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Articles 61 - 90 of 207
Full-Text Articles in Other Computer Sciences
Improving Robot Darwinian Particle Swarm Optimization Using Quantum-Behaved Swarm Theory For Robot Exploration And Communication, Duaa Mehiar
Student Works (2020-2029)
Despite the significance of the Robotic Darwinian Particle Swarm Optimization (RDPSO) algorithm on swarm-robot exploration and communication, there remain notable gaps such as premature and slow convergence, collisions between robots, and communication breaks and constraints. The quantum computing theory has several advantages that can improve the searching capabilities of PSO-based algorithms. However, there has yet an attempt to adopt quantum behaviour onto robot-based system such as the RDPSO. In this study, a new algorithm called the Quantum Robot Darwinian Particle Swarm Optimization (QRDPSO) is contributed with the hypothesis that quantum behaving particles can address the RDPSO main gaps; i.e. to …
Exploring The Efficiency Of Self-Organizing Software Teams With Game Theory, Clay Stevens, Jared Soundy, Hau Chan
Exploring The Efficiency Of Self-Organizing Software Teams With Game Theory, Clay Stevens, Jared Soundy, Hau Chan
School of Computing: Conference and Workshop Papers
Over the last two decades, software development has moved away from centralized, plan-based management toward agile methodologies such as Scrum. Agile methodologies are founded on a shared set of core principles, including self-organizing software development teams. Such teams are promoted as a way to increase both developer productivity and team morale, which is echoed by academic research. However, recent works on agile neglect to consider strategic behavior among developers, particularly during task assignment–one of the primary functions of a self-organizing team. This paper argues that self-organizing software teams could be readily modeled using game theory, providing insight into how agile …
Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis
Design Project: Smart Headband, John Michel, Jack Durkin, Noah Lewis
Williams Honors College, Honors Research Projects
Concussion in sports is a prevalent medical issue. It can be difficult for medical professionals to diagnose concussions. With the fast pace nature of many sports, and the damaging effects of concussions, it is important that any concussion risks are assessed immediately. There is a growing trend of wearable technology that collects data such as steps and provides the wearer with in-depth information regarding their performance. The Smart Headband project created a wearable that can record impact data and provide the wearer with a detailed analysis on their risk of sustaining a concussion. The Smart Headband uses accelerometers and gyroscopes …
Distributed Cross-Community Collaboration For The Cloud-Based Energy Management Service, Yu-Wen Chen, J. Morris Chang
Distributed Cross-Community Collaboration For The Cloud-Based Energy Management Service, Yu-Wen Chen, J. Morris Chang
Publications and Research
Customers’ participation is a critical factor for inte-grating the distributed energy resources via demand response and demand-side management programs, especially when customers become prosumers. Incentives need to be delivered by the energy management service to attract prosumers to operate their distributed energy resources and electricity loads grid-friendly actively. The cloud-based energy management service enables virtual trading for customers within the same community to minimize cost and smooth the fluctuation. With the potential fast-growing number of service providers and customers, the needs exist for efficiently collaborating across multiple service providers and customers. This paper proposes the distributed cross-community collaboration (XCC) for …
Game-Theoretic Analysis Of Effort Allocation Of Contributors To Public Projects, Jared Soundy, Chenhao Wang, Clay Stevens, Hau Chan
Game-Theoretic Analysis Of Effort Allocation Of Contributors To Public Projects, Jared Soundy, Chenhao Wang, Clay Stevens, Hau Chan
School of Computing: Conference and Workshop Papers
Public projects can succeed or fail for many reasons such as the feasibility of the original goal and coordination among contributors. One major reason for failure is that insufficient work leaves the project partially completed. For certain types of projects anything short of full completion is a failure (e.g., feature request on software projects in GitHub). Therefore, project success relies heavily on individuals allocating sufficient effort. When there are multiple public projects, each contributor needs to make decisions to best allocate his/her limited effort (e.g., time) to projects while considering the effort allocation decisions of other strategic contributors and his/her …
Use Of Image Processing Algorithms For Mine Originating Waste Grain Size Determination, Sebastian Iwaszenko
Use Of Image Processing Algorithms For Mine Originating Waste Grain Size Determination, Sebastian Iwaszenko
Journal of Sustainable Mining
The utilization of mineral wastes from the mining industry is one of most challenging phases in the raw materials life cycle. In many countries, there are piles of mineral waste materials that date back to the previous century. There is also a constant stream of accompanying mineral matter excavated during everyday mine operation. This stream of waste matter is particularly notable for deep coal mining. Grain size composition of waste mineral matter is one of most important characteristics of coal originating waste material. This paper presents the use of image analysis for the determination of grain size composition of mineral …
Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary
Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …
A Fortran-Keras Deep Learning Bridge For Scientific Computing, Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi
A Fortran-Keras Deep Learning Bridge For Scientific Computing, Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi
Engineering Faculty Articles and Research
Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way …
Reducing Run-Time Adaptation Space Via Analysis Of Possible Utility Bounds, Clay Stevens, Hamid Bagheri
Reducing Run-Time Adaptation Space Via Analysis Of Possible Utility Bounds, Clay Stevens, Hamid Bagheri
School of Computing: Conference and Workshop Papers
Self-adaptive systems often employ dynamic programming or similar techniques to select optimal adaptations at run-time. These techniques suffer from the “curse of dimensionality", increasing the cost of run-time adaptation decisions. We propose a novel approach that improves upon the state-of-the-art proactive self-adaptation techniques to reduce the number of possible adaptations that need be considered for each run-time adaptation decision. The approach, realized in a tool called Thallium, employs a combination of automated formal modeling techniques to (i) analyze a structural model of the system showing which configurations are reachable from other configurations and (ii) compute the utility that can be …
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Engineering Faculty Articles and Research
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …
Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi
Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi
Engineering Faculty Articles and Research
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization …
Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale
Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale
Electronic Theses and Dissertations
The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …
Power-Over-Tether Uas Leveraged For Nearly Indefinite Meteorological Data Acquisition In The Platte River Basin, Daniel Rico, Carrick Detweiler, Francisco Munoz-Arriola
Power-Over-Tether Uas Leveraged For Nearly Indefinite Meteorological Data Acquisition In The Platte River Basin, Daniel Rico, Carrick Detweiler, Francisco Munoz-Arriola
School of Computing: Conference and Workshop Papers
The integration of unmanned aerial systems (UASs) has increased in the field of agriculture. These systems can provide data that was previously difficult to obtain to help increase efficiency and production. Typical commercial off the shelf (COTS) UASs have significant limitations in the form of small payloads, and short flight times which inhibit their ability to provide significant quantities of useful data. We present the development of a novel power-over-tether UAS that leverages the physical presence of the tether to integrate sensors at multiple altitudes along the tether. The UAS can acquire data nearly indefinitely to sense atmospheric conditions and …
A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan
A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan
University of the Pacific Theses and Dissertations
The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …
The Trolley Problem In Virtual Reality, Jungsu Pak, Ariane Guirguis, Nicholas Mirchandani, Scott Cummings, Uri Maoz
The Trolley Problem In Virtual Reality, Jungsu Pak, Ariane Guirguis, Nicholas Mirchandani, Scott Cummings, Uri Maoz
Student Scholar Symposium Abstracts and Posters
Would people react to the Trolley problem differently based on the medium? Immersive Virtual Reality Driving Simulator was used to examine participants respond to the trolley problem in a realistic and controlled simulated environment.
Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor
Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor
School of Computing: Dissertations, Theses, and Student Research
Formal concept analysis (FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …
Vrsensory: Designing Inclusive Virtual Games With Neurodiverse Children, Ben Wasserman, Derek Prate, Bryce Purnell, Alex Muse, Kaitlyn Abdo, Kendra Day, Louanne Boyd
Vrsensory: Designing Inclusive Virtual Games With Neurodiverse Children, Ben Wasserman, Derek Prate, Bryce Purnell, Alex Muse, Kaitlyn Abdo, Kendra Day, Louanne Boyd
Engineering Faculty Articles and Research
We explore virtual environments and accompanying interaction styles to enable inclusive play. In designing games for three neurodiverse children, we explore how designing for sensory diversity can be understood through a formal game design framework. Our process reveals that by using sensory processing needs as requirements we can make sensory and social accessible play spaces. We contribute empirical findings for accommodating sensory differences for neurodiverse children in a way that supports inclusive play. Specifically, we detail the sensory driven design choices that not only support the enjoyability of the leisure activities, but that also support the social inclusion of sensory-diverse …
Identifying Hourly Traffic Patterns With Python Deep Learning, Christopher L. Leavitt
Identifying Hourly Traffic Patterns With Python Deep Learning, Christopher L. Leavitt
Computer Engineering
This project was designed to explore and analyze the potential abilities and usefulness of applying machine learning models to data collected by parking sensors at a major metro shopping mall. By examining patterns in rates at which customer enter and exit parking garages on the campus of the Bellevue Collection shopping mall in Bellevue, Washington, a recurrent neural network will use data points from the previous hours will be trained to forecast future trends.
Grammar-Based Procedurally Generated Village Creation Tool, Kevin Matthew Graves
Grammar-Based Procedurally Generated Village Creation Tool, Kevin Matthew Graves
Computer Engineering
This project is a 3D village generator tool for Unity. It consists of three components: a building, mountain, and river generator. All of these generators use grammar-based procedural generation in order to create a unique and logical village and landscape each time the program is run.
Mapping In The Humanities: Gis Lessons For Poets, Historians, And Scientists, Emily W. Fairey
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 …
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
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 …
Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead
Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead
Engineering Faculty Articles and Research
Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …
Agent-Based Modeling And Simulation Approaches In Stem Education Research, Shanna R. Simpson-Singleton, Xiangdong Che
Agent-Based Modeling And Simulation Approaches In Stem Education Research, Shanna R. Simpson-Singleton, Xiangdong Che
Journal of International Technology and Information Management
The development of best practices that deliver quality STEM education to all students, while minimizing achievement gaps, have been solicited by several national agencies. ABMS is a feasible approach to provide insight into global behavior based upon the interactions amongst agents and environments. In this review, we systematically surveyed several modeling and simulation approaches and discussed their applications to the evaluation of relevant theories in STEM education. It was found that ABMS is optimal to simulate STEM education hypotheses, as ABMS will sensibly present emergent theories and causation in STEM education phenomena if the model is properly validated and calibrated.
Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi
Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi
Engineering Faculty Articles and Research
Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM)— find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust …
A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab
A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab
Electronic Theses and Dissertations
The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Masters Theses & Specialist Projects
In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to Go (MCTS) with a supervised learning method (neural networks) …
Investigating On Through Glass Via Based Rf Passives For 3-D Integration, Libo Qian, Jifei Sang, Yinshui Xia, Jian Wang, Peiyi Zhao
Investigating On Through Glass Via Based Rf Passives For 3-D Integration, Libo Qian, Jifei Sang, Yinshui Xia, Jian Wang, Peiyi Zhao
Mathematics, Physics, and Computer Science Faculty Articles and Research
Due to low dielectric loss and low cost, glass is developed as a promising material for advanced interposers in 2.5-D and 3-D integration. In this paper, through glass vias (TGVs) are used to implement inductors for minimal footprint and large quality factor. Based on the proposed physical structure, the impact of various process and design parameters on the electrical characteristics of TGV inductors is investigated with 3-D electromagnetic simulator HFSS. It is observed that TGV inductors have identical inductance and larger quality factor in comparison with their through silicon via counterparts. Using TGV inductors and parallel plate capacitors, a compact …
Data Mining Ancient Script Image Data Using Convolutional Neural Networks, Shruti Daggumati, Peter Revesz
Data Mining Ancient Script Image Data Using Convolutional Neural Networks, Shruti Daggumati, Peter Revesz
School of Computing: Conference and Workshop Papers
The recent surge in ancient scripts has resulted in huge image libraries of ancient texts. Data mining of the collected images enables the study of the evolution of these ancient scripts. In particular, the origin of the Indus Valley script is highly debated. We use convolutional neural networks to test which Phoenician alphabet letters and Brahmi symbols are closest to the Indus Valley script symbols. Surprisingly, our analysis shows that overall the Phoenician alphabet is much closer than the Brahmi script to the Indus Valley script symbols.
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …
Distributed Iterative Graph Processing Using Nosql With Data Locality, Ayam Pokhrel
Distributed Iterative Graph Processing Using Nosql With Data Locality, Ayam Pokhrel
LSU Master's Theses
A tremendous amount of data is generated every day from a wide range of sources such as social networks, sensors, and application logs. Among them, graph data is one type that represents valuable relationships between various entities. Analytics of large graphs has become an essential part of business processes and scientific studies because it leads to deep and meaningful insights into the related domain based on the connections between various entities. However, the optimal processing of large-scale iterative graph computations is very challenging due to the issues like fault tolerance, high memory requirement, parallelization, and scalability. Most of the contemporary …