Artificial Intelligence and Robotics Commons^™

Analyzing Decision-Making In Robot Soccer For Attacking Behaviors, Justin Rodney

USF Tampa Graduate Theses and Dissertations

In robotics soccer, decision-making is critical to the performance of a team’s SoftwareSystem. The University of South Florida’s (USF) RoboBulls team implements behavior for the robots by using traditional methods such as analytical geometry to path plan and determine whether an action should be taken. In recent works, Machine Learning (ML) and Reinforcement Learning (RL) techniques have been used to calculate the probability of success for a pass or goal, and even train models for performing low-level skills such as traveling towards a ball and shooting it towards the goal[1, 2]. Open-source frameworks have been created for training Reinforcement Learning …

Fighting Mass Diffusion Of Fake News On Social Media, Abdallah Musmar

USF Tampa Graduate Theses and Dissertations

Fake news has been considered one of the most challenging problems in the last few years. The effects of spreading fake news over social media platforms are widely observed across the globe as the depth and velocity of fake news reach far more than real news (Vosoughi et al., 2018). The plan for the following dissertation is to investigate the mass spread of fake news across social media and propose a framework to fight the spread of fake news by mixing preventive methods that could hinder the overall percentage of fake news sharing. We plan to create a study on …

Machine Learning For Species Habitat Analysis, Abigail Lavallin

USF Tampa Graduate Theses and Dissertations

Management and conservation initiatives will always be controlled by finite resources, whether financialor temporal. Understanding a species’ spatial ecology, and how its requirements vary across habitats and locations is key to a successful species management plan. During recent decades, it has been noted how many species populations have declined, despite conservation practices working to increase their numbers. The most prevalent impacts affecting fauna populations have come from anthropogenic change in the form of habitat loss and destruction, along with fragmentation, and global climate change. There is a clear need for management practices to now operate on an entire landscape instead …

Adaptive Network Slicing In Fog Ran For Iot With Heterogeneous Latency And Computing Requirements: A Deep Reinforcement Learning Approach, Almuthanna Nassar

USF Tampa Graduate Theses and Dissertations

In view of the recent advances in Internet of Things (IoT) devices and the emerging new breed of smart city applications and intelligent vehicular systems driven by artificial intelligence, fog radio access network (F-RAN) has been recently introduced for the next generation wireless communications. The capability of F-RAN has emerged to overcome the latency limitations of cloud-RAN (C-RAN) and assure the quality-of-service (QoS) requirements of the ultra-reliable-low-latency-communication (URLLC) for IoT applications. To this end, fog nodes (FNs) are equipped with computing, signal processing and storage capabilities to extend the inherent operations and services of the cloud to the edge. However, …

Optimization And Machine Learning Methods For Solving Combinatorial Problems In Urban Transportation, Aigerim Bogyrbayeva

USF Tampa Graduate Theses and Dissertations

This dissertation investigates three applications of emerging technologies for urban trans- portation. In the first chapter, we design a new market for fractional ownership of au- tonomous vehicles (AVs), in which an AV is co-leased by a group of individuals. We present a practical iterative auction based on the combinatorial clock auction to match the interested customers together and determine their payments. In designing such an auction, we con- sider continuous-time items (time slots) which are defined by bidders, and naturally exploit driverless mobility of AVs to form co-leasing groups. To relieve the computational burdens of both bidders and the …

An Automated Framework For Connected Speech Evaluation Of Neurodegenerative Disease: A Case Study In Parkinson's Disease, Sai Bharadwaj Appakaya

USF Tampa Graduate Theses and Dissertations

Neurodegenerative diseases affect millions of people around the world. The progressive degeneration worsens the symptoms, heavily impacting the quality of life of the patients as well as the caregivers. Speech production is one of the physiological processes affected by neurodegenerative diseases like Alzheimer’s disease, amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD). Speech is the most basic form of communication, and the effect of neurodegeneration degrades speech production, thereby reducing social interaction and mental well-being. PD is the second most common neurodegenerative disease affecting speech production in 90% of the diagnosed individuals. Speech analysis methods for PD in clinical methods …

Exploring The Use Of Neural Transformers For Psycholinguistics, Antonio Laverghetta Jr.

USF Tampa Graduate Theses and Dissertations

Deep learning has the potential to help solve numerous problems in cognitive science andeducation, by providing us a way to model the cognitive profiles of individual people. If this were possible, it would allow us to design targeted tests and suggest specific remediation based on each individual’s needs. On the flip side, employing techniques from psychology can give us insight into the underlying skillsets neural networks have acquired during training, addressing the interpretability concern. This thesis explores these ideas in the context of transformer language models, which have achieved state-of-the-art results on virtually every natural language processing (NLP) task. First, …

Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak

USF Tampa Graduate Theses and Dissertations

The commercial platforms that use recommender systems can collect relevant information to produce useful recommendations to the platform users. However, these sources usually contain missing values, imbalanced and heterogeneous data, and noisy observations. Such characteristics render the process of exploiting the information nontrivial, as one should carefully address them during the data fusion process. In addition to the degenerative characteristics, some entries can be fake, i.e., they can be the outcomes of malicious intents to manipulate the system. These entries should be eliminated before incorporation to any recommendation task. Detecting such malicious attacks quickly and accurately and then mitigating them …

Understanding The Complex Ethical Landscape Of Artificial Intelligence Adoptions, Chrissann R. Ruehle

USF Tampa Graduate Theses and Dissertations

Although Artificial Intelligence (AI) has existed since the 1950’s, it has experienced a series of expansions and declines over the years. Currently, AI is on an upward trajectory and has prompted the fourth industrial revolution as many scientists have noted. Some firms have rapidly embraced this technology and experienced growth while others have been slow to adopt. Naturally, this expansion often has societal impacts. The aim of this study is to explore ethical considerations that arise during the adoption of this technology. This research addressed three questions: 1. How do market and regulatory forces reportedly shape Artificial Intelligence adoptions? 2. …

Next-Generation Self-Organizing Communications Networks: Synergistic Application Of Machine Learning And User-Centric Technologies, Chetana V. Murudkar

USF Tampa Graduate Theses and Dissertations

The telecommunications industry is going through a metamorphic journey where the 5G and 6G technologies will be deeply rooted in the society forever altering how people access and use information. In support of this transformation, this dissertation proposes a fundamental paradigm shift in the design, performance assessment, and optimization of wireless communications networks developing the next-generation self-organizing communications networks with the synergistic application of machine learning and user-centric technologies.

This dissertation gives an overview of the concept of self-organizing networks (SONs), provides insight into the “hot” technology of machine learning (ML), and offers an intuitive understanding of the user-centric (UC) …

Action Recognition Using The Motion Taxonomy, Maxat Alibayev

USF Tampa Graduate Theses and Dissertations

In the last years, modern action recognition frameworks with deep architectures have achieved impressive results on the large-scale activity datasets. All state-of-the-art models share one common attribute: two-stream architectures. One deep model takes RGB frames, while the other model is fed with pre-computed optical flow vectors. The outputs of both models are combined to be used as a final probability distribution for the action classes. When comparing the results of individual models with the fused model, it is common to see that that latter method is more superior. Researchers explain that phenomena with the fact that optical flow vectors serve …

Artificial Intelligence Towards The Wireless Channel Modeling Communications In 5g, Saud Mobark Aldossari

USF Tampa Graduate Theses and Dissertations

Channel prediction is a mathematical predicting of the natural propagation of the signal that helps the receiver to approximate the affected signal, which plays an important role in highly mobile or dynamic channels. The standard wireless communication channel modeling can be facilitated by either deterministic or stochastic channel methodologies. The deterministic approach is based on the electromagnetic theories and every single object in that environment has to be known in that propagation space and an example of this method is ray tracing. While the stochastic modeling method is based on measurements that involve statistical distributions of the channel parameters and …

Functional Object-Oriented Network: A Knowledge Representation For Service Robotics, David Andrés Paulius Ramos

USF Tampa Graduate Theses and Dissertations

In this dissertation, we discuss our work behind the development of the functional object-oriented network (abbreviated as FOON), a graphical knowledge representation for robotic manipulation and understanding of its own actions and (potentially) the intentions of humans in the household. Based on the theory of affordance, this representation captures manipulations and their effects on actions through the coupling of object and motion nodes as fundamental learning units known as functional units. The activities currently represented in FOON are cooking related, but this representation can be extended to other activities that involve manipulation of objects which result in observable changes of …

Evaluating Conversation Agent Impact On Student Experience In A Distance Education Course, Grover Walters

USF Tampa Graduate Theses and Dissertations

We explore the efficacy of conversation agents operating as an instructional aid in a distance education course. Two aspects of efficacy are considered—conversation agent impact on student perceptions of the experience, and how different design features of the agent affect student perceptions of engagement. Evaluation of the agent is accomplished by collecting data from 24 undergraduate participants separated into random groups. We conduct two rounds of mixedmethod evaluation. Between the two rounds, a modification to the agent occurs based on the outcome of the first evaluation. Findings include limitations related to phrasing and data persistence features of the design that …

Detecting Digitally Forged Faces In Online Videos, Neilesh Sambhu

USF Tampa Graduate Theses and Dissertations

We use Rossler’s FaceForensics dataset of 1004 online videos and their corresponding forged counterparts [1] to investigate the ability to distinguish digitally forged facial images from original images automatically with deep learning. The proposed convolutional neural network is much smaller than the current state-of-the-art solutions. Nevertheless, the network maintains a high level of accuracy (99.6%), all while using the entire FaceForensics dataset and not including any temporal information. We implement majority voting and show the impact on accuracy (99.67%), where only 1 video of 300 is misclassified. We examine why the model misclassified this one video. In terms of tuning …

Experiences Of Using Intelligent Virtual Assistants By Visually Impaired Students In Online Higher Education, Michele R. Forbes

USF Tampa Graduate Theses and Dissertations

In today’s world, the attainment of higher education impacts the acquisition of competitive employment and, thus, quality of life. As a group, persons with disabilities continually fall behind others in such academic progress, requiring new efforts to support their earning of advanced credentials. Though highly beneficial for these individuals, obtaining a degree comes with elevated levels of stress. As enrollment of students with disabilities grows in all formats of higher education, those involved must understand the stress endured by these students and how to diminish it. Theories speculate that technology, such as intelligent virtual assistants, may be a viable tool …

Robotic Motion Generation By Using Spatial-Temporal Patterns From Human Demonstrations, Yongqiang Huang

USF Tampa Graduate Theses and Dissertations

Robots excel in manufacturing facilities because the tasks are repetitive and do not change. However, when the tasks change, which happens in almost all tasks that humans perform daily, such as cutting, pouring, and grasping, etc., robots perform much worse. We aim at teaching robots to perform tasks that are subject to change using demonstrations collected from humans, a problem referred to as learning from demonstration (LfD).

LfD consists of two parts: the data of human demonstrations, and the algorithm that extracts knowledge from the data to perform the same motions. Similarly, this thesis is divided into two parts. The …

Emotion Recognition Using Deep Convolutional Neural Network With Large Scale Physiological Data, Astha Sharma

USF Tampa Graduate Theses and Dissertations

Classification of emotions plays a very important role in affective computing and has real-world applications in fields as diverse as entertainment, medical, defense, retail, and education. These applications include video games, virtual reality, pain recognition, lie detection, classification of Autistic Spectrum Disorder (ASD), analysis of stress levels, and determining attention levels. This vast range of applications motivated us to study automatic emotion recognition which can be done by using facial expression, speech, and physiological data.

A person’s physiological signals such are heart rate, and blood pressure are deeply linked with their emotional states and can be used to identify a …

A Machine Learning Framework To Classify Mosquito Species From Smart-Phone Images, Mona Minakshi

USF Tampa Graduate Theses and Dissertations

Mosquito borne diseases have been a constant scourge across the globe resulting in numerous diseases with debilitating consequences, and also death. To derive trends on population of mosquitoes in an area, trained personnel lay traps, and after collecting trapped specimens, they spend hours under a microscope to inspect each specimen for identifying the actual species and logging it. This is vital, because multiple species of mosquitoes can reside in any area, and the vectors that some of them carry are not the same ones carried by others. The species identification process is naturally laborious, and imposes severe cognitive burden, since …

A Fall Prevention System For The Elderly And Visually Impaired, Yueng Santiago De La Hoz Isaza

USF Tampa Graduate Theses and Dissertations

The World Health Organization claims that there are more than 285 million blind and visually impaired people in the world. In the US, 25 million Americans suffer from total or partial vision loss. As a result of their impairment, they struggle with mobility problems, especially the risk of falling. According to the National Council On Aging, falls are among the primary causes for fatal injury and they are the most common cause of non-fatal trauma-related hospital admissions among older adults. Visibility, an organization that helps visually impaired people, reports that people with visual impairments are twice as likely to fall …

Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc

USF Tampa Graduate Theses and Dissertations

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …

Context-Based Human Activity Recognition Using Multimodal Wearable Sensors, Pratool Bharti

USF Tampa Graduate Theses and Dissertations

In the past decade, Human Activity Recognition (HAR) has been an important part of the regular day to day life of many people. Activity recognition has wide applications in the field of health care, remote monitoring of elders, sports, biometric authentication, e-commerce and more. Each HAR application needs a unique approach to provide solutions driven by the context of the problem. In this dissertation, we are primarily discussing two application of HAR in different contexts. First, we design a novel approach for in-home, fine-grained activity recognition using multimodal wearable sensors on multiple body positions, along with very small Bluetooth beacons …

Semantic Description Of Activities In Videos, Fillipe Dias Moreira De Souza

USF Tampa Graduate Theses and Dissertations

Description of human activities in videos results not only in detection of actions and objects but also in identification of their active semantic relationships in the scene. Towards this broader goal, we present a combinatorial approach that assumes availability of algorithms for detecting and labeling objects and actions, albeit with some errors. Given these uncertain labels and detected objects, we link them into interpretative structures using domain knowledge encoded with concepts of Grenander’s general pattern theory. Here a semantic video description is built using basic units, termed generators, that represent labels of objects or actions. These generators have multiple out-bonds, …

Digital Integration, Jacob C. Boccio

USF Tampa Graduate Theses and Dissertations

Artificial intelligence is an emerging technology; something far beyond smartphones, cloud integration, or surgical microchip implantation. Utilizing the work of Ray Kurzweil, Nick Bostrom, and Steven Shaviro, this thesis investigates technology and artificial intelligence through the lens of the cinema. It does this by mapping contemporary concepts and the imagined worlds in film as an intersection of reality and fiction that examines issues of individual identity and alienation. I look at a non-linear timeline of films involving machine advancement, machine intelligence, and stages of post-human development; Elysium (2013) and Surrogates (2009) are about technology as an extension of the self, …

Scene-Dependent Human Intention Recognition For An Assistive Robotic System, Kester Duncan

USF Tampa Graduate Theses and Dissertations

In order for assistive robots to collaborate effectively with humans for completing everyday tasks, they must be endowed with the ability to effectively perceive scenes and more importantly, recognize human intentions. As a result, we present in this dissertation a novel scene-dependent human-robot collaborative system capable of recognizing and learning human intentions based on scene objects, the actions that can be performed on them, and human interaction history. The aim of this system is to reduce the amount of human interactions necessary for communicating tasks to a robot. Accordingly, the system is partitioned into scene understanding and intention recognition modules. …

Robotic Swarming Without Inter-Agent Communication, Daniel Jonathan Standish

USF Tampa Graduate Theses and Dissertations

Many physical and algorithmic swarms utilize inter-agent communication to achieve advanced swarming behaviors. These swarms are inspired by biological swarms that can be seen throughout nature and include bee swarms, ant colonies, fish schools, and bird flocks. These biological swarms do not utilize inter-agent communication like their physical and algorithmic counterparts. Instead, organisms in nature rely on a local awareness of other swarm members that facilitates proper swarm motion and behavior. This research aims to pursue an effective swarm algorithm using only line-of-sight proximity information and no inter-agent communication. It is expected that the swarm performance will be lower than …

A Location-Aware Architecture Supporting Intelligent Real-Time Mobile Applications, Sean J. Barbeau

USF Tampa Graduate Theses and Dissertations

This dissertation presents LAISYC, a modular location-aware architecture for intelligent real-time mobile applications that is fully-implementable by third party mobile app developers and supports high-precision and high-accuracy positioning systems such as GPS. LAISYC significantly improves device battery life, provides location data authenticity, ensures security of location data, and significantly reduces the amount of data transferred between the phone and server. The design, implementation, and evaluation of LAISYC using real mobile phones include the following modules: the GPS Auto-Sleep module saves battery energy when using GPS, maintaining acceptable movement tracking (approximately 89% accuracy) with an approximate average doubling of battery life. …

A Location-Based Incentive Mechanism For Participatory Sensing Systems With Budget Constraints, Luis Gabriel Jaimes

USF Tampa Graduate Theses and Dissertations

Participatory Sensing (PS) systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their

cellular phones. Users agree to use their cellular phone resources to sense and transmit the data of interest because these data will be used to address a collective problem that otherwise would

be very difficult to assess and solve. However, this new data collection paradigm has not been very successful yet mainly because of the lack of incentives for participation and privacy concerns. Without adequate incentive and privacy guaranteeing …

A Spatially Explicit Agent Based Model Of Muscovy Duck Home Range Behavior, James Howard Anderson

USF Tampa Graduate Theses and Dissertations

ABSTRACT

Research in GIScience has identified agent-based simulation methodologies as effective in the study of complex adaptive spatial systems (CASS). CASS are characterized by the emergent nature of their spatial expressions and by the changing relationships between their constituent variables and how those variables act on the system's spatial expression over time. Here, emergence refers to a CASS property where small-scale, individual action results in macroscopic or system-level patterns over time. This research develops and executes a spatially-explicit agent based model of Muscovy Duck home range behavior. Muscovy duck home range behavior is regarded as a complex adaptive spatial system …