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A Novel Approach On Energy Of Λj-Dominating Single-Valued Neutrosophic Graph Structure, S.N. Suber Bathusha, S. Angelin Kavitha Raj
A Novel Approach On Energy Of Λj-Dominating Single-Valued Neutrosophic Graph Structure, S.N. Suber Bathusha, S. Angelin Kavitha Raj
Neutrosophic Systems with Applications
The concept of dominance is one of the most important ideas in graph theory for handling random events, and it has drawn the interest from many scholars. Research related to graph energy has garnered a lot focus recently. The application of single-valued neutrosophic graphs (SVNGs) for energy, Laplacian energy, and dominating energy has been recommended by previous studies. In this research, we apply the concepts of single-valued neutrosophic sets (SVNS) to graph structures (GSs) and investigate some intriguing features of single-valued neutrosophic graph structures (SVNGS). Moreover, the notions of λJ-dominating energy GS in an SVNGS environment are analyzed in this …
Visual Analytics In Explaining Neural Networks With Neuron Clustering, Gulsum Alicioglu, Bo Sun
Visual Analytics In Explaining Neural Networks With Neuron Clustering, Gulsum Alicioglu, Bo Sun
College of Science & Mathematics Departmental Research
Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitive domains such as healthcare. Visual analytics (VA) combined with DL methods have been widely used to discover data insights, but they often encounter visual clutter (VC) issues. This study presents a compact neural network (NN) view design to reduce the visual clutter in explaining the DL model components for domain experts and end users. We utilized clustering algorithms to group hidden neurons based on their activation similarities. …
Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi
Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi
Thesis/ Dissertation Defenses
In recent years, artificial intelligence (AI) techniques have been used for source code authorship attribution, which is the process of identifying the original author of a given piece of code. With the advancement of AI technologies like ChatGPT, which can generate code, there is a need to accurately identify whether a piece of code is written by a human or generated by a machine. This is crucial for intellectual property protection, cybersecurity, and software forensics. The main objective of this thesis is to review existing research on source code authorship attribution and conduct several experiments to determine the best AI …
Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama
Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama
Neutrosophic Systems with Applications
Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, …
Enhancing Cybersecurity Awareness In The United Arab Emirates: An Assessment Of Current Practices And The Development Of An Ai-Enhanced Mobile Application, Meera Alalawi
Thesis/ Dissertation Defenses
In today's interconnected world, individuals, private corporations, public institutions, and governments face increasingly sophisticated cyber threats and attacks, highlighting the critical need for individuals and organizations to understand cybersecurity comprehensively. Cyberattacks have affected many countries and infrastructures in different sectors worldwide, including the United Arab Emirates (UAE), which has become a main target for cybercrime due to its booming economy and tourism. The UAE considers cybersecurity an increasingly critical issue in our digital world, and increasing cybersecurity awareness among residents is essential to protect themselves and their organizations from cyberattacks. The primary objectives of this study are to identify key …
Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama
Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama
Neutrosophic Systems with Applications
Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, …
Improving Implicit Communication In Remote Collaboration Through Augmented Reality And Digital Twins, Nicholas Levergne
Improving Implicit Communication In Remote Collaboration Through Augmented Reality And Digital Twins, Nicholas Levergne
LSU Master's Theses
Large scale digital twinning projects are beginning to emerge across the tech industry. Within these projects is a desire to integrate augmented reality capabilities into industrial workflows. However, research on augmented reality technology for remote collaboration lacks ecologically valid studies of real world scenarios. Additionally, prior remote collaboration literature is focused on white-collar applications instead of blue-collar field work. Prior AR collaboration software is similarly limited, with most software allowing mixed camera views and annotation that requires participants to be stationary. This thesis introduces SpectAR, an augmented reality and desktop remote collaboration software suite developed in Unreal Engine 5.1.1. SpectAR …
Modeling Metaverse Perceptions For Bolstering Traffic Safety Using Novel Trss-Based Owcm-Ram Mcdm Techniques: Purposes And Strategies, Mona Mohamed, Asmaa Elsayed, Marwa Sharawi
Modeling Metaverse Perceptions For Bolstering Traffic Safety Using Novel Trss-Based Owcm-Ram Mcdm Techniques: Purposes And Strategies, Mona Mohamed, Asmaa Elsayed, Marwa Sharawi
Neutrosophic Systems with Applications
The Metaverse has the potential to revolutionize various aspects of human life, including transportation systems. The integration of the Metaverse into intelligent transportation systems has the potential to significantly improve traffic safety in smart cities. By creating a virtual replica of the physical world, the Metaverse can provide a platform for testing new traffic management systems, road designs, and vehicle technologies in a controlled and safe environment before implementing them in the real world. One way to integrate the Metaverse into intelligent transportation systems (ITS) is by enhancing traffic safety. This can be achieved by developing an evaluation model that …
Ai Is A Viable Alternative To High Throughput Screening: A 318-Target Study, Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, Adrian Stecula, Andreana Rosnik, Ann Marie O’Sullivan, Aram Davtyan, Ben Samudio, Bill Thomas, Brad Worley, Brittany Butler, Christian Laggner, Desiree Thayer, Ehsan Moharreri, Greg Friedland, Ha Truong, Henry Van Den Bedem, Ho Leung Ng, Kate Stafford, Krishna Sarangapani, Kyle Giesler, Lien Ngo, Michael Mysinger, Mostafa Ahmed, Nicholas J. Anthis, Niel Henriksen, Arthur L. Haas, Et Al
Ai Is A Viable Alternative To High Throughput Screening: A 318-Target Study, Izhar Wallach, Denzil Bernard, Kong Nguyen, Gregory Ho, Adrian Morrison, Adrian Stecula, Andreana Rosnik, Ann Marie O’Sullivan, Aram Davtyan, Ben Samudio, Bill Thomas, Brad Worley, Brittany Butler, Christian Laggner, Desiree Thayer, Ehsan Moharreri, Greg Friedland, Ha Truong, Henry Van Den Bedem, Ho Leung Ng, Kate Stafford, Krishna Sarangapani, Kyle Giesler, Lien Ngo, Michael Mysinger, Mostafa Ahmed, Nicholas J. Anthis, Niel Henriksen, Arthur L. Haas, Et Al
School of Medicine Faculty Publications
High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, …
Modeling Metaverse Perceptions For Bolstering Traffic Safety Using Novel Trss-Based Owcm-Ram Mcdm Techniques: Purposes And Strategies, Mona Mohamed, Asmaa Elsayed, Marwa Sharawi
Modeling Metaverse Perceptions For Bolstering Traffic Safety Using Novel Trss-Based Owcm-Ram Mcdm Techniques: Purposes And Strategies, Mona Mohamed, Asmaa Elsayed, Marwa Sharawi
Neutrosophic Systems with Applications
The Metaverse has the potential to revolutionize various aspects of human life, including transportation systems. The integration of the Metaverse into intelligent transportation systems has the potential to significantly improve traffic safety in smart cities. By creating a virtual replica of the physical world, the Metaverse can provide a platform for testing new traffic management systems, road designs, and vehicle technologies in a controlled and safe environment before implementing them in the real world. One way to integrate the Metaverse into intelligent transportation systems (ITS) is by enhancing traffic safety. This can be achieved by developing an evaluation model that …
Advancing Text Summarization And Classification: Deep Insights From Transformer-Based Statistical Learning, Kun Bu
USF Tampa Graduate Theses and Dissertations
Artificial Intelligence (AI) is a part of human's daily life nowadays. Machine Learning (ML) as one aspect from AI has been rapidly developing during the past two decades, especially from the statistical learning approaches, which emphasized the use of probability and statistics to model data, such as Support Vector Machines (SVMs) for classification and regression tasks to the ensemble learning techniques, such as Random Forest, Gradient Boosting Machine (GBM), and stacking. Ensemble learning has evolved into a pivotal concept in contemporary machine learning, empowering practitioners to amalgamate multiple models to enhance generalization, accuracy, and robustness. As the field of machine …
Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang
Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang
Mathematics, Physics, and Computer Science Faculty Articles and Research
Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …
Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Institute for ECHO Articles and Research
The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …
Update From Aristotle To Newton, From Sets To Fuzzy Sets, And From Sigmoid To Relu: What Do All These Transitions Have In Common?, Christian Servin, Olga Kosheleva, Vladik Kreinovich
Update From Aristotle To Newton, From Sets To Fuzzy Sets, And From Sigmoid To Relu: What Do All These Transitions Have In Common?, Christian Servin, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
In this paper, we show that there is a -- somewhat unexpected -- common trend behind several seemingly unrelated historic transitions: from Aristotelian physics to modern (Newton's) approach, from crisp sets (such as intervals) to fuzzy sets, and from traditional neural networks, with close-to-step-function sigmoid activation functions to modern successful deep neural networks that use a completely different ReLU activation function. In all these cases, the main idea of the corresponding transition can be explained, in mathematical terms, as going from the first order to second order differential equations.
Navigating The Maze: The Role Of Pre-Enrollment Socio-Cultural And Institutional Factors In Higher Education In The Age Of Ai, Emily Barnes, James Hutson
Navigating The Maze: The Role Of Pre-Enrollment Socio-Cultural And Institutional Factors In Higher Education In The Age Of Ai, Emily Barnes, James Hutson
Faculty Scholarship
This article explores the complex interplay between pre-enrollment socio-cultural and institutional factors and their impact on the higher education landscape. It challenges traditional metrics of academic achievement, presenting a nuanced perspective on student success that emphasizes the importance of socio-economic backgrounds, cultural capital, and K-12 education quality. The analysis extends to the significant role of institutional attributes in shaping student readiness and decision-making processes. The study advocates for the integration of artificial intelligence (AI)-driven assessments by higher education institutions to cater to the diverse needs of the student body, promoting an inclusive and supportive learning environment. Anchored in an extensive …
Rethinking Plagiarism In The Era Of Generative Ai, James Hutson
Rethinking Plagiarism In The Era Of Generative Ai, James Hutson
Faculty Scholarship
The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator …
Mcfadden's Discrete Choice And Softmax Under Interval (And Other) Uncertainty: Revisited, Bartlomiej Jacek Kubica, Olga Kosheleva, Vladik Kreinovich
Mcfadden's Discrete Choice And Softmax Under Interval (And Other) Uncertainty: Revisited, Bartlomiej Jacek Kubica, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
Studies of how people actually make decisions have led to an empirical formula that predicts the probability of different decisions based on the utilities of different alternatives. This formula is known as McFadden's formula, after a Nobel prize winning economist who discovered it. A similar formula -- known as softmax -- describes the probability that the classification predicted by a deep neural network is correct, based on the neural network's degrees of confidence in the object belonging to each class. In practice, we usually do not know the exact values of the utilities -- or of the degrees of confidence. …
How To Gauge Inequality And Fairness: A Complete Description Of All Decomposable Versions Of Theil Index, Saeid Tizpaz-Niari, Olga Kosheleva, Vladik Kreinovich
How To Gauge Inequality And Fairness: A Complete Description Of All Decomposable Versions Of Theil Index, Saeid Tizpaz-Niari, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
In general, in statistics, the most widely used way to describe the difference between different elements of a sample if by using standard deviation. This characteristic has a nice property of being decomposable: e.g., to compute the mean and standard deviation of the income overall the whole US, it is sufficient to compute the number of people, mean, and standard deviation over each state; this state-by-state information is sufficient to uniquely reconstruct the overall standard deviation. However, e.g., for gauging income inequality, standard deviation is not very adequate: it provides too much weight to outliers like billionaires, and thus, does …
Paradox Of Causality And Paradoxes Of Set Theory, Alondra Baquier, Bradley Beltran, Gabriel Miki-Silva, Olga Kosheleva, Vladik Kreinovich
Paradox Of Causality And Paradoxes Of Set Theory, Alondra Baquier, Bradley Beltran, Gabriel Miki-Silva, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
Logical paradoxes show that human reasoning is not always fully captured by the traditional 2-valued logic, that this logic's extensions -- such as multi-valued logics -- are needed. Because of this, the study of paradoxes is important for research on multi-valued logics. In this paper, we focus on paradoxes of set theory. Specifically, we show their analogy with the known paradox of causality, and we use this analogy to come up with similar set-theoretic paradoxes.
Number Representation With Varying Number Of Bits, Anuradha Choudhury, Md Ahsanul Haque, Saeefa Rubaiyet Nowmi, Ahmed Ann Noor Ryen, Sabrina Saika, Vladik Kreinovich
Number Representation With Varying Number Of Bits, Anuradha Choudhury, Md Ahsanul Haque, Saeefa Rubaiyet Nowmi, Ahmed Ann Noor Ryen, Sabrina Saika, Vladik Kreinovich
Departmental Technical Reports (CS)
In a computer, usually, all real numbers are stored by using the same number of bits: usually, 8 bytes, i.e., 64 bits. This amount of bits enables us to represent numbers with high accuracy -- up to 19 decimal digits. However, in most cases -- whether we process measurement results or whether we process expert-generated membership degrees -- we do not need that accuracy, so most bits are wasted. To save space, it is therefore reasonable to consider representations with varying number of bits. This would save space used for representing numbers themselves, but we would also need to store …
Data Fusion Is More Complex Than Data Processing: A Proof, Robert Alvarez, Salvador Ruiz, Martine Ceberio, Vladik Kreinovich
Data Fusion Is More Complex Than Data Processing: A Proof, Robert Alvarez, Salvador Ruiz, Martine Ceberio, Vladik Kreinovich
Departmental Technical Reports (CS)
Empirical data shows that, in general, data fusion takes more computation time than data processing. In this paper, we provide a proof that data fusion is indeed more complex than data processing.
How To Fairly Allocate Safety Benefits Of Self-Driving Cars, Fernando Munoz, Christian Servin, Vladik Kreinovich
How To Fairly Allocate Safety Benefits Of Self-Driving Cars, Fernando Munoz, Christian Servin, Vladik Kreinovich
Departmental Technical Reports (CS)
In this paper, we describe how to fairly allocated safety benefits of self-driving cars between drivers and pedestrians -- so as to minimize the overall harm.
Using Known Relation Between Quantities To Make Measurements More Accurate And More Reliable, Niklas Winnewisser, Felix Mett, Michael Beer, Olga Kosheleva, Vladik Kreinovich
Using Known Relation Between Quantities To Make Measurements More Accurate And More Reliable, Niklas Winnewisser, Felix Mett, Michael Beer, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
Most of our knowledge comes, ultimately, from measurements and from processing measurement results. In this, metrology is very valuable: it teaches us how to gauge the accuracy of the measurement results and of the results of data processing, and how to calibrate the measuring instruments so as to reach the maximum accuracy. However, traditional metrology mostly concentrates on individual measurements. In practice, often, there are also relations between the current values of different quantities. For example, there is usually an known upper bound on the difference between the values of the same quantity at close moments of time or at …
Assessing The Usability And Feasibility Of Digital Assistant Tools For Direct Support Professionals: Participatory Design And Pilot-Testing, Patrice Dolhonde Tremoulet, Andrea Lobo, Christina A. Simmons, Ganesh Baliga, Matthew Brady
Assessing The Usability And Feasibility Of Digital Assistant Tools For Direct Support Professionals: Participatory Design And Pilot-Testing, Patrice Dolhonde Tremoulet, Andrea Lobo, Christina A. Simmons, Ganesh Baliga, Matthew Brady
College of Science & Mathematics Departmental Research
Background: The United States is experiencing a direct support professional (DSP) crisis, with demand far exceeding supply. Although generating documentation is a critical responsibility, it is one of the most wearisome aspects of DSPs’ jobs. Technology that enables DSPs to log informal time-stamped notes throughout their shift could help reduce the burden of end-of-shift documentation and increase job satisfaction, which in turn could improve the quality of life of the individuals with intellectual and developmental disabilities (IDDs) whom DSPs support. However, DSPs, with varied ages, levels of education, and comfort using technology, are not likely to adopt tools that detract …
Somewhat Surprisingly, (Subjective) Fuzzy Technique Can Help To Better Combine Measurement Results And Expert Estimates Into A Model With Guaranteed Accuracy: Digital Twins And Beyond, Niklas Winnewisser, Michael Beer, Olga Kosheleva, Vladik Kreinovich
Somewhat Surprisingly, (Subjective) Fuzzy Technique Can Help To Better Combine Measurement Results And Expert Estimates Into A Model With Guaranteed Accuracy: Digital Twins And Beyond, Niklas Winnewisser, Michael Beer, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
To understand how different factors and different control strategies will affect a system -- be it a plant, an airplane, etc. -- it is desirable to form an accurate digital model of this system. Such models are known as digital twins. To make a digital twin as accurate as possible, it is desirable to incorporate all available knowledge of the system into this model. In many cases, a significant part of this knowledge comes in terms of expert statements, statements that are often formulated by using imprecise ("fuzzy") words from natural language such as "small", "very possible", etc. To translate …
Smu Libraries – An Enabling Partner In Ai Information Literacy, Samantha Seah, Zhe Benedict Yeo, Lukas Tschopp
Smu Libraries – An Enabling Partner In Ai Information Literacy, Samantha Seah, Zhe Benedict Yeo, Lukas Tschopp
Research Collection Library
SMU Libraries plays a pivotal role in advancing AI information literacy within the larger need for digital literacy skills in the SMU community. In this presentation, participants will get an overview of SMU Libraries' engagement and partnerships with the academic community and will showcase initiatives and resources supporting AI literacy. This includes a discussion of insights from the scholarly literature, research findings and critical perspectives to inform teaching and learning practices related to AI. Speakers will share SMU Libraries’ contributions towards awareness and adoption of AI through a portfolio of successful collaborations and initiatives with partners and stakeholders within and …
Ct Image Segmentation Using Optimization Techniques Under Neutrosophic Domain, Doaa El-Shahat, Nourhan Talal, Jun Ye, Wen-Hua Cui
Ct Image Segmentation Using Optimization Techniques Under Neutrosophic Domain, Doaa El-Shahat, Nourhan Talal, Jun Ye, Wen-Hua Cui
Neutrosophic Systems with Applications
In this paper, we introduce a hybrid technique between optimization algorithms and neutrosophic theory. This new hybridization can deal with uncertainties in brain computed tomography (CT) images in three different memberships very effectively. To prove the real-time application of this theory, a new segmentation method for brain CT medical images is presented. The grayscale medical image suffers from uncertainties and inconsistencies in the gray levels due to their bad luminance. The proposed technique addressed this problem by performing neutrosophic operations on gray levels based on the S membership function.
How Difficult Is It To Comprehend A Program That Has Significant Repetitions: Fuzzy-Related Explanations Of Empirical Results, Christian Servin, Olga Kosheleva, Vladik Kreinovich
How Difficult Is It To Comprehend A Program That Has Significant Repetitions: Fuzzy-Related Explanations Of Empirical Results, Christian Servin, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
In teaching computing and in gauging the programmers' productivity, it is important to property estimate how much time it will take to comprehend a program. There are techniques for estimating this time, but these techniques do not take into account that some program segments are similar, and this similarity decreases the time needed to comprehend the second segment. Recently, experiments were performed to describe this decrease. These experiments found an empirical formula for the corresponding decrease. In this paper, we use fuzzy-related ideas to provide commonsense-based theoretical explanation for this empirical formula.
Enhancing Rotating Machinery Fault Diagnosis: A Dual-Head Attention Mechanism In Deep Learning Neural Networks, Qing Snyder
Enhancing Rotating Machinery Fault Diagnosis: A Dual-Head Attention Mechanism In Deep Learning Neural Networks, Qing Snyder
Dissertations
Rotating machinery is crucial to production efficiency and safety in manufacturing industries for an extended time. Ensuring machinery reliability necessitates effective diagnostic systems, particularly for rotating bearings, the key components of such equipment. Fault diagnosis in rotating machinery is essential to prevent failures and minimize downtime, thereby playing an important role in industrial operations. The application of advanced neural network techniques in industry has risen recently. Among these, attention-based neural networks, especially the Transformer models, are originally noteworthy for their sequential data handling capability. This research delves into attention-based algorithms for rotating machinery fault diagnosis, signifying a substantial advancement in …
Why Bernstein Polynomials: Yet Another Explanation, Olga Kosheleva, Vladik Kreinovich
Why Bernstein Polynomials: Yet Another Explanation, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
In many computational situations -- in particular, in computations under interval or fuzzy uncertainty -- it is convenient to approximate a function by a polynomial. Usually, a polynomial is represented by coefficients at its monomials. However, in many cases, it turns out more efficient to represent a general polynomial by using a different basis -- of so-called Bernstein polynomials. In this paper, we provide a new explanation for the computational efficiency of this basis.