Digital Commons Network^™

A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong

Computer Science Faculty Publications

Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …

Enabling Cybersecure And Resilient Smart Distribution Grid With Edge Devices, Partha Sarathi Sarker

Graduate Theses, Dissertations, and Problem Reports (ETD)

The smart distribution grid achieves enhanced efficiency and flexibility through the utilization of advanced communication infrastructure, digital devices, and robust computation and control capabilities. Grid-edge devices including distributed energy resources (DERs) like solar photovoltaics (PV), battery storage systems, and intelligent electric loads like electric vehicles and smart appliances, are contributing to the growth of the smart distribution grid. This evolution is transforming the grid into a multifaceted network of interconnected devices and systems, enabling bidirectional data communication and power flows. However, these new layers of data integration and control in the smart grid introduce vulnerabilities to cyberattacks and accidental failures, …

Optimizing The Placement Of Multiple Uav--Lidar Units Under Road Priority And Resolution Requirements, Zachary Michael Osterwisch

Masters Theses

"Real-time road traffic information is crucial for intelligent transportation systems (ITS) applications, like traffic navigation or emergency response management, but acquiring such data is tremendously challenging in practice because of the high costs and inefficient placement of sensors. Some modern ITS applications contribute to this problem by equipping vehicles with multiple light detection and ranging (LiDAR) sensors, which are expensive and gather data inefficiently; one solution that avoids vehicle-mounted LiDAR acquisition has been to install elevated LiDAR instruments along roadways, but this approach remains unrefined. The eventual development of sixth-generation (6G) wireless communication will enable new, creative solutions to solve …

Indonesian Language Term Extraction Using Multi-Task Neural Network, Joan Santoso, Esther Irawati Setiawan, Fransiskus Xaverius Ferdinandus, Gunawan Gunawan, Leonel Hernandez Collantes

Knowledge Engineering and Data Science

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two …

Examining Early Elementary Computer Science Identity Repertoires Within A Curriculum: Implications For Epistemologically Pluralistic Identities, Eleanor Richard, Shakhnoza Kayumova

Journal of Computer Science Integration

As computer science (CS) enters an increasing number of elementary classrooms, researchers must investigate the representations of what kinds of people are presented as doing computer science within CS curricula. In this paper, we explore a widely used, freely accessible, web-based, early elementary CS curriculum to examine the kinds of identity repertoires (behaviors, actions, skills, and socioemotional norms) that are promoted as representative of being/becoming a CS person. More specifically, we draw on identity studies and employ critical discourse analysis to examine how the kinds of norms and repertoires of CS practice made available in the curricular materials might construct …

Coronacredits: Program Innovations To Aid Student Completion Of Disrupted Fieldwork Abroad Due To The Covid-19 Pandemic, Benning W. Tieke, Marcela Pino Alcaraz, Melissa Armstrong

Journal of International Engineering Education

The Spring 2020 semester provided unique challenges for global experiences of all types to meet the intended learning objectives for students due to the COVID-19 pandemic disruption. This was especially true for experiential language and cultural immersion programs where engineering students were in the midst of their fieldwork experience abroad. The COVID-19 disruption presented unique challenges to recreate language and cultural understanding within international engineering fieldwork experiences in the US. This article outlines the response to the COVID-19 pandemic by the Interdisciplinary Global Programs (IGP) at Northern Arizona University (NAU). The IGP response was an innovative interdisciplinary and cross-institutional collaboration …

Putting The Coronavirus To Work: Developing A Global Engineering Program During A Pandemic, Cynthia S. Chalupa

Journal of International Engineering Education

In the aftermath of COVID-19 shutdowns at campuses across the U.S. in the spring of 2020, student enrollments have fallen and budgets have been severely constrained. To counteract the current and long-term repercussions of the pandemic on institutions of higher education, administrators have called for innovative program development and strategic transformation. In the past, many engineering and world languages departments may have considered the task of creating a collaborative degree program insurmountable or undesirable despite existing models that are successful (e.g. University of Rhode Island’s IEP program). In the era of COVID-19, however, innovative programs combining language with disciplines outside …

Do People Perceive Alexa As Gendered? A Cross-Cultural Study Of People’S Perceptions, Expectations, And Desires Of Alexa, Leopoldina Fortunati, Autumn P. Edwards, Anna Maria Manganelli, Chad Edwards, Federico De Luca

Human-Machine Communication

Mainly, the scholarly debate on Alexa has focused on sexist/anti-woman gender representations in the everyday life of many families, on a cluster of themes such as privacy, insecurity, and trust, and on the world of education and health. This paper takes another stance and explores via online survey methodology how university student respondents in two countries (the United States, n = 333; and Italy, n = 322) perceive Alexa’s image and gender, what they expect from this voice-based assistant, and how they would like Alexa to be. Results of a free association exercise showed that Alexa’s image was scarcely embodied …

A Model For Equity-Oriented Prek-12 Engineering, Christine M. Cunningham, Gregory J. Kelly

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

In this paper, we address the societally important issue of developing a more equitable approach to preK-12 engineering education. Our primary emphasis is on K-8 grades—a time when first impressions of engineering may be developed. Calls for increased participation by all students, including those who have been historically marginalized, motivate the need for theoretically grounded ways of developing and assessing educational programs. This paper draws from sociocultural learning theory and applies four theoretical and empirical analyses to derive design principles for equity that can inform curriculum, instruction, and assessment of preK-12 engineering education programs. We present a model for equity-oriented …

Enhanced Neurologic Concept Recognition Using A Named Entity Recognition Model Based On Transformers, Sima Azizi, Daniel B. Hier, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks …

Investigation Of Models To Predict The Remaining Useful Life Of Prestressed Concrete Cylinder Pipe, Ahmad Mahmoud Jibreen

Civil Engineering Dissertations - Archive

The water distribution system in the United States consists of 2.2 million miles of pipelines with water main breaks that the American Society of Civil Engineers (ASCE) 2021 Report Card estimated results in six billion gallons of drinking water lost daily. Detecting the state of pipeline deterioration should always be a top priority in the water pipeline industry This dissertation statistically evaluates data from more than 70,000 electromagnetic inspections that have been performed since the installation of these pipes in 1971. The scope of this dissertation is related to performance and degradation of prestressed concrete cylinder pipe (PCCP) which is …

Identification Of Factors Contributing To Traffic Crashes By Analysis Of Text Narratives, Cristian D. Arteaga-Sanchez

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature …

Nigeria’S Budding Digital Economy: Coping With Disruptive Technology, Olugbenga Agboola

Economic and Financial Review

For a thriving and inclusive digital economy, African countries such as Nigeria need to build the critical foundations of the digital economy (World Bank Group, 2019). These foundations are interdependent and require public and private sector solutions.

Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan

School of Computing: Dissertations, Theses, and Student Research

Deep neural networks (DNNs) can perform impressively in many natural language processing (NLP) tasks, but their black-box nature makes them inherently challenging to explain or interpret. Self-Explanatory models are a new approach to overcoming this challenge, generating explanations in human-readable languages besides task objectives like answering questions. The main focus of this thesis is the explainability of NLP tasks, as well as how attention methods can help enhance performance. Three different attention modules are proposed, SimpleAttention, CrossSelfAttention, and CrossModality. It also includes a new dataset transformation method called Two-Documents that converts every dataset into two separate documents required by the …

A Voice And Facial Recognition System To Protect Students From Being Forgotten Inside School Buses In The Kingdom Of Saudi Arabia, Ibrahim Alferaih

Theses and Dissertations

In this research, we discuss the situation of students in the three to eight year old age group. Every day in the Kingdom of Saudi Arabia during school days, more than 1,000,000 students are transported throughout the country by more than 15,000 school buses. Everything is dependent on the human element. During school transportation around the world, many children are forgotten inside the school bus and with many of them eventually going missing or even dying. The bus driver is usually busy and tired due to the focus on the road, which may make him lose focus on those on …

Deep Learning Approach To Speech Recognition: A Signal Extractor & Producer For Artificial General Intelligence, Ahmad Zuhair S. Hasanain

Theses and Dissertations

The efficient use of a communication bandwidth starts with the data source. The features of the speech signals can be extracted and reconstructed to lower the Internet traffic of the acoustic artificial agents and to increase the quality of the automatic speech recognition systems. The Speech Quefrency Transform (SQT) is hereby introduced in the work to enrich the communication space between the artificial agents and mankind. We describe the motivation, methodology, and deep learning approach in detail as we apply the SQT technology to several applications: sharp pitch track extraction, real-time speech communications, and emotion recognition. The results were excellent. …

Optimized Learning Using Fuzzy-Inference-Assisted Algorithms For Deep Learning, Miroslava Barua

Open Access Theses & Dissertations

For years, researchers in Artificial Intelligence (AI) and Deep Learning (DL) observed that performance of a Deep Learning Network (DLN) could be improved by using larger and larger datasets coupled with complex network architectures. Although these strategies yield remarkable results, they have limits, dictated by data quantity and quality, rising costs by the increased computational power, or, more frequently, by long training times on networks that are very large. Training DLN requires laborious work involving multiple layers of densely connected neurons, updates to millions of network parameters, while potentially iterating thousands of times through millions of entries in a big …

Hybrid Model For Making Decision Methods In Wireless Sensor Networks Through Neuro-Fuzzy Inference System, Martha Lucia Torres

Open Access Theses & Dissertations

Considering the complexity and multiple alternatives for technology decisions in Wireless Sensor Networks (WSNs), a multicriteria selection method (MCDM) is an appropriate approach for choosing the best option in technical projects. Purely quantitative decision-making procedures have currently been created based on client requirements and recommendations from industry professionals in many domains. In this context, implementation and operational costs could be increasing due to technical problems and additional processes. In order to prevent future difficulties and obtain a more accurate technology selection, a new method was being developed to involve qualitative and quantitative parameters taken from real scenarios and technical literature …

Comparison Of Fuzzy And Neural Network Computing Techniques For Performance Prediction Of An Industrial Copper Flotation Circuit, Ivana Jovanović, Fardis Nakhaei, Daniel Kržanović, Vesna Conić, Daniela Urošević

Mining Engineering Faculty Research & Creative Works

This paper presents the development and validation of five different soft computing methods for flotation performance prediction: (1) two models based on fuzzy logic (Mamdani and Takagi-Sugeno fuzzy inference system) and (2) three models based on artificial neural networks. Copper content in the ore feed, collector dosage in the rougher and the scavenger flotation circuits, slurry pH in the rougher flotation circuit and frother consumption were selected as input parameters to estimate the copper grade and recovery of final concentrate, as well as the copper content in the final tailings of the flotation plant. The training and evaluation of the …

Performance Comparison Of Several Control Algorithms For Tracking Control Of Pantograph Mechanism, Laylaa Mahmoud Eng, Mohamed Shamseldin Dr, Mohamed Sallam Dr, Abdelghany Mohamed Prof

Future Engineering Journal

A sort of parallel manipulator known as a pantograph robot mechanism was created primarily for industrial requests that required high precision and satisfied speed. While tracking a chosen trajectory profile requires a powerful controller. Because it has four active robot links and one robot passive link in place of just two links like the open chain does, it can carry more loads than the open chain robot mechanism while maintaining accuracy and stability. The calculated model for a closed chain pantograph robot mechanism presented in this paper takes into account the boundary conditions. For the purpose of simulating the dynamics …

Relative Rate Observer Self-Tuning Of Fuzzy Pid Virtual Inertia Control For An Islanded Microgrid, Ahmed H. Mohamed, Helmy M. El Zoghby, Mohiy Bahgat, A. M. Abdel Ghany

Future Engineering Journal

Expanding the usage of renewable energy in islanded microgrids leads to a reduction in its total inertia. Low inertia microgrids have difficulties in voltage and frequency control. That affected saving its stability and preventing a blackout. To improve low inertia islanded microgrids' dynamic response and save their stability, this paper presented relative rate observer self-tuning fuzzy PID (RROSTF-PID) based on virtual inertia control (VIC) for an islanded microgrid with a high renewable energy sources (RESs) contribution. RROSTF-PID based on VIC's success in showing remarkable improvement in the microgrid's dynamic response and enhancement of its stability. Moreover, it handles different contingency …

Science, Technology, Engineering, And Mathematics (Stem) Project-Based Learning (Pbl) Education: A New Mexico Case Study For Equity And Inclusion, Kimberly A. Scheerer

Teacher Education, Educational Leadership & Policy ETDs

This research addresses how student participation in Science, Technology, Engineering, and Mathematics (STEM) project-based learning (PBL) education activities encourages underrepresented minority student achievement in STEM career field trajectories. Seven New Mexico high school counselors and 12 STEM organization personnel were interviewed during this study. Their responses represent the nuanced professional voices where New Mexico public education intersects with STEM student interest and cultural influence.

For students, STEM PBL can foster deep integration across educational disciplines and enhance STEM career trajectory interest and readiness. STEM education converged with PBL methodologies has the ability to leverage community support while broadening student networks. …

Towards More Task-Generalized And Explainable Ai Through Psychometrics, Alec Braynen

USF Tampa Graduate Theses and Dissertations

In this work, we propose that adopting the methods, principles, and guidelines of the field of psychometrics can help the Artificial Intelligence (AI) community to build more task-generalizable and explainable AI. Three arguments are presented and explored. These arguments are that psychometrics can help by providing 1) a framework for formulating better datasets, 2) psychometric AI data that can lead to models of generalization in AI, and 3) explainable AI through more informative evaluations.

A review of psychometrics and psychological generalization is performed, along with an overview of evaluation, generalization, and explainability in AI. Various ideas are presented throughout for …

Comparison Study Of Consumer’S Perception Toward Urban Air Mobility In The United States And Rest Of The World Using Social Media Information, S M Toki Tahmid

USF Tampa Graduate Theses and Dissertations

Urban Air Mobility (UAM) is a wide concept that will enable access to on-demand air mobility, cargo delivery, medical applications, and emergency services that is run through a connected and integrated transportation system. This study aims to investigate social media data to compare the major topics discussed in the United States and rest of the world and perform a sentiment analysis to see if the comments are positive, negative, or neutral. Twitter was the primary source of information that was used in this thesis, as it is a place where people interact, create, and share information and ideas online. In …

Spam Detection Using Machine Learning And Deep Learning, Olubodunde Agboola

LSU Doctoral Dissertations

Text messages are essential these days; however, spam texts have contributed negatively to the success of this communication mode. The compromised authenticity of such messages has given rise to several security breaches. Using spam messages, malicious links have been sent to either harm the system or obtain information detrimental to the user. Spam SMS messages as well as emails have been used as media for attacks such as masquerading and smishing ( a phishing attack through text messaging), and this has threatened both the user and service providers. Therefore, given the waves of attacks, the need to identify and remove …

Artificial Intelligence And Applications, Sanjay Singh Dr.

Technical Collection

I work in the broad areas of computational intelligence, artificial intelligence, neural networks, machine learning, deep learning, game theory, mathematical logic, and natural language processing. I am also actively working in the area of algorithmic fairness and explainable AI (XAI). Currently, we are developing neuro-symbolic logic learning systems for common sense reasoning, which aims to augment the existing conventional artificial intelligence, which is logically based. The neuro-symbolic logic-based systems will provide more accurate results than their GOAI (Good Old Artificial Intelligence) version. We are also working on the area of abstractive summarization methods. We intend to develop an efficient abstractive …

Utilizing Motion And Spatial Features For Sign Language Gesture Recognition Using Cascaded Cnn And Lstm Models, Hamzah Luqman, Elsayed Elalfy

Turkish Journal of Electrical Engineering and Computer Sciences

Sign language is a language produced by body parts gestures and facial expressions. The aim of an automatic sign language recognition system is to assign meaning to each sign gesture. Recently, several computer vision systems have been proposed for sign language recognition using a variety of recognition techniques, sign languages, and gesture modalities. However, one of the challenging problems involves image preprocessing, segmentation, extraction and tracking of relevant static and dynamic features related to manual and nonmanual gestures from different images in sequence. In this paper, we studied the efficiency, scalability, and computation time of three cascaded architectures of convolutional …

A Rule-Based/Bpso Approach To Produce Low-Dimensional Semantic Basis Vectors Set, Atefe Pakzad, Morteza Analoui

Turkish Journal of Electrical Engineering and Computer Sciences

The present study aims to generate low-dimensional explicit distributional semantic vectors. In explicit semantic vectors, each dimension corresponds to a word, which makes word vectors interpretable. In this study, a new approach is proposed to obtain low-dimensional explicit semantic vectors. Firstly, the suggested approach considers three criteria, namely, word similarity, number of zeros, and word frequency as features for words in a corpus. Next, some rules are extracted to obtain the initial basis words using a decision tree which is drawn based on the three features. Secondly, a binary weighting method is proposed based on the binary particle swarm optimization …

Asking The Right Questions To Solve Algebraic Word Problems, Ege Yi̇ği̇t Çeli̇k, Zeynel Orulluoğlu, Ridvan Mertoğlu, Selma Teki̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Word algebra problems are among challenging AI tasks as they combine natural language understanding with a formal equation system. Traditional approaches to the problem work with equation templates and frame the task as a template selection and number assignment to the selected template. The recent deep learning-based solutions exploit contextual language models like BERT and encode the natural language text to decode the corresponding equation system. The proposed approach is similar to the template-based methods as it works with a template and fills in the number slots. Nevertheless, it has contextual understanding because it adopts a question generation and answering …

The Role Of Generation Iv Nuclear Reactors In Decarbonising International Shipping : A Multi-Criteria Decision Making (Mcdm) Framework For Matching Potential Decarbonisation Pathways To Different Ship Types And Size, Claudianus David Kunze

World Maritime University Dissertations

No abstract provided.