Physical Sciences and Mathematics Commons^™

Nearest Centroid: A Bridge Between Statistics And Machine Learning, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

In order to guide our students of machine learning in their statistical thinking, we need conceptually simple and mathematically defensible algorithms. In this paper, we present the Nearest Centroid algorithm (NC) algorithm as a pedagogical tool, combining the key concepts behind two foundational algorithms: K-Means clustering and K Nearest Neighbors (k- NN). In NC, we use the centroid (as defined in the K-Means algorithm) of the observations belonging to each class in our training data set and its distance from a new observation (similar to k-NN) for class prediction. Using this obvious extension, we will illustrate how the concepts of …

Walls Have Ears: Eavesdropping User Behaviors Via Graphics-Interrupt-Based Side Channel, Haoyu Ma, Jianwen Tian, Debin Gao, Jia Chunfu

Research Collection School Of Computing and Information Systems

Graphics Processing Units (GPUs) are now playing a vital role in many devices and systems including computing devices, data centers, and clouds, making them the next target of side-channel attacks. Unlike those targeting CPUs, existing side-channel attacks on GPUs exploited vulnerabilities exposed by application interfaces like OpenGL and CUDA, which can be easily mitigated with software patches. In this paper, we investigate the lower-level and native interface between GPUs and CPUs, i.e., the graphics interrupts, and evaluate the side channel they expose. Being an intrinsic profile in the communication between a GPU and a CPU, the pattern of graphics interrupts …

Unsupervised Structural Graph Node Representation Learning, Mikel Joaristi

Boise State University Theses and Dissertations

Unsupervised Graph Representation Learning methods learn a numerical representation of the nodes in a graph. The generated representations encode meaningful information about the nodes' properties, making them a powerful tool for tasks in many areas of study, such as social sciences, biology or communication networks. These methods are particularly interesting because they facilitate the direct use of standard Machine Learning models on graphs. Graph representation learning methods can be divided into two main categories depending on the information they encode, methods preserving the nodes connectivity information, and methods preserving nodes' structural information. Connectivity-based methods focus on encoding relationships between nodes, …

Hierarchical Aggregation Of Multidimensional Data For Efficient Data Mining, Safaa Khalil Alwajidi

Dissertations

Big data analysis is essential for many smart applications in areas such as connected healthcare, intelligent transportation, human activity recognition, environment, and climate change monitoring. Traditional data mining algorithms do not scale well to big data due to the enormous number of data points and the velocity of their generation. Mining and learning from big data need time and memory efficiency techniques, albeit the cost of possible loss in accuracy. This research focuses on the mining of big data using aggregated data as input. We developed a data structure that is to be used to aggregate data at multiple resolutions. …

In The Margins: Reconsidering The Range And Contribution Of Diazotrophs In Nearshore Environments, Corday R. Selden

OES Theses and Dissertations

Dinitrogen (N₂) fixation enables primary production and, consequently, carbon dioxide drawdown in nitrogen (N) limited marine systems, exerting a powerful influence over the coupled carbon and N cycles. Our understanding of the environmental factors regulating its distribution and magnitude are largely based on the range and sensitivity of one genus, Trichodesmium. However, recent work suggests that the niche preferences of distinct diazotrophic (N₂ fixing) clades differ due to their metabolic and ecological diversity, hampering efforts to close the N budget and model N₂ fixation accurately. Here, I explore the range of N₂ fixation …

Enhanced Traffic Incident Analysis With Advanced Machine Learning Algorithms, Zhenyu Wang

Computational Modeling & Simulation Engineering Theses & Dissertations

Traffic incident analysis is a crucial task in traffic management centers (TMCs) that typically manage many highways with limited staff and resources. An effective automatic incident analysis approach that can report abnormal events timely and accurately will benefit TMCs in optimizing the use of limited incident response and management resources. During the past decades, significant efforts have been made by researchers towards the development of data-driven approaches for incident analysis. Nevertheless, many developed approaches have shown limited success in the field. This is largely attributed to the long detection time (i.e., waiting for overwhelmed upstream detection stations; meanwhile, downstream stations …

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak

USF Tampa Graduate Theses and Dissertations

The commercial platforms that use recommender systems can collect relevant information to produce useful recommendations to the platform users. However, these sources usually contain missing values, imbalanced and heterogeneous data, and noisy observations. Such characteristics render the process of exploiting the information nontrivial, as one should carefully address them during the data fusion process. In addition to the degenerative characteristics, some entries can be fake, i.e., they can be the outcomes of malicious intents to manipulate the system. These entries should be eliminated before incorporation to any recommendation task. Detecting such malicious attacks quickly and accurately and then mitigating them …

Using Data Analytics To Predict Students Score, Nang Laik Ma, Gim Hong Chua

Research Collection School Of Computing and Information Systems

Education is very important to Singapore, and the government has continued to invest heavily in our education system to become one of the world-class systems today. A strong foundation of Science, Technology, Engineering, and Mathematics (STEM) was what underpinned Singapore's development over the past 50 years. PISA is a triennial international survey that evaluates education systems worldwide by testing the skills and knowledge of 15-year-old students who are nearing the end of compulsory education. In this paper, the authors used the PISA data from 2012 and 2015 and developed machine learning techniques to predictive the students' scores and understand the …

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets And Machine Learning, S. Majumdar, Ryan G. Smith, J. J. Butler, V. Lakshmi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests -- …

Machine Learning Integrated Design For Additive Manufacturing, Jingchao Jiang, Yi Xiong, Zhiyuan Zhang, David W. Rosen

Research Collection School Of Computing and Information Systems

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and …

Base-Package Recommendation Framework Based On Consumer Behaviours In Iptv Platform, Kuruparan Shanmugalingam, Ruwinda Ranganayanke, Chanka Gunawardhaha, Rajitha Navarathna

Research Collection School Of Computing and Information Systems

Internet Protocol TeleVision (IPTV) provides many services such as live television streaming, time-shifted media, and Video On Demand (VOD). However, many customers do not engage properly with their subscribed packages due to a lack of knowledge and poor guidance. Many customers fail to identify the proper IPTV service package based on their needs and to utilise their current package to the maximum. In this paper, we propose a base-package recommendation model with a novel customer scoring-meter based on customers behaviour. Initially, our paper describes an algorithm to measure customers engagement score, which illustrates a novel approach to track customer engagement …

A New Efficient Method To Detect Genetic Interactions For Lung Cancer Gwas, Jennifer Luyapan, Xuemei Ji, Siting Li, Xiangjun Xiao, Dakai Zhu, Eric J. Duell, David C. Christiani, Matthew B. Schabath, Susanne M. Arnold, Shanbeh Zienolddiny, Hans Brunnström, Olle Melander, Mark D. Thornquist, Todd A. Mackenzie, Christopher I. Amos, Jiang Gui

Markey Cancer Center Faculty Publications

BACKGROUND: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of …

Exploring The Potential Of Sparse Coding For Machine Learning, Sheng Yang Lundquist

Dissertations and Theses

While deep learning has proven to be successful for various tasks in the field of computer vision, there are several limitations of deep-learning models when compared to human performance. Specifically, human vision is largely robust to noise and distortions, whereas deep learning performance tends to be brittle to modifications of test images, including being susceptible to adversarial examples. Additionally, deep-learning methods typically require very large collections of training examples for good performance on a task, whereas humans can learn to perform the same task with a much smaller number of training examples.

In this dissertation, I investigate whether the use …

Applications Of Ai In Business, Industry, Government, Healthcare, And Environment, University Of Maine Artificial Intelligence Initiative

General University of Maine Publications

UMaine AI draws top talent and leverages a distinctive set of capabilities from the University of Maine and other collaborating institutions from across Maine and beyond, while it also recruits world-class talent from across the nation and the world. It is centered at the University of Maine, leveraging the university’s strengths across disciplines, including computing and information sciences, engineering, health and life sciences, business, education, social sciences, and more.

Asymptotically-Optimal Topological Nearest-Neighbor Filtering, Read Sandström, Jory Denny, Nancy M. Amato

Department of Math & Statistics Faculty Publications

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to a significant computational bottleneck for problems which require many configurations to find a solution. In this work, we develop a method of mapping configurations of a jointed robot to neighborhoods in the workspace that supports fast search for configurations in nearby neighborhoods. This expedites nearest-neighbor search by locating a small set of the most likely candidates for connecting to the query with a local plan. We show that this filtering technique can preserve asymptotically-optimal …

The Future Of Work Now: Automl At 84.51°And Kroger, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon.

Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn

Research Collection School Of Computing and Information Systems

Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware. …

Co2vec: Embeddings Of Co-Ordered Networks Based On Mutual Reinforcement, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across …

European Floating Strike Lookback Options: Alpha Prediction And Generation Using Unsupervised Learning, Tristan Lim, Aldy Gunawan, Chin Sin Ong

Research Collection School Of Computing and Information Systems

This research utilized the intrinsic quality of European floating strike lookback call options, alongside selected return and volatility parameters, in a K-means clustering environment, to recommend an alpha generative trading strategy. The result is an elegant easy-to-use alpha strategy based on the option mechanisms which identifies investment assets with high degree of significance. In an upward trending market, the research had identified European floating strike lookback call option as an evaluative criterion and investable asset, which would both allow investors to predict and profit from alpha opportunities. The findings will be useful for (i) buy-side investors seeking alpha generation and/or …

Integrated Multiparametric Radiomics And Informatics System For Characterizing Breast Tumor Characteristics With The Oncotypedx Gene Assay, Michael A. Jacobs, Christopher B. Umbricht, Vishwa S. Parekh, Riham H. El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff

Radiology Faculty Publications

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained …

A Big Data Lake For Multilevel Streaming Analytics, Ruoran Liu, Haruna Isah, Farhana Zulkernine

Publications and Scholarship

Large organizations are seeking to create new architectures and scalable platforms to effectively handle data management challenges due to the explosive nature of data rarely seen in the past. These data management challenges are largely posed by the availability of streaming data at high velocity from various sources in multiple formats. The changes in data paradigm have led to the emergence of new data analytics and management architecture. This paper focuses on storing high volume, velocity and variety data in the raw formats in a data storage architecture called a data lake. First, we present our study on the limitations …

Semantic-Driven Unsupervised Image-To-Image Translation For Distinct Image Domains, Wesley Ackerman

Theses and Dissertations

We expand the scope of image-to-image translation to include more distinct image domains, where the image sets have analogous structures, but may not share object types between them. Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains (SUNIT) is built to more successfully translate images in this setting, where content from one domain is not found in the other. Our method trains an image translation model by learning encodings for semantic segmentations of images. These segmentations are translated between image domains to learn meaningful mappings between the structures in the two domains. The translated segmentations are then used as the basis …

Developing Employment Environments Where Individuals With Asd Thrive: Using Machine Learning To Explore Employer Policies And Practices, Amy Jane Griffiths, Amy E. Hurley Hanson, Cristina M. Giannantonio, Sneha Kohli Mathur, Kayleigh Hyde, Erik Linstead

Education Faculty Articles and Research

An online survey instrument was developed to assess employers’ perspectives on hiring job candidates with Autism Spectrum Disorder (ASD). The investigators used K-means clustering to categorize companies in clusters based on their hiring practices related to individuals with ASD. This methodology allowed the investigators to assess and compare the various factors of businesses that successfully hire employees with ASD versus those that do not. The cluster analysis indicated that company structures, policies and practices, and perceptions, as well as the needs of employers and employees, were important in determining who would successfully hire individuals with ASD. Key areas that require …

Exploring The Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, And Autism Quotient To Identify Eating Disorder Vulnerability: A Cluster Analysis, Natalia Stewart Rosenfield, Erik Linstead

Engineering Faculty Articles and Research

Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder …

Embedded Power Optimization Method Based On User Behavior, Wang Hai, Gao Ling, Dongqi Chen, Ren Jie

Journal of System Simulation

Abstract: In recent years, with the rapid development of embedded device represented by mobile phone and tablet computer, low power technology has been one of the hotspots in the embedded research field. Because the battery capacity of embedded device is limited due to its restricted volume and weight, there are often users suffering the problem that their phone battery being dead. There are many research directions in embedded low power field at present. The relationship between low power and user behavior recognition was aimed, which started with recognizing user behavior using machine learning and then obtains the user’s daily usage …

Volcano Video Data Characterized And Classified Using Computer Vision And Machine Learning Algorithms, Alex J. C. Witsil, Jeffrey B. Johnson

Geosciences Faculty Publications and Presentations

Video cameras are common at volcano observatories, but their utility is often limited during periods of crisis due to the large data volume from continuous acquisition and time requirements for manual analysis. For cameras to serve as effective monitoring tools, video frames must be synthesized into relevant time series signals and further analyzed to classify and characterize observable activity. In this study, we use computer vision and machine learning algorithms to identify periods of volcanic activity and quantify plume rise velocities from video observations. Data were collected at Villarrica Volcano, Chile from two visible band cameras located ~17 km from …

A Hybrid Framework Using A Qubo Solver For Permutation-Based Combinatorial Optimization, Siong Thye Goh, Sabrish Gopalakrishnan, Jianyuan Bo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial problems effectively using a high-performance quadratic unconstrained binary optimization (QUBO) solver. To do so, transformations are required to change a constrained optimization model to an unconstrained model that involves parameter tuning. We propose techniques to overcome the challenges in using a QUBO solver that typically comes with limited numbers of bits. First, to smooth the energy landscape, we reduce the magnitudes of the input without compromising optimality. We propose a machine learning approach to tune the parameters for good performance effectively. To handle possible infeasibility, we introduce …

Joint 1d And 2d Neural Networks For Automatic Modulation Recognition, Luis M. Rosario Morel

Theses and Dissertations

The digital communication and radar community has recently manifested more interest in using data-driven approaches for tasks such as modulation recognition, channel estimation and distortion correction. In this research we seek to apply an object detector for parameter estimation to perform waveform separation in the time and frequency domain prior to classification. This enables the full automation of detecting and classifying simultaneously occurring waveforms. We leverage a lD ResNet implemented by O'Shea et al. in [1] and the YOLO v3 object detector designed by Redmon et al. in [2]. We conducted an in depth study of the performance of these …

London Heathrow Airport Uses Real-Time Analytics For Improving Operations, Xiaojia Guo, Yael Grushka-Cockayne, Bert De Reyck

Research Collection Lee Kong Chian School Of Business

Improving airport collaborative decision making is at the heart of airport operations centers (APOCs) recently established in several major European airports. In this paper, we describe a project commissioned by Eurocontrol, the organization in charge of the safety and seamless flow of European air traffic. The project’s goal was to examine the opportunities offered by the colocation and real-time data sharing in the APOC at London’s Heathrow airport, arguably the most advanced of its type in Europe. We developed and implemented a pilot study of a real-time data-sharing and collaborative decision-making process, selected to improve the efficiency of Heathrow’s operations. …