Physical Sciences and Mathematics Commons^™

Neural Network Architecture Optimization Using Reinforcement Learning, Raghav Vadhera

Computer Science and Engineering Dissertations

Deep learning has emerged as an increasingly valuable tool, employed across a myriad of applications. However, the intricacies of deep learning systems, stemming from their sensitivity to specific network architectures, have rendered them challenging for non-experts to harness, thus highlighting the need for automatic network architecture optimization. Prior research predominantly optimizes a network for a single problem through architecture search, necessitating extensive training of various architectures during optimization.\\ To tackle this issue and unlock the potential for transferability across tasks, this dissertation presents a groundbreaking approach that employs Reinforcement Learning to develop a network optimization policy based on an abstract …

Practical Indirect Control Flow Analysis For Binary Executables, Haotian Zhang

Computer Science and Engineering Dissertations

Resolving indirect control flow is one of the fundamental challenges in binary analysis. Improving the accuracy of the indirect control flow analysis is vital to the binary analysis domain. Many analysis algorithms and security techniques rely on a precise indirect control flow result, such as recursive disassembling, control flow integrity, data-flow analysis, etc. Incorrect or even inaccuracy indirect control flow analysis results can compromise or even break the assumptions of these analyses. This thesis explores this topic from two directions, altering the indirect control flow analysis to make it more suitable for different scenarios and improving the accuracy of indirect …

Toward A Deeper Integration Of Low-Fidelity Sketches Into Mobile Application Development, Soumik Mohian

Computer Science and Engineering Dissertations

Mobile application development often starts with creating low-fidelity sketches of user interfaces. Integrating these sketches into the software development process can reduce repetition, narrow the gap between user perception and final implementation, and improve app resilience. In this study, we introduce the DoodleUINet dataset, which comprises over 10K sketches of UI elements. Our Doodle2App tool converts low-fidelity sketches into a single-page, compilable Android app. At the same time, our PSDoodle provides an interactive, partial sketch-based search engine with a top-10 screen retrieval accuracy comparable to the state-of-the-art SWIRE line of work but with a 50% reduction in the average required …

Enhancing Health Tweet Classification: An Evaluation Of Transformer-Based Models For Comprehensive Analysis, Foram Pankajbhai Patel

Computer Science and Engineering Theses

The task of health tweet classification entails identifying whether a given tweet is health-related or not. While existing research in this area has made significant progress in classifying tweets into specific sub-domains of health, such as mental health, COVID-19, or specific diseases, there is a need for a more comprehensive approach that considers a broader range of health-related topics. This thesis addresses this need by proposing a diverse and comprehensive dataset that includes various existing health-related datasets, data collected through a keyword-based approach, and manually annotated data. However, the use of health-related keywords in a figurative or non-health context poses …

Toward Digital Phenotyping: Human Activity Representation For Embodied Cognition Assessment, Mohammad Zakizadehghariehali

Computer Science and Engineering Dissertations

Cognition is the mental process of acquiring knowledge and understanding through thought, experience and senses. Based on Embodied Cognition theory, physical activities are an important manifestation of cognitive functions. As a result, they can be employed to both assess and train cognitive skills. In order to assess various cognitive measures, the ATEC system has been proposed. It consists of physical exercises with different variations and difficulty levels, designed to provide assessment of executive and motor functions. This thesis focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. Representation learning …

Optimizing Resource Utilization, Efficiency And Scalability In Deep Learning Systems, Xiaofeng Wu

Computer Science and Engineering Dissertations

This thesis addresses the challenges of utilization, efficiency, and scalability faced by deep learning systems, which are essential for high-performance training and serving of deep learning models. Deep learning systems play a critical role in developing accurate and complex models for various applications, including image recognition, natural language understanding, and speech recognition. This research focuses on understanding and developing deep learning systems that encompass data preprocessing, resource management, multi-tenancy, and distributed model training. The thesis proposes several solutions to improve the performance, scalability, and efficiency of deep learning applications. Firstly, we introduce SwitchFlow, a scheduling framework that addresses the limitations …

Survey On Outdoor Navigation Applications For People With Visual Impairment, Fatmaelzahraa Eltaher, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever

Datasets

Outdoor navigation is a very challenging activity for People who suffer from Blindness or Visually Impairment (PBVI). Having examined the current literature, we conclude that there are very few publications providing a nuanced understanding of how PBVI undertake a journey in an outdoor environment and what their main challenges and obstacles are. To throw some light on this gap, we conducted a questionnaire in collaboration with the National Council for the Blind Ireland (NCBI) for 49 PBVI. Our questionnaire gathers information about key aspects related to PBVI outdoor navigation such as support tools/devices, hazards, journey preparation, crossing roads, and understanding …

Csc 71010/Csci 77100: Programming Languages/Software Engineering, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.

Wala Quick Start, Raffi T. Khatchadourian

Open Educational Resources

Setting up and trying the TJ Watson Library for Analysis (WALA).

Building An Ast Eclipse Plug-In, Raffi T. Khatchadourian

Open Educational Resources

Complete the Building an AST Eclipse Plug-in assignment. Once it works, find a medium-sized open-source Java project to run your plugin on. You may want to explore GitHub. Import the project into Eclipse and run your plug-in on it. Report on the following, which may require you to change some of the source code so that it is convenient:

1. Project name.
2. Project URL.
3. Project description.
4. The number of classes in the project.
5. The number of user-defined methods in the project.
6. For each class, the number of method calls.
7. Statistics about the method calls:
   1. The total number of method calls …

Working With Control-Flow Graphs, Raffi T. Khatchadourian

Open Educational Resources

No abstract provided.

Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain

All Works

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …

Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi

All Works

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (IoT), and data streaming, challenges such as monitoring and management remain unresolved. Edge IoT devices produce and stream huge amounts of sample sources, which can incur significant processing, computation, and storage costs during local updates using all data samples. Many research initiatives have improved the algorithm for FL in homogeneous networks. However, in the typical distributed learning application scenario, data is generated …

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Forecasting Networks Links With Laplace Characteristic And Geographical Information In Complex Networks, Muhammad Wasim, Feras Al-Obeidat, Fernando Moreira, Haji Gul, Adnan Amin

All Works

Forecasting links in a network is a crucial task in various applications such as social networks, internet traffic management, and data mining. Many studies on forecasting links in social networks and on other networks have been conducted over the last decade. In this paper, we propose a novel method based on graph Laplacian eigenmaps for predicting the geographic location of nodes in complex networks. Our method utilizes the adjacency matrix of the network and generates a scoring matrix that captures the similarity between nodes in terms of their geographic location. By transforming the distance matrices into score matrices using exponential …

Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi

All Works

No abstract provided.

Accuracy Of Spectral Indices Assessing Fire Severity Utilizing Maximum And Minimum Pixel Values, Jarrad Mckercher, David Blake, Eddie Van Etten

Research Datasets

This data set contains all Spectral Indices created using Google Earth Engine through Google Collaborate. 16 Spectral Indices were created that utilise different image collection and pixel value parameters to map the burn severity of the 2021 Wooroloo Bushfire.

An Experiment On The Effects Of Using Color To Visualize Requirements Analysis Tasks: Supplemental Material, Yesugen Baatartogtokh, Irene Foster, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper: "An Experiment on the Effects of using Color to Visualize Requirements Analysis Tasks".
This paper is a scientific evaluation of the effectiveness and usability of EVO. We conduct an experiment to measure any effect of using colors to represent evidence pairs.

Visualizations For User-Supported State Space Exploration Of Goal Models: Supplemental Material, Yesugen Baatartogtokh, Irene Foster, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the research paper entitled, "Visualizations for User-supported State Space Exploration of Goal Models". This paper presents a technique for valuation-based filtering and coloring to assist users in understanding a solution space and selecting custom states from it. This supplement contains the data from our initial evaluation and associated models.

Data From: Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Systems Science Faculty Datasets

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Graph Representation Learning For Heterogeneous Multimodal Biomedical Data, Nhat Chau Tran

Computer Science and Engineering Dissertations

The emergence of high-throughput sequencing technology has generated a wealth of “multi-omics” data, capturing information about different types of biomolecules at multiple levels. Since large-scale genomics, transcriptomics, and proteomics data are becoming publicly available, integrated systems analysis utilizing these data sources has taken the front seat in deriving valuable insights for identifying cancer biomarkers or predicting interactions and functions for novel molecules such as LncRNAs. The graph representation learning paradigm can address these challenging tasks as among the most promising approaches to improve predictions over sparsely annotated molecular entities and to provide representation capacity and interpretability over heterogeneous and hierarchically …

Intuitive Robot Integration Via Virtual Reality Workspaces, Minh Tram

Computer Science and Engineering Theses

As robots become increasingly prominent in diverse industrial settings, the desire for an accessible and reliable system has correspondingly increased. Yet, the task of meaningfully assessing the feasibility of introducing a new robotic component, or adding more robots into an existing infrastructure, remains a challenge. This is due to both the logistics of acquiring a robot and the need for expert knowledge in setting it up. In this paper, we address these concerns by developing a purely virtual simulation of a robotic system. Our proposed framework enables natural human-robot interaction through a visually immersive representation of the workspace. The main …

Approximate Query Processing Using Deep Learning And Database Techniques, Shohedul Hasan

Computer Science and Engineering Dissertations

Data is generated at an unprecedented rate surpassing our ability to analyze them. In real applications, it is often impractical to find an exact answer by traversing the entire data. As a result, Approximate Query Processing (AQP) is getting extremely popular, which finds an approximate answer in a quick time by sacrificing a fraction of accuracy. This dissertation focuses on developing different AQP techniques to solve fundamental database problems using deep learning. Moreover, we build a fast and scalable algorithm for Quantile Regression, a well-known regression technique that can help minimize the uncertainty in the recent deep learning-based AQP solutions, …

Data Discovery Analysis On Complex Time Series Data, Peter Lawrence Severynen

Computer Science and Engineering Theses

Complex time series are a ubiquitous form of data in the modern world. They have wide application across many different fields of scientific inquiry and business endeavor. Time series are used to understand and forecast weather patterns, voting patterns, computer network traffic, population health outcomes, demographic changes, the results of scientific experiments, and the performance of stocks and mutual funds. But time series can be difficult to analyze by conventional methods when the data is multivariate, incomplete, or in different formats. To address these issues, an investigation of several multivariate time series datasets was performed using the methods of automatic …

Understanding Human Actions: Cognitive Assessment And Action Segmentation Using Human Object Interaction, Saif Sayed

Computer Science and Engineering Dissertations

Automatic understanding of human behavior has several applications in medicine and surveillance. Analysing human actions can enable cognitive assessment of children by measuring their hyperactivity and response inhibition which can give physicians better understanding of their cognitive state. Automatic and non-invasive assessment for cognitive disorders will increase the affordability and reach for these detection methods and can prove life-changing in child’s development. Human activity can also be analysed in common settings such as cooking in kitchen and understanding the information of human object interaction can give priors on the underlying activity they are performing. In the first section, we focus …

Model Transformations Between Sequence Diagram And Activity Diagram With Qvto, Yutong Xia

Undergraduate Student Research Internships Conference

Complex software systems are specified by various models denoting the behavior of the system components, the exchanges of messages and data among components, the intents of the system stakeholders, the flow of system processes, and the structure of the system as a collection of modules.

When such systems are maintained and evolved (e.g. by adding new functionality, fixing bugs, or porting to a new operating environment), one or more of these models are altered. This brings the system specification to an inconsistent state since some models reflect the new behavior while other models were not appropriately evolved.

This research presents …

Towards High Performance Cancer Staging From Histology Images, Ashwin Raju

Computer Science and Engineering Dissertations

Digital Pathology (DP) has been recently used in replacement to traditional microscopy samples as it easy to navigate and can be analysed, processed and saved. With the invention of Digital pathology, there has been exponential increase of automated process to make the life of Doctors easier. One such automated process is Artificial Intelligence (AI) where the AI is used as an assistant to Humans and to make the analysis and guide the experts. With the advent of AI and in particular Deep Learning, research has been divided and focused to solve multiple problems in Digital Pathology. One such important application …

Hand Analysis From Depth Images, Mohammad Rezaei

Computer Science and Engineering Dissertations

Hand analysis using vision systems is necessary for interaction between people and digital devices and thus is crucial in many applications relating to computer vision and human computer interaction (HCI). The proposed dissertation will explore hand analysis from depth images along two lines: hand part segmentation and 3D hand pose estimation. First, we investigate hand part segmentation from depth images, which is formulated as a semantic segmentation task. We explore a method aimed at determining for every pixel what hand part it belongs to. This method attempts to perform this task without requiring the ground-truth segmentation labels for training. It …

Semi Automatic Hand Pose Annotation Using A Single Depth Camera, Marnim Galib

Computer Science and Engineering Dissertations

This thesis investigates the problem of 3D hand pose annotation using a single depth camera. While hand pose annotations are critically important for training deep neural networks, creating such reliable training data is challenging and manual labor intensive. Current datasets that rely on manual annotation on real images are limited in size due to the difficulty of annotating them. Although, large datasets have been generated using tracking based methods followed by manual refinement, these methods are prone to annotation errors due to tracking failure. Synthetic images have also been used to create large datasets but synthetic frames does not capture …

Gan-Based Domain Translation For Hand Pose Estimation And Face Reconstruction, Farnaz Farahanipad

Computer Science and Engineering Dissertations

Deep learning solutions for hand pose estimation are now very reliant on comprehensive datasets covering diverse camera perspectives, lighting conditions, shapes, and pose variations. Since, acquiring such datasets is a challenging task that may be infeasible for many novel applications, several studies aim to develop semi/self supervised learning methods, that learn to estimate hand pose from a few labeled/unlabeled data. Therefore, in this dissertation, we investigate new advances in semi/self supervised learning which will remove the bottleneck of obtaining time-consuming frameby- frame manual annotations through generative adversarial networks (GANs). To handle above mentioned challenges, this thesis makes the following contributions. …