Physical Sciences and Mathematics Commons^™

Deciphering Trends And Tactics: Data-Driven Techniques For Forecasting Information Spread And Detecting Coordinated Campaigns In Social Media, Kin Wai Ng Lugo

USF Tampa Graduate Theses and Dissertations

The main objective of this dissertation is to develop models that predict and investigate the spread of information in social media over time. In this context, we consider topics of discussions as the information that spreads. Thus, we are interested in forecasting the number of messages per day in a future interval of time. We take a data-driven approach, in which we compare our results with real datasets from a multitude of socio-political contexts and from multiple social media platforms, specifically, Twitter and YouTube.

We identified a number of challenges related to forecasting social media time series per topic. First, …

Evaluating Methods For Improving Dnn Robustness Against Adversarial Attacks, Laureano Griffin

USF Tampa Graduate Theses and Dissertations

Deep learning has become more widespread as advances in the field continue. As aresult, making sure deep learning is safe has become a priority. A seemingly normal image with intentional pixel changes can cause a well-trained model to misclassify the image with high confidence. Those kinds of images are called adversarial attacks. Adversarial training has been developed to defend against adversarial attacks. This thesis evaluates different adversarial training methods against a variety of adversarial attacks. The key metrics for evaluation are classification accuracy and training time. This thesis also experiments with an improvement on an existing adversarial training method, the …

Graph Analysis On Social Networks, Shen Lu

USF Tampa Graduate Theses and Dissertations

With the development of transportation network, social network, and communication network, there are many applications in streaming data. For example, traffic congestion happens between the origin and destination of daily trips. Traffic analysis can help plan the trips so that traffic congestion can be avoided. Social network and communication network represent the behaviors of the entire population. People build connections based on their hobbies, daily activities, photos, videos, simple messages, and even anonymous web surfing. All of these can be turned into commercial use, such as product marketing, business network building, and technology trending. Data science is about how to …

Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin

USF Tampa Graduate Theses and Dissertations

Neonates can not express their pain like an adult person. Due to the lacking of proper muscle growth and inability to express non-verbally, it is difficult to understand their emotional status. In addition, if the neonates are under any treatment or left monitored after any major surgeries (post-operative), it is more difficult to understand their pain due to the side effect of medications and the caring system (i.e. intubated, masked face, covered body with blanket, etc.). In a clinical environment, usually, bedside nurses routinely observe the neonate and measure the pain status following any standard clinical pain scale. But current …

Video Anomaly Detection: Practical Challenges For Learning Algorithms, Keval Doshi

USF Tampa Graduate Theses and Dissertations

Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of several existing methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, real-time decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Furthermore, several critical tasks such as continual learning, model interpretability and cross-domain adaptability are completely neglected in existing works. Motivated by these research gaps, in this dissertation we discuss our …

Automated Identification Of Stages In Gonotrophic Cycle Of Mosquitoes Using Computer Vision Techniques, Sherzod Kariev

USF Tampa Graduate Theses and Dissertations

In this paper, we design Computer Vision techniques to determine stages in the Gonotrophic cycle of mosquitoes. The dataset for our problem came from 125 adult female mosquitoes - each of which belonged to one of three species - Aedes aegypti, Culex quinquefasciatus, and Anopheles stephensi. The mosquitoes were raised in a lab and passed through all fourGonotrophic stages (Un-fed, Fully-fed, Semi-gravid, and Gravid). At each stage, their images were captured on a plain background via a Xiaomi smartphone, resulting in a dataset of 1784 images. The images were then augmented using standard techniques to generate a larger dataset of …

Design, Deployment, And Validation Of Computer Vision Techniques For Societal Scale Applications, Arup Kanti Dey

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence techniques have ensued a significant impact on our daily lives. Numerous applications in so many diverse fields have been made possible by AI algorithms today, and there are many more yet to come. In this dissertation, we design, deploy and validate computer vision algorithms for innovative and high-impact societal scale applications.We specifically focus on two applications in this dissertation: Detection of distracted driving and Detection of breeding habitats of mosquito vectors.

Distracted driving on roads is a major problem around the world. Distracted driving is the case where a driver diverts his/her focus from the road and engages …

Knowledge Extraction And Inference Based On Visual Understanding Of Cooking Contents, Ahmad Babaeian Babaeian Jelodar

USF Tampa Graduate Theses and Dissertations

In this dissertation, we discuss our work on analyzing cooking content for the ultimate goal ofautomatic robotic manipulation. For a robot to perform a cooking task, it will need to both have an understanding of the scene and utilize prior knowledge. We will explore two main sub-problems: knowledge extraction and inference, and visual understanding of the scene in this dissertation. Visual understanding of a scene, requires algorithms that can visually infer information from a single image or video. Many algorithms in the area of image classification, object detection, or activity recognition can be used in this area. Although great advances …

Strategies In Botnet Detection And Privacy Preserving Machine Learning, Di Zhuang

USF Tampa Graduate Theses and Dissertations

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this dissertation, we present two P2P botnet detection systems, PeerHunter and Enhanced PeerHunter. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Enhanced PeerHunter is an …

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, …

Automating The Classification Of Mosquito Specimens Using Image Processing Techniques, Mona Minakshi

USF Tampa Graduate Theses and Dissertations

According to WHO (World Health Organization) reports, among all animals, mosquitoes are responsible for the most deaths worldwide. Mosquito borne diseases continue to pose grave dangers to global health. In 2015 alone, 214 million cases of malaria were registered worldwide. According to Centers for Disease Control and Prevention (CDC) report published in 2016, 62,500 suspected case of Zika were reported to the Puerto Rico Department of Health (PRDH) out of which 29,345 cases were found positive. The year 2019 was recorded as the worst for dengue in South East Asia. There are close to 4,500 species of mosquitoes (spread across …

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 …

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 …