robot, NAO, to properly localize and play the instrument (Xylophone) using the robot’s arms. Module two allows NAO to play customized songs …

Black Beyond Measure: An Antideficit Exploration Of Cultural Capital Within A National Society Of Black Engineers (Nsbe) Chapter At A Predominantly White Institution (Pwi), Rhonda Harley

Dissertations

Historically, Black students have been excluded from Predominately White institutions (PWI) longer than welcomed to attend and matriculate (Harper et al., 2009). Due to this lack of inclusion, African American students' educational experiences often center on academic disparities, inequality of opportunity, and under-preparedness in career planning within the American education system. While there has been a fair amount of research on the lack of representation of Black students in the engineering disciplines, the heavy focus on quantitative data offers little insight into the unique ways students succeed and overcome institutional and systemic barriers in pursuit of their degree. Undergraduate experiences, …

Du Undergraduate Showcase: Research, Scholarship, And Creative Works: Abstracts, Emma Aggeler, Elena Arroway, Daisy T. Booker, Justin Bravo, Kyle Bucholtz, Megan Burnham, Nicole Choi, Spencer Cockerell, Rosie Contino, Jackson Garske, Kaitlyn Glover, Caroline Hamilton, Haley Hartmann, Madalyne Heiken, Colin Holter, Leah Huzjak, Alyssa Jeng, Cole Jernigan, Chad Kashiwa, Adelaide Kerenick, Emily King, Abigail Langeberg, Maddie Leake, Meredith Lemons, Alec Mackay, Greer Mckinley, Ori Miller, Guy Milliman, Katherine Miromonti, Audrey Mitchell, Lauren Moak, Megan Morrell, Gelella Nebiyu, Zdenek Otruba, Toni V. Panzera, Kassidy Patarino, Sneha Patil, Alexandra Penney, Kevin Persky, Caitlin Pham, Gabriela Recinos, Mary Ringgenberg, Chase Routt, Olivia Schneider, Roman Shrestha, Arlo Simmerman, Alec Smith, Tessa Smith, Nhi-Lac Thai, Kyle Thurmann, Casey Tindall, Amelia Trembath, Maria Trubetskaya, Zachary Vangelisti, Peter Vo, Abby Walker, David Winter, Grayden Wolfe, Leah York

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Showcase.

Modeling Multi-Criteria Decision-Making Problems With Applications In Last Mile Delivery And School Safety Assessment, Ayat Alrahahleh

Theses and Dissertations

The last-mile delivery option has become a focal point of academic research and industrial development in recent years. Multiple factors such as increased demands on delivery flexibility, customer requirements, delivery urgency, and many others are enforcing to adopt this option. For fulfilling this paradigm shift in delivery and providing additional flexibility, drones can be considered as a viable option to use for last-mile delivery cases. Numerous drones are available in the market with varying capacities and functionalities, posing a significant challenge for decision-makers to select the most appropriate drone type for a specific application. For this purpose, this study proposes …

Ai-Synthesized Speech : Generation And Detection, Ehab Alsayed Albadawy Abdrabuh

Legacy Theses & Dissertations (2009 - 2024)

From speech to images, and videos, advances in machine learning have led to dramatic improvements in the quality and realism of so-called AI-synthesized content. While there are many exciting and interesting applications, this type of content can also be used to create convincing and dangerous fakes. We seek to develop forensic techniques that can distinguish a real human voice from a synthesized voice. We observe that deep neural networks used to synthesize speech introduce specific and unusual artifacts not typically found in human speech. Although not necessarily audible, we develop various detection algorithms to measure these artifacts and be able …

Twitter Account Classification Using Account Metadata: Organizationvs. Individual, Yusuf Mucahi̇t Çeti̇nkaya, Mesut Gürlek, İsmai̇l Hakki Toroslu, Pinar Karagöz

Turkish Journal of Electrical Engineering and Computer Sciences

Organizations present their existence on social media to gain followers and reach out to the crowds. Social media-related tasks and applications, such as social media graph construction, sentiment analysis, and bot detection, are required to identify the entities' account types. Some applications focus on personal accounts, whereas others only need nonpersonal accounts. This paper addresses the account classification problem using only minimum amount of data, which is the metadata of the account's profile. The proposed approach classifies accounts either as organization or individual, in a language-independent manner, without collecting the accounts' tweet content. The model uses a long short term …

A Novel Deep Reinforcement Learning Based Stock Price Prediction Using Knowledge Graph And Community Aware Sentiments, Anil Berk Altuner, Zeynep Hi̇lal Ki̇li̇mci̇

Turkish Journal of Electrical Engineering and Computer Sciences

Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the prediction of stock prices using sentiments of community and knowledge graph. For this purpose, we firstly construct a social knowledge graph of users by analyzing relations between connections. After that, time series analysis of related stock and sentiment analysis is blended with deep reinforcement methodology. Turkish version of Bidirectional Encoder Representations from …