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Rubik's Cube: A Visual And Tactile Learning Of Algorithms And Patterns, Lawrence Muller 2019 CUNY La Guardia Community College

Rubik's Cube: A Visual And Tactile Learning Of Algorithms And Patterns, Lawrence Muller

Open Educational Resources

This is a classroom activity report on teaching algorithms as part of a second course in computer programming. Teaching an algorithm in an introductory level programming class is often a dry task for the instructor and the rewards for the student are abstract. To make the learning of algorithms and software more rewarding, this assignment employs a Rubik’s cube.


Optimal Distribution Of Testing Resources Between Different System Levels, Griselda Acosta, Eric Smith, Vladik Kreinovich 2019 University of Texas at El Paso

Optimal Distribution Of Testing Resources Between Different System Levels, Griselda Acosta, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

When designing a system, we need to perform testing and checking on all levels of the system hierarchy, from the most general system level to the most detailed level. Our resources are limited, so we need to find the best way to allocate these resources, i.e., we need to decide how much efforts to use of each of the levels. In this paper, we formulate this problem in precise terms, and provide a solution to the resulting optimization problem.


A Simple Quantitative Model Of Cognitive Tradeoff Phenomenon, Griselda Acosta, Eric Smith, Vladik Kreinovich 2019 University of Texas at El Paso

A Simple Quantitative Model Of Cognitive Tradeoff Phenomenon, Griselda Acosta, Eric Smith, Vladik Kreinovich

Departmental Technical Reports (CS)

A recent study of chimpanzees has shown that on the individual basis, they are, surprisingly, much better than humans in simple tasks requiring intelligence and memory. A usual explanation -- called cognitive tradeoff -- is that a human brain has sacrificed some of its data processing (computation) abilities in favor of enhancing the ability to communicate; as a result, while individual humans may not be as smart as possible, jointly, we can solve complex problems. A similar cognitive tradeoff phenomenon can be observed in computer clusters: the most efficient computer clusters are not formed from the fastest, most efficient computers, they are ...


Logarithms Are Not Infinity: A Rational Physics-Related Explanation Of The Mysterious Statement By Lev Landau, Francisco Zapata, Olga Kosheleva, Vladik Kreinovich 2019 University of Texas at El Paso

Logarithms Are Not Infinity: A Rational Physics-Related Explanation Of The Mysterious Statement By Lev Landau, Francisco Zapata, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Nobel-prize winning physicist Lev Landau liked to emphasize that logarithms are not infinity -- meaning that from the physical viewpoint, logarithms of infinite values are not really infinite. Of course, from a literally mathematical viewpoint, this statement does not make sense: one can easily prove that logarithm of infinity is infinite. However, when a Nobel-prizing physicist makes a statement, you do not want to dismiss it, you want to interpret it. In this paper, we propose a possible physical explanation of this statement. Namely, in physics, nothing is really infinite: according to modern physics, even the Universe is finite in size ...


Why Grade Distribution Is Often Multi-Modal: An Uncertainty-Based Explanation, Olga Kosheleva, Christian Servin, Vladik Kreinovich 2019 University of Texas at El Paso

Why Grade Distribution Is Often Multi-Modal: An Uncertainty-Based Explanation, Olga Kosheleva, Christian Servin, Vladik Kreinovich

Departmental Technical Reports (CS)

There are many different independent factors that affect student grades. There are many physical situations like this, in which many different independent factors affect a phenomenon, and in most such situations, we encounter normal distribution -- in full accordance with the Central Limit Theorem, which explains that in such situations, distribution should be close to normal. However, the grade distribution is definitely not normal -- it is multi-modal. In this paper, we explain this strange phenomenon, and, moreover, we explain several observed features of this multi-modal distribution.


How To Fuse Expert Knowledge: Not Always "And" But A Fuzzy Combination Of "And" And "Or", Christian Servin, Olga Kosheleva, Vladik Kreinovich 2019 El Paso Community College

How To Fuse Expert Knowledge: Not Always "And" But A Fuzzy Combination Of "And" And "Or", Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In the non-fuzzy (e.g., interval) case, if two expert's opinions are consistent, then, as the result of fusing the knowledge of these two experts, we take the intersection of the two sets (e.g., intervals) describing the expert's opinions. In the experts are inconsistent, i.e., if the intersection is empty, then a reasonable idea is to assume that at least of these experts is right, and thus, to take the union of the two corresponding sets. In practice, expert opinions are often imprecise; this imprecision can be naturally described in terms of fuzzy logic -- a technique ...


Derivation Of Louisville-Bratu-Gelfand Equation From Shift- Or Scale-Invariance, Leobardo Valera, Martine Ceberio, Vladik Kreinovich 2019 University of Texas at El Paso

Derivation Of Louisville-Bratu-Gelfand Equation From Shift- Or Scale-Invariance, Leobardo Valera, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

Louisville-Bratu-Gelfand equation appear in many different physical situations ranging from combustion to explosions to astrophysics. The fact that the same equation appears in many different situations seems to indicate that this equation should not depend on any specific physical process, that it should be possible to derive it from general principles. This is indeed what we show in this paper: that this equation can be naturally derived from basic symmetry requirements.


Extending Set Functors To Generalised Metric Spaces, Adriana Balan, Alexander Kurz, Jiří Velebil 2019 University Politehnica of Bucharest

Extending Set Functors To Generalised Metric Spaces, Adriana Balan, Alexander Kurz, Jiří Velebil

Mathematics, Physics, and Computer Science Faculty Articles and Research

For a commutative quantale V, the category V-cat can be perceived as a category of generalised metric spaces and non-expanding maps. We show that any type constructor T (formalised as an endofunctor on sets) can be extended in a canonical way to a type constructor TV on V-cat. The proof yields methods of explicitly calculating the extension in concrete examples, which cover well-known notions such as the Pompeiu-Hausdorff metric as well as new ones.

Conceptually, this allows us to to solve the same recursive domain equation X ≅ TX in different categories (such as sets and metric spaces) and we ...


An Internet Based Intelligent Argumentation System For Collaborative Engineering Design, Xiaoqing Frank Liu, Samir Raorane, Man Zheng, Ming-Chuan Leu 2019 Missouri University of Science and Technology

An Internet Based Intelligent Argumentation System For Collaborative Engineering Design, Xiaoqing Frank Liu, Samir Raorane, Man Zheng, Ming-Chuan Leu

Ming C. Leu

Modern product design is a very complicated process which involves groups of designers, manufacturers, suppliers, and customer representatives. Conflicts are unavoidable in collaboration among multiple stakeholders, who have different objectives, requirements, and priorities. Unfortunately, current web-based collaborative engineering design systems do not support collaborative conflict resolution. In this paper, we will develop an intelligent computational argumentation model to enable management of a large scale argumentation network, and resolution of conflicts based on argumentation from many participants. A web-based intelligent argumentation tool is developed as a part of a web-based collaborative engineering design system based on the above model to resolve ...


Modeling Of Cloud-Based Digital Twins For Smart Manufacturing With Mt Connect, Liwen Hu, Ngoc-Tu Nguyen, Wenjin Tao, Ming-Chuan Leu, Xiaoqing Frank Liu, Rakib Shahriar, S M Nahian Al Sunny 2019 Missouri University of Science and Technology

Modeling Of Cloud-Based Digital Twins For Smart Manufacturing With Mt Connect, Liwen Hu, Ngoc-Tu Nguyen, Wenjin Tao, Ming-Chuan Leu, Xiaoqing Frank Liu, Rakib Shahriar, S M Nahian Al Sunny

Ming C. Leu

The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model ...


An Overview Of Cryptography (Updated Version 24 January 2019), Gary C. Kessler 2019 Embry-Riddle Aeronautical University

An Overview Of Cryptography (Updated Version 24 January 2019), Gary C. Kessler

Publications

There are many aspects to security and many applications, ranging from secure commerce and payments to private communications and protecting health care information. One essential aspect for secure communications is that of cryptography. But it is important to note that while cryptography is necessary for secure communications, it is not by itself sufficient. The reader is advised, then, that the topics covered here only describe the first of many steps necessary for better security in any number of situations.


Spectral Clustering For Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series, Logan Blakely 2019 Portland State University

Spectral Clustering For Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series, Logan Blakely

Dissertations and Theses

The increasing demand for and prevalence of distributed energy resources (DER) such as solar power, electric vehicles, and energy storage, present a unique set of challenges for integration into a legacy power grid, and accurate models of the low-voltage distribution systems are critical for accurate simulations of DER. Accurate labeling of the phase connections for each customer in a utility model is one area of grid topology that is known to have errors and has implications for the safety, efficiency, and hosting capacity of a distribution system. This research presents a methodology for the phase identification of customers solely using ...


An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine 2019 Southern Methodist University

An Evaluation Of Training Size Impact On Validation Accuracy For Optimized Convolutional Neural Networks, Jostein Barry-Straume, Adam Tschannen, Daniel W. Engels, Edward Fine

SMU Data Science Review

In this paper, we present an evaluation of training size impact on validation accuracy for an optimized Convolutional Neural Network (CNN). CNNs are currently the state-of-the-art architecture for object classification tasks. We used Amazon’s machine learning ecosystem to train and test 648 models to find the optimal hyperparameters with which to apply a CNN towards the Fashion-MNIST (Mixed National Institute of Standards and Technology) dataset. We were able to realize a validation accuracy of 90% by using only 40% of the original data. We found that hidden layers appear to have had zero impact on validation accuracy, whereas the ...


Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran 2019 Southern Methodist University

Comparisons Of Performance Between Quantum And Classical Machine Learning, Christopher Havenstein, Damarcus Thomas, Swami Chandrasekaran

SMU Data Science Review

In this paper, we present a performance comparison of machine learning algorithms executed on traditional and quantum computers. Quantum computing has potential of achieving incredible results for certain types of problems, and we explore if it can be applied to machine learning. First, we identified quantum machine learning algorithms with reproducible code and had classical machine learning counterparts. Then, we found relevant data sets with which we tested the comparable quantum and classical machine learning algorithm's performance. We evaluated performance with algorithm execution time and accuracy. We found that quantum variational support vector machines in some cases had higher ...


Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal 2019 Southern Methodist University

Comparative Study Of Sentiment Analysis With Product Reviews Using Machine Learning And Lexicon-Based Approaches, Heidi Nguyen, Aravind Veluchamy, Mamadou Diop, Rashed Iqbal

SMU Data Science Review

In this paper, we present a comparative study of text sentiment classification models using term frequency inverse document frequency vectorization in both supervised machine learning and lexicon-based techniques. There have been multiple promising machine learning and lexicon-based techniques, but the relative goodness of each approach on specific types of problems is not well understood. In order to offer researchers comprehensive insights, we compare a total of six algorithms to each other. The three machine learning algorithms are: Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting. The three lexicon-based algorithms are: Valence Aware Dictionary and Sentiment Reasoner (VADER), Pattern ...


Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater 2019 Southern Methodist University

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide ...


Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks 2019 Southern Methodist University

Improving Gas Well Economics With Intelligent Plunger Lift Optimization Techniques, Atsu Atakpa, Emmanuel Farrugia, Ryan Tyree, Daniel W. Engels, Charles Sparks

SMU Data Science Review

In this paper, we present an approach to reducing bottom hole plunger dwell time for artificial lift systems. Lift systems are used in a process to remove contaminants from a natural gas well. A plunger is a mechanical device used to deliquefy natural gas wells by removing contaminants in the form of water, oil, wax, and sand from the wellbore. These contaminants decrease bottom-hole pressure which in turn hampers gas production by forming a physical barrier within the well tubing. As the plunger descends through the well it emits sounds which are recorded at the surface by an echo-meter that ...


Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard 2019 Yale University

Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard

Yale Day of Data

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim ...


Computer Organization With Mips, Seth D. Bergmann 2019 Rowan University

Computer Organization With Mips, Seth D. Bergmann

Open Educational Resources

This book is intended to be used for a first course in computer organization, or computer architecture. It assumes that all digital components can be constructed from fundamental logic gates. The book begins with number representation schemes and assembly language for the MIPS architecture, including assembler directives, pseudo-operations, and floating point instructions. It then describes the machine language instruction formats, and shows the student how to translate an assembly language program to machine language. This is followed by a chapter which describes how to construct an assembler for MIPS. This chapter may be omitted without loss of continuity. This is ...


A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas 2019 Southern Methodist University

A Comparative Evaluation Of Recommender Systems For Hotel Reviews, Ryan Khaleghi, Kevin Cannon, Raghuram Srinivas

SMU Data Science Review

There has been increasing growth in deployment of recommender systems across Internet sites, with various models being used. These systems have been particularly valuable for review sites, as they seek to add value to the user experience to gain market share and to create new revenue streams through deals. Hotels are a prime target for this effort, as there is a large number for most destinations and a lot of differentiation between them. In this paper, we present an evaluation of two of the most popular methods for hotel review recommender systems: collaborative filtering and matrix factorization. The accuracy of ...


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