Algorithm-Based Fault Tolerance At Scale,
2022
University of Alabama in Huntsville
Algorithm-Based Fault Tolerance At Scale, Joshua Dennis Booth
Summer Community of Scholars (RCEU and HCR) Project Proposals
No abstract provided.
Games For One, Games For Two: Computationally Complex Fun For Polynomial-Hierarchical Families,
2022
Claremont Colleges
Games For One, Games For Two: Computationally Complex Fun For Polynomial-Hierarchical Families, Kye Shi
HMC Senior Theses
In the first half of this thesis, we explore the polynomial-time hierarchy, emphasizing an intuitive perspective that associates decision problems in the polynomial hierarchy to combinatorial games with fixed numbers of turns. Specifically, problems in �� are thought of as 0-turn games, ���� as 1-turn “puzzle” games, and in general ��ₖ�� as ��-turn games, in which decision problems answer the binary question, “can the starting player guarantee a win?” We introduce the formalisms of the polynomial hierarchy through this perspective, alongside definitions of ��-turn CIRCUIT SATISFIABILITY games, whose ��ₖ��-completeness is assumed from prior work (we briefly justify this assumption …
Finding Optimal Cayley Map Embeddings Using Genetic Algorithms,
2022
Rollins College
Finding Optimal Cayley Map Embeddings Using Genetic Algorithms, Jacob Buckelew
Honors Program Theses
Genetic algorithms are a commonly used metaheuristic search method aimed at solving complex optimization problems in a variety of fields. These types of algorithms lend themselves to problems that can incorporate stochastic elements, which allows for a wider search across a search space. However, the nature of the genetic algorithm can often cause challenges regarding time-consumption. Although the genetic algorithm may be widely applicable to various domains, it is not guaranteed that the algorithm will outperform other traditional search methods in solving problems specific to particular domains. In this paper, we test the feasibility of genetic algorithms in solving a …
Machine Learning In Requirements Elicitation: A Literature Review,
2022
Concordia University, Montreal, Quebec, Canada
Machine Learning In Requirements Elicitation: A Literature Review, Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
Engineering Management & Systems Engineering Faculty Publications
A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords …
Mitigation Of Algorithmic Bias To Improve Ai Fairness,
2022
William & Mary
Mitigation Of Algorithmic Bias To Improve Ai Fairness, Kathy Wang
Cybersecurity Undergraduate Research Showcase
As artificial intelligence continues to evolve rapidly with emerging innovations, mass-scale digitization could be disrupted due to unfair algorithms with historically biased data. With the rising concerns of algorithmic bias, detecting biases is essential in mitigating and implementing an algorithm that promotes inclusive representation. The spread of ubiquitous artificial intelligence means that improving modeling robustness is at its most crucial point. This paper examines the omnipotence of artificial intelligence and its resulting bias, examples of AI bias in different groups, and a potential framework and mitigation strategies to improve AI fairness and remove AI bias from modeling techniques.
A Human-Centered Approach To Improving Adolescent Online Sexual Risk Detection Algorithms,
2022
University of Central Florida
A Human-Centered Approach To Improving Adolescent Online Sexual Risk Detection Algorithms, Afsaneh Razi
Electronic Theses and Dissertations, 2020-2023
Computational risk detection has the potential to protect especially vulnerable populations from online victimization. Conducting a comprehensive literature review on computational approaches for online sexual risk detection led to the identification that the majority of this work has focused on identifying sexual predators after-the-fact. Also, many studies rely on public datasets and third-party annotators to establish ground truth and train their algorithms, which do not accurately represent young social media users and their perspectives to prevent victimization. To address these gaps, this dissertation integrated human-centered approaches to both creating representative datasets and developing sexual risk detection machine learning models to …
Spaghetti Tracer: A Framework For Tracing Semiregular Filamentous Densities In 3d Tomograms,
2022
Old Dominion University
Spaghetti Tracer: A Framework For Tracing Semiregular Filamentous Densities In 3d Tomograms, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers
Computer Science Faculty Publications
Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D …
The Locus Algorithm: A Novel Technique For Identifying Optimised Pointings For Differential Photometry,
2022
Dublin Institute for Advanced Studies
The Locus Algorithm: A Novel Technique For Identifying Optimised Pointings For Differential Photometry, Oisin Creaner, Kevin Nolan Mr, E. Hickey, N. Smith
Articles
Studies of the photometric variability of astronomical sources from ground-based telescopes must overcome atmospheric extinction effects. Differential photometry by reference to an ensemble of reference stars which closely match the target in terms of magnitude and colour can mitigate these effects. This Paper describes the design, implementation, and operation of a novel algorithm – The Locus Algorithm – which enables optimised differential photometry. The Algorithm is intended to identify, for a given target and observational parameters, the Field of View (FoV) which includes the target and the maximum number of reference stars similar to the target. A collection of objects …
Decoding Cyclic Codes Via Gröbner Bases,
2022
Colby College
Decoding Cyclic Codes Via Gröbner Bases, Eduardo Sosa
Honors Theses
In this paper, we analyze the decoding of cyclic codes. First, we introduce linear and cyclic codes, standard decoding processes, and some standard theorems in coding theory. Then, we will introduce Gr¨obner Bases, and describe their connection to the decoding of cyclic codes. Finally, we go in-depth into how we decode cyclic codes using the key equation, and how a breakthrough by A. Brinton Cooper on decoding BCH codes using Gr¨obner Bases gave rise to the search for a polynomial-time algorithm that could someday decode any cyclic code. We discuss the different approaches taken toward developing such an algorithm and …
Lasso: Listing All Subset Sums Obediently For Evaluating Unbounded Subset Sums,
2022
University of Montana
Lasso: Listing All Subset Sums Obediently For Evaluating Unbounded Subset Sums, Christopher N. Burgoyne, Travis J. Wheeler
Graduate Student Theses, Dissertations, & Professional Papers
In this study we present a novel algorithm, LASSO, for solving the unbounded and bounded subset sum problem. The LASSO algorithm was designed to solve the unbounded SSP quickly and to return all subsets summing to a target sum. As speed was the highest priority, we benchmarked the run time performance of LASSO against implementations of some common approaches to the bounded SSP, as well as the only comparable implementation for solving the unbounded SSP that we could find. In solving the bounded SSP, our algorithm had a significantly faster run time than the competing algorithms when the target sum …
A Game Theoretical Model Of Radiological Terrorism Defense,
2022
Purdue University
A Game Theoretical Model Of Radiological Terrorism Defense, Shraddha Rane, Jason Timothy Harris
International Journal of Nuclear Security
Radiological dispersal devices (RDD) pose a threat to the United States. Healthcare facilities housing high-risk radioactive materials and devices are potentially easy targets for unauthorized access and are vulnerable to malevolent acts of theft or sabotage. The three most attractive candidates for use in RDD considered in this study are: 60Co (radiosurgery devices), 137Cs (blood irradiators) and 192Ir (brachytherapy high dose radiation device). The threat posed by RDDs has led to evaluating the security risk of radioactive materials and defending against attacks. The concepts of risk analysis used in conjunction with game theory lay the foundations of …
The Performance Optimization Of Asp Solving Based On Encoding Rewriting And Encoding Selection,
2022
University of Kentucky
The Performance Optimization Of Asp Solving Based On Encoding Rewriting And Encoding Selection, Liu Liu
Theses and Dissertations--Computer Science
Answer set programming (ASP) has long been used for modeling and solving hard search problems. These problems are modeled in ASP as encodings, a collection of rules that declaratively describe the logic of the problem without explicitly listing how to solve it. It is common that the same problem has several different but equivalent encodings in ASP. Experience shows that the performance of these ASP encodings may vary greatly from instance to instance when processed by current state-of-the-art ASP grounder/solver systems. In particular, it is rarely the case that one encoding outperforms all others. Moreover, running an ASP system on …
Matrix Interpretations And Tools For Investigating Even Functionals,
2022
University of Kentucky
Matrix Interpretations And Tools For Investigating Even Functionals, Benjamin Stringer
Theses and Dissertations--Computer Science
Even functionals are a set of polynomials evaluated on the terms of hollow symmetric matrices. Their properties lend themselves to applications such as counting subgraph embeddings in generic (weighted or unweighted) host graphs and computing moments of binary quadratic forms, which occur in combinatorial optimization. This research focuses primarily on counting subgraph embeddings, which is traditionally accomplished with brute-force algorithms or algorithms curated for special types of graphs. Even functionals provide a method for counting subgraphs algebraically in time proportional to matrix multiplication and is not restricted to particular graph types. Counting subgraph embeddings can be accomplished by evaluating a …
A Simple Algorithm For Generating A New Two Sample Type-Ii Progressive Censoring With Applications,
2022
Mansoura University
A Simple Algorithm For Generating A New Two Sample Type-Ii Progressive Censoring With Applications, E. M. Shokr, Rashad Mohamed El-Sagheer, Mahmoud Mansour, H. M. Faied, B. S. El-Desouky
Basic Science Engineering
In this article, we introduce a simple algorithm to generating a new type-II progressive censoring scheme for two samples. It is observed that the proposed algorithm can be applied for any continues probability distribution. Moreover, the description model and necessary assumptions are discussed. In addition, the steps of simple generation algorithm along with programming steps are also constructed on real example. The inference of two Weibull Frechet populations are discussed under the proposed algorithm. Both classical and Bayesian inferential approaches of the distribution parameters are discussed. Furthermore, approximate confidence intervals are constructed based on the asymptotic distribution of the maximum …
Interpretable Design Of Reservoir Computing Networks Using Realization Theory,
2022
Linkedin
Interpretable Design Of Reservoir Computing Networks Using Realization Theory, Wei Miao, Vignesh Narayanan, Jr-Shin Li
Publications
The reservoir computing networks (RCNs) have been successfully employed as a tool in learning and complex decision-making tasks. Despite their efficiency and low training cost, practical applications of RCNs rely heavily on empirical design. In this article, we develop an algorithm to design RCNs using the realization theory of linear dynamical systems. In particular, we introduce the notion of α-stable realization and provide an efficient approach to prune the size of a linear RCN without deteriorating the training accuracy. Furthermore, we derive a necessary and sufficient condition on the irreducibility of the number of hidden nodes in linear RCNs based …
Reinforcement Learning Applied To The Shoals Marine Laboratory Smart Grid,
2022
University of New Hampshire - Main Campus
Reinforcement Learning Applied To The Shoals Marine Laboratory Smart Grid, Daniel C. Mattson
Honors Theses and Capstones
Reinforcement learning (RL) techniques have been applied to smart grids with a variety of applications. The most common objective is to optimize profit for one actor in the system. The goal of this work is to apply different RL models to the smart grid at the Shoals Marine Laboratory (SML) located on Appledore Island, Maine in an effort to reduce costs and minimize the amount of nonrenewable energy consumed on the island. The RL models implemented resulted in more sustainable practices in simulations, with a linear spline model outperforming the naive policy the SML currently uses. Future work includes extending …
On Discovering Motifs And Frequent Patterns In Spatial Trajectories With Discrete Fréchet Distance,
2022
Singapore Management University
On Discovering Motifs And Frequent Patterns In Spatial Trajectories With Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Jiahao Zhang, Kai Wang
Research Collection School Of Computing and Information Systems
The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between two trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering similar subtrajectories within a single trajectory or across multiple trajectories. In this paper, we adopt DFD as the similarity measure, and study two representative trajectory analysis problems, namely, motif discovery and frequent pattern discovery. Due to the time complexity of DFD, these tasks are computationally challenging. We address that challenge with a suite of …
A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic,
2022
Singapore Management University
A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic, Xuan Bach Le, Shang-Wei Lin, Jun Sun, David Sanan
Research Collection School Of Computing and Information Systems
It is well-known that quantum programs are not only complicated to design but also challenging to verify because the quantum states can have exponential size and require sophisticated mathematics to encode and manipulate. To tackle the state-space explosion problem for quantum reasoning, we propose a Hoare-style inference framework that supports local reasoning for quantum programs. By providing a quantum interpretation of the separating conjunction, we are able to infuse separation logic into our framework and apply local reasoning using a quantum frame rule that is similar to the classical frame rule. For evaluation, we apply our framework to verify various …
Rankings Of Mma Fighters,
2022
Minnesota State University, Mankato
Rankings Of Mma Fighters, Michael Schaefer
All Graduate Theses, Dissertations, and Other Capstone Projects
Ranking is an essential process that allows sporting authorities to determine the relative performance of athletes. While ranking is straightforward in some sports, it is more complicated in MMA (mixed martial arts), where competition is often fragmented. This paper describes the mathematics behind four existing ranking algorithms: Elo’s System, Massey’s Method, Colley’s Method, and Google’s PageRank, and shows how to adapt them to rank MMA fighters in the UFC (Ultimate Fighting Championship). We also provide a performance analysis for each ranking method.
A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways,
2022
Old Dominion University
A Comparison Of Deep Learning Algorithms On Image Data For Detecting Floodwater On Roadways, Sarp Salih, Kuzlu Murat, Zhao Yanxiao, Cetin Mecit
Engineering Technology Faculty Publications
Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask Region-Based Convolutional Neural Networks (Mask-R-CNN) and Generative Adversarial Networks (GAN) algorithms. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performances of the algorithms are assessed in accurately detecting the floodwater captured in images. The results show …
