Digital Commons Network^™

Accessibility In User Reviews For Mobile Apps: An Automated Detection Approach, Murtaza Tamjeed

Theses

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many restrictions. Recent work demonstrated that user reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this thesis is to support the automated identification of accessibility in user reviews, to help practitioners in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as …

Machine-Assisted Translation By Human-In-The-Loop Crowdsourcing For Bambara, Allahsera Auguste Tapo

Theses

Language is more than a tool of conveying information; it is utilized in all aspects of our lives. Yet only a small number of languages in the 7,000 languages worldwide are highly resourced by human language technologies (HLT). Despite African languages representing over 2,000 languages, only a few African languages are highly resourced, for which there exists a considerable amount of parallel digital data.

We present a novel approach to machine translation (MT) for under-resourced languages by improving the quality of the model using a paradigm called ``humans in the Loop.''

This thesis describes the work carried out to create …

Development And Identification Of Metrics To Predict The Impact Of Dimension Reduction Techniques On Classical Machine Learning Algorithms For Still Highway Images, Wasim Akram Khan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

We are witnessing an influx of data - images, texts, video, etc. Their high dimensionality and large volume make it challenging to apply machine learning to obtain actionable insight. This thesis explores several aspects pertaining to dimensional reduction: dimension reduction methods, metrics to measure distortion, image preprocessing, etc. Faster training and inference time on reduced data and smaller models which can be deployed on commodity hardware are a critical advantage of dimension reduction. For this study, classical machine learning methods were explored owing to their solid mathematical foundation and interpretability.

The dataset used is a time series of images from …

Predictive Insights For Improving The Resilience Of Global Food Security Using Artificial Intelligence, Meng Leong How, Yong Jiet Chan, Sin Mei Cheah

Research Collection Lee Kong Chian School Of Business

Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

School of Public Health Faculty Research

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

Critical Media, Information, And Digital Literacy: Increasing Understanding Of Machine Learning Through An Interdisciplinary Undergraduate Course, Barbara R. Burke, Elena Machkasova

Irish Communication Review

Widespread use of Artificial Intelligence in all areas of today’s society creates a unique problem: algorithms used in decision-making are generally not understandable to those without a background in data science. Thus, those who use out-of-the-box Machine Learning (ML) approaches in their work and those affected by these approaches are often not in a position to analyze their outcomes and applicability.

Our paper describes and evaluates our undergraduate course at the University of Minnesota Morris, which fosters understanding of the main ideas behind ML. With Communication, Media & Rhetoric and Computer Science faculty expertise, students from a variety of majors, …

Identification Of Fatigue-Related Kinematic Changes In Elite Runners Using A Support Vector Machine Approach, Bernd J. Stetter, Felix Möhler, Frieder C. Krafft, Stefan Sell, Thorsten Stein

ISBS Proceedings Archive

Understanding the kinematic changes underlying fatigue is essential in running biomechanics. The aim of this study was to identify fatigue-related kinematic changes in elite runners using a support vector machine approach. Full-body kinematics of thirteen trained runners were recorded in a non-fatigued and a fatigued state during treadmill running at their individual fatigue-speed. A support vector machine was trained and used to identify kinematic differences between the non-fatigued and fatigued state based on principal component scores. Strides during non-fatigued and fatigued running could be separated with 99.4% classification accuracy. Four upper limb (two shoulder and two elbow), four lower limb …

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …

Predicting The Long-Term Stability Of Compact Multiplanet Systems, Daniel Tamayo, Miles Cranmer, Samuel Hadden, Hanno Rein, Peter Battaglia, Alysa Obertas, Philip J. Armitage, Shirley Ho, David N. Spergel, Christian Gilbertson, Naireen Hussain, Ari Silburt, Daniel Jontof-Hutter, Kristen Menou

Pacific Faculty Work

We combine analytical understanding of resonant dynamics in two-planet systems with machine-learning techniques to train a model capable of robustly classifying stability in compact multiplanet systems over long timescales of 109 orbits. Our Stability of Planetary Orbital Configurations Klassifier (SPOCK) predicts stability using physically motivated summary statistics measured in integrations of the first 104 orbits, thus achieving speed-ups of up to 105 over full simulations. This computationally opens up the stability-constrained characterization of multiplanet systems. Our model, trained on_100,000 three-planet systems sampled at discrete resonances, generalizes both to a sample spanning a continuous period-ratio range, as well as to a …

Southwest Pacific Tropical Cyclone Frequency And Intensity Related To Observed And Modeled Geophysical And Aerosol Variables, Rupsa Bhowmick

LSU Doctoral Dissertations

The dissertation focuses on western region of Southwest Pacific Ocean (SWPO)

basin (135E - 180, and 5S - 35S) tropical cyclone (TC) climatology using observed

and modeled data. The classification-based machine learning approach

identifies the synoptic geophysical and aerosol environment favorable or unfavorable

for TC intensification and intensity change prior to landfall incorporating

observational and satellite data. A multiple poisson regression model with varying

temporal monthly lags was used to build a relationship between the number of

monthly TC days with basin wide average dust aerosol optical depth (AOD), sea

surface temperature (SST), and upper ocean temperature (UOT). This idea …

Atmospheric Contrail Detection With A Deep Learning Algorithm, Nasir Siddiqui

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Department of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) …

Automatic Detection Of Dynamic And Static Activities Of The Older Adults Using A Wearable Sensor And Support Vector Machines, Jian Zhang, Rahul Soangra, Thurmon E. Lockhart

Physical Therapy Faculty Articles and Research

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant …

Reconciling Risk And Equality, Christopher Slobogin

Vanderbilt Law School Faculty Publications

States have increasingly resorted to statistically-derived risk algorithms to determine when diversion from prison should occur, whether sentences should be enhanced, and the level of security and treatment a prisoner requires. The federal government has jumped on the bandwagon in a big way with the First Step Act, which mandated that a risk assessment instrument be developed to determine which prisoners can be released early on parole. Policymakers are turning to these algorithms because they are thought to be more accurate and less biased than judges and correctional officials, making them useful tools for reducing prison populations through identification of …

A Nonlinear Distortion Removal Based On Deep Neural Network For Underwater Acoustic Ofdm Communication With The Mitigation Of Peak To Average Power Ratio, Xuefei Ma, Waleed Raza, Zhiqiang Wu, Muhammad Bilal, Ziqi Zhou, Amir Ali

Electrical Engineering Faculty Publications

Machine learning and deep learning algorithms have proved to be a powerful tool for developing data-driven signal processing algorithms for challenging engineering problems. This paper studies the modern machine learning algorithm for modeling nonlinear devices like power amplifiers (PAs) for underwater acoustic (UWA) orthogonal frequency divisional multiplexing (OFDM) communication. The OFDM system has a high peak to average power ratio (PAPR) in the time domain because the subcarriers are added coherently via inverse fast Fourier transform (IFFT). This causes a higher bit error rate (BER) and degrades the performance of the PAs; hence, it reduces the power efficiency. For long-range …

Application Of Predictive Modelling To Improve The Discharge Process In Hospitals, Sayed Hisham, Shahina Abdul Rasheed, Brayal Dsouza

Open Access archive

Objectives: To find out the factors influencing discharge process turnaround time (TAT) and to accurately predict the discharge process TAT. Methods: The discharge process of cardiology department inpatients in a tertiary care hospital was mapped over a month. The likely factors influencing discharge TAT were tested for significance by ANOVA. Multiple linear regression (MLR) was used to predict the TAT. The sample was divided into testing and training sets for regression. A model was generated using the training set and compared with the testing set for accuracy. Results: After a process map was plot-ted, the significant factors influencing the TAT …

Theoretical Investigation Of The Biomass Conversion On Transition Metal Surfaces Based On Density Functional Theory Calculations And Machine Learning, Wenqiang Yang

Theses and Dissertations

During the past decades, heterogenous catalyzed conversion of biomass to hydrocarbons with similar or identical properties to conventional fossil fuels has gained significantly academic and industrial interest. However, the conventional heterogeneous catalysts such as sulfided NiMo/Al₂O₃ and CoMo/Al₂O₃ used have various drawbacks, such as short catalyst lifetime and high sulfur content of product. To overcome the limitations of the conventional sulfided catalysts, new catalysts must be developed, which requires a better understanding of the reaction mechanism of the biomass conversion. Based on density functional theory, in this thesis, we reported a computational calculation study …

Prediction Of Feed Utilization Performance In Clarias Gariepinus Using Multiple Linear Regression In Machine Learning, Adekunle Oluwatosin Familusi

Journal of Bioresource Management

Machine learning models can be used to make predictions about nutrient utilization performance index using available proximate analysis data on feed composition. Data from similar experiments on nutrient utilization performance was used to fit a multiple linear regression model for the prediction of four performance indexes. The Specific Growth Rate and percentage inclusion with strength of 0.57 was noted along with a negative relationship between protein efficiency and protein content. A negative relationship between Nitrogen Free Extract (NFE) and Protein Efficiency Ratio (PER) at NFE content ≥25 % was observed. PER was predicted with 85 % accuracy, while Weight Gain …

Data-Driven And Model-Based Methods With Physics-Guided Machine Learning For Damage Identification, Zhiming Zhang

LSU Doctoral Dissertations

Structural health monitoring (SHM) has been widely used for structural damage diagnosis and prognosis of a wide range of civil, mechanical, and aerospace structures. SHM methods are generally divided into two categories: (1) model-based methods; (2) data-driven methods. Compared with data-driven SHM, model-based methods provide an updated physics-based numerical model that can be used for damage prognosis when long-term data is available. However, the performance of model-based methods is susceptible to modeling error in establishing the numerical model, which is usually unavoidable due to model simplification and omission. The major challenge of data-driven SHM methods lies in data insufficiency, e.g., …

Exploiting Earth Observation Data To Impute Groundwater Level Measurements With An Extreme Learning Machine, Steven Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson

Faculty Publications

Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture …

In Search Of Trustworthy And Transparent Intelligent Systems With Human-Like Cognitive And Reasoning Capabilities, Nikhil R. Pal

Journal Articles

At present we are witnessing a tremendous interest in Artificial Intelligence (AI), particularly in Deep Learning (DL)/Deep Neural Networks (DNNs). One of the reasons appears to be the unmatched performance achieved by such systems. This has resulted in an enormous hope on such techniques and often these are viewed as all—cure solutions. But most of these systems cannot explain why a particular decision is made (black box) and sometimes miserably fail in cases where other systems would not. Consequently, in critical applications such as healthcare and defense practitioners do not like to trust such systems. Although an AI system is …

Literature Review: How U.S. Government Documents Are Addressing The Increasing National Security Implications Of Artificial Intelligence, Bert Chapman

Libraries Faculty and Staff Scholarship and Research

This article emphasizes the increasing importance of artificial intelligence (AI) in military and national security policy making. It seeks to inform interested individuals about the proliferation of publicly accessible U.S. government and military literature on this multifaceted topic. An additional objective of this endeavor is encouraging greater public awareness of and participation in emerging public policy debate on AI's moral and national security implications..

Optimized 3d Reconstruction For Infrastructure Inspection With Automated Structure From Motion And Machine Learning Methods, Samuel Arce Munoz

Theses and Dissertations

Infrastructure monitoring is being transformed by the advancements on remote sensing, unmanned vehicles and information technology. The wide interaction among these fields and the availability of reliable commercial technology are helping pioneer intelligent inspection methods based on digital 3D models. Commercially available Unmanned Aerial Vehicles (UAVs) have been used to create 3D photogrammetric models of industrial equipment. However, the level of automation of these missions remains low. Limited flight time, wireless transfer of large files and the lack of algorithms to guide a UAV through unknown environments are some of the factors that constraint fully automated UAV inspections. This work …

Pathways To The Native Storyteller: A Method To Enable Computational Story Understanding, Aramide O. Kehinde

College of Computing and Digital Media Dissertations

The primary objective of this thesis is to develop a method that uses machine learning algorithms to enable computational story understanding. This research is conducted with the aim of establishing a system called the Native Storyteller that plans and creates storytelling experiences for human users. The paper first establishes the desired capabilities of the system and then deep dives into how to enable story understanding, which is the core ability the system needs to function. As such, the research places emphasis on natural language processing and its application to solving key problems in this context. Namely, machine representation of story …

Self-Supervision Initialization For Semantic Segmentation Networks, Kenneth Alexopoulos

Theses

Convolutional neural networks excel at extracting features from signals. These features are able to be utilized for many downstream tasks. These tasks include object recognition, object detection, depth estimation, pixel level semantic segmentation, and more. These tasks can be used for applications such as autonomous driving where images captured by a camera can be used to give a detailed understanding of the scene. While these models are impressive, they can fail to generalize to new environments. This forces the cumbersome process of collecting images from multifarious environments and annotating them by hand. Annotating thousands or millions of images is both …

Conference Roundup: Smart Cataloging - Beginning The Move From Batch Processing To Automated Classification, Rachel S. Evans

Articles, Chapters and Online Publications

This article reviewed the Amigos Online Conference titled “Work Smarter, Not Harder: Innovating Technical Services Workflows” keynote session delivered by Dr. Terry Reese on February 13, 2020. Excerpt:

"As the developer of MarcEdit, a popular metadata suite used widely across the library community, Reese’s current work is focused on the ways in which libraries might leverage semantic web techniques in order to transform legacy library metadata into something new. So many sessions related to using new technologies in libraries or academia, although exciting, are not practical enough to put into everyday use by most librarians. Reese’s keynote, titled Smart Cataloging: …

A Unified Decentralized Trust Framework For Detection Of Iot Device Attacks In Smart Homes, Hussein Salim Qasim Alsheakh

Dissertations

Trust in Smart Home technology security is a primary concern for consumers, which can prevent them from adopting smart home services. Such concerns are due to following reasons; (i) nature of IoT devices– which due to their limited computational and resource capabilities, cannot support traditional on-device security controls (ii) any breach to cyber-attacks have an immediate impact on the smart homeowner, compared to traditional cyber-attacks (iii) a large variety of different applications and services under the umbrella of make an overarching security framework for smart homes fundamentally challenging for both providers to offer and owners to manage.

This dissertation offers …

Free Space Detection And Trajectory Planning For Autonomous Robot, Zachary Ross Winger

Computer Science and Software Engineering

Autonomous robots need to know what is around them and where it is safe for them to move to. Because having this ability is so important, Dr. Seng and myself have created a model to predict the free space in front of his autonomous robot, Herbie. We then use this prediction to enforce a driving policy to ensure Herbie drives around safely.

Integrated Framework For Data Quality And Security Evaluation On Mobile Devices, Igor Khokhlov

Theses

Data quality (DQ) is an important concept that is used in the design and employment of information, data management, decision making, and engineering systems with multiple applications already available for solving specific problems. Unfortunately, conventional approaches to DQ evaluation commonly do not pay enough attention or even ignore the security and privacy of the evaluated data. In this research, we develop a framework for the DQ evaluation of the sensor originated data acquired from smartphones, that incorporates security and privacy aspects into the DQ evaluation pipeline. The framework provides support for selecting the DQ metrics and implementing their calculus by …

A Machine Learning Approach For Vulnerability Curation, Yang Chen, Andrew E. Santosa, Ming Yi Ang, Abhishek Sharma, Asankhaya Sharma, David Lo

Research Collection School Of Computing and Information Systems

Software composition analysis depends on database of open-source library vulerabilities, curated by security researchers using various sources, such as bug tracking systems, commits, and mailing lists. We report the design and implementation of a machine learning system to help the curation by by automatically predicting the vulnerability-relatedness of each data item. It supports a complete pipeline from data collection, model training and prediction, to the validation of new models before deployment. It is executed iteratively to generate better models as new input data become available. We use self-training to significantly and automatically increase the size of the training dataset, opportunistically …

Using Machine Learning To Optimize Predictive Models Used For Big Data Analytics In Various Sports Events, Akhil Kumar Gour

Master's Projects

In today’s world, data is growing in huge volume and type day by day. Historical data can hence be leveraged to predict the likelihood of the events which are to occur in the future. This process of using statistical or any other form of data to predict future outcomes is commonly termed as predictive modelling. Predictive modelling is becoming more and more important and is trending because of several reasons. But mainly, it enables businesses or individual users to gain accurate insights and allows to decide suitable actions for a profitable outcome.

Machine learning techniques are generally used in order …