Computer Engineering Commons^™

Practical Considerations And Applications For Autonomous Robot Swarms, Rory Alan Hector

LSU Doctoral Dissertations

In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

Performing Memory Forensics For Object Recovery From Android Application Memory, Sneha Sudhakaran

LSU Doctoral Dissertations

The analysis of application-specific behavior has become an increasingly important technique in cyber forensics and incident response. The ability to determine the precise actions taken by a user can be the difference between a successful analysis and one that fails to meet its goals. The precise actions includes URLs visited, files downloaded, messages sent and received, images viewed, and data accessed. Evidence extraction from application memory at runtime is an effective solution to successfully extract valuable objects allocated by each application, and it is evident that there is a need for more Android forensics analysis tools that support recovering evidence …

Human-Machine Communication: Complete Volume 4

Human-Machine Communication

This is the complete volume of HMC Volume 4.

Embracing Ai-Based Education: Perceived Social Presence Of Human Teachers And Expectations About Machine Teachers In Online Education, Jihyun Kim, Kelly Merrill Jr., Kun Xu, Deanna D. Sellnow

Human-Machine Communication

Technological advancements in education have turned the idea of machines as teachers into a reality. To better understand this phenomenon, the present study explores how college students develop expectations (or anticipations) about a machine teacher, particularly an AI teaching assistant. Specifically, the study examines whether students’ previous experiences with online courses taught by a human teacher would influence their expectations about AI teaching assistants in future online courses. An online survey was conducted to collect data from college students in the United States. Findings indicate that positively experienced social presence of a human teacher helps develop positive expectations about an …

Sex With Robots And Human-Machine Sexualities: Encounters Between Human-Machine Communication And Sexuality Studies, Marco Dehnert

Human-Machine Communication

Sex robots are a controversial topic. Understood as artificial-intelligence enhanced humanoid robots designed for use in partnered and solo sex, sex robots offer ample opportunities for theorizing from a Human-Machine Communication (HMC) perspective. This comparative literature review conjoins the seemingly disconnected literatures of HMC and sexuality studies (SeS) to explore questions surrounding intimacy, love, desire, sex, and sexuality among humans and machines. In particular, I argue for understanding human-machine sexualities as communicative sexuotechnical-assemblages, extending previous efforts in both HMC and SeS for more-than-human, ecological, and more fluid approaches to humans and machines, as well as to sex and sexuality. This …

I Get By With A Little Help From My Bots: Implications Of Machine Agents In The Context Of Social Support, Austin Beattie, Andrew C. High

Human-Machine Communication

In this manuscript we discuss the increasing use of machine agents as potential sources of support for humans. Continued examination of the use of machine agents, particularly chatbots (or “bots”) for support is crucial as more supportive interactions occur with these technologies. Building off extant research on supportive communication, this manuscript reviews research that has implications for bots as support providers. At the culmination of the literature review, several propositions regarding how factors of technological efficacy, problem severity, perceived stigma, and humanness affect the process of support are proposed. By reviewing relevant studies, we integrate research on human-machine and supportive …

Considering The Context To Build Theory In Hci, Hri, And Hmc: Explicating Differences In Processes Of Communication And Socialization With Social Technologies, Andrew Gambino, Bingjie Liu

Human-Machine Communication

The proliferation and integration of social technologies has occurred quickly, and the specific technologies with which we engage are ever-changing. The dynamic nature of the development and use of social technologies is often acknowledged by researchers as a limitation. In this manuscript, however, we present a discussion on the implications of our modern technological context by focusing on processes of socialization and communication that are fundamentally different from their interpersonal corollary. These are presented and discussed with the goal of providing theoretical building blocks toward a more robust understanding of phenomena of human-computer interaction, human-robot interaction, human-machine communication, and interpersonal …

Image Provenance Analysis, Daniel Moreira, William Theisen, Walter Scheirer, Aparna Bharati, Joel Brogan, Anderson Rocha

Computer Science: Faculty Publications and Other Works

The literature of multimedia forensics is mainly dedicated to the analysis of single assets (such as sole image or video files), aiming at individually assessing their authenticity. Different from this, image provenance analysis is devoted to the joint examination of multiple assets, intending to ascertain their history of edits, by evaluating pairwise relationships. Each relationship, thus, expresses the probability of one asset giving rise to the other, through either global or local operations, such as data compression, resizing, color-space modifications, content blurring, and content splicing. The principled combination of these relationships unveils the provenance of the assets, also constituting an …

Using Deep Neural Network And Transformers To Extract Graphene Compounds And Properties, Ayman Ibn Jaman

Computer Science Graduate Research Workshop

No abstract provided.

Visual Homing For Robot Teams: Do You See What I See?, Damian Lyons, Noah Petzinger

Faculty Publications

Visual homing is a lightweight approach to visual navigation which does not require GPS. It is very attractive for robot platforms with a low computational capacity. However, a limitation is that the stored home location must be initially within the field of view of the robot. Motivated by the increasing ubiquity of camera information we propose to address this line-of-sight limitation by leveraging camera information from other robots and fixed cameras. To home to a location that is not initially within view, a robot must be able to identify a common visual landmark with another robot that can be used …

K-Means Clustering Using Gravity Distance, Ajinkya Vishwas Indulkar

Masters Theses & Specialist Projects

Clustering is an important topic in data modeling. K-means Clustering is a well-known partitional clustering algorithm, where a dataset is separated into groups sharing similar properties. Clustering an unbalanced dataset is a challenging problem in data modeling, where some group has a much larger number of data points than others. When a K-means clustering algorithm with Euclidean distance is applied to such data, the algorithm fails to form good clusters. The standard K-means tends to split data into smaller clusters during a clustering process evenly.

We propose a new K-means clustering algorithm to overcome the disadvantage by introducing a different …

The Causal Fairness Field Guide: Perspectives From Social And Formal Sciences, Alycia Carey, Xintao Wu

Computer Science and Computer Engineering Faculty Publications and Presentations

Over the past several years, multiple different methods to measure the causal fairness of machine learning models have been proposed. However, despite the growing number of publications and implementations, there is still a critical lack of literature that explains the interplay of causality-based fairness notions with the social sciences of philosophy, sociology, and law. We hope to remedy this issue by accumulating and expounding upon the thoughts and discussions of causality-based fairness notions produced by both social and formal (specifically machine learning) sciences in this field guide. In addition to giving the mathematical backgrounds of several popular causality-based fair machine …

Cova Cci Undergrad Cyber Research, Nana Jeffrey

Cybersecurity Undergraduate Research Showcase

Is your digital assistant your worst enemy? Modern technology has impacted our lives in a positive way making tasks that were once time consuming become more convenient. For example a few years ago writing down your grocery list with a paper and pen was a norm, now with technology we have access to IoT devices such as smart fridges that can inform us on what items are low in stock, send a message to our digital assistants such as iOS Siri and Amazon's Alexa to remind us to buy those groceries. Although these digital assistants have helped make our daily …

Towards Improved Inertial Navigation By Reducing Errors Using Deep Learning Methodology, Hua Chen, Tarek M. Taha, Vamsy P. Chodavarapu

Electrical and Computer Engineering Faculty Publications

Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage. The INS unit incorporates an Inertial Measurement Unit (IMU) to process the linear acceleration and angular velocity data to obtain orientation, position, and velocity information using mechanization equations. In this work, we describe a novel deep-learning-based methodology, using Convolutional Neural Networks (CNN), to reduce errors from MEMS IMU sensors. We develop a CNN-based approach that can learn from the responses of a particular inertial sensor …

Discovering Ways To Increase Inclusivity For Dyslexic Students In Computing Education, Felicia Hellems, Sajal Bhatia

School of Computer Science & Engineering Faculty Publications

The years accompanying entrance into the university system are often characterized by a period of great transformation. These years can also be wrought with difficulties for many students, difficulties which are often compounded in students with disabilities (SWD). Reports from the U.S. Department of Education show that as recently as 2015--16, 19% of undergraduate students experienced some form of disability1. Additionally, statistics show that SWD tend to have lower post secondary completion rates than their counterparts [3]. A review of pertinent literature has shown that there still exist gaps within the field of computing education (CE) for teaching cybersecurity concepts …

Assessing Security Risks With The Internet Of Things, Faith Mosemann

Senior Honors Theses

For my honors thesis I have decided to study the security risks associated with the Internet of Things (IoT) and possible ways to secure them. I will focus on how corporate, and individuals use IoT devices and the security risks that come with their implementation. In my research, I found out that IoT gadgets tend to go unnoticed as a checkpoint for vulnerability. For example, often personal IoT devices tend to have the default username and password issued from the factory that a hacker could easily find through Google. IoT devices need security just as much as computers or servers …

Generative Adversarial Networks Take On Hand Drawn Sketches: An Application To Louisiana Culture And Mardi Gras Fashion, Stephanie Hines

Honors Capstones

No abstract provided.

Improving Feature Generalizability With Multitask Learning In Class Incremental Learning, Dong Ma, Chi Ian Tang, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Many deep learning applications, like keyword spotting [1], [2], require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL). The major challenge in CIL is catastrophic forgetting, i.e., preserving as much of the old knowledge as possible while learning new tasks. Various techniques, such as regularization, knowledge distillation, and the use of exemplars, have been proposed to resolve this issue. However, prior works primarily focus on the incremental learning step, while ignoring the optimization during the base model training. We hypothesise that a more transferable and generalizable feature representation from the base model would …

Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li

Research Collection School Of Computing and Information Systems

Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been designed for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g., sequence, structure and network data. However, it is challenging to effectively integrate these data sources and automatically extract features for different link prediction tasks. Results: In …

Verifiable Searchable Encryption Framework Against Insider Keyword-Guessing Attack In Cloud Storage, Yinbin Miao, Robert H. Deng, Kim-Kwang Raymond Choo, Ximeng Liu, Hongwei Li

Research Collection School Of Computing and Information Systems

Searchable encryption (SE) allows cloud tenants to retrieve encrypted data while preserving data confidentiality securely. Many SE solutions have been designed to improve efficiency and security, but most of them are still susceptible to insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. Also in existing SE solutions, a semi-honest-but-curious cloud server may deliver incorrect search results by performing only a fraction of retrieval operations honestly (e.g., to save storage space). To address these two challenging issues, we first construct the basic Verifiable SE Framework (VSEF), which can withstand …

Quadratic Neural Network Architecture As Evaluated Relative To Conventional Neural Network Architecture, Reid Taylor

Senior Theses

Current work in the field of deep learning and neural networks revolves around several variations of the same mathematical model for associative learning. These variations, while significant and exceptionally applicable in the real world, fail to push the limits of modern computational prowess. This research does just that: by leveraging high order tensors in place of 2nd order tensors, quadratic neural networks can be developed and can allow for substantially more complex machine learning models which allow for self-interactions of collected and analyzed data. This research shows the theorization and development of mathematical model necessary for such an idea to …

On The Reliability Of Wearable Sensors For Assessing Movement Disorder-Related Gait Quality And Imbalance: A Case Study Of Multiple Sclerosis, Steven Díaz Hernández

USF Tampa Graduate Theses and Dissertations

Approximately 33 million American adults had a movement disorder associated with medication use, ear infections, injury, or neurological disorders in 2008, with over 18 million people affected by neurological disorders worldwide. Physical therapists assist people with movement disorders by providing interventions to reduce pain, improve mobility, avoid surgeries, and prevent falls and secondary complications of neurodegenerative disorders. Current gait assessments used by physical therapists, such as the Multiple Sclerosis Walking Scale, provide only semi-quantitative data, and cannot assess walking quality in detail or describe how one’s walking quality changes over time. As a result, quantitative systems have grownas useful tools …

A Combined Approach For Private Indexing Mechanism, Pranita Maruti Desai Ms., Vijay Maruti Shelake Mr.

Journal of Digital Forensics, Security and Law

Private indexing is a set of approaches for analyzing research data that are similar or resemble similar ones. This is used in the database to keep track of the keys and their values. The main subject of this research is private indexing in record linkage to secure the data. Because unique personal identification numbers or social security numbers are not accessible in most countries or databases, data linkage is limited to attributes such as date of birth and names to distinguish between the number of records and the real-life entities they represent. For security reasons, the encryption of these identifiers …

Research On 3d Path Planning Algorithm Based On Fast Rrt Algorithm, Zhaoqiang Li, Shiyu Zhang

Journal of System Simulation

Abstract: RRT (rapidly exploring random tree) algorithm is a sampling-based path planning algorithm, which can search a path in high-dimensional environment. The traditional RRT algorithm has the problems of low node utilization and large amount of calculation. To solve these problems, the fast RRT* (Quick RRT*) algorithm is improved by optimizing the strategy of reselection of parent node and pruning range, improving the sampling method and introducing adaptive step size, which makes the algorithm time-consuming and path length shorter. At the same time, the node connection screening strategy is added to eliminate the excessive turning angle in the path. …

Research On Real-Time Motion Matching Of Shadow Play Based On Kinect, Chuanqian Tang, Zhiqiang Liu, Yijun Su, Xiaojing Liu

Journal of System Simulation

Abstract: In the inheritance of shadow play culture, due to the aging of the audience and the discontinuity of inheritance, the shadow play culture is gradually facing decline. Real-time matching of shadow play movements based on Kinect can inject new vitality into traditional shadow play culture. According to the characteristics of shadow play, a joint point shadow play model is constructed, and the static digitization of shadow play is realized. The human body depth image is obtained based on Kinect, and the human skeleton point coordinates are obtained through segmentation mask and machine learning to generate the human skeleton.Bone …

Effectiveness Evaluation Of Surface Ship Air Defense And Antimissile Combat In Complex Electromagnetic Environment, Gaofeng Zhang, Liang Wu

Journal of System Simulation

Abstract: In order to effectively evaluate the effectiveness of surface ship air defense and antimissile combat in complex electromagnetic environment, a surface ship air defense and antimissile combat effectiveness index system is established considering the influence of equipment, environment and human behavior, the evaluation process of surface ship air defense and antimissile combat effectiveness based on analytic hierarchy process(AHP) is proposed, and a hierarchical structure model of effectiveness evaluation is constructed including five levels of target layer, sub-efficiency layer, capability layer, constraint layer and plan layer. The application shows that the evaluation process and structure model can fully reflect the …

New Embedded Simulation Technology For Smart Internet Of Things, Bohu Li, Xudong Chai, Lin Zhang, Duzheng Qing, Guoqiang Shi, Tingyu Lin, Liqin Guo, Chen Yang, Mu Gu, Zhengxuan Jia, Hui Gong, Zhen Tang

Journal of System Simulation

Abstract: Human society in the new development era and journey is facing the new situation. The operation paradigm, technology and ecosystem of industries related to the national economy and people's livelihood、national security are changing significantly towards the digital, networked, cloud-based and intelligent "Smart Internet of Things". The new embedded simulation technology, with the capabilities of online and continuous analysis, cognition, learning, decision-making, operation and optimization,is urgently needed for the development of "Smart Internet of Things". "Smart Internet of Things" is briefly introduced and the connotation, characteristics and application mode of the new embedded simulation technology are proposed and its architecture, …

Open Cloud Architecture Design For Complex Product Modeling And Simulation System, Guoqiang Shi, Zewei Liu, Tingyu Lin, Zhao Xu, Xingyi Yang, Liqin Guo, Zhengxuan Jia

Journal of System Simulation

Abstract: Aiming at the problem that the complex product modeling and simulation system focuses on co-simulation of heterogeneous models and cannot realize the on-demand sharing and collaboration of simulation resources, this paper proposes an open cloud architecture for complex product modeling and simulation systems, realized on-demand sharing and collaboration of cross-organizational simulation software and hardware resources, thereby supporting complex product system-wide, full-lifecycle, anytime, anywhere, real-time, coherent, and transparently requesting accessing and obtaining simulation services. The object-process methodology (OPM) is used to model and deduce the simulation interoperability of the system and the on-demand sharing and collaborative process of simulation resources. …

Research On Integrated Scheduling Of Agv And Machine In Flexible Job Shop, Kui Chen, Li Bi, Wenya Wang

Journal of System Simulation

Abstract: Aiming at the flexible job shop scheduling problem with AGV (automated guided vehicle), a dual resource integrated scheduling optimization model with the objective of minimizing makespan is established. In the process of population initialization, a heuristic initialization method is proposed to improve the quality of population initial solution and accelerate the convergence speed of the algorithm. A hybrid discrete particle swarm optimization algorithm that can effectively avoid premature maturation is proposed by combining the competitive learning mechanism and the random restart mechanism to address the disadvantages of discrete particle swarm algorithms that are prone to premature maturation. Simulation experiments …