Computer Engineering Commons^™

R2gan: Cross-Modal Recipe Retrieval With Generative Adversarial Network, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Yanbin Hao

Research Collection School Of Computing and Information Systems

Representing procedure text such as recipe for crossmodal retrieval is inherently a difficult problem, not mentioning to generate image from recipe for visualization. This paper studies a new version of GAN, named Recipe Retrieval Generative Adversarial Network (R2GAN), to explore the feasibility of generating image from procedure text for retrieval problem. The motivation of using GAN is twofold: learning compatible cross-modal features in an adversarial way, and explanation of search results by showing the images generated from recipes. The novelty of R2GAN comes from architecture design, specifically a GAN with one generator and dual discriminators is used, which makes the …

Robot Navigation In Cluttered Environments With Deep Reinforcement Learning, Ryan Weideman

Master's Theses

The application of robotics in cluttered and dynamic environments provides a wealth of challenges. This thesis proposes a deep reinforcement learning based system that determines collision free navigation robot velocities directly from a sequence of depth images and a desired direction of travel. The system is designed such that a real robot could be placed in an unmapped, cluttered environment and be able to navigate in a desired direction with no prior knowledge. Deep Q-learning, coupled with the innovations of double Q-learning and dueling Q-networks, is applied. Two modifications of this architecture are presented to incorporate direction heading information that …

Health And Wellness Information System, Monica Rangel

Electronic Theses, Projects, and Dissertations

The greatest wealth is health. It is sometimes said your health is a function of what you are not doing, not what you are currently doing. The degree to which individuals can attain, process, and comprehend the necessary health information and services they need to make proper health decisions is vital for optimal health and well-being.

This project documents the analysis, design, development, and implementation of a prototype web-based data-driven health & wellness system targeted for college students. The architecture for this system uses business intelligence to develop a smart online platform for real-time analysis based on inputs entered by …

Security Practices: Keeping Individuals Safe And Aware In The Cyber World, Annie Respicio

Electronic Theses, Projects, and Dissertations

We currently live in a day and age where nearly everyone uses electronic devices and connects to the web. Whether it be from a desktop, laptop, or smartphone, staying connected and having information at your fingertips is easier than ever. Although technology has become so intermingled with our daily lives, the idea around security is not as momentous as it should be. As mentioned by the Multi-State Information Sharing and Analysis Center (MS-ISAC), “based on recent statistics, the average unprotected computer can be compromised in a matter of minutes. The majority of individuals who thought their computers were safe…were wrong.” …

Electrical Machines For Harsh Environments, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

There have been several megatrends over the past several years that have been driving research activities in the area of electrical machines and drives. These include transportation electrification (land vehicles, aerospace, ships, locomotives), expanded role of renewables (wind, solar, wave…etc.), and smart grid among others. One of those megatrends that continues to become more critical is electrical machines for harsh environments. In many applications, production has been moved to remote locations and harsh environments. These include subsea oil exploration, sour/acid gas compression, downhole and geothermal pumps, underground mining, and aerospace applications. This paper will provide an overview of electrical machine …

Multiframe Coded Computation For Distributed Uplink Channel Decoding, Brinell F. Monteiro

Theses

The latest 5G technology in wireless communication has led to an increasing demand for higher data rates and low latencies. The overall latency of the system in a cloud radio access network is greatly affected by the decoding latency in the uplink channel. Various proposed solutions suggest using network function virtualization (NFV). NFV is the process of decoupling the network functions from hardware appliances. This provides the exibility to implement distributed computing and network coding to effectively reduce the decoding latency and improve the reliability of the system. To ensure the system is cost effective, commercial off the shelf (COTS) …

Confucian Robot Ethics, Qin Zhu, Tom Williams, Ruchen Wen

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

In the literature of artificial moral agents (AMAs), most work is influenced by either deontological or utilitarian frameworks. It has also been widely acknowledged that these Western “rule-based” ethical theories have encountered both philosophical and computing challenges. To tackle these challenges, this paper explores a non-Western, role-based, Confucian approach to robot ethics. In this paper, we start by providing a short introduction to some theoretical fundamentals of Confucian ethics. Then, we discuss some very preliminary ideas for constructing a Confucian approach to robot ethics. Lastly, we briefly share a couple of empirical studies our research group has recently conducted that …

A Ulysses Pact With Artificial Systems. How To Deliberately Change The Objective Spirit With Cultured Ai, Bruno Gransche

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The article introduces a concept of cultured technology, i.e. intelligent systems capable of interacting with humans and showing (or simulating) manners, of following customs and of socio-sensitive considerations. Such technologies might, when deployed on a large scale, influence and change the realm of human customs, traditions, standards of acceptable behavior, etc. This realm is known as the "objective spirit" (Hegel), which usually is thought of as being historically changing but not subject to deliberate human design. The article investigates the question of whether the purposeful design of interactive technologies (as cultured technologies) could enable us to shape modes of …

On The Responsibility For Uses Of Downstream Software, Marty J. Wolf, Keith W. Miller, Frances S. Grodzinsky

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

In this paper we explore an issue that is different from whether developers are responsible for the direct impact of the software they write. We examine, instead, in what ways, and to what degree, developers are responsible for the way their software is used “downstream.” We review some key scholarship analyzing responsibility in computing ethics, including some recent work by Floridi. We use an adaptation of a mechanism developed by Floridi to argue that there are features of software that can be used as guides to better distinguish situations where a software developer might share in responsibility for the software’s …

Stackcbpred: A Stacking Based Prediction Of Protein-Carbohydrate Binding Sites From Sequence, Suraj Gattani

LSU New Orleans Theses and Dissertations

Carbohydrate-binding proteins play vital roles in many vital biological processes and study of these interactions, at residue level, are useful in treating many critical diseases. Analyzing the local sequential environments of the binding and non-binding regions to predict the protein-carbohydrate binding sites is one of the challenging problems in molecular and computational biology. Prediction of such binding sites, directly from sequences, using computational methods, can be useful to fast annotate the binding sites and guide the experimental process. Because the number of carbohydrate-binding residues is significantly lower than non-carbohydrate-binding residues, most of the methods developed are biased towards over predicting …

Scalable Community Detection Using Distributed Louvain Algorithm, Naw Safrin Sattar

LSU New Orleans Theses and Dissertations

Community detection (or clustering) in large-scale graph is an important problem in graph mining. Communities reveal interesting characteristics of a network. Louvain is an efficient sequential algorithm but fails to scale emerging large-scale data. Developing distributed-memory parallel algorithms is challenging because of inter-process communication and load-balancing issues. In this work, we design a shared memory-based algorithm using OpenMP, which shows a 4-fold speedup but is limited to available physical cores. Our second algorithm is an MPI-based parallel algorithm that scales to a moderate number of processors. We also implement a hybrid algorithm combining both. Finally, we incorporate dynamic load-balancing in …

Long Term Software Quality And Reliability Assurance In A Small Company, Eric Abuta

Computer Science and Engineering Theses and Dissertations

Demonstrating software reliability across multiple software releases has become essential in making informed decisions of upgrading software releases without impacting significantly end users' characterized processes and software quality standards. Standard defect and workload data normally collected in a typical small software development organization can be used for this purpose. Objective of this study was to demonstrate how to measure software reliability in multiple releases and whether continuous defect fixes and code upgrades increased software reliability. This study looked at techniques such as trend test that evaluated software system's overall trend and stability, input domain reliability models (IDRM) that assessed system's …

Spring 2019

In The Loop

Lab Life: New and improved School of Computing labs inspire research in robotics and engineering; A Festival to Remember: Students thrive in film festival environments; Next Gen Data Scientists: Data Science Academy introduces high school students to the big world of data; A Well-Designed Design Colloquium; Game Face: "Sausage Sports Club: creator and CDM alumnus Chris Wade shares the story behind his playful Nintendo Switch game: Connected from Afar: Graduate student Sierra Sellman makes the most of her online degree program; Seen and Heard; Bravo! Bravo!

Lattice Dynamics Of Orthorhombic Ndgao3, Alyssa Mock, Rafal Korlacki, Sean Knight, Megan Stokey, A. Fritz, Vanya Darakchieva, Mathias Schubert

Department of Electrical and Computer Engineering: Faculty Publications

A complete set of infrared-active and Raman-active lattice modes is obtained from density functional theory calculations for single-crystalline centrosymmetric orthorhombic neodymium gallate. The results for infraredactive modes are compared with an analysis of the anisotropic long-wavelength properties using generalized spectroscopic ellipsometry. The frequency-dependent dielectric function tensor and dielectric loss function tensor of orthorhombic neodymium gallium oxide are reported in the spectral range of 80–1200 cm⁻¹. A combined eigendielectric displacement vector summation and dielectric displacement loss vector summation approach augmented by considerations of lattice anharmonicity is utilized to describe the experimentally determined tensor elements. All infrared-active transverse and longitudinal …

Lattice Dynamics Of Orthorhombic Ndgao3, A. Mock, Rafal Korlacki, Sean Knight, Megan Stokey, A. Fritz, V. Darakchieva, Mathias Schubert

Department of Electrical and Computer Engineering: Faculty Publications

A complete set of infrared-active and Raman-active lattice modes is obtained from density functional theory calculations for single-crystalline centrosymmetric orthorhombic neodymium gallate. The results for infraredactive modes are compared with an analysis of the anisotropic long-wavelength properties using generalized spectroscopic ellipsometry. The frequency-dependent dielectric function tensor and dielectric loss function tensor of orthorhombic neodymium gallium oxide are reported in the spectral range of 80–1200 cm⁻¹. A combined eigendielectric displacement vector summation and dielectric displacement loss vector summation approach augmented by considerations of lattice anharmonicity is utilized to describe the experimentally determined tensor elements. All infrared-active transverse and longitudinal …

Quantified Measurement Of The Tilt Effect In A Family Of Café Wall Illusions, Nasim Nematzadeh Dr., David Martin Powers Prof.

MODVIS Workshop

This abstract explores the tilt effect in a family of Café Wall illusions using a Classical Gaussian Receptive Field model (CRF). Our model constructs an intermediate representation called edge map at multiple scales (Fig. 1) that reveals tilt cues and clues involved in the illusory perception of the Café Wall pattern. We investigate a wide range of parameters of the stimulus including mortar width, luminance, tiles contrast, and phase of the tile displacement (the stimuli in Fig. 2). We show that this simple bioplausible model, simulating the contrast sensitivity of the retinal ganglion cells, can not only detect the tilts …

Mapping In The Humanities: Gis Lessons For Poets, Historians, And Scientists, Emily W. Fairey

Open Educational Resources

User-friendly Geographic Information Systems (GIS) is the common thread of this collection of presentations, and activities with full lesson plans. The first section of the site contains an overview of cartography, the art of creating maps, and then looks at historical mapping platforms like Hypercities and Donald Rumsey Historical Mapping Project. In the next section Google Earth Desktop Pro is introduced, with lessons and activities on the basics of GE such as pins, paths, and kml files, as well as a more complex activity on "georeferencing" an historic map over Google Earth imagery. The final section deals with ARCGIS Online …

Your Internet Data Is Rotting, Paul Royster

University of Nebraska-Lincoln Libraries: Faculty Publications

The internet is growing, but old information continues to disappear daily.

Many MySpace users were dismayed to discover earlier this year that the social media platform lost 50 million files uploaded between 2003 and 2015. The failure of MySpace to care for and preserve its users’ content should serve as a reminder that relying on free third-party services can be risky. MySpace has probably preserved the users’ data; it just lost their content. The data was valuable to MySpace; the users’ content less so.

Preserving content or intellectual property on the internet presents a conundrum. If it’s accessible, then it …

Management And Security Of Multi-Cloud Applications, Lav Gupta

McKelvey School of Engineering Graduate Student Theses & Dissertations

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers' virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the …

Toward Controllable And Robust Surface Reconstruction From Spatial Curves, Zhiyang Huang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying …

Real-Time Reliable Middleware For Industrial Internet-Of-Things, Chao Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation contributes to the area of adaptive real-time and fault-tolerant systems research, applied to Industrial Internet-of-Things (IIoT) systems. Heterogeneous timing and reliability requirements arising from IIoT applications have posed challenges for IIoT services to efficiently differentiate and meet such requirements. Specifically, IIoT services must both differentiate processing according to applications' timing requirements (including latency, event freshness, and relative consistency of each other) and enforce the needed levels of assurance for data delivery (even as far as ensuring zero data loss). It is nontrivial for an IIoT service to efficiently differentiate such heterogeneous IIoT timing/reliability requirements to fit each application, …

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available …

A Constrained Box Algorithm For Imbalanced Data In Remote Sensing Images, Wajira Abeysinghe

Master of Science in Computer Science Theses

Imbalanced data is a common problem in machine learning where the number of observations that belong to one class is significantly lower than other classes. Due to the skewed distribution among the classes, most classification algorithms fail to classify minority instances effectively. The class imbalance problem can be found in many domains such as credit card fraud detection and rare diseases diagnosis.

Imbalanced data is a prominent issue also in remote sensing images (RSI) which are used to obtain information of earth resources and the surrounding environment. RSI are collected by special cameras that capture information from a specific wavelength …

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater

SMU Data Science Review

Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. A number of successful object detection systems have been proposed in recent years that are based on CNNs. In this paper, an empirical evaluation of three recent meta-architectures: SSD (Single Shot multi-box Detector), R-CNN (Region-based CNN) and R-FCN (Region-based Fully Convolutional Networks) was conducted to measure how fast and accurate they are in identifying objects on the road, such as vehicles, pedestrians, …

Anonymous And Efficient Message Authentication Scheme For Smart Grid, Libing Wu, Jing Wang, Sherali Zeadally, Debiao He

Information Science Faculty Publications

Smart grid has emerged as the next-generation electricity grid with power flow optimization and high power quality. Smart grid technologies have attracted the attention of industry and academia in the last few years. However, the tradeoff between security and efficiency remains a challenge in the practical deployment of the smart grid. Most recently, Li et al. proposed a lightweight message authentication scheme with user anonymity and claimed that their scheme is provably secure. But we found that their scheme fails to achieve mutual authentication and mitigate some typical attacks (e.g., impersonation attack, denial of service attack) in the smart grid …

High-Torque-Density Low-Cost Magnetic Gear Utilizing Hybrid Magnets And Advanced Materials, Ali Hussain Al-Qarni, Fan Wu, Ayman M. El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Two major challenges of existing high-performance magnetic gears are: (i) High content of rare-earth permanent magnets which results in high cost as well as price fluctuation; (ii) Conflict between mechanical and electromagnetic performances, especially in the design of highspeed rotor. A magnetic gear using a blend of magnet types, i.e NdFeB, or Dy-free NdFeB and ferrites, is proposed in this paper. The goal is to bring down the cost while retaining comparable torque-transducing performance to a baseline magnetic gear only using rare-earth NdFeB magnets. A variety of topologies based on different combinations of magnet types and geometric shapes have been …

Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja

Honors Scholar Theses

Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In …

Worker Demographics And Earnings On Amazon Mechanical Turk: An Exploratory Analysis, Kotaro Hara, Kristy Milland, Benjamin V. Hanrahan, Chris Callison-Burch, Abigail Adams, Saiph Savage, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

Prior research reported that workers on Amazon Mechanical Turk (AMT) are underpaid, earning about $2/h. But the prior research did not investigate the difference in wage due to worker characteristics (e.g., country of residence). We present the first data-driven analysis on wage gap on AMT. Using work log data and demographic data collected via online survey, we analyse the gap in wage due to different factors. We show that there is indeed wage gap; for example, workers in the U.S. earn $3.01/h while those in India earn $1.41/h on average.

Low-Energy Acceleration Of Binarized Convolutional Neural Networks Using A Spin Hall Effect Based Logic-In-Memory Architecture, Ashkan Samiee, Payal Borulkar, Ronald F. Demara, Peiyi Zhao, Yu Bai

Engineering Faculty Articles and Research

Deep Learning (DL) offers the advantages of high accuracy performance at tasks such as image recognition, learning of complex intelligent behaviors, and large-scale information retrieval problems such as intelligent web search. To attain the benefits of DL, the high computational and energy-consumption demands imposed by the underlying processing, interconnect, and memory devices on which software-based DL executes can benefit substantially from innovative hardware implementations. Logic-in-Memory (LIM) architectures offer potential approaches to attaining such throughput goals within area and energy constraints starting with the lowest layers of the hardware stack. In this paper, we develop a Spintronic Logic-in-Memory (S-LIM) XNOR neural …

Computationally Efficient Optimization Of A Five-Phase Flux-Switching Pm Machine Under Different Operating Conditions, Hao Chen, Xiangdong Liu, Nabeel Demerdash, Ayman M. El-Refaie, Zhen Chen, Jiangbiao He

Electrical and Computer Engineering Faculty Research and Publications

This paper investigates the comparative design optimizations of a five-phase outer-rotor flux-switching permanent magnet (FSPM) machine for in-wheel traction applications. To improve the comprehensive performance of the motor, two kinds of large-scale design optimizations under different operating conditions are performed and compared, including the traditional optimization performed at the rated operating point and the optimization targeting the whole driving cycles. Three driving cycles are taken into account, namely, the urban dynamometer driving schedule (UDDS), the highway fuel economy driving schedule (HWFET), and the combined UDDS/HWFET, representing the city, highway, and combined city/highway driving, respectively. Meanwhile, the computationally efficient finite-element analysis …