Tradao: A Visual Analytics System For Trading Algorithm Optimization, 2021 Singapore Management University

#### Tradao: A Visual Analytics System For Trading Algorithm Optimization, Ka Wing Tsang, Haotian Li, Fuk Ming Lam, Yifan Mu, Yong Wang, Huamin Qu

*Research Collection School Of Computing and Information Systems*

With the wide applications of algorithmic trading, it has become critical for traders to build a winning trading algorithm to beat the market. However, due to the lack of efficient tools, traders mainly rely on their memory to manually compare the algorithm instances of a trading algorithm and further select the best trading algorithm instance for the real trading deployment. We work closely with industry practitioners to discover and consolidate user requirements and develop an interactive visual analytics system for trading algorithm optimization. Structured expert interviews are conducted to evaluateTradAOand a representative case study is documented for illustrating the system ...

The “Knapsack Problem” Workbook: An Exploration Of Topics In Computer Science, 2021 CUNY La Guardia Community College

#### The “Knapsack Problem” Workbook: An Exploration Of Topics In Computer Science, Steven Cosares

*Open Educational Resources*

This workbook provides discussions, programming assignments, projects, and class exercises revolving around the “Knapsack Problem” (KP), which is widely a recognized model that is taught within a typical Computer Science curriculum. Throughout these discussions, we use KP to introduce or review topics found in courses covering topics in Discrete Mathematics, Mathematical Programming, Data Structures, Algorithms, Computational Complexity, etc. Because of the broad range of subjects discussed, this workbook and the accompanying spreadsheet files might be used as part of some CS capstone experience. Otherwise, we recommend that individual sections be used, as needed, for exercises relevant to a course in ...

Fake News Analysis And Graph Classification On A Covid-19 Twitter Dataset, 2021 San Jose State University

#### Fake News Analysis And Graph Classification On A Covid-19 Twitter Dataset, Kriti Gupta

*Master's Projects*

Earlier researches have showed that the spread of fake news through social media can have a huge impact to society and also to individuals in an extremely negative way. In this work we aim to study the spread of fake news compared to real news in a social network. We do that by performing classical social network analysis to discover various characteristics, and formulate the problem as a binary classification, where we have graphs modeling the spread of fake and real news. For our experiments we rely on how news are propagated through a popular social media services such as ...

A Comparison Of Word Embedding Techniques For Similarity Analysis, 2021 University of Arkansas, Fayetteville

#### A Comparison Of Word Embedding Techniques For Similarity Analysis, Tyler Gerth

*Computer Science and Computer Engineering Undergraduate Honors Theses*

There have been a multitude of word embedding techniques developed that allow a computer to process natural language and compare the relationships between different words programmatically. In this paper, similarity analysis, or the testing of words for synonymic relations, is used to compare several of these techniques to see which performs the best. The techniques being compared all utilize the method of creating word vectors, reducing words down into a single vector of numerical values that denote how the word relates to other words that appear around it. In order to get a holistic comparison, multiple analyses were made, with ...

Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, 2021 University of Arkansas, Fayetteville

#### Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai

*Computer Science and Computer Engineering Undergraduate Honors Theses*

Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent ...

Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, 2021 University of Arkansas, Fayetteville

#### Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, Z. Gunner Lawless, Dana Dittoe, Dale R. Thompson, Steven C. Ricke

*Computer Science and Computer Engineering Undergraduate Honors Theses*

Bioinformatic analysis is a time-consuming process for labs performing research on various microbiomes. Researchers use tools like Qiime2 to help standardize the bioinformatic analysis methods, but even large, extensible platforms like Qiime2 have drawbacks due to the attention required by researchers. In this project, we propose to automate additional standard lab bioinformatic procedures by eliminating the existing manual process of determining the trim and truncate locations for paired end 2 sequences. We introduce a new Qiime2 plugin called TruncTrimmer to automate the process that usually requires the researcher to make a decision on where to trim and truncate manually after ...

Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, 2021 University of Arkansas, Fayetteville

#### Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri

*Computer Science and Computer Engineering Undergraduate Honors Theses*

In the modern age of social media and networks, graph representations of real-world phenomena have become incredibly crucial. Often, we are interested in understanding how entities in a graph are interconnected. Graph Neural Networks (GNNs) have proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been ...

Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, 2021 Embry-Riddle Aeronautical University

#### Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, James Hendrickson

*PhD Dissertations and Master's Theses*

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering ...

Evolving Efficient Floor Plans For Hospital Emergency Rooms, 2021 University of Nebraska at Omaha

#### Evolving Efficient Floor Plans For Hospital Emergency Rooms, Alex Ramsey

*Theses/Capstones/Creative Projects*

Genetic Algorithms find wide use in optimization problems across many fields of research, including crowd simulation. This paper proposes that genetic algorithms could be used to create better floor plans for hospital emergency rooms, potentially saving critical time in high risk situations. The genetic algorithm implemented makes use of a hospital-specific crowd simulation to accurately evaluate the effectiveness of produced layouts. The results of combining genetic algorithms with a crowd simulation are promising. Future work may improve upon these results to produce better, more optimal hospital floor plans.

Deep Learning And Optimization In Visual Target Tracking, 2021 Utah State University

#### Deep Learning And Optimization In Visual Target Tracking, Mohammadreza Javanmardi

*All Graduate Theses and Dissertations*

Visual tracking is the process of estimating states of a moving object in a dynamic frame sequence. It has been considered as one of the most paramount and challenging topics in computer vision. Although numerous tracking methods have been introduced, developing a robust algorithm that can handle different challenges still remains unsolved. In this dissertation, we introduce four different trackers and evaluate their performance in terms of tracking accuracy on challenging frame sequences. Each of these trackers aims to address the drawbacks of their peers. The first developed method is called a structured multi-task multi-view tracking (SMTMVT) method, which exploits ...

Quantum Simulation Using High-Performance Computing, 2021 Grand Valley State University

#### Quantum Simulation Using High-Performance Computing, Collin Beaudoin, Christian Trefftz, Zachary Kurmas

*Masters Theses*

Hermitian matrix multiplication is one of the most common actions that is performed on quantum matrices, for example, it is used to apply observables onto a given state vector/density matrix.

ρ→Hρ

Our goal is to create an algorithm to perform the matrix multiplication within the constraints of QuEST [1], a high-performance simulator for quantum circuits. QuEST provides a system-independent platform for implementing and simulating quantum algorithms without the need for access to quantum machines. The current implementation of QuEST supports CUDA, MPI, and OpenMP, which allows programs to run on a wide variety of systems.

Implications Of The Quantum Dna Model For Information Sciences, 2021 University of Tennessee Health Science Center

#### Implications Of The Quantum Dna Model For Information Sciences, F. Matthew Mihelic

*Faculty Publications*

The DNA molecule can be modeled as a quantum logic processor, and this model has been supported by pilot research that experimentally demonstrated non-local communication between cells in separated cell cultures. This modeling and pilot research have important implications for information sciences, providing a potential architecture for quantum computing that operates at room temperature and is scalable to millions of qubits, and including the potential for an entanglement communication system based upon the quantum DNA architecture. Such a system could be used to provide non-local quantum key distribution that could not be blocked by any shielding or water depth, would ...

Compact Representations Of Uncertainty In Clustering, 2021 University of Massachusetts Amherst

#### Compact Representations Of Uncertainty In Clustering, Craig Stuart Greenberg

*Doctoral Dissertations*

Flat clustering and hierarchical clustering are two fundamental tasks, often used to discover meaningful structures in data, such as subtypes of cancer, phylogenetic relationships, taxonomies of concepts, and cascades of particle decays in particle physics. When multiple clusterings of the data are possible, it is useful to represent uncertainty in clustering through various probabilistic quantities, such as the distribution over partitions or tree structures, and the marginal probabilities of subpartitions or subtrees.

Many compact representations exist for structured prediction problems, enabling the efficient computation of probability distributions, e.g., a trellis structure and corresponding Forward-Backward algorithm for Markov models that ...

Toward Improving Understanding Of The Structure And Biophysics Of Glycosaminoglycans, 2021 University of New England

#### Toward Improving Understanding Of The Structure And Biophysics Of Glycosaminoglycans, Elizabeth K. Whitmore

*Electronic Theses and Dissertations*

Glycosaminoglycans (GAGs) are the linear carbohydrate components of proteoglycans (PGs) that mediate PG bioactivities, including signal transduction, tissue morphogenesis, and matrix assembly. To understand GAG function, it is important to understand GAG structure and biophysics at atomic resolution. This is a challenge for existing experimental and computational methods because GAGs are heterogeneous, conformationally complex, and polydisperse, containing up to 200 monosaccharides. Molecular dynamics (MD) simulations come close to overcoming this challenge but are only feasible for short GAG polymers. To address this problem, we developed an algorithm that applies conformations from unbiased all-atom explicit-solvent MD simulations of short GAG polymers ...

Data-Driven Approaches To Complex Materials: Applications To Amorphous Solids, 2021 The University of Southern Mississippi

#### Data-Driven Approaches To Complex Materials: Applications To Amorphous Solids, Dil Kumar Limbu

*Dissertations*

While conventional approaches to materials modeling made significant contributions and advanced our understanding of materials properties in the past decades, these approaches often cannot be applied to disordered materials (e.g., glasses) for which accurate total-energy functionals or forces are either not available or it is infeasible to employ due to computational complexities associated with modeling disordered solids in the absence of translational symmetry. In this dissertation, a number of information-driven probabilistic methods were developed for the structural determination of a range of materials including disordered solids to transition metal clusters. The ground-state structures of transition-metal clusters of iron, nickel ...

Dijkstra’S Pathfinder, 2021 Coastal Carolina University

#### Dijkstra’S Pathfinder, Taylor F. Malamut

*Honors Theses*

Dijkstra’s algorithm has been widely studied and applied since it was first published in 1959. This research shows that Dijkstra’s algorithm can be used to find the shortest path between two stations on the Washington D.C. Metro. After exploring different types of research and applying Dijkstra’s algorithm, it was found that the algorithm will always yield the shortest path, even if visually a shorter path was initially expected.

Toward A Quantum Neural Network: Proposing The Qaoa Algorithm To Replace A Feed Forward Neural Network, 2021 University of Nevada, Las Vegas

#### Toward A Quantum Neural Network: Proposing The Qaoa Algorithm To Replace A Feed Forward Neural Network, Erick Serrano

*Undergraduate Research Symposium Posters*

With a surge in popularity of machine learning as a whole, many researchers have sought optimization methods to reduce the complexity of neural networks; however, only recent attempts have been made to optimize neural networks via quantum computing methods. In this paper, we describe the training process of a feed forward neural network (FFNN) and the time complexity of the training process. We highlight the inefficiencies of the FFNN training process, particularly when implemented with gradient descent, and introduce a call to action for optimization of a FFNN. Afterward, we discuss the strides made in quantum computing to improve the ...

A Comprehensive Mapping And Real-World Evaluation Of Multi-Object Tracking On Automated Vehicles, 2021 Embry-Riddle Aeronautical University

#### A Comprehensive Mapping And Real-World Evaluation Of Multi-Object Tracking On Automated Vehicles, Alexander Bassett

*PhD Dissertations and Master's Theses*

Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field.

This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of ...

Agent-Based Computational Economics: Overview And Brief History, 2021 Iowa State University

#### Agent-Based Computational Economics: Overview And Brief History, Leigh Tesfatsion

*Economics Working Papers*

Scientists seek to understand how real-world systems work. Models devised for scientific purposes must always simplify reality. However, scientists should be permitted to tailor these simplifications to purposes at hand; they should not be forced to distort reality in specific predetermined ways in order to apply a modeling approach. Adherence to this modeling precept was a key goal motivating my development of Agent-Based Computational Economics (ACE), a variant of agent-based modeling characterized by seven specific modeling principles. This perspective provides an overview of ACE and a brief history of its development.

Network-Based Analysis Of Early Pandemic Mitigation Strategies: Solutions, And Future Directions, 2021 Syracuse University

#### Network-Based Analysis Of Early Pandemic Mitigation Strategies: Solutions, And Future Directions, Pegah Hozhabrierdi, Raymond Zhu, Maduakolam Onyewu, Sucheta Soundarajan

*Northeast Journal of Complex Systems (NEJCS)*

Despite the large amount of literature on mitigation strategies for pandemic spread, in practice, we are still limited by na\"ive strategies, such as lockdowns, that are not effective in controlling the spread of the disease in long term. One major reason behind adopting basic strategies in real-world settings is that, in the early stages of a pandemic, we lack knowledge of the behavior of a disease, and so cannot tailor a more sophisticated response. In this study, we design different mitigation strategies for early stages of a pandemic and perform a comprehensive analysis among them. We then propose a ...