Theory and Algorithms Commons^™

The Locals Casino As A Social Network – Can An Interconnected Community Of Players Detect Differences In Hold?, Jason D. Fiege, Anastasia (Stasi) D. Baran

International Conference on Gambling & Risk Taking

Abstract

It is difficult for individual players to detect differences in theoretical hold between slot machines without playing an unrealistically large number of games. This difficulty occurs because the fractional loss incurred by a player converges only slowly to the theoretical hold in the presence of volatility designed into slot pay tables. Nevertheless, many operators believe that players can detect changes in hold or differences compared to competition, especially in a locals casino market, and therefore resist increasing holds. Instead of investigating whether individual players can detect differences in hold, we ask whether a population of casino regulars who share …

Feature Selection From Clinical Surveys Using Semantic Textual Similarity, Benjamin Warner

McKelvey School of Engineering Theses & Dissertations

Survey data collected from human subjects can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names …

Hidden Stratagem - Microtargeting: The Future Of Conflict, Jessica Dawson

ACI Books & Book Chapters

In September 2020, General Paul Nakasone, NSA Director and Commander of U.S. Cyber Command, called foreign influence operations “the next great disruptor.”[1] Nearly every intelligence agency in the United States government has been sounding the alarm over targeted influence operations enabled by social media companies since at least 2016, even though some of these operations started earlier. What often goes unstated and even less understood is the digital surveillance economy underlying these platforms and how this economic structure of trading free access for data collection about individuals’ lives poses a national security threat. Harvard sociologist Shoshana Zuboff calls this phenomenon …

Universal Computation Using Self-Assembling, Crisscross Dna Slats, Jackson S. Bullard

Computer Science and Computer Engineering Undergraduate Honors Theses

I first give a brief introduction to formal models of computation. I then present three different approaches for computation in the aTAM. I later detail generating systems of crisscross slats given an arbitrary algorithm encoded in the form of a Turing machine. Crisscross slats show potential due to their high levels of cooperativity, so it is hoped that implementations utilizing slats are more robust to various growth errors compared to the aTAM. Finally, my software converts arbitrary crisscross slat systems into various physical representations that assist in analyzing their potential to be realized in experiments.

Immersive Learning Environments For Computer Science Education, Dillon Buchanan

Electronic Theses and Dissertations

This master's thesis explores the effectiveness of an educational intervention using an interactive notebook to support and supplement instruction in a foundational-level programming course. A quantitative, quasi-experimental group comparison method was employed, where students were placed into either a control or a treatment group. Data was collected from assignment and final grades, as well as self-reported time spent using the notebook. Independent t-tests and correlation were used for data analysis. Results were inconclusive but did indicate that the intervention had a possible effect. Further studies may explore better efficacy, implementation, and satisfaction of interactive notebooks across a larger population and …

Hidden Strategum, Jessica Dawson

West Point Books

In September 2020, General Paul Nakasone, NSA Director and Commander of U.S. Cyber Command, called foreign influence operations “the next great disruptor.”[1] Nearly every intelligence agency in the United States government has been sounding the alarm over targeted influence operations enabled by social media companies since at least 2016, even though some of these operations started earlier. What often goes unstated and even less understood is the digital surveillance economy underlying these platforms and how this economic structure of trading free access for data collection about individuals’ lives poses a national security threat. Harvard sociologist Shoshana Zuboff calls this phenomenon …

Analysis Of Honeypots In Detecting Tactics, Techniques, And Procedure (Ttp) Changes In Threat Actors Based On Source Ip Address, Carson Reynolds, Andy Green

Symposium of Student Scholars

The financial and national security impacts of cybercrime globally are well documented. According to the 2020 FBI Internet Crime Report, financially motivated threat actors committed 86% of reported breaches, resulting in a total loss of approximately $4.1 billion in the United States alone. In order to combat this, our research seeks to determine if threat actors change their tactics, techniques, and procedures (TTPs) based on the geolocation of their target’s IP address. We will construct a honeypot network distributed across multiple continents to collect attack data from geographically separate locations concurrently to answer this research question. We will configure the …

Using Azure Automl To Analyze The Effect Of Attendance And Seat Choice On University Student Grades, Ac Hýbl

Campus Research Day

Teachers often claim that class attendance and sitting at the front of a classroom improves student grades. This study employs machine learning on a private University's attendance data to analyze this claim. We perform a correlation analysis in Azure by training regression models. No correlation is found. Next we use the K-means clustering algorithm in Azure. At k=2 clusters, a cluster with perfect attendance shows a higher average grade than a cluster with a late attendance average. Seat choice within the classroom does not prove important to the clustering models.

Investigating The Use Of Recurrent Neural Networks In Modeling Guitar Distortion Effects, Caleb Koch, Scott Hawley, Andrew Fyfe

Belmont University Research Symposium (BURS)

Guitar players have been modifying their guitar tone with audio effects ever since the mid-20th century. Traditionally, these effects have been achieved by passing a guitar signal through a series of electronic circuits which modify the signal to produce the desired audio effect. With advances in computer technology, audio “plugins” have been created to produce audio effects digitally through programming algorithms. More recently, machine learning researchers have been exploring the use of neural networks to replicate and produce audio effects initially created by analog and digital effects units. Recurrent Neural Networks have proven to be exceptional at modeling audio effects …

Time Evolution Is A Source Of Bias In The Wolf Algorithm For Largest Lyapunov Exponents, Kolby Brink, Tyler Wiles, Nicholas Stergiou, Aaron Likens

UNO Student Research and Creative Activity Fair

Human movement is inherently variable by nature. One of the most common analytical tools for assessing movement variability is the largest Lyapunov exponent (LyE) which quantifies the rate of trajectory divergence or convergence in an n-dimensional state space. One popular method for assessing LyE is the Wolf algorithm. Many studies have investigated how Wolf’s calculation of the LyE changes due to sampling frequency, filtering, data normalization, and stride normalization. However, a surprisingly understudied parameter needed for LyE computation is evolution time. The purpose of this study is to investigate how the LyE changes as a function of evolution time …

Toward A Simulation Model Complexity Measure, J. Scott Thompson, Douglas D. Hodson, Michael R. Grimaila, Nicholas Hanlon, Richard Dill

Faculty Publications

Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of the complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. The key terms model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes …

Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba

SMU Data Science Review

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …

Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn

SMU Data Science Review

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

A Modified Hopfield Network For The K-Median Problem, Cody Rossiter

Electronic Thesis and Dissertation Repository

The k-median problem is a clustering problem where given n locations one wants to select k locations such that the total distance between every non-selected location and its nearest selected location is minimized. The problem has applications in several fields, including network design, resource allocation, and data mining.

There is currently limited research on applying neural networks to combinatorial optimization problems and we contribute by presenting a modified Hopfield network for the k-median problem. Hopfield networks are a type of neural network that can be applied to combinatorial optimization problems but often run slowly and produce poor solutions.

Our modifications …

Solving Fjssp With A Genetic Algorithm, Michael John Srouji

Computer Science and Software Engineering

The Flexible Job Shop Scheduling Problem is an NP-Hard combinatorial problem. This paper aims to find a solution to this problem using genetic algorithms, and discuss the effectiveness of this. Initially, I did exploratory work on whether neural networks would be effective or not, and found a lot of trade offs between using neural networks and chromosome sequencing. In the end, I decided to use chromosome sequencing over neural networks, due to the scope of my problem being on a small scale rather than on a large scale.

Therefore, the genetic algorithm was implemented using chromosome sequencing. My chromosomes were …

Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …

Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese

Faculty Scholarship at Penn Carey Law

Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, …

Combinatorics Syllabus, Tugce Ozdemir

Open Educational Resources

No abstract provided.

Patch-Wise Training With Convolutional Neural Networks To Synthetically Upscale Cfd Simulations, John P. Romano, Alec C. Brodeur, Oktay Baysal

Mechanical & Aerospace Engineering Faculty Publications

This paper expands the authors’ prior work[1], which focuses on developing a convolutional neural network (CNN) model capable of mapping time-averaged, unsteady Reynold’s-averaged Navier-Stokes (URANS) simulations to higher resolution results informed by time-averaged detached eddy simulations (DES). The authors present improvements over the prior CNN autoencoder model that result from hyperparameter optimization, increased data set augmentation through the adoption of a patch-wise training approach, and the predictions of primitive variables rather than vorticity magnitude. The training of the CNN model developed in this study uses the same URANS and DES simulations of a transonic flow around several NACA 4-digit airfoils …