Computer Engineering Commons^™

Secured Data Masking Framework And Technique For Preserving Privacy In A Business Intelligence Analytics Platform, Osama Ali

Electronic Thesis and Dissertation Repository

The main concept behind business intelligence (BI) is how to use integrated data across different business systems within an enterprise to make strategic decisions. It is difficult to map internal and external BI’s users to subsets of the enterprise’s data warehouse (DW), resulting that protecting the privacy of this data while maintaining its utility is a challenging task. Today, such DW systems constitute one of the most serious privacy breach threats that an enterprise might face when many internal users of different security levels have access to BI components. This thesis proposes a data masking framework (iMaskU: Identify, Map, Apply, …

A Scalable, Chunk-Based Slicer For Cooperative 3d Printing, Jace J. Mcpherson

Computer Science and Computer Engineering Undergraduate Honors Theses

Cooperative 3D printing is an emerging technology that aims to increase the 3D printing speed and to overcome the size limit of the printable object by having multiple mobile 3D printers (printhead-carrying mobile robots) work together on a single print job on a factory floor. It differs from traditional layer-by-layer 3D printing due to requiring multiple mobile printers to work simultaneously without interfering with each other. Therefore, a new approach for slicing a digital model and generating commands for the mobile printers is needed, which has not been discussed in literature before. We propose a chunk-by-chunk based slicer that divides …

A Validation Study Of Time Series Data Forecasting Using Neural Networks, Marco Martinez, Jeremy Evert

Student Research

Artificial Intelligence(AI) is a growing topic in Computer Science and has many uses in real world applications. One application is using Al, or more specifically Neural Networks to model data and predict outcomes. Neural Networks have been used in the past to predict weather changes, create facial recognition software , and to create self-driving cars. Our project is a validation study of, “Modeling Time Series Data With Deep Fourier Neural Networks” by Gashler and Ashmore, 2016. Here we show that a neural network can be trained to be an effective predictor of weather patterns in Alaska over several years. Our …

Web-Based Archaeology And Collaborative Research, Fabrizio Galeazzi, Heather Richards-Rissetto

Department of Anthropology: Faculty Publications

While digital technologies have been part of archaeology for more than fifty years, archaeologists still look for more efficient methodologies to integrate digital practices of fieldwork recording with data management, analysis, and ultimately interpretation.This Special Issue of the Journal of Field Archaeology gathers international scholars affiliated with universities, organizations, and commercial enterprises working in the field of Digital Archaeology. Our goal is to offer a discussion to the international academic community and practitioners. While the approach is interdisciplinary, our primary audience remains readers interested in web technology and collaborative platforms in archaeology

Plataforma Computacional Para El Tratamiento De Habilidades Cognitivas Para Niños Con Autismo, Elkin Leandro Rodriguez Zamudio

Ingeniería en Automatización

En el año 2013 se publicó una nueva versión del manual diagnóstico y estadístico para las enfermedades mentales DSM-V (Manual diagnóstico y estadístico de los trastornos mentales). Entre otros cambios que no son motivo de este trabajo la nueva versión Fusiona varios de los trastornos generalizados del desarrollo, trastorno generalizado del desarrollo no especificado, y el síndrome de asperger en lo que ahora se conoce como ‘trastorno del espectro autista’ (TEA), el cual es un conjunto de alteraciones heterogéneas a nivel del neuro-desarrollo que normalmente inicia desde la infancia y que implica alteraciones en la comunicación e interacción social, también …

Advanced Recurrent Network-Based Hybrid Acoustic Models For Low Resource Speech Recognition, Jian Kang, Wei-Qiang Zhang, Wei-Wei Liu, Jia Liu, Michael T. Johnson

Electrical and Computer Engineering Faculty Publications

Recurrent neural networks (RNNs) have shown an ability to model temporal dependencies. However, the problem of exploding or vanishing gradients has limited their application. In recent years, long short-term memory RNNs (LSTM RNNs) have been proposed to solve this problem and have achieved excellent results. Bidirectional LSTM (BLSTM), which uses both preceding and following context, has shown particularly good performance. However, the computational requirements of BLSTM approaches are quite heavy, even when implemented efficiently with GPU-based high performance computers. In addition, because the output of LSTM units is bounded, there is often still a vanishing gradient issue over multiple layers. …

A Study Of Scalability And Cost-Effectiveness Of Large-Scale Scientific Applications Over Heterogeneous Computing Environment, Arghya K. Das

LSU Doctoral Dissertations

Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data.

In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures …

Roborodentia Robot: Treadbot, Stephen C. Schmidt

Computer Science and Software Engineering

This document is a summary of my contest entry to the 2018 Cal Poly Roborodentia competition. It is meant to be a process overview and design outline of the mechanical, electrical, and software components of my robot.

Vehicle Pseudonym Association Attack Model, Pierson Yieh

Master's Theses

With recent advances in technology, Vehicular Ad-hoc Networks (VANETs) have grown in application. One of these areas of application is Vehicle Safety Communication (VSC) technology. VSC technology allows for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that enhance vehicle safety and driving experience. However, these newly developing technologies bring with them a concern for the vehicular privacy of drivers. Vehicles already employ the use of pseudonyms, unique identifiers used with signal messages for a limited period of time, to prevent long term tracking. But can attackers still attack vehicular privacy even when vehicles employ a pseudonym change strategy? The major contribution …

Automated Pruning Of Greenhouse Indeterminate Tomato Plants, Joey M. Angeja

Master's Theses

Pruning of indeterminate tomato plants is vital for a profitable yield and it still remains a manual process. There has been research in automated pruning of grapevines, trees, and other plants, but tomato plants have yet to be explored. Wage increases are contributing to the depleting profits of greenhouse tomato farmers. Rises in population are the driving force behind the need for efficient growing techniques. The major contribution of this thesis is a computer vision algorithm for detecting greenhouse tomato pruning points without the use of depth sensors. Given an up-close 2-D image of a tomato stem with the background …

Artificial Neural Network-Based Robotic Control, Justin Ng

Master's Theses

Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.

Towards Autonomous Localization Of An Underwater Drone, Nathan Sfard

Master's Theses

Autonomous vehicle navigation is a complex and challenging task. Land and aerial vehicles often use highly accurate GPS sensors to localize themselves in their environments. These sensors are ineffective in underwater environments due to signal attenuation. Autonomous underwater vehicles utilize one or more of the following approaches for successful localization and navigation: inertial/dead-reckoning, acoustic signals, and geophysical data. This thesis examines autonomous localization in a simulated environment for an OpenROV Underwater Drone using a Kalman Filter. This filter performs state estimation for a dead reckoning system exhibiting an additive error in location measurements. We evaluate the accuracy of this Kalman …

Resource Brokering In Grid Computing, Adrian T. Bienkowski

Electronic Thesis and Dissertation Repository

Grid Computing has emerged in the academia and evolved towards the bases of what is currently known as Cloud Computing and Internet of Things (IoT). The vast collection of resources that provide the nature for Grid Computing environment is very complex; multiple administrative domains control access and set policies to the shared computing resources. It is a decentralized environment with geographically distributed computing and storage resources, where each computing resource can be modeled as an autonomous computing entity, yet collectively can work together. This is a class of Cooperative Distributed Systems (CDS). We extend this by applying characteristic of open …

Design Of A Distributed Real-Time E-Health Cyber Ecosystem With Collective Actions: Diagnosis, Dynamic Queueing, And Decision Making, Yanlin Zhou

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we develop a framework for E-health Cyber Ecosystems, and look into different involved actors. The three interested parties in the ecosystem including patients, doctors, and healthcare providers are discussed in 3 different phases. In Phase 1, machine-learning based modeling and simulation analysis is performed to remotely predict a patient's risk level of having heart diseases in real time. In Phase 2, an online dynamic queueing model is devised to pair doctors with patients having high risk levels (diagnosed in Phase 1) to confirm the risk, and provide help. In Phase 3, a decision making paradigm is proposed …

The 3d Abstract Tile Assembly Model Is Intrinsically Universal, Aaron Koch, Daniel Hader, Matthew J. Patitz

Computer Science and Computer Engineering Undergraduate Honors Theses

In this paper, we prove that the three-dimensional abstract Tile Assembly Model (3DaTAM) is intrinsically universal. This means that there is a universal tile set in the 3DaTAM which can be used to simulate any 3DaTAM system. This result adds to a body of work on the intrinsic universality of models of self-assembly, and is specifically motivated by a result in FOCS 2016 showing that any intrinsically universal tile set for the 2DaTAM requires nondeterminism (i.e. undirectedness) even when simulating directed systems. To prove our result we have not only designed, but also fully implemented what we believe to be …

Handwritten Digit Recognition By Multi-Class Support Vector Machines, Yu Wang

MSU Graduate Theses

Support Vector Machine(SVM) is a widely-used tool for pattern classification problems. The main idea behind SVM is to separate two different groups with a hyperplane which makes the margin of these two groups maximized. It doesn't require any knowledge about the object we are focused on, since it can catch the features automatically. The idea of SVM can be easily generalized to nonlinear model by a mapping from the original space to a high-dimensional feature space, and they construct a max-margin linear classifier in the high dimensional feature space.

This thesis will investigate the basic idea of SVM and apply …

Early Alert Of At-Risk Students: An Ontology-Driven Framework, Elias S. Lopez

Electrical and Computer Engineering ETDs

As higher education continues to adapt to the constantly shifting conditions that society places on institutions, the enigma of student attrition continues to trouble universities. Early alerts for students who are at-risk academically have been introduced as a method for solving student attrition at these institutions. Early alert systems are designed to provide students who are academically at-risk a prompt indication so that they may correct their performance and make progress towards successful semester completion. Many early alert systems have been introduced and implemented at various institutions with varying levels of success. Currently, early alert systems employ different techniques for …

Library Awesome Sauce Undergraduate Research, Jeremy Evert, Phillip Joe Fitzsimmons, Hector Lucas

Faculty Articles & Research

Library Awesome Sauce Undergraduate Research was a presentation at the 2018 CADRE Conference in Stillwater, OK. The presenters discussed their collaboration on a video project to film interviews of students giving progress reports about their software engineering projects. The videos were posted on the institutional repository.

The speakers discussed Student-Led research and the role that academic libraries play in facilitating student and faculty research and publishing for all disciplines on campus.

Image Processing Applications In Real Life: 2d Fragmented Image And Document Reassembly And Frequency Division Multiplexed Imaging, Houman Kamran Habibkhani

LSU Doctoral Dissertations

In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied.

In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the …

Affective Computing For Emotion Detection Using Vision And Wearable Sensors, Alphonsus Keary

PhDs

The research explores the opportunities, challenges, limitations, and presents advancements in computing that relates to, arises from, or deliberately influences emotions (Picard, 1997). The field is referred to as Affective Computing (AC) and is expected to play a major role in the engineering and development of computationally and cognitively intelligent systems, processors and applications in the future. Today the field of AC is bolstered by the emergence of multiple sources of affective data and is fuelled on by developments under various Internet of Things (IoTs) projects and the fusion potential of multiple sensory affective data streams. The core focus of …

Reconocimiento Automático De Partituras Para Guitarra Por Medio De Visión Artificial Y Redes Neuronales Artificiales, Jeferson Camilo Varela Cañón

Ingeniería en Automatización

Cuando se comienza a tomar lecciones de guitarra, el primer elemento de aprendizaje son las acordes básicos (Do, Re, Mi, Fa, Sol, La, Si) o notas naturales. La tablatura es el siguiente paso donde explícitamente se indica la posición de los dedos en el diapasón, este método es sencillo pero carece de información al momento de interpretar; por ello es necesario leer la partitura musical, siendo una tarea complicada para muchas personas que hasta ahora inician con el aprendizaje retrasando así el mismo; mediante el reconocimiento de imágenes, algoritmo OCR(Optical Carácter Recognition) y RNA (Redes Neuronales Artificiales) se desarrolló una …

Examining A Hate Speech Corpus For Hate Speech Detection And Popularity Prediction, Filip Klubicka, Raquel Fernandez

Other resources

As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other …

Is It Worth It? Budget-Related Evaluation Metrics For Model Selection, Filip Klubicka, Giancarlo Salton, John D. Kelleher

Conference papers

Projects that set out to create a linguistic resource often do so by using a machine learning model that pre-annotates or filters the content that goes through to a human annotator, before going into the final version of the resource. However, available budgets are often limited, and the amount of data that is available exceeds the amount of annotation that can be done. Thus, in order to optimize the benefit from the invested human work, we argue that the decision on which predictive model one should employ depends not only on generalized evaluation metrics, such as accuracy and F-score, but …

The Rhetoric Of Science Education And Technology, Iwasan D. Kejawa

School of Computing: Faculty Publications

Nearly thousands of science experiments are performed both on humans and animals every year in the United States (Gregory, 1999). Does Science enormously play a role in the well-beings of individual in the society? Research has found that science education is through motivation and satisfying the needs of humans. The scientific world is part of an elongated human development. This can be substantiated with the use and evolution of TECHNOLOGY and SCIENCE (Minton, 2004). Education of the entities that comprise the need to achieve the goal of TECHNOLOGY and SCIENCE which are important issues of today. Research has shown that …

Adapt At Semeval-2018 Task 9: Skip-Gram Word Embeddings For Unsupervised Hypernym Discovery In Specialised Corpora, Alfredo Maldonado, Filip Klubicka

Other resources

This paper describes a simple but competitive unsupervised system for hypernym discovery. The system uses skip-gram word embeddings with negative sampling, trained on specialised corpora. Candidate hypernyms for an input word are predicted based on cosine similar- ity scores. Two sets of word embedding mod- els were trained separately on two specialised corpora: a medical corpus and a music indus- try corpus. Our system scored highest in the medical domain among the competing unsu- pervised systems but performed poorly on the music industry domain. Our approach does not depend on any external data other than raw specialised corpora.

Quantitative Fine-Grained Human Evaluation Of Machine Translation Systems: A Case Study On English To Croatian, Filip Klubicka, Antonio Toral, Victor Manuel Sanchez-Cartagena

Articles

This paper presents a quantitative fine-grained manual evaluation approach to comparing the performance of different machine translation (MT) systems. We build upon the well-established Multidimensional Quality Metrics (MQM) error taxonomy and implement a novel method that assesses whether the differences in performance for MQM error types between different MT systems are statistically significant. We conduct a case study for English-to- Croatian, a language direction that involves translating into a morphologically rich language, for which we compare three MT systems belonging to different paradigms: pure phrase-based, factored phrase-based and neural. First, we design an MQM-compliant error taxonomy tailored to the relevant …