Physical Sciences and Mathematics Commons^™

Critical Media, Information, And Digital Literacy: Increasing Understanding Of Machine Learning Through An Interdisciplinary Undergraduate Course, Barbara R. Burke, Elena Machkasova

Irish Communication Review

Widespread use of Artificial Intelligence in all areas of today’s society creates a unique problem: algorithms used in decision-making are generally not understandable to those without a background in data science. Thus, those who use out-of-the-box Machine Learning (ML) approaches in their work and those affected by these approaches are often not in a position to analyze their outcomes and applicability.

Our paper describes and evaluates our undergraduate course at the University of Minnesota Morris, which fosters understanding of the main ideas behind ML. With Communication, Media & Rhetoric and Computer Science faculty expertise, students from a variety of majors, …

Maximum Likelihood Estimation Of Species Trees And Anomaly Zone Detection Using Ranked Gene Trees, Anastasiia Kim

Mathematics & Statistics ETDs

A phylogenetic tree represents the evolutionary relationships among a set of organisms. Gene trees can be used to reconstruct phylogenetic trees. The methods in this dissertation focus on the gene tree topologies with emphasis on ranked gene tree topologies. A ranked tree depicts the order in which nodes appear in the tree together with topological relationships among gene lineages. One challenge that arises during phylogenetic inference is the existence of the anomaly zones, the regions of branch-length space in the species tree that can produce gene trees that have topologies differing from the species tree topology but are more probable …

Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Department of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) …

Algorithmic Robot Design: Label Maps, Procrustean Graphs, And The Boundary Of Non-Destructiveness, Shervin Ghasemlou

Theses and Dissertations

This dissertation is focused on the problem of algorithmic robot design. The process of designing a robot or a team of robots that can reliably accomplish a task in an environment requires several key elements. How the problem is formulated can play a big role in the design process. The ability of the model to correctly reflect the environment, the events, and different pieces of the problem is crucial. Another key element is the ability of the model to show the relationship between different designs of a single system. These two elements can enable design algorithms to navigate through the …

Strategies And Algorithms Of Sudoku, Callie Weaver

Mathematics Senior Capstone Papers

This paper discusses different strategies for the game of Sudoku and how those strategies relate to other problem solving techniques while also attempting to use those other techniques in a way that improves the strategies for Sudoku. This includes a thorough analysis of the general algorithm and an algorithm that is formed by the Occupancy Theorem and Preemptive Sets. This paper also compares these algorithms that directly relate to Sudoku with algorithms to similar combinatorial problems such as the Traveling Salesman problem and more. With the study of game theory becoming more popular, these strategies have also been shown to …

Quantum Computing And Quantum Algorithms, Daniel Serban

Senior Honors Theses

The field of quantum computing and quantum algorithms is studied from the ground up. Qubits and their quantum-mechanical properties are discussed, followed by how they are transformed by quantum gates. From there, quantum algorithms are explored as well as the use of high-level quantum programming languages to implement them. One quantum algorithm is selected to be implemented in the Qiskit quantum programming language. The validity and success of the resulting computation is proven with matrix multiplication of the qubits and quantum gates involved.

Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui

Student Research, Papers, and Creative Works

Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Deparment of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) …

Noise Resilience Of Variational Quantum Compiling, Kunal Sharma, Sumeet Khatri2, M. Cerezo, Patrick J. Coles

Faculty Publications

Variational hybrid quantum-classical algorithms (VHQCAs) are near-term algorithms that leverage classical optimization to minimize a cost function, which is efficiently evaluated on a quantum computer. Recently VHQCAs have been proposed for quantum compiling, where a target unitary U is compiled into a short-depth gate sequence V. In this work, we report on a surprising form of noise resilience for these algorithms. Namely, we find one often learns the correct gate sequence V (i.e. the correct variational parameters) despite various sources of incoherent noise acting during the cost-evaluation circuit. Our main results are rigorous theorems stating that the optimal variational parameters …

Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple

Faculty Publications

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and …

Towards A Novel Generalized Chinese Remainder Algorithm For Extended Rabin Cryptosystem, Justin Zhan, Peter J. Shiue, Shen C. Huang, Benjamin J. Lowe

Mathematical Sciences Faculty Research

This paper proposes a number of theorems and algorithms for the Chinese Remainder Theorem, which is used to solve a system of linear congruences, and the extended Rabin cryptosystem, which accepts a key composed of an arbitrary finite number of distinct primes. This paper further proposes methods to relax the condition on the primes with trade-offs in the time complexity. The proposed algorithms can be used to provide ciphertext indistinguishability. Finally, this paper conducts extensive experimental analysis on six large data sets. The experimental results show that the proposed algorithms are asymptotically tight to the existing decryption algorithm in the …

Development Of Machine Learning Tutorials For R, John Pintar

All Undergraduate Theses and Capstone Projects

Machine learning (ML) techniques developed in computer science have revolutionized nearly every sector of industry. Despite the prevalence and usefulness of ML, students outside of computer science rarely receive training in ML. Students frequently receive training in statistical analysis, often using the software package R, which is free, open source, and has additional downloadable modules. A popular module is the ML package caret, which contains 238 different ML algorithms, each with 0-9 hyperparameters. caret is powerful, flexible, and provides consistent syntax across algorithms. In the hands of an experienced practitioner, this tunability is welcomed and can increase accuracy. However, when …

Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …

Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed

Dissertations

Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project …

Fractals As Basis For Design And Critique, John Charles Driscoll

Dissertations and Theses

The design profession is responding to the complex systems represented by architecture and planning by increasingly incorporating the power of computer technology into the design process. This represents a paradigm shift, and requires that designers rise to the challenge of both embracing modern technologies to perform increasingly sophisticated tasks without compromising their objective to create meaningful and environmentally sensitive architecture. This dissertation investigated computer-based fractal tools applied within a traditional architectural charette towards a design process with the potential to address the complex issues architects and planners face today. We developed and presented an algorithm that draws heavily from fractal …

Scheduling Multiple Parallel Jobs Online, Kefu Lu

McKelvey School of Engineering Graduate Student Theses & Dissertations

The prevalence of parallel processing has only increased in recent years. Today, most computing machines available on the market shifted from using single processors to possessing a multicore architecture. Naturally, there has been considerable work in developing parallel programming languages and frameworks which programmers can use to leverage the computing power of these machines. These languages allow users to create programs with internal parallelism. The next, and crucial, step is to ensure that the computing system can efficiently execute these parallel jobs. Executing a single parallel job efficiently is a very well-studied problem in parallel computing. In the area of …

Assessing Computational Thinking, Daniel Duckworth

2009 - 2019 ACER Research Conferences

This paper provides some context for the role of computation thinking (CT) in the Australian Curriculum, an abridged literature review of CT as a problem-solving framework from the International Computer and Information Literacy Study (ICILS) 2018 assessment framework and some examples of how CT has been used to solve real-world problems. Finally, this paper presents ways to teach and assess CT.

New Algorithms For Large Datasets And Distributions, Sutanu Gayen

School of Computing: Dissertations, Theses, and Student Research

In this dissertation, we make progress on certain algorithmic problems broadly over two computational models: the streaming model for large datasets and the distribution testing model for large probability distributions.

First we consider the streaming model, where a large sequence of data items arrives one by one. The computer needs to make one pass over this sequence, processing every item quickly, in a limited space. In Chapter 2 motivated by a bioinformatics application, we consider the problem of estimating the number of low-frequency items in a stream, which has received only a limited theoretical work so far. We give an …

Graph Pebbling Algorithms And Lemke Graphs, Charles A. Cusack, Aaron Green, Airat Bekmetjev, Mark Powers

Faculty Publications

Given a simple, connected graph, a pebbling configuration (or just configuration) is a function from its vertex set to the nonnegative integers. A pebbling move between adjacent vertices removes two pebbles from one vertex and adds one pebble to the other. A vertex r is said to be reachable from a configuration if there exists a sequence of pebbling moves that places at least one pebble on r. A configuration is solvable if every vertex is reachable. The pebbling number π(G) of a graph G is the minimum integer such that every configuration of size π(G) on G …

Can Algorithms Help Us Decide Who To Trust?, David De Cremer, Jack Mcguire, Yorck Hesselbarth, Ke M Mai

Research Collection Lee Kong Chian School Of Business

The use of artificial intelligence (AI) and algorithms is increasing within organizations to manage business processes, hire employees, and automate routine organizational decision making. This comes as no surprise, since the application of simple linear algorithms have been shown to outperform human judgment in the accuracy of many administrative tasks. A 2017 Accenture survey also revealed that 85% of executives want to invest more extensively in AI-related technologies over the next three years.

The Right To Human Intervention: Law, Ethics And Artificial Intelligence, Maria Kanellopoulou - Botti, Fereniki Panagopoulou, Maria Nikita, Anastasia Michailaki

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The paper analyses the new right of human intervention in use of information technology, automatization processes and advanced algorithms in individual decision-making activities. Art. 22 of the new General Data Protection Regulation (GDPR) provides that the data subject has the right not to be subject to a fully automated decision on matters of legal importance to her interests, hence the data subject has a right to human intervention in this kind of decisions.

Incorporating Pathway Information Into Feature Selection Towards Better Performed Gene Signatures, Suyan Tian, Chi Wang, Bing Wang

Biostatistics Faculty Publications

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, …

Extending And Improving Tla+, Amy House

Theses and Dissertations

TLA+ is a mathematically precise specification language based on set theory. We provide a reusable TLA+ module for specifying asynchronous message-passing systems. The syntax and semantics of the TLA+ language are introduced. An example algorithm is translated from pseudocode to TLA+ to show the new module's use. TLC is used to model-check the translated algorithm. An established parallel algorithm for detecting strongly connected components of a graph is translated to PlusCal, a pseudolanguage that can be mechanically translated into TLA+, then a formal correctness proof is given. This algorithm is one proposed to improve the efficiency of liveness checking in …

The Non-Mechanical Beam Steering Of Light In Reflective Inverse Diffusion, Eric K. Nagamine

Theses and Dissertations

Wavefront shaping is a technique that uses spatial light modulators to conjugate the phase of light incident on a rough surface, such that the light will refocus after reflection. This refocusing effect is called reflective inverse diffusion. There currently are two different approaches used to achieve reflective inverse diffusion: iterative methods and matrix methods. Iterative methods find one phase mask which allows for reflected light to be focused at a single, specific position, with results that are immediately available and continuously improving. Matrix methods calculate the complex matrix which describes the rough surface and allows for reflected light to be …

Pascal's Triangle Modulo N And Its Applications To Efficient Computation Of Binomial Coefficients, Zachary Warneke

Honors Program: Senior Projects (Public)

In this thesis, Pascal's Triangle modulo n will be explored for n prime and n a prime power. Using the results from the case when n is prime, a novel proof of Lucas' Theorem is given. Additionally, using both the results from the exploration of Pascal's Triangle here, as well as previous results, an efficient algorithm for computation of binomial coefficients modulo n (a choose b mod n) is described, and its time complexity is analyzed and compared to naive methods. In particular, the efficient algorithm runs in O(n log(a)) time (as opposed to …

Smart Parking Systems Design And Integration Into Iot, Charles M. Menne

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

This paper looks at two smart parking reservation algorithms, and examines the ongoing efforts to connect smart systems of different domains in a city's infrastructure. The reservation algorithms are designed to improve the performance of smart parking systems. The first algorithm considers the distance between parking areas and the number of free parking spaces in determining a parking space. The second algorithm uses distance between parking areas and driver destination, parking price, and the number of unoccupied spaces for each parking area. Neither of these smart parking systems cover how they could fit into a larger scale smart system. As …

Dynamic Light Scattering Optical Coherence Tomography To Probe Motion Of Subcellular Scatterers., Nico J J Arezza, Marjan Razani, Michael C Kolios

Medical Biophysics Publications

Optical coherence tomography (OCT) is used to provide anatomical information of biological systems but can also provide functional information by characterizing the motion of intracellular structures. Dynamic light scattering OCT was performed on intact, control MCF-7 breast cancer cells and cells either treated with paclitaxel to induce apoptosis or deprived of nutrients to induce oncosis. Autocorrelations (ACs) of the temporal fluctuations of OCT intensity signals demonstrate a significant decrease in decorrelation time after 24 h in both the paclitaxel-treated and nutrient-deprived cell groups but no significant differences between the two groups. The acquired ACs were then used as input for …

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.

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …

Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya

Computer Science Faculty Publications

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the …

On Hybrid Temporal Basis Functions For Stable Numerical Solution Of Time Domain Boundary Integral Equations, Fang Q. Hu

Mathematics & Statistics Faculty Publications

Problems in unsteady aerodynamics and aeroacoustics can sometimes be formulated as integral equations, such as the boundary integral equations. Numerical discretization of integral equations in the time domain often leads to so-called March-On-in-Time (MOT) schemes. In the literature, the temporal basis functions used in MOT schemes have been largely limited to low-order shifted Lagrange basis functions. In order to evaluate the accuracy and effectiveness of the temporal basis functions, a Fourier analysis of the temporal interpolation schemes is carried out. Based on the Fourier analysis, the spectral resolutions of various temporal basis functions are quantified. It is argued that hybrid …