An Efficient Annealing-Assisted Differential Evolution For Multi-Parameter Adaptive Latent Factor Analysis, 2022 Chongqing University of Post and Telecommunications

#### An Efficient Annealing-Assisted Differential Evolution For Multi-Parameter Adaptive Latent Factor Analysis, Qing Li, Guansong Pang, Mingsheng Shang

*Research Collection School Of Computing and Information Systems*

A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis ...

Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, 2022 James Madison University

#### Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, Julian Gouffray

*James Madison Undergraduate Research Journal (JMURJ)*

This paper examines how the Bitcoin-altcoin return relationship has evolved in periods between 2015 and 2020. To understand this relation, we observe data on the cryptocurrency Bitcoin and prominent altcoins Ethereum, Litecoin, Ripple, Stellar, and Monero, which collectively represent over 90% of the market throughout the observed period. We employ a vector autoregressive model (VAR) to produce forecast error variance decompositions, orthogonal impulse response functions, and Granger-causality tests. We find evidence that Bitcoin return variation has increasingly explained altcoin returns and that market inefficiency increased between 2017 and 2020, as shown by increased Granger causality between Bitcoin and altcoins. These ...

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, 2022 Southern Methodist University

#### Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

*SMU Data Science Review*

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model ...

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, 2022 Southern Methodist University

#### Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

*SMU Data Science Review*

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements ...

Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, 2022 Wuhan University

#### Towards An Optimal Bus Frequency Scheduling: When The Waiting Time Matters, Songsong Mo, Zhifeng Bao, Baihua Zheng, Zhiyong Peng

*Research Collection School Of Computing and Information Systems*

Reorganizing bus frequencies to cater for actual travel demands can significantly save the cost of the public transport system. This paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold can be maximized. We propose two variants of the problem, FAST and FASTCO, to cater for different application needs and prove that both are NP-hard. To solve FAST effectively and efficiently, we first present an ...

An Analysis Of The Friendship Paradox And Derived Sampling Methods, 2022 The Graduate Center, City University of New York

#### An Analysis Of The Friendship Paradox And Derived Sampling Methods, Yitzchak Novick

*Dissertations, Theses, and Capstone Projects*

The friendship paradox (FP) is the famous sampling-bias phenomenon that leads to the seemingly paradoxical truth that, on average, people’s friends have more friends than they do. Among the many far-reaching research findings the FP inspired is a sampling method that samples neighbors of vertices in a graph in order to acquire random vertices that are of higher expected degree than average.

Our research examines the friendship paradox on a local level. We seek to quantify the impact of the FP on an individual vertex by defining the vertex’s “friendship index”, a measure of the extent to which ...

On The Cryptographic Deniability Of The Signal Protocol, 2022 The Graduate Center, City University of New York

#### On The Cryptographic Deniability Of The Signal Protocol, Nihal Vatandas

*Dissertations, Theses, and Capstone Projects*

Offline deniability is the ability to a posteriori deny having participated in a particular communication session. This property has been widely assumed for the Signal messaging application, yet no formal proof has appeared in the literature. In this work, we present the first formal study of the offline deniability of the Signal protocol. Our analysis shows that building a deniability proof for Signal is non-trivial and requires strong assumptions on the underlying mathematical groups where the protocol is run.

To do so, we study various implicitly authenticated key exchange protocols, including MQV, HMQV, and 3DH/X3DH, the latter being the ...

Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, 2022 Western University

#### Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, Adib Sedig

*Undergraduate Student Research Internships Conference*

This project is primarily inspired by three papers: Colin Camerer and Xiaomin Li’s (2019 working paper)—*Using Visual Salience in Empirical Game Theory*, Ryan Oprea’s (2020)—*What Makes a Rule Complex?*, and Caplin et. al.’s (2011)—*Search and Satisficing*. Over the summer, I worked towards constructing a model of choice for the visual coordination game that can model player behavior more accurately than traditional game theoretic predictions. It attempts to do so by incorporating a degree of bias towards salience into a cellular automaton search algorithm and utilizing it alongside a sequential search mechanism of satisficing. This ...

The Design And Implementation Of A High-Performance Polynomial System Solver, 2022 The University of Western Ontario

#### The Design And Implementation Of A High-Performance Polynomial System Solver, Alexander Brandt

*Electronic Thesis and Dissertation Repository*

This thesis examines the algorithmic and practical challenges of solving systems of polynomial equations. We discuss the design and implementation of triangular decomposition to solve polynomials systems exactly by means of symbolic computation.

Incremental triangular decomposition solves one equation from the input list of polynomials at a time. Each step may produce several different components (points, curves, surfaces, etc.) of the solution set. Independent components imply that the solving process may proceed on each component concurrently. This so-called component-level parallelism is a theoretical and practical challenge characterized by irregular parallelism. Parallelism is not an algorithmic property but rather a geometrical ...

Distributed Learning With Automated Stepsizes, 2022 Clemson University

#### Distributed Learning With Automated Stepsizes, Benjamin Liggett

*All Theses*

Stepsizes for optimization problems play a crucial role in algorithm convergence, where the stepsize must undergo tedious manual tuning to obtain near-optimal convergence. Recently, an adaptive method for automating stepsizes was proposed for centralized optimization. However, this method is not directly applicable to decentralized optimization because it allows for heterogeneous agent stepsizes. Furthermore, directly using consensus between agent stepsizes to mitigate stepsize heterogeneity can decrease performance and even lead to divergence.

This thesis proposes an algorithm to remedy the tedious manual tuning of stepsizes in decentralized optimization. Our proposed algorithm automates the stepsize and uses dynamic consensus between agents’ stepsizes ...

Unsupervised Contrastive Representation Learning For Knowledge Distillation And Clustering, 2022 Clemson University

#### Unsupervised Contrastive Representation Learning For Knowledge Distillation And Clustering, Fei Ding

*All Dissertations*

Unsupervised contrastive learning has emerged as an important training strategy to learn representation by pulling positive samples closer and pushing negative samples apart in low-dimensional latent space. Usually, positive samples are the augmented versions of the same input and negative samples are from different inputs. Once the low-dimensional representations are learned, further analysis, such as clustering, and classification can be performed using the representations. Currently, there are two challenges in this framework. First, the empirical studies reveal that even though contrastive learning methods show great progress in representation learning on large model training, they do not work well for small ...

Variational Graph Author Topic Modeling, 2022 Singapore Management University

#### Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw

*Research Collection School Of Computing and Information Systems*

While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal ...

Submodularity And Local Search Approaches For Maximum Capture Problems Under Generalized Extreme Value Models, 2022 Phenikaa University

#### Submodularity And Local Search Approaches For Maximum Capture Problems Under Generalized Extreme Value Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

*Research Collection School Of Computing and Information Systems*

We study the maximum capture problem in facility location under random utility models, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured user demand is maximized, assuming that each customer chooses among all available facilities according to a random utility maximization model. We employ the generalized extreme value (GEV) family of discrete choice models and show that the objective function in this context is monotonic and submodular. This finding implies that a simple greedy heuristic can always guarantee a (1−1/e) approximation solution. We further develop a new algorithm combining ...

2-Dimensional String Problems: Data Structures And Quantum Algorithms, 2022 Louisiana State University

#### 2-Dimensional String Problems: Data Structures And Quantum Algorithms, Dhrumilkumar Patel

*LSU Master's Theses*

The field of stringology studies algorithms and data structures used for processing strings efficiently. The goal of this thesis is to investigate 2-dimensional (2D) variants of some fundamental string problems, including \textit{Exact Pattern Matching} and \textit{Longest Common Substring}.

In the 2D pattern matching problem, we are given a matrix $\M[1\dd n,1\dd n]$ that consists of $N = n \times n$ symbols drawn from an alphabet $\Sigma$ of size $\sigma$. The query consists of a $ m \times m$ square matrix $\PP[1\dd m, 1\dd m]$ drawn from the same alphabet, and the task is ...

A Nature-Inspired Approach For Scenario-Based Validation Of Autonomous Systems, 2022 Embry-Riddle Aeronautical University

#### A Nature-Inspired Approach For Scenario-Based Validation Of Autonomous Systems, Quentin Goss, Mustafa Akbas

*Beyond: Undergraduate Research Journal*

Scenario-based approaches are cost and time effective solutions to autonomous cyber-physical system testing to identify bugs before costly methods such as physical testing in a controlled or uncontrolled environment. Every bug in an autonomous cyber-physical system is a potential safety risk. This paper presents a scenario-based method for finding bugs and estimating boundaries of the bug profile. The method utilizes a nature-inspired approach adapting low discrepancy sampling with local search. Extensive simulations demonstrate the performance of the approach with various adaptations.

On The Total Set Chromatic Number Of Graphs, 2022 Ateneo de Manila University

#### On The Total Set Chromatic Number Of Graphs, Mark Anthony C. Tolentino, Gerone Russel J. Eugenio, Mari-Jo P. Ruiz

*Theory and Applications of Graphs*

Given a vertex coloring *c* of a graph, the neighborhood color set of a vertex is defined to be the set of all of its neighbors’ colors. The coloring *c* is called a set coloring if any two adjacent vertices have different neighborhood color sets. The set chromatic number χ_{s}(G) of a graph G is the minimum number of colors required in a set coloring of G. In this work, we investigate a total analog of set colorings; that is, we study set colorings of the total graph of graphs. Given a graph *G* = (*V*, *E*), its total ...

Intuitr: A Theorem Prover For Intuitionistic Propositional Logic, 2022 University of Minnesota - Morris

#### Intuitr: A Theorem Prover For Intuitionistic Propositional Logic, Erik Rauer

*Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal*

A constructive proof proves the existence of a mathematical object by giving the steps necessary to construct said object. Proofs of this type can be interpreted as an algorithm for creating such an object. Intuitionistic Propositional Logic (IPL) is a propositional logic system wherein all valid proofs are constructive. intuitR is a theorem prover for IPL, that is, it determines whether a given formula is valid in IPL or not. In this paper, we describe how intuitR determines the validity of a formula and review its performance. When compared on a benchmark set of problems, intuitR was determined to solve ...

Filling Gaps On The Pareto Front In Multi- And Many-Objective Optimization, 2022 University of Minnesota - Morris

#### Filling Gaps On The Pareto Front In Multi- And Many-Objective Optimization, Richard Lussier

*Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal*

Pareto fronts offer insight into the best found solutions of a given problem. Several algorithms have been developed to help maintain a well-distributed Pareto front and therefore offer a wide variety of solutions. However, in real-world problems, the Pareto front isn’t necessarily a continuous surface and may contain holes and/or discontinuous lines. These irregular areas on the Pareto front are considered gaps. These gaps can either be natural or artificial. In their research, Pellicer, Escudero, Alzueta, and Deb suggest a three-step procedure to find, validate, and fill these gaps. First, they developed an algorithm to generate gap points ...

Review Of Some Existing Qml Frameworks And Novel Hybrid Classical-Quantum Neural Networks Realising Binary Classification For The Noisy Datasets, 2022 Singapore Management University

#### Review Of Some Existing Qml Frameworks And Novel Hybrid Classical-Quantum Neural Networks Realising Binary Classification For The Noisy Datasets, N. Schetakis, D. Aghamalyan, Paul Robert Griffin, M. Boguslavsky

*Research Collection School Of Computing and Information Systems*

One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC-ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for ...

Space-Efficient Algorithms And Verification Schemes For Graph Streams, 2022 Dartmouth College

#### Space-Efficient Algorithms And Verification Schemes For Graph Streams, Prantar Ghosh

*Dartmouth College Ph.D Dissertations*

Structured data-sets are often easy to represent using graphs. The prevalence of massive data-sets in the modern world gives rise to big graphs such as web graphs, social networks, biological networks, and citation graphs. Most of these graphs keep growing continuously and pose two major challenges in their processing: (a) it is infeasible to store them entirely in the memory of a regular server, and (b) even if stored entirely, it is incredibly inefficient to reread the whole graph every time a new query appears. Thus, a natural approach for efficiently processing and analyzing such graphs is reading them as ...