Physical Sciences and Mathematics Commons^™

Analysis Of Github Pull Requests, Canon Ellis

Computer Science and Engineering Theses and Dissertations

The popularity of the software repository site GitHub has created a rise in the Pull Based Development Models' use. An essential portion of pull-based development is the creation of Pull Requests. Pull Requests often have to be reviewed by an individual to be approved and accepted into the Master branch of a software repository. The reviewing process can often be time-consuming and introduce a relatively high level of lost development time. This paper examines thousands of pull requests to understand the most valuable metadata of pull requests. We then introduce metrics in comparing the metadata of pull requests to understand …

Analyzing Performance, Energy Consumption, And Reliability Of Mobile Applications, Osama Barack

Computer Science and Engineering Theses and Dissertations

Mobile applications have become a high priority for software developers. Researchers and practitioners are working toward improving and optimizing the energy efficiency and performance of mobile applications due to the capacity limitation of mobile device processors and batteries. In addition, mobile applications have become popular among end-users, developers have introduced a wide range of features that increase the complexity of application code.

To improve and enhance the maintainability, extensibility, and understandability of application code, refactoring techniques were introduced. However, implementing such techniques to mobile applications affects energy efficiency and performance. To evaluate and categorize software implementation and optimization efficiency, several …

Improving A Wireless Localization System Via Machine Learning Techniques And Security Protocols, Zachary Yorio

Masters Theses, 2020-current

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make …

Deep Neural Network Based Student Response Modeling With Uncertainty, Multimodality And Attention, Xinyi Ding

Computer Science and Engineering Theses and Dissertations

In this thesis, I investigate deep neural network based student response modeling, more specifically Knowledge Tracing (KT). Knowledge Tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered, thus adjusting curriculum accordingly. Deep neural network based knowledge tracing models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have achieved significant improvements compared with conventional probabilistic models. There are mainly two goals in this thesis: 1) To have a better understanding of existing deep neural network based models and their predictions through visualization and through incorporating uncertainties. 2) To improve the performance of …

The Use Of Evidential Reasoning Model With Biomarkers In Pancreatic Cancer Prediction, Qianhui Fan

Master's Projects

In this project, an evidential reasoning model is built to amalgamate factors that could be used in early detection of pancreatic cancer. Our machine learning model outputs a probability of a given patient having prostate cancer based on various input variables. These variables include health history factors, such as smoking and medical history, technical artifacts, such as biopsy sequencing technology, and genomic biomarkers such as mutational, transcriptional and methylomic profiles, cfDNA, and copy number variation. The dataset used in this project is a part of The Cancer Genome Atlas (TCGA) project and was collected from the National Cancer Institute (NIH) …

Multigrid For The Nonlinear Power Flow Equations, Enrique Pereira Batista

Mathematics Theses and Dissertations

The continuously changing structure of power systems and the inclusion of renewable
energy sources are leading to changes in the dynamics of modern power grid,
which have brought renewed attention to the solution of the AC power flow equations.
In particular, development of fast and robust solvers for the power flow problem
continues to be actively investigated. A novel multigrid technique for coarse-graining
dynamic power grid models has been developed recently. This technique uses an
algebraic multigrid (AMG) coarsening strategy applied to the weighted
graph Laplacian that arises from the power network's topology for the construction
of coarse-grain approximations to …

Metric Learning Via Linear Embeddings For Human Motion Recognition, Byoungdoo Kong

Masters Theses

We consider the application of Few-Shot Learning (FSL) and dimensionality reduction to the problem of human motion recognition (HMR). The structure of human motion has unique characteristics such as its dynamic and high-dimensional nature. Recent research on human motion recognition uses deep neural networks with multiple layers. Most importantly, large datasets will need to be collected to use such networks to analyze human motion. This process is both time-consuming and expensive since a large motion capture database must be collected and labeled. Despite significant progress having been made in human motion recognition, state-of-the-art algorithms still misclassify actions because of characteristics …

Reasoning About User Feedback Under Identity Uncertainty In Knowledge Base Construction, Ariel Kobren

Doctoral Dissertations

Intelligent, automated systems that are intertwined with everyday life---such as Google Search and virtual assistants like Amazon’s Alexa or Apple’s Siri---are often powered in part by knowledge bases (KBs), i.e., structured data repositories of entities, their attributes, and the relationships among them. Despite a wealth of research focused on automated KB construction methods, KBs are inevitably imperfect, with errors stemming from various points in the construction pipeline. Making matters more challenging, new data is created daily and must be integrated with existing KBs so that they remain up-to-date. As the primary consumers of KBs, human users have tremendous potential to …

Understanding The Dynamic Visual World: From Motion To Semantics, Huaizu Jiang

Doctoral Dissertations

We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in …

The Algorithm Project Research And Modeling Of Information Systems, Victoria Kuznetsova, S.B. Dovletova, Mixriddin Raximov, Gulnora Muxtorova, Kim Yelena

Bulletin of TUIT: Management and Communication Technologies

Pre-project research is a strategic stage of the object design process, based on the results of which a decision is made on the level of competitiveness, development prospects, setting a task for the project, labor intensity and feasibilityof creating a system in general.

The existing methods of pre-project research have a high degree of generalization and are practically not formalized in any way. The disadvantage of these methods is that they consider only specific individual prototypes and are aimed at finding solutions to current problems and eliminating individual shortcomings of a particular prototype. Thus, it Is Concluded that It Is …

Machine Learning Model Selection For Predicting Global Bathymetry, Nicholas P. Moran

University of New Orleans Theses and Dissertations

This work is concerned with the viability of Machine Learning (ML) in training models for predicting global bathymetry, and whether there is a best fit model for predicting that bathymetry. The desired result is an investigation of the ability for ML to be used in future prediction models and to experiment with multiple trained models to determine an optimum selection. Ocean features were aggregated from a set of external studies and placed into two minute spatial grids representing the earth's oceans. A set of regression models, classification models, and a novel classification model were then fit to this data and …

A Federated Deep Autoencoder For Detecting Iot Cyber Attacks, Christopher M. Regan

Master of Science in Computer Science Theses

Internet of Things (IoT) devices are mass-produced and rapidly released to the public in a rough state. IoT devices are produced by various companies satisfying various goals, such as monitoring the environment, senor trigger cameras, on-demand electrical switches. These IoT devices are produced by companies to meet a market demand quickly, producing a rough software solution that customers or other enterprises willingly buy with the expectation they will have software updates after production. These IoT devices are often heterogeneous in nature, only to receive updates at infrequently intervals, and can remain out of sight on a home or office network …

Algorithms For Massive, Expensive, Or Otherwise Inconvenient Graphs, David Tench

Doctoral Dissertations

A long-standing assumption common in algorithm design is that any part of the input is accessible at any time for unit cost. However, as we work with increasingly large data sets, or as we build smaller devices, we must revisit this assumption. In this thesis, I present some of my work on graph algorithms designed for circumstances where traditional assumptions about inputs do not apply.
1. Classical graph algorithms require direct access to the input graph and this is not feasible when the graph is too large to fit in memory. For computation on massive graphs we consider the dynamic …

System Design For Digital Experimentation And Explanation Generation, Emma Tosch

Doctoral Dissertations

Experimentation increasingly drives everyday decisions in modern life, as it is considered by some to be the gold standard for determining cause and effect within any system. Digital experiments have expanded the scope and frequency of experiments, which can range in complexity from classic A/B tests to contextual bandits experiments, which share features with reinforcement learning. Although there exists a large body of prior work on estimating treatment effects using experiments, this prior work did not anticipate the new challenges and opportu- nities introduced by digital experimentation. Novel errors and threats to validity arise at the intersection of software and …

Visualization Of Large Networks Using Recursive Community Detection, Xinyuan Fan

Master's Projects

Networks show relationships between people or things. For instance, a person has a social network of friends, and websites are connected through a network of hyperlinks. Networks are most commonly represented as graphs, so graph drawing becomes significant for network visualization. An effective graph drawing can quickly reveal connections and patterns within a network that would be difficult to discern without visual aid. But graph drawing becomes a challenge for large networks. Am- biguous edge crossings are inevitable in large networks with numerous nodes and edges, and large graphs often become a complicated tangle of lines. These issues greatly reduce …

Automatically Classifying Non-Functional Requirements With Feature Extraction And Supervised Machine Learning Techniques, Mahtab Ezzatikarami

Electronic Thesis and Dissertation Repository

Abstract. Context and Motivation: Non-functional requirements (NFRs) of a system need to be classified into different types such as usability, performance, etc. This would enable stakeholders to ensure the completeness of their work by extracting specific NFRs related to their expertise. Question/Problem: Because of the size and complexity of requirement specification documents, the manual classification of NFRs is time-consuming, labour-intensive, and error-prone. We thus need an automated solution that can provide a highly accurate and efficient categorization of NFRs. Principal ideas/results: In this investigation, using natural language processing and supervised machine learning (SML) techniques, we investigate with feature extraction techniques …

Methodological Aspects Of Distance Learning For Developing The Professional Competence Of Students Of The Direction "Computer Engineering, B Kuznetsova, Gulnora Muxtarova, Umida Azimova, Kim Yelena

Bulletin of TUIT: Management and Communication Technologies

This work is based on the use of distance learning technologies in education, which will make it possible to individualize training, and in turn contributes to the formation of professionally important qualities for students of the direction of "Computer Engineering". The experimental work was aimed at developing a technology for the formation of students' professional competence.

The article shows that the mastery by students of knowledge, skills and abilities in the field of computer engineering was aimed at their conscious application in solving problems of the educational and cognitive process, and subsequently in professional activity.

The article presents the results …

A Preliminary Analysis Of How A Software Organization’S Maturity And Size Affect Its Intellectual Property Portfolio, Daniel Gifford

Master of Science in Software Engineering Theses

Intellectual property, commonly known as IP, is complex. The four main types of software IP, which is what this thesis will focus on, are patents, trade secrets, trademarks, and copyright. Patents, trade secrets, and copyrights were all studied by this thesis. Software IP is unique in that it can by copyrighted. Different IP owners, which can be businesses of different types, individuals, and universities, often have different strategies as to how to use their IP portfolio. This thesis studies differences in IP usage between these entities specifically in the field of software. Large and small software companies were analyzed specifically. …

Quantifying Deepfake Detection Accuracy For A Variety Of Natural Settings, Pratikkumar Prajapati

Master's Projects

Deep fakes are videos generated from a starting video of a person where that person's face has been swapped for someone else's. In this report, we describe our work to develop general, deep learning-based models to classify Deep Fake content. Our first experiments involved simple Convolution Neural Network (CNN)-based models where we varied how individual frames from the source video were passed to the CNN. These simple models tended to give low accuracy scores for discriminating fake versus non-fake videos of less than 60%. We then developed three more sophisticated models: one based on choosing test frames, one based on …

Malware Classification Using Lstms, Dennis Dang

Master's Projects

Signature and anomaly based detection have long been quintessential techniques used in malware detection. However, these techniques have become increasingly ineffective as malware becomes more complex. Researchers have therefore turned to deep learning to construct better performing models. In this project, we create four different long-short term memory (LSTM) models and train each model to classify malware by family type. Our data consists of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP) such as word embedding and bidirection LSTMs (biLSTM). We also use convolutional neural networks (CNN). We found that our model consisting of …

Bioinformatics Metadata Extraction For Machine Learning Analysis, Zachary Tom

Master's Projects

Next generation sequencing (NGS) has revolutionized the biological sciences. Today, entire genomes can be rapidly sequenced, enabling advancements in personalized medicine, genetic diseases, and more. The National Center for Biotechnology Information (NCBI) hosts the Sequence Read Archive (SRA) containing vast amounts of valuable NGS data. Recently, research has shown that sequencing errors in conventional NGS workflows are key confounding factors for detecting mutations. Various steps such as sample handling and library preparation can introduce artifacts that affect the accuracy of calling rare mutations. Thus, there is a need for more insight into the exact relationship between various steps of the …

Regional Integration: Physician Perceptions On Electronic Medical Record Use And Impact In South West Ontario, Sadiq Raji

Electronic Thesis and Dissertation Repository

Regional initiatives in the health care context in Canada are typically organized and administered along geographic boundaries or operational units. Regional integration of Electronic Medical Records (EMR) has been continuing across Canadian provinces in recent years, yet the use and impact of regionally integrated EMRs are not routinely assessed and questions remain about their impact on and use in physicians’ practices. Are stated goals of simplifying connections and sharing of electronic health information collected and managed by many health services providers being met? What are physicians’ perspectives on the use and impact of regionally integrated EMR? In this thesis, I …

Applying Front End Compiler Process To Parse Polynomials In Parallel, Amha W. Tsegaye

Electronic Thesis and Dissertation Repository

Parsing large expressions, in particular large polynomial expressions, is an important task for computer algebra systems. Despite of the apparent simplicity of the problem, its efficient software implementation brings various challenges. Among them is the fact that this is a memory bound application for which a multi-threaded implementation is necessarily limited by the characteristics of the memory organization of supporting hardware.

In this thesis, we design, implement and experiment with a multi-threaded parser for large polynomial expressions. We extract parallelism by splitting the input character string, into meaningful sub-strings that can be parsed concurrently before being merged into a single …

Research On Recovering Of Complex Networks Based On Boundary Nodes Of Giant Connected Component, Zhe Wang, Jianhua Li, Kang Dong

Journal of System Simulation

Abstract: Network recovery is an important way to solve the inevitable failure,and the reasonable recovery strategy can reduce the cost of resource and improve the network robustness.In order to study the dynamic behavior of recovery process and the relationship between recovery and network robustness,a Recovery Model of Boundary Nodes (RMBN) based on boundary of giant connected component is proposed,and two network Recovery strategies,Average Recovery of Boundary Nodes (ARBN) strategy and Priority Recovery of Boundary Nodes (PRBN) strategy are designed.The simulation results of different recovery strategies on three network models show that with the increase of recovery ratio,the …

Non-Cooperative Target Feature Point Cloud Registration Optimization Based On Icp Algorithm, Wei Liang, Muyao Xue, Huo Ju, Jinjie Zhang

Journal of System Simulation

Abstract: Aiming at the pose measurement caused by non-cooperative targets in visual measurement that cannot provide cooperation information,the ICP(Iterative Closest Point) algorithm is used to register the point cloud down-sampling data acquired at different times to complete the relative pose measurement of the target.The point cloud data of the target at the current moment is obtained using the structure from motion algorithm and the feature point matching algorithms are compared based on threshold matching and optical flow matching method.The extracted feature points are reconstructed by triangulation.The relative pose changes of the object at different times are calculated by using …

Frequency Regulation Signal Reduction Methods For Aluminum Smelters, Zejian Feng, Shengfei Li, Shouzhen Zhu, Zhiyun Li, Xiaomin Bai

Journal of System Simulation

Abstract: The motion delay of tap changer of aluminum smelter rectifier downgrades frequency response precision in following high frequency regulation signals.Aiming to improve the regulation performance of aluminum smelter loads based on adjusting dynamics,a fast regulation signal reduction technique is proposed,which is motion control threshold-based.A simulation-based decision technique of threshold values of the signal reduction algorithm's is devised to improve the performances of frequency response.Simulation results verify the efficacy of the techniques in promoting the frequency response precision to a certain production level,and contribute a practical way to support aluminum smelter‘s provision of frequency regulation services …

Research On Fuzzy Control And Optimization For Traffic Lights At Single Intersection, Jiajia Liu, Xingquan Zuo

Journal of System Simulation

Abstract: Aiming at the traffic signal control at urban single intersection,a fuzzy control method for traffic lights is presented.The method is based on a four-phase phasing sequence to control the traffic lights at a single intersection.Inputs of the fuzzy controller are the number of vehicles in line and the arrival rate of vehicles,and the output is the green light extension time of the current green light phase.A genetic algorithm (GA) is used to optimize fuzzy rules and membership functions of the fuzzy control system to improve the performance of the fuzzy controller.The fuzzy control method is realized by using …

Research On Dissemination And Control Of Public Opinion Based On Multilayer Coupled Network, Chen Shuai

Journal of System Simulation

Abstract: In order to study the influence of information interaction between multi-platforms on the dissemination and control of public opinion,taking Wechat and Weibo for example,a public opinion communication and control model based on multi-layer coupled network including Wechat layer,Weibo layer and control layer is constructed using multi-agent modeling method and improved SEIR model.On Anylogic platform,a simulation experiment was conducted on the event that “the use of materials by the Hubei Red Cross Society raises doubts”,and the effects factors such as single/dual platform,control range,control dynamics,control time and interaction between platforms were analyzed.The media guidance and government intervention strategies under multi-platform …

Application Of Simulation Technology In Football Overall Attack Training, Xu Nuo, Shuanglong Liu, Fugao Jiang

Journal of System Simulation

Abstract: Simulation technology has a broad prospect in the field of football application.At present,football attack training is mainly based on audio-visual,experience and on-the-spot practice,but it lacks professional scene representation and systematic theoretical support. Visual C++ development platform and simulation programming method are used to virtualize the football training and reproduce the overall attack drill of football under different training scenes.The results show that the simulation technology can achieve more training situations. It can be used as an auxiliary tool for football overall attack training,enrich the training means,help players accurately understand the tactical system,and promote the scientific development of football training.

Human-Computer Interaction Speech Emotion Recognition Based On Random Forest And Convolution Feature Learning, Wang Jing, Hongyan Liu, Fangfang Liu, Qingqing Wang

Journal of System Simulation

Abstract: Focus on the different speech features of different types of people in the automatic speech emotion recognition of emotional robots,a random forest for speech emotion recognition is proposed,and a preliminary simulation experiment of emotional social robot system based on convolution feature learning is carried out.The results show that the emotional robot can track in real time,the seven basic emotions of excitement,anger,sadness,happiness,surprise,fear and neutrality.By using non personalized speech emotion features,the original personalized speech emotion features are supplemented,and the general emotion and special emotion are extracted.For emotional robot,using these indicators has a certain application prospect in the simulation experiment …