Building Interpretable Methods For Identifying Bridge Maintenance Patterns,
2022
University of Nebraska at Omaha
Building Interpretable Methods For Identifying Bridge Maintenance Patterns, Akshay Kale
Computer Science Graduate Research Workshop
No abstract provided.
Foam-Based Floatovoltaics: A Potential Solution To Disappearing Terminal Natural Lakes,
2022
Western University
Foam-Based Floatovoltaics: A Potential Solution To Disappearing Terminal Natural Lakes, Koami Soulemane Hayibo, Joshua M. Pearce
Electrical and Computer Engineering Publications
Terminal lakes are disappearing worldwide because of direct and indirect human activities. Floating photovoltaics (FPV) are a synergistic system with increased energy output because of water cooling, while the FPV reduces water evaporation. This study explores how low-cost foam-based floatovoltaic systems can mitigate the disappearance of natural lakes. A case study is performed on 10%–50% FPV coverage of terminal and disappearing Walker Lake. Water conservation is investigated with a modified Penman-Monteith evapotranspiration method and energy generation is calculated with an operating temperature model experimentally determined from foam-based FPV. Results show FPV saves 52,000,000 m3/year of water and US$6,000,000 at 50% …
A New Application Of The Central Limit Theorem,
2022
Southeastern University
A New Application Of The Central Limit Theorem, Kenneth Winters
Selected Honors Theses
This paper discusses the Central Limit Theorem (CLT) and its applications. The paper gives an introduction to what the CLT is and how it can be applied to real life. Additionally, the paper gives a conceptual understanding of the theorem through various examples and visuals. The paper discusses the applications of the CLT in fields such as computer science, psychology, and political science. The author then suggests a new mathematical theorem as an application of the CLT and provides a proof of the theorem. The new theorem relates to expected value and probabilities of random variables and provides a link …
Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques In Blue Tarpaulin Inspection,
2022
Jacksonville State University
Disaster Site Structure Analysis: Examining Effective Remote Sensing Techniques In Blue Tarpaulin Inspection, Madeline G. Miles
Theses
This thesis aimed to evaluate three methods of analyzing blue roofing tarpaulin (tarp) placed on homes in post natural disaster zones with remote sensing techniques by assessing the different methods- image segmentation, machine learning (ML), and supervised classification. One can determine which is the most efficient and accurate way of detecting blue tarps. The concept here was that using the most efficient and accurate way to locate blue tarps can aid federal, state, and local emergency management (EM) operations and homeowners. In the wake of a natural disaster such as a tornado, hurricane, thunderstorm, or similar weather events, roofs are …
Unsupervised Learning With Word Embeddings Captures Quiescent Knowledge From Covid-19 And Materials Science Literature,
2022
Western Michigan University
Unsupervised Learning With Word Embeddings Captures Quiescent Knowledge From Covid-19 And Materials Science Literature, Tasnim H. Gharaibeh
Dissertations
Millions of scientific papers are produced each year and the scientific literature is continuing to grow at a head-spinning speed. Thus, massive scientific knowledge exists in solid text, but all these publications make it difficult, if not impossible, for researchers to keep in up to date with discoveries, even within a narrow scientific area. This massive amount of information also makes it difficult to find implicit and hidden connections, relationships, and dependencies within the information that may guide the direction of future research or lead to valuable new insights. So, there is a need for algorithms or models that can …
The Role Of Physical Geographic Features In Video Games,
2022
Jacksonville State University
The Role Of Physical Geographic Features In Video Games, Dexter Ferguson
Theses
This thesis examines how physical geographic features affect individuals when interacting with video game environments. There are very few studies on the geography of video games. I aim to bridge the gap between game studies and geography, a naturally occurring link. The observation of elements such as the geography of an area is critical when exploring an individual’s perception of a video game environment. Individuals interact with the geography of their location on a day-to-day basis, whether it be physical or human geography; how they can relate a video game’s geography to the real world dramatically affects their immersion. An …
Assessing Security Risks With The Internet Of Things,
2022
Liberty University
Assessing Security Risks With The Internet Of Things, Faith Mosemann
Senior Honors Theses
For my honors thesis I have decided to study the security risks associated with the Internet of Things (IoT) and possible ways to secure them. I will focus on how corporate, and individuals use IoT devices and the security risks that come with their implementation. In my research, I found out that IoT gadgets tend to go unnoticed as a checkpoint for vulnerability. For example, often personal IoT devices tend to have the default username and password issued from the factory that a hacker could easily find through Google. IoT devices need security just as much as computers or servers …
Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture,
2022
University of South Carolina
Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor
Senior Theses
Current work in the field of deep learning and neural networks revolves around several variations of the same mathematical model for associative learning. These variations, while significant and exceptionally applicable in the real world, fail to push the limits of modern computational prowess. This research does just that: by leveraging high order tensors in place of 2nd order tensors, quadratic neural networks can be developed and can allow for substantially more complex machine learning models which allow for self-interactions of collected and analyzed data. This research shows the theorization and development of mathematical model necessary for such an idea to …
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection,
2022
PhD Candidate, Faculty of Architecture - Design & Built Environment, Beirut Arab University, Lebanon
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb
Architecture and Planning Journal (APJ)
Natural and human-made disasters have significant impacts on monumental buildings, threatening them from being deteriorated. If no rapid consolidations took into consideration traumatic accidents would endanger the existence of precious sites. In this context, Beirut's enormous 4th of August 2020 explosion damaged an estimated 640 historical monuments, many volunteers assess damages for more than a year to prevent the more crucial risk of demolitions. This research aims to assist the collaboration ability among photogrammetry science, Artificial Intelligence Model (AIM) and Architectural Coding to optimize the process for better coverage and scientific approach of data specific to the crack disorders to …
Objective Measure Of Working Memory Capacity Using Eye Movements,
2022
Old Dominion University
Objective Measure Of Working Memory Capacity Using Eye Movements, James Owens, Gavindya Jayawardena, Yasasi Abeysinghe, Vikas G. Ashok, Sampath Jayarathna
Undergraduate Research Symposium
Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term …
Fusionai, A Dna-Sequence-Based Deep Learning Protocol Reduces The False Positives Of Human Fusion Gene Prediction,
2022
The Texas Medical Center Library
Fusionai, A Dna-Sequence-Based Deep Learning Protocol Reduces The False Positives Of Human Fusion Gene Prediction, Xiaobo Zhou
Faculty, Staff and Student Publications
Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).
How Graduate Student Fellows Enhance What A Center For Digital Scholarship Does,
2022
University of Notre Dame
How Graduate Student Fellows Enhance What A Center For Digital Scholarship Does, Ben B. Chiewphasa
Transforming Libraries for Graduate Students
Multiple disciplines are increasingly embracing data science and digital scholarship. However, insufficient training for digital and computational methodologies within subject/departmental silos means that these needs often get overlooked. Opportunities for learning how to teach technical concepts (i.e., how to handle troubleshooting, live participatory coding, etc.) are also rare or non-existent via departmental offerings. To respond to these needs, the Navari Family Center for Digital Scholarship launched its Pedagogy Fellowship Program in Fall 2021 where Notre Dame PhD students/candidates build their instructional expertise and experience related to digital scholarship with an added bonus of enhancing their competitiveness on the job market. …
Split Classification Model For Complex Clustered Data,
2022
University of Nebraska - Lincoln
Split Classification Model For Complex Clustered Data, Katherine Gerot
Honors Program: Senior Projects (Public)
Classification in high-dimensional data has generated tremendous interest in a multitude of fields. Data in higher dimensions often tend to reside in non-Euclidean metric space. This prevents Euclidean-based classification methodologies, such as regression, from reliably modeling the data. Many proposed models rely on computationally-complex embedding to convert the data to a more usable format. Others, namely the Support Vector Machine, rely on kernel manipulation to implicitly describe the "feature space" to arrive at a non-linear decision boundary. The proposed methodology in this paper seeks to classify complex data in a relatively computationally-simple and explainable manner.
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data,
2022
School of Computer Science, Qufu Normal University, Rizhao 276826, China
Bcse: Blockchain-Based Trusted Service Evaluation Model Over Big Data, Fengyin Li, Xinying Yu, Rui Ge, Yanli Wang, Yang Cui, Huiyu Zhou
Big Data Mining and Analytics
The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new …
Big Data With Cloud Computing: Discussions And Challenges,
2022
University Institute of Computing, Chandigarh University, Mohali 140413, India
Big Data With Cloud Computing: Discussions And Challenges, Amanpreet Kaur Sandhu
Big Data Mining and Analytics
With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, …
Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking,
2022
School of Computing and Artifical Intelligence, Southwest Jiaotong University, Chengdu 611756, China
Exploiting More Associations Between Slots For Multi-Domain Dialog State Tracking, Hui Bai, Yan Yang, Jie Wang
Big Data Mining and Analytics
Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the …
Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis,
2022
CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, P.O. Box 2735, Beijing 100190, China
Sampling With Prior Knowledge For High-Dimensional Gravitational Wave Data Analysis, He Wang, Zhoujian Cao, Yue Zhou, Zong-Kuan Guo, Zhixiang Ren
Big Data Mining and Analytics
Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior distributions. In this study, we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training dataset. Accordingly, the more relevant regions of the high-dimensional feature space are covered by additional data points, such that the model can learn the subtle but important details. We adapt the normalizing flow …
Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective,
2022
Department of Anhui Province Key Laboratory of Big Data Analysis and Application (BDAA), School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
Toward Intelligent Financial Advisors For Identifying Potential Clients: A Multitask Perspective, Qixiang Shao, Runlong Yu, Hongke Zhao, Chunli Liu, Mengyi Zhang, Hongmei Song, Qi Liu
Big Data Mining and Analytics
Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue for IFAs, i.e., identifying users who are willing to purchase the portfolios. Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. In this study, we formalize a potential conversion relationship, i.e., user→activated user→client and decompose this relationship into …
A Comparison Of Computational Approaches For Intron Retention Detection,
2022
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
A Comparison Of Computational Approaches For Intron Retention Detection, Jiantao Zheng, Cuixiang Lin, Zhenpeng Wu, Hong-Dong Li
Big Data Mining and Analytics
Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an …
Ingredient Classification Using Food Ontology,
2022
University of Nebraska at Omaha
Ingredient Classification Using Food Ontology, Ricky Flores
UNO Student Research and Creative Activity Fair
A food label provides some of the most crucial information for a food product. The food label is a key resource for many health-conscious consumers for understanding ingredients. It is also vital for individuals to avoid food allergens or help patients follow dietary recommendations. While the food labels in the United States are regulated by the Food and Drug Administration (FDA) many labels contain additional information or statements that are not regulated. Moreover, the food label may be complex or contain terminology that the layperson may not understand. Evidence has indicated that consumers often find nutrition labels confusing, especially when …
