Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Artificial intelligence (4)
- Business-to-business sales (2)
- Content personalization (2)
- Customer experience (2)
- Information Encoding (2)
-
- LSTM (2)
- Navigation (2)
- Photo-realistic avatars (2)
- Place Cells (2)
- Synthetic media (2)
- Transfer Learning (2)
- AI (1)
- Dimension reduction (1)
- EV charging (1)
- Federated Learning (1)
- Home energy management system (1)
- Hospital capacity (1)
- Language model (1)
- Large language models (1)
- NAEP (1)
- NLP (1)
- Non-IID data (1)
- Privacy Preservation (1)
- Quantitative literacy (1)
- Reliability (1)
- Resource allocation (1)
- Sea level rise (1)
- Validity (1)
- Publication Type
Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros
Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros
USF Tampa Graduate Theses and Dissertations
The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …
Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros
Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros
USF Tampa Graduate Theses and Dissertations
The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …
Human Vs Machine: Hyper-Realistic Avatars And Their Efficacy As A Communication Channel, Jill S. Schiefelbein
Human Vs Machine: Hyper-Realistic Avatars And Their Efficacy As A Communication Channel, Jill S. Schiefelbein
USF Tampa Graduate Theses and Dissertations
Hyper-realistic avatars (HRAs), a form of synthetic media, are custom-created digital embodiments of a human, created by capturing and combining that person’s video and vocal likeness. This is the first known study of the efficacy of videos delivered by hyper-realistic avatars as a communication channel in comparison to videos delivered by their human counterparts. An experiment testing how information retention, engagement, and trust vary between viewers of videos delivered by a real human, videos delivered by the HRA representing that same human, and videos delivered by the HRA that discloses to viewers that it is a hyper-realistic avatar is presented. …
Human Vs Machine: Hyper-Realistic Avatars And Their Efficacy As A Communication Channel, Jill S. Schiefelbein
Human Vs Machine: Hyper-Realistic Avatars And Their Efficacy As A Communication Channel, Jill S. Schiefelbein
USF Tampa Graduate Theses and Dissertations
Hyper-realistic avatars (HRAs), a form of synthetic media, are custom-created digital embodiments of a human, created by capturing and combining that person’s video and vocal likeness. This is the first known study of the efficacy of videos delivered by hyper-realistic avatars as a communication channel in comparison to videos delivered by their human counterparts. An experiment testing how information retention, engagement, and trust vary between viewers of videos delivered by a real human, videos delivered by the HRA representing that same human, and videos delivered by the HRA that discloses to viewers that it is a hyper-realistic avatar is presented. …
A Psychometric Analysis Of Natural Language Inference Using Transformer Language Models, Antonio Laverghetta Jr.
A Psychometric Analysis Of Natural Language Inference Using Transformer Language Models, Antonio Laverghetta Jr.
USF Tampa Graduate Theses and Dissertations
Large language models (LLMs) are poised to transform both academia and industry. But the excitement around these generative AIs has also been met with concern for the true extent of their capabilities. This dissertation helps to address these questions by examining the capabilities of LLMs using the tools of psychometrics. We focus on analyzing the capabilities of LLMs on the task of natural language inference (NLI), a foundational benchmark often used to evaluate new models. We demonstrate that LLMs can reliably predict the psychometric properties of NLI items were those items administered to humans. Through a series of experiments, we …
Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen
Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen
USF Tampa Graduate Theses and Dissertations
Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …
Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo
Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo
USF Tampa Graduate Theses and Dissertations
Optimization, which refers to making the best or most out of a system, is critical for an organization's strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have …
Artificial Intelligence, Basic Skills, And Quantitative Literacy, Gizem Karaali
Artificial Intelligence, Basic Skills, And Quantitative Literacy, Gizem Karaali
Numeracy
The introduction in November 2022 of ChatGPT, a freely available language-based artificial intelligence, has led to concerns among some educators about the feasibility and benefits of teaching basic writing and critical thinking skills to students in the context of easily accessed, AI-based cheating mechanisms. As of now, ChatGPT can write pretty convincing student-level prose, but it is still not very good at answering quantitatively rich questions. Therefore, for the time being, the preceding concerns may not be shared by a large portion of the numeracy education community. However, as Google and WolframAlpha are definitely capable of answering standard and some …