Engineering Commons^™

Sensitivity Analysis Of An Agent-Based Simulation Model Using Reconstructability Analysis, Andey M. Nunes, Martin Zwick, Wayne Wakeland

Systems Science Faculty Publications and Presentations

Reconstructability analysis, a methodology based on information theory and graph theory, was used to perform a sensitivity analysis of an agent-based model. The NetLogo BehaviorSpace tool was employed to do a full 2^k factorial parameter sweep on Uri Wilensky’s Wealth Distribution NetLogo model, to which a Gini-coefficient convergence condition was added. The analysis identified the most influential predictors (parameters and their interactions) of the Gini coefficient wealth inequality outcome. Implications of this type of analysis for building and testing agent-based simulation models are discussed.

How Live Streaming And Twitch Have Changed The Gaming Industry, Krystal Ruiz

ART 108: Introduction to Games Studies

Live streaming in itself has become a booming industry in which its content consists of “streamers” who live broadcast numerous events and real-time interactions while simultaneously chatting with viewers drawing huge and increasing numbers (Adamovich). Twitch has especially excelled at garnering attention as one of the most popular live streaming platforms that focuses on broadcasting and viewing video game content (Adamovich). Twitch has grown rapidly within the last few years asserting its dominance as one of the major forces in the games industry and becoming a multi-billion-dollar industry (Adamovich). For example, according to Descrier, in 2016 there were approximately 292 …

Survey On Deep Neural Networks In Speech And Vision Systems, M. Alam, Manar D. Samad, Lasitha Vidyaratne, ‪Alexander Glandon, Khan M. Iftekharuddin

Computer Science Faculty Research

This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in speech and vision applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent speech and vision systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent …

A Novel Spatiotemporal Prediction Method Of Cumulative Covid-19 Cases, Junzhe Cai

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

Prediction methods are important for many applications. In particular, an accurate prediction for the total number of cases for pandemics such as the Covid-19 pandemic could help medical preparedness by providing in time a sufficient supply of testing kits, hospital beds and medical personnel. This thesis experimentally compares the accuracy of ten prediction methods for the cumulative number of Covid-19 pandemic cases. These ten methods include two types of neural networks and extrapolation methods based on best fit linear, best fit quadratic, best fit cubic and Lagrange interpolation, as well as an extrapolation method from Revesz. We also consider the …

Suffix Tree, Minwise Hashing And Streaming Algorithms For Big Data Analysis In Bioinformatics, Sairam Behera

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

In this dissertation, we worked on several algorithmic problems in bioinformatics using mainly three approaches: (a) a streaming model, (b) sux-tree based indexing, and (c) minwise-hashing (minhash) and locality-sensitive hashing (LSH). The streaming models are useful for large data problems where a good approximation needs to be achieved with limited space usage. We developed an approximation algorithm (Kmer-Estimate) using the streaming approach to obtain a better estimation of the frequency of k-mer counts. A k-mer, a subsequence of length k, plays an important role in many bioinformatics analyses such as genome distance estimation. We also developed new methods that use …

Representational Learning Approach For Predicting Developer Expertise Using Eye Movements, Sumeet Maan

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

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on …

Optimal Collaborative Path Planning For Unmanned Surface Vehicles Carried By A Parent Boat Along A Planned Route, Ari Carisza Graha Prasetia, I-Lin Wang, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In this paper, an effective mechanism using a fleet of unmanned surface vehicles (USVs) carried by a parent boat (PB) is proposed to complete search or scientific tasks over multiple target water areas within a shorter time . Specifically, multiple USVs can be launched from the PB to conduct such operations simultaneously, and each USV can return to the PB for battery recharging or swapping and data collection in order to continue missions in a more extended range. The PB itself follows a planned route with a flexible schedule taking into consideration locational constraints or collision avoidance in a real-world …

Covid-19 And Mental Health/Substance Use Disorders On Reddit: A Longitudinal Study, Amanuel Alambo, Swati Padhee, Tanvi Banerjee, Krishnaprasad Thirunarayan

Computer Science and Engineering Faculty Publications

COVID-19 pandemic has adversely and disproportionately impacted people suffering from mental health issues and substance use problems. This has been exacerbated by social isolation during the pandemic and the social stigma associated with mental health and substance use disorders, making people reluctant to share their struggles and seek help. Due to the anonymity and privacy they provide, social media emerged as a convenient medium for people to share their experiences about their day to day struggles. Reddit is a well-recognized social media platform that provides focused and structured forums called subreddits, that users subscribe to and discuss their experiences with …

Machine Learning Augmentation Micro-Sensors For Smart Device Applications, Mohammad H. Hasan

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Novel smart technologies such as wearable devices and unconventional robotics have been enabled by advancements in semiconductor technologies, which have miniaturized the sizes of transistors and sensors. These technologies promise great improvements to public health. However, current computational paradigms are ill-suited for use in novel smart technologies as they fail to meet their strict power and size requirements. In this dissertation, we present two bio-inspired colocalized sensing-and-computing schemes performed at the sensor level: continuous-time recurrent neural networks (CTRNNs) and reservoir computers (RCs). These schemes arise from the nonlinear dynamics of micro-electro-mechanical systems (MEMS), which facilitates computing, and the inherent ability …

Digital And Mixed Domain Hardware Reduction Algorithms And Implementations For Massive Mimo, Najath A. Mohomed

FIU Electronic Theses and Dissertations

Emerging 5G and 6G based wireless communications systems largely rely on multiple-input-multiple-output (MIMO) systems to reduce inherently extensive path losses, facilitate high data rates, and high spatial diversity. Massive MIMO systems used in mmWave and sub-THz applications consists of hundreds perhaps thousands of antenna elements at base stations. Digital beamforming techniques provide the highest flexibility and better degrees of freedom for phased antenna arrays as compared to its analog and hybrid alternatives but has the highest hardware complexity.

Conventional digital beamformers at the receiver require a dedicated analog to digital converter (ADC) for every antenna element, leading to ADCs for …

Defense By Deception Against Stealthy Attacks In Power Grids, Md Hasan Shahriar

FIU Electronic Theses and Dissertations

Cyber-physical Systems (CPSs) and the Internet of Things (IoT) are converging towards a hybrid platform that is becoming ubiquitous in all modern infrastructures. The integration of the complex and heterogeneous systems creates enormous space for the adversaries to get into the network and inject cleverly crafted false data into measurements, misleading the control center to make erroneous decisions. Besides, the attacker can make a critical part of the system unavailable by compromising the sensor data availability. To obfuscate and mislead the attackers, we propose DDAF, a deceptive data acquisition framework for CPSs' hierarchical communication network. Each switch in the hierarchical …

Formal Concept Analysis Applications In Bioinformatics, Sarah Roscoe

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

Bioinformatics is an important field that seeks to solve biological problems with the help of computation. One specific field in bioinformatics is that of genomics, the study of genes and their functions. Genomics can provide valuable analysis as to the interaction between how genes interact with their environment. One such way to measure the interaction is through gene expression data, which determines whether (and how much) a certain gene activates in a situation. Analyzing this data can be critical for predicting diseases or other biological reactions. One method used for analysis is Formal Concept Analysis (FCA), a computing technique based …

Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt

Mechanical and Materials Engineering Faculty Publications and Presentations

Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network’s intended function, without the use of global optimization ormachine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuronmodel have fundamental similarities to those of …

Multi-User Verifiable Searchable Symmetric Encryption For Cloud Storage, Xueqiao Liu, Guomin Yang, Guomin Yang

Research Collection School Of Computing and Information Systems

In a cloud data storage system, symmetric key encryption is usually used to encrypt files due to its high efficiency. In order allow the untrusted/semi-trusted cloud storage server to perform searching over encrypted data while maintaining data confidentiality, searchable symmetric encryption (SSE) has been proposed. In a typical SSE scheme, a users stores encrypted files on a cloud storage server and later can retrieve the encrypted files containing specific keywords. The basic security requirement of SSE is that the cloud server learns no information about the files or the keywords during the searching process. Some SSE schemes also offer additional …

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating …

Multi-Echo Quantitative Susceptibility Mapping For Strategically Acquired Gradient Echo (Stage) Imaging, Sara Gharabaghi, Saifeng Liu, Ying Wang, Yongsheng Chen, Sagar Buch, Mojtaba Jokar, Thomas Wischgoll, Nasser H. Kashou, Chunyan Zhang, Bo Wu, Jingliang Cheng, E. Mark Haacke

Computer Science and Engineering Faculty Publications

Purpose: To develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. Methods: The proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The …

Finite-Time State Estimation For An Inverted Pendulum Under Input-Multiplicative Uncertainty, Sergey V. Drakunov, William Mackunis, Anu Kossery Jayaprakash, Krishna Bhavithavya Kidambi, Mahmut Reyhanoglu

Publications

A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous …

Investigating Factors Predicting Effective Learning In A Cs Professional Development Program For K-12 Teachers, Patrick Morrow

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

The demand for K-12 Computer Science (CS) education is growing and there is not an adequate number of educators to match the demand. Comprehensive research was carried out to investigate and understand the influence of a summer two-week professional development (PD) program on teachers’ CS content and pedagogical knowledge, their confidence in such knowledge, their interest in and perceived value of CS, and the factors influencing such impacts. Two courses designed to train K-12 teachers to teach CS, focusing on both concepts and pedagogy skills were taught over two separate summers to two separate cohorts of teachers. Statistical and SWOT …

A User Study Of A Wearable System To Enhance Bystanders’ Facial Privacy, Alfredo J. Perez, Sherali Zeadally, Scott Griffith, Luis Y. Matos Garcia, Jaouad A. Mouloud

Information Science Faculty Publications

The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called …

Integrated Cyberattack Detection And Resilient Control Strategies Using Lyapunov-Based Economic Model Predictive Control, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

The use of an integrated system framework, characterized by numerous cyber/physical components (sensor measurements, signals to actuators) connected through wired/wireless networks, has not only increased the ability to control industrial systems, but also the vulnerabilities to cyberattacks. State measurement cyberattacks could pose threats to process control systems since feedback control may be lost if the attack policy is not thwarted. Motivated by this, we propose three detection concepts based on Lyapunov‐based economic model predictive control (LEMPC) for nonlinear systems. The first approach utilizes randomized modifications to an LEMPC formulation online to potentially detect cyberattacks. The second method detects attacks when …

Agent-Based Modelling Of Values: The Case Of Value Sensitive Design For Refugee Logistics, Christine Boshuijzen-Van Burken, Ross J. Gore, Frank Dignum, Lamber Royakkers, Phillip Wozny, F. Leron Shults

VMASC Publications

We have used value sensitive design as a method to develop an agent-based model of values in humanitarian logistics for refugees. Schwartz’s theory of universal values is implemented in the model in such a way that agents can make value trade-offs, which are operationalized into a measure of refugee wellbeing and a measure of public opinion about how the refugee logistics is being handled. By trying out different ‘value scenarios’, stakeholders who are responsible for, or involved in refugee logistics can have insights into the effects of various value choices. The model is visualized and made usable as a platform …

Camera Placement Meeting Restrictions Of Computer Vision, Sara Aghajanzadeh, Roopasree Naidu, Shuo-Han Chen, Caleb Tung, Abhinav Goel, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

In the blooming era of smart edge devices, surveillance cam- eras have been deployed in many locations. Surveillance cam- eras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cam- eras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a signifi- cant restriction of computer vision: in order to track a moving object, the object must occupy enough pixels. The number of pixels depends on many factors (how far away is the object? What is the camera resolution? What is the focal length?). …

Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools, Murat Kuzlu, Umit Cali, Vinayak Sharma, Özgür Güler

Engineering Technology Faculty Publications

Over the last two decades, Artificial Intelligence (AI) approaches have been applied to various applications of the smart grid, such as demand response, predictive maintenance, and load forecasting. However, AI is still considered to be a ‘‘black-box’’ due to its lack of explainability and transparency, especially for something like solar photovoltaic (PV) forecasts that involves many parameters. Explainable Artificial Intelligence (XAI) has become an emerging research field in the smart grid domain since it addresses this gap and helps understand why the AI system made a forecast decision. This article presents several use cases of solar PV energy forecasting using …

Analysis Of Cloud Bursting On Openstack Infrastructure To Aws, Bao Pham, Ronald C. Jones, Majid Shaalan

Other Student Works

Cloud computing is the development of distributed and parallel computing that seeks to provide a new model of business computing by automating services and efficiently storing proprietary data. Cloud bursting is one of the cloud computing techniques that adopts the hybrid cloud model which seeks to expand the resources of a private cloud through the integration with a public cloud infrastructure. In this paper, the viability of cloud bursting is experimented and an attempt to integrate AWS EC2 onto an Openstack cloud environment using the Openstack OMNI driver is conducted.

Deep Reinforcement Learning Approach To Solve Dynamic Vehicle Routing Problem With Stochastic Customers, Waldy Joe, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In real-world urban logistics operations, changes to the routes and tasks occur in response to dynamic events. To ensure customers’ demands are met, planners need to make these changes quickly (sometimes instantaneously). This paper proposes the formulation of a dynamic vehicle routing problem with time windows and both known and stochastic customers as a route-based Markov Decision Process. We propose a solution approach that combines Deep Reinforcement Learning (specifically neural networks-based TemporalDifference learning with experience replay) to approximate the value function and a routing heuristic based on Simulated Annealing, called DRLSA. Our approach enables optimized re-routing decision to be generated …

Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu

Research Collection School Of Computing and Information Systems

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online …

Online Traffic Signal Control Through Sample-Based Constrained Optimization, Srishti Dhamija, Alolika Gon, Pradeep Varakantham, William Yeoh

Research Collection School Of Computing and Information Systems

Traffic congestion reduces productivity of individuals by increasing time spent in traffic and also increases pollution. To reduce traffic congestion by better handling dynamic traffic patterns, recent work has focused on online traffic signal control. Typically, the objective in traffic signal control is to minimize expected delay over all vehicles given the uncertainty associated with the vehicle turn movements at intersections. In order to ensure responsiveness in decision making, a typical approach is to compute a schedule that minimizes the delay for the expected scenario of vehicle movements instead of minimizing expected delay over the feasible vehicle movement scenarios. Such …

Cleanpage: Fast And Clean Document And Whiteboard Capture, Jane Courtney

Articles

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and …

Memory Foreshadow: Memory Forensics Of Hardware Cryptocurrency Wallets – A Tool And Visualization Framework, Tyler Thomas, Mathew Piscitelli, Ilya Shavrov, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

We present Memory FORESHADOW: Memory FOREnSics of HArDware cryptOcurrency Wallets. To the best of our knowledge, this is the primary account of cryptocurrency hardware wallet client memory forensics. Our exploratory analysis revealed forensically relevant data in memory including transaction history, extended public keys, passphrases, and unique device identifiers. Data extracted with FORESHADOW can be used to associate a hardware wallet with a computer and allow an observer to deanonymize all past and future transactions due to hierarchical deterministic wallet address derivation. Additionally, our novel visualization framework enabled us to measure both the persistence and integrity of artifacts produced by the …

Exploring The Learning Efficacy Of Digital Forensics Concepts And Bagging & Tagging Of Digital Devices In Immersive Virtual Reality, Courtney Hassenfeldt, Jillian Jacques, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

This work presents the first account of evaluating learning inside a VR experience created to teach Digital Forensics (DF) concepts, and a hands-on laboratory exercise in Bagging & Tagging a crime scene with digital devices. First, we designed and developed an immersive VR experience which included a lecture and a lab. Next, we tested it with (n = 57) participants in a controlled experiment where they were randomly assigned to a VR group or a physical group. Both groups were subjected to the same lecture and lab, but one was in VR and the other was in the real world. …