Computer Engineering Commons^™

Implementing Asynchronous Linear Solvers Using Non-Uniform Distributions, Erik Jensen, Evan C. Coleman, Masha Sosonkina

Computational Modeling & Simulation Engineering Faculty Publications

Asynchronous iterative methods present a mechanism to improve the performance of algorithms for highly parallel computational platforms by removing the overhead associated with synchronization among computing elements. This paper considers a class of asynchronous iterative linear system solvers that employ randomization to determine the component update orders, specifically focusing on the effects of drawing the order from non-uniform distributions. Results from shared-memory experiments with a two-dimensional finite-difference discrete Laplacian problem show that using distributions favoring the selection of components with a larger contribution to the residual may lead to faster convergence than selecting uniformly. Multiple implementations of the randomized asynchronous …

A Discrimination Aware Model To Predict Childhood Literacy Levels, Kate Byrne

Dissertations

It is illegal in Ireland to discriminate in the provision of education on the basis of multiple characteristics including gender, race and religion. While the increased use of machine learning models can open multiple avenues to identify early intervention strategies in education, caution must be exercised to ensure that any intervention does not discriminate with respect to a protected class. Poor literacy in childhood can have long term effects as the child ages, including on employment and mental health outcomes. Early intervention is key in mitigating this. In this dissertation, a model was created that predicted the outcome of a …

An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram

Dissertations

In a world where anybody can share their views, opinions and make it sound like these are facts about the current situation of the world, Fake News poses a huge threat especially to the reputation of people with high stature and to organizations. In the political world, this could lead to opposition parties making use of this opportunity to gain popularity in their elections. In the medical world, a fake scandalous message about a medicine giving side effects, hospital treatment gone wrong or even a false message against a practicing doctor could become a big menace to everyone involved in …

Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy

Dissertations

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known …

Adapting Microservices In The Cloud With Faas, Mateusz Pietraszewski

Dissertations

This project involves benchmarking, microservices and Function-as-a-service (FaaS) across the dimensions of performance and cost. In order to do a comparison this paper proposes a benchmark framework.

Lightgwas: A Novel Genome-Wide Association Study Procedure, Bruno Ambrozio

Dissertations

This dissertation proposes LightGWAS, a novel machine learning procedure for genome-wide association study (GWAS) based on LightGBM and k-fold cross-validation. The conducted literature review identified that the currently available GWAS implementations rely on massive manual quality control steps to address statistical issues, such as controlling for false-positive inflation and power reduction. It also showed they demand a specific GWAS method for each type of genomic dataset morphology, which consequently increases the human dependency and open margins for misleadings. LightGWAS is a potential single, resilient, autonomous and scalable solution to address such concerns. Through this research, LightGWAS was contrasted against the …

Physical Randomness Can Help In Computations, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Can we use some so-far-unused physical phenomena to compute something that usual computers cannot? Researchers have been proposing many schemes that may lead to such computations. These schemes use different physical phenomena ranging from quantum-related to gravity-related to using hypothetical time machines. In this paper, we show that, in principle, there is no need to look into state-of-the-art physics to develop such a scheme: computability beyond the usual computations naturally appears if we consider such a basic notion as randomness.

Nyku: A Social Robot For Children With Autism Spectrum Disorders, Dan Stephan Stoianovici

Electronic Theses and Dissertations

The continued growth of Autism Spectrum Disorders (ASD) around the world has spurred a growth in new therapeutic methods to increase the positive outcomes of an ASD diagnosis. It has been agreed that the early detection and intervention of ASD disorders leads to greatly increased positive outcomes for individuals living with the disorders. Among these new therapeutic methods, Robot-Assisted Therapy (RAT) has become a hot area of study. Recent works have shown that high functioning ASD children have an affinity for interacting with robots versus humans. It is proposed that this is due to a less complex set of communication …

Microgrid-Enabled Reactive Power Support To Enhance Grid Economics, Sarhan Hasan

Electronic Theses and Dissertations

Reactive power plays an essential role in voltage control and stability in electric power systems. Various Volt/VAR techniques are utilized in electric power systems to maintain the voltage profile within defined acceptable limits and accordingly provide reliability and stability. Reactive power has been commonly generated through large-scale synchronous generators or distributed capacitor banks to provide proper transmission and distribution level system management, however, reactive power can be further used as an effective means to reduce total system operation cost. In this dissertation, an optimal reactive power model is proposed to determine the optimal nodal reactive powers that result in the …

Xylo-Bot: A Therapeutic Robot-Based Music Platform For Children With Autism, Huanghao Feng

Electronic Theses and Dissertations

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills, including motor control, emotional facial expressions, and eye gaze / joint attention. This Ph.D. dissertation focuses on studying the feasibility and effectiveness of using a social robot, called NAO, and a toy music instrument, xylophone, at modeling and improving the social responses and behaviors of children with ASD. In our investigation, we designed an autonomous social interactive music teaching system to fulfill this mission.

A novel modular robot-music teaching system consisting of three modules is presented. Module 1 provides an autonomous self-awareness positioning system for the …

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 …

Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal

Electronic Theses and Dissertations

The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …

Acue Certificate In Effective College Instruction Curated Reflections, Fadi Muheidat

Q2S Enhancing Pedagogy

The ACUE course is full of useful, best practice, experts’ opinions, and elaborated research on effective teaching. I feel I need another round of reading and watching the videos and classroom scenarios and go through the state-of-the-art techniques and work samples. My peers who participated in this course enriched the discussion and their own experiences best practices. Samples of reflections and implementation strategies listed below.

Developing An Inclusive K-12 Outreach Model, Karen Nolan, Roisin Faherty, Keith Quille, Brett A. Becker, Susan Bergin

Conference Papers

This paper outlines the longitudinal development of a K-12 outreachmodel, to promote Computer Science in Ireland. Over a three-yearperiod, it has been piloted to just under 9700 K-12 students fromalmost every county in Ireland. The model consists of a two-hourcamp that introduces students to a range of Computer Sciencetopics: addressing computing perceptions, introduction to codingand exploration of computational thinking. The model incorporateson-site school delivery and is available at no cost to any interestedschool across Ireland. The pilot study so far collected over 3400surveys (pre- and post-outreach delivery).Schools from all over Ireland self-selected to participate, includ-ing male only, female only and …

Named Entity Recognition In Spanish Biomedical Literature: Short Review And Bert Model, Liliya Akhtyamova

Conference Papers

Named Entity Recognition (NER) is the rst step for knowledge acquisition when we deal with an unknown corpus of texts. Having received these entities, we have an opportunity to form parameters space and to solve problems of text mining as concept normalization, speech recognition, etc. The recent advances in NER are related to the technology of word embeddings, which transforms text to the form being effective for Deep Learning. In the paper, we show how NER detects pharmacological substances, compounds, and proteins in the dataset obtained from the Spanish Clinical Case Corpus (SPACCC). To achieve this goal, we use contextualized …

Annual Report 2019-2020, Depaul University College Of Computing And Digital Media

CDM Annual Reports

LETTER FROM THE DEAN

As I write this letter wrapping up the 2019-20 academic year, we remain in a global pandemic that has profoundly altered our lives. While many things have changed, some stayed the same: our CDM community worked hard, showed up for one another, and continued to advance their respective fields. A year that began like many others changed swiftly on March 11th when the University announced that spring classes would run remotely. By March 28th, the first day of spring quarter, we had moved 500 CDM courses online thanks to the diligent work of our faculty, staff, …

Timcc: On Data Freshness In Privacy-Preserving Incentive Mechanism Design For Continuous Crowdsensing Using Reverse Auction, Xiaoqiang Ma, Weiwei Deng, Feng Wang, Menglan Hu, Fei Chen, Mohammad Mehedi Hassan

Faculty and Student Publications

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very …

Promoting A Growth Mindset In Cs1: Does One Size Fit All? A Pilot Study, Keith Quille, Susan Bergin

Conference Papers

This paper describes a pilot intervention conducted in CS1, in the
academic year of 2016-2017. The intervention was based on the
work of Dweck, promoting a growth Mindset in an effort to in-
crease performance in introductory programming. The study also
examined data from a previous year (as a control group) to compare
and contrast the results. Multiple factors related to programming
performance were recorded with the control and treatment group,
which were measured at multiple intervals throughout the course,
to monitor changes as the pilot intervention was implemented.
This study found a significant increase in programming perfor-
mance when …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Water Conservation Potential Of Self-Funded Foam-Based Flexible Surface-Mounted Floatovoltaics, Koami Soulemane Hayibo, Pierce Mayville, Ravneet Kaur Kailey, Joshua M. Pearce

Electrical and Computer Engineering Publications

A potential solution to the coupled water–energy–food challenges in land use is the concept of floating photovoltaics or floatovoltaics (FPV). In this study, a new approach to FPV is investigated using a flexible crystalline silicon-based photovoltaic (PV) module backed with foam, which is less expensive than conventional pontoon-based FPV. This novel form of FPV is tested experimentally for operating temperature and performance and is analyzed for water-savings using an evaporation calculation adapted from the Penman–Monteith model. The results show that the foam-backed FPV had a lower operating temperature than conventional pontoon-based FPV, and thus a 3.5% higher energy output per …

Mac Protocols For Terahertz Communication: A Comprehensive Survey, Saim Ghafoor, Noureddine Boujnah, Mubashir Husain Rehmani, Alan Davy

Publications

Terahertz communication is emerging as a future technology to support Terabits per second link with highlighting features as high throughput and negligible latency. However, the unique features of the Terahertz band such as high path loss, scattering, and reflection pose new challenges and results in short communication distance. The antenna directionality, in turn, is required to enhance the communication distance and to overcome the high path loss. However, these features in combine negate the use of traditional medium access protocols (MAC). Therefore, novel MAC protocol designs are required to fully exploit their potential benefits including efficient channel access, control message …

Comparative Analysis Of Single-Cell Transcriptomics In Human And Zebrafish Oocytes, Handan Can, Sree K. Chanumolu, Elena Gonzalez-Muñoz, Sukumal Prukudom, Hasan H. Otu, Jose Cibelli

Department of Electrical and Computer Engineering: Faculty Publications

Background: Zebrafish is a popular model organism, which is widely used in developmental biology research. Despite its general use, the direct comparison of the zebrafish and human oocyte transcriptomes has not been well studied. It is significant to see if the similarity observed between the two organisms at the gene sequence level is also observed at the expression level in key cell types such as the oocyte.

Results: We performed single-cell RNA-seq of the zebrafish oocyte and compared it with two studies that have performed single-cell RNA-seq of the human oocyte. We carried out a comparative analysis of genes expressed …

Cdc42-Dependent Transfer Of Mir301 From Breast Cancer-Derived Extracellular Vesicles Regulates The Matrix Modulating Ability Of Astrocytes At The Blood–Brain Barrier, Golnaz Morad, Cassandra C. Daisy, Hasan H. Otu, Towia A. Libermann, Simon T. Dillon, Marsha A. Moses

Department of Electrical and Computer Engineering: Faculty Publications

Breast cancer brain metastasis is a major clinical challenge and is associated with a dismal prognosis. Understanding the mechanisms underlying the early stages of brain metastasis can provide opportunities to develop efficient diagnostics and therapeutics for this significant clinical challenge. We have previously reported that breast cancer-derived extracellular vesicles (EVs) breach the blood–brain barrier (BBB) via transcytosis and can promote brain metastasis. Here, we elucidate the functional consequences of EV transport across the BBB. We demonstrate that brain metastasis-promoting EVs can be internalized by astrocytes and modulate the behavior of these cells to promote extracellular matrix remodeling in vivo. We …

Ieee Access Special Section Editorial: Green Signal Processing For Wireless Communicationsand Networking, Wen-Long Chin, David Shiung, Yi Qian, Woongsup Lee, Andres Kwasinki, Yansha Deng

Department of Electrical and Computer Engineering: Faculty Publications

No abstract provided.

Energy-Efficient Signal Conversion And In-Memory Computing Using Emerging Spin-Based Devices, Soheil Salehi Mobarakeh

Electronic Theses and Dissertations, 2020-2023

New approaches are sought to maximize the signal sensing and reconstruction performance of Internet-of-Things (IoT) devices while reducing their dynamic and leakage energy consumption. Recently, Compressive Sensing (CS) has been proposed as a technique aimed at reducing the number of samples taken per frame to decrease energy, storage, and data transmission overheads. CS can be used to sample spectrally-sparse wide-band signals close to the information rate rather than the Nyquist rate, which can alleviate the high cost of hardware performing sampling in low-duty IoT applications. In my dissertation, I am focusing mainly on the adaptive signal acquisition and conversion circuits …

Statistical And Stochastic Learning Algorithms For Distributed And Intelligent Systems, Jiang Bian

Electronic Theses and Dissertations, 2020-2023

In the big data era, statistical and stochastic learning for distributed and intelligent systems focuses on enhancing and improving the robustness of learning models that have become pervasive and are being deployed for decision-making in real-life applications including general classification, prediction, and sparse sensing. The growing prospect of statistical learning approaches such as Linear Discriminant Analysis and distributed Learning being used (e.g., community sensing) has raised concerns around the robustness of algorithm design. Recent work on anomalies detection has shown that such Learning models can also succumb to the so-called 'edge-cases' where the real-life operational situation presents data that are …

Topical Review Of Vulnerability Management For Local Hampton Roads Industry, Gregory W. Hubbard Jr., Matthew Eunice

OUR Journal: ODU Undergraduate Research Journal

The progress towards an interconnected digital world offers an exciting level of advancement for humanity. Unfortunately, this “online” connection is not safe from the threats and dangers typically associated with physical operations. With the foundation of Cyber Command of DoD cyberspace, the United States Government is taking a prominent stance in cyberspace operations. Like the federal government, both industries and individuals are not immune and are oftentimes unknowingly at risk to cyberattack. This report hopes to bring awareness to common vulnerabilities in multi-user networks by describing a historical background on cyber security as well as outlining current methods of vulnerability …

Assessment Of Dobhoff Tube Malposition On Radiographs Using Deep Learning, Kevin George, Paras Lakhani, Md

Phase 1

Introduction: Dobhoff tubes (DHT) are narrow-bore flexible devices that deliver enteral nutrition for critically ill patients. Tracheobronchial insertion of DHTs presents a significant risk for pulmonary complications. Thus, DHT insertion requires radiologist confirmation of correct placement with chest x-ray (CXR), increasing clinical delays. To address this, we demonstrate the novel application of Deep Convolutional Neural Networks (DCNNs) to automatically and accurately identify DHTs in CXRs in real time.

Methods: 141 de-identified HIPAA compliant frontal view chest radiographs containing DHTs in various positions were obtained. The DHTs were first manually segmented and verified by a board certified radiologist. Images were split …