Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications,
2022
Old Dominion University
Post-Quantum Secure Identity-Based Encryption Scheme Using Random Integer Lattices For Iot-Enabled Ai Applications, Dharminder Dharminder, Ashok Kumar Das, Sourav Saha, Basudeb Bera, Athanasios V. Vasilakos
VMASC Publications
Identity-based encryption is an important cryptographic system that is employed to ensure confidentiality of a message in communication. This article presents a provably secure identity based encryption based on post quantum security assumption. The security of the proposed encryption is based on the hard problem, namely Learning with Errors on integer lattices. This construction is anonymous and produces pseudo random ciphers. Both public-key size and ciphertext-size have been reduced in the proposed encryption as compared to those for other relevant schemes without compromising the security. Next, we incorporate the constructed identity based encryption (IBE) for Internet of Things (IoT) applications, …
The Performance Optimization Of Asp Solving Based On Encoding Rewriting And Encoding Selection,
2022
University of Kentucky
The Performance Optimization Of Asp Solving Based On Encoding Rewriting And Encoding Selection, Liu Liu
Theses and Dissertations--Computer Science
Answer set programming (ASP) has long been used for modeling and solving hard search problems. These problems are modeled in ASP as encodings, a collection of rules that declaratively describe the logic of the problem without explicitly listing how to solve it. It is common that the same problem has several different but equivalent encodings in ASP. Experience shows that the performance of these ASP encodings may vary greatly from instance to instance when processed by current state-of-the-art ASP grounder/solver systems. In particular, it is rarely the case that one encoding outperforms all others. Moreover, running an ASP system on …
Don't Give Me That Story! -- A Human-Centered Framework For Usable Narrative Planning,
2022
University of Kentucky
Don't Give Me That Story! -- A Human-Centered Framework For Usable Narrative Planning, Rachelyn Farrell
Theses and Dissertations--Computer Science
Interactive or branching stories are engaging and can be embedded into digital systems for a variety of purposes, but their size and complexity makes it difficult and time-consuming for humans to author them. Narrative planning algorithms can automatically generate large branching stories with guaranteed causal consistency, using a hand-authored library of story content pieces. The usability of such a system depends on both the quality of the narrative model upon which it is built and the ability of the user to create the story content library.
Current narrative planning algorithms use either a limited or no model of character belief, …
Development Of Accurate And Efficient Computational Methodologies For Predicting Protein-Ligand And Protein-Protein Binding Free Energies,
2022
University of Kentucky
Development Of Accurate And Efficient Computational Methodologies For Predicting Protein-Ligand And Protein-Protein Binding Free Energies, Alexander Hamilton Williams
Theses and Dissertations--Pharmacy
Computational modeling is an invaluable tool in the drug discovery process either for small ligand or protein therapeutics. The widespread availability of protein X-Ray Crystal and Cryo-Electron Microscopy (Cryo-EM) structures has allowed for more accurate molecular dynamics (MD) simulations that are not reliant on methods such as homology modeling, which may produce structures that require significant computational time to demonstrate their stability. In this thesis we describe several novel methodologies for the computationally efficient modeling of protein/ligand and protein/protein complexes that may be employed within both large-scale virtual screenings and lead compound optimization. These methodologies may also be utilized in …
On-Ice Detection, Classification, Localization And Tracking Of Anthropogenic Acoustic Sources With Machine Learning,
2022
Michigan Technological University
On-Ice Detection, Classification, Localization And Tracking Of Anthropogenic Acoustic Sources With Machine Learning, Steven J. Whitaker
Dissertations, Master's Theses and Master's Reports
Arctic acoustics have been of concern in recent years for the US navy. First-year ice is now the prevalent factor in ice coverage in the Arctic, which changes the previously understood acoustic properties. Due to the ice melting each year, anthropogenic sources in the Arctic region are more common: military exercises, shipping, and tourism. For the navy, it is of interest to detect, classify, localize, and track these sources to have situational awareness of these surroundings. Because the sources are on-water or on-ice, acoustic radiation propagates at a longer distance and so acoustics are the method by which the sources …
Supporting Stylized Language Models Using Multi-Modality Features,
2022
University of Kentucky
Supporting Stylized Language Models Using Multi-Modality Features, Chengxi Li
Theses and Dissertations--Computer Science
As AI and machine learning systems become more common in our everyday lives, there is an increased desire to construct systems that are able to seamlessly interact and communicate with humans. This typically means creating systems that are able to communicate with humans via natural language. Given the variance of natural language, this can be a very challenging task. In this thesis, I explored the topic of humanlike language generation in the context of stylized language generation. Stylized language generation involves producing some text that exhibits a specific, desired style. In this dissertation, I specifically explored the use of multi-modality …
Smart Decision-Making Via Edge Intelligence For Smart Cities,
2022
University of Kentucky
Smart Decision-Making Via Edge Intelligence For Smart Cities, Nathaniel Hudson
Theses and Dissertations--Computer Science
Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and …
Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions,
2022
Old Dominion University
Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Automatic classification of child facial expressions is challenging due to the scarcity of image samples with annotations. Transfer learning of deep convolutional neural networks (CNNs), pretrained on adult facial expressions, can be effectively finetuned for child facial expression classification using limited facial images of children. Recent work inspired by facial age estimation and age-invariant face recognition proposes a fusion of facial landmark features with deep representation learning to augment facial expression classification performance. We hypothesize that deep transfer learning of child facial expressions may also benefit from fusing facial landmark features. Our proposed model architecture integrates two input branches: a …
Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things,
2022
Old Dominion University
Ascp-Iomt: Ai-Enabled Lightweight Secure Communication Protocol For Internet Of Medical Things, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das, Sachin Shetty, Muhammad Khurram Khan, Joel J.P.C. Rodrigues
VMASC Publications
The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be …
Behavioral Predictive Analytics Towards Personalization For Self-Management – A Use Case On Linking Health-Related Social Needs,
2022
CUNY Queens College
Behavioral Predictive Analytics Towards Personalization For Self-Management – A Use Case On Linking Health-Related Social Needs, Bon Sy, Michael Wassil, Helene Connelly, Alisha Hassan
Publications and Research
The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize patient engagement in diabetes self-management, and to gain insights on the potential of infusing a chatbot with NLP technology for discovering health-related social needs. In the U.S., less than 25% of patients actively engage in self-health management even though self-health management has been reported to associate with improved health outcomes and reduced healthcare costs. The proposed behavioral predictive analytics relies on manifold clustering to identify subpopulations segmented by behavior readiness characteristics that exhibit non-linear properties. For each subpopulation, an individualized auto-regression model and …
A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets,
2022
Missouri University of Science and Technology
A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets, Francesco Betti Sorbelli, Federico Coro, Sajal K. Das, Emanuele Di Bella, Lara Maistrello, Lorenzo Palazzetti, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
In this work, we consider the problem of using a drone to collect information within orchards in order to scout insect pests, i.e., the stink bug Halyomorpha halys. An orchard can be modeled as an aisle-graph, which is a regular and constrained data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone's energy is limited, only a subset of locations in the orchard can be visited with a fully charged …
Generation Of High Performing Morph Datasets,
2022
West Virginia University
Generation Of High Performing Morph Datasets, Kelsey Lynn O'Haire
Graduate Theses, Dissertations, and Problem Reports (ETD)
Facial recognition systems play a vital role in our everyday lives. We rely on this technology from menial tasks to issues as vital as national security. While strides have been made over the past ten years to improve facial recognition systems, morphed face images are a viable threat to the reliability of these systems. Morphed images are generated by combining the face images of two subjects. The resulting morphed face shares the likeness of the contributing subjects, confusing both humans and face verification algorithms. This vulnerability has grave consequences for facial recognition systems used on international borders or for law …
A Domain Adaptation Approach For Segmenting Cell Instances In Microscopy Data,
2022
West Virginia University
A Domain Adaptation Approach For Segmenting Cell Instances In Microscopy Data, Matthew R. Keaton
Graduate Theses, Dissertations, and Problem Reports (ETD)
Automated cellular instance segmentation is a process that has been utilized for accelerating biological research since before the deep learning era, and recent advancements have produced higher quality results with less effort from the biologist. Most current endeavors focus on completely cutting the researcher out of the picture by generating highly generalized models. However, these models invariably fail when faced with novel data and effectively opt to miss out on the full capabilities of deep learning in pursuit of this goal. In our work, we demonstrate how, with even a minimal amount of annotated data, dominant approaches in this space …
Classifying Blood Glucose Levels Through Noninvasive Features,
2022
West Virginia University
Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy
Graduate Theses, Dissertations, and Problem Reports (ETD)
Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline …
Determining Power System Fault Location Using Neural Network Approach,
2022
University of Kentucky
Determining Power System Fault Location Using Neural Network Approach, Edward O. Ojini
Theses and Dissertations--Electrical and Computer Engineering
Fault location remains an extremely pivotal feature of the electric power grid as it ensures efficient operation of the grid and prevents large downtimes during fault occurrences. This will ultimately enhance and increase the reliability of the system. Since the invention of the electric grid, many approaches to fault location have been studied and documented. These approaches are still effective and are implemented in present times, and as the power grid becomes even more broadened with new forms of energy generation, transmission, and distribution technologies, continued study on these methods is necessary. This thesis will focus on adopting the artificial …
Contextualized Vector Embeddings For Malware Detection,
2022
San Jose State University
Contextualized Vector Embeddings For Malware Detection, Vinay Pandya
Master's Projects
Malware classification is a technique to classify different types of malware which form an integral part of system security. The aim of this project is to use context dependant word embeddings to classify malware. Tansformers is a novel architecture which utilizes self attention to handle long range dependencies. They are particularly effective in many complex natural language processing tasks such as Masked Lan- guage Modelling(MLM) and Next Sentence Prediction(NSP). Different transfomer architectures such as BERT, DistilBert, Albert, and Roberta are used to generate context dependant word embeddings. These embeddings would help in classifying different malware samples based on their similarity …
Adversarial Attacks On Speech Separation Systems,
2022
San Jose State University
Adversarial Attacks On Speech Separation Systems, Kendrick Trinh
Master's Projects
Speech separation is a special form of blind source separation in which the objective is to decouple two or more sources such that they are distinct. The need for such an ability grows as speech activated device usage increases in our every day life. These systems, however, are susceptible to malicious actors. In this work, we repurpose proven adversarial attacks and leverage them against a combination speech separation and speech recognition system. The attack adds adversarial noise to a mixture of two voices such that the two outputs of the speech separation system are similarly transcribed by the speech recognition …
Exploring Algorithmic Literacy For College Students: An Educator’S Roadmap,
2022
Loyola Marymount University
Exploring Algorithmic Literacy For College Students: An Educator’S Roadmap, Susan Gardner Archambault
LMU Theses and Dissertations
Research shows that college students are largely unaware of the impact of algorithms on their everyday lives. Also, most university students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aimed to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors, and pedagogical considerations to aid faculty in teaching algorithmic literacy to college students. Eleven individual, semi-structured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. Findings suggested three sets of knowledge components that would contribute to students’ algorithmic literacy: general …
Secure Multi-Robot Adaptive Information Sampling With Continuous, Periodic And Opportunistic Connectivity,
2022
University of North Florida
Secure Multi-Robot Adaptive Information Sampling With Continuous, Periodic And Opportunistic Connectivity, Tamim Khatib
UNF Graduate Theses and Dissertations
Multi-robot teams are an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, natural disaster aftermath surveying, and pollution tracking. In a coordinated multi-robot information sampling scenario, robots share their collected information amongst one another to form better predictions. These robot teams are often assembled from untrusted devices, making the verification of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of continuous, periodic, or opportunistic connectivity and are limited in their energy budget and computational power. In this thesis, we study how to secure the information …
Learning Robot Motion From Creative Human Demonstration,
2022
Virginia Commonwealth University
Learning Robot Motion From Creative Human Demonstration, Charles C. Dietzel
Theses and Dissertations
This thesis presents a learning from demonstration framework that enables a robot to learn and perform creative motions from human demonstrations in real-time. In order to satisfy all of the functional requirements for the framework, the developed technique is comprised of two modular components, which integrate together to provide the desired functionality. The first component, called Dancing from Demonstration (DfD), is a kinesthetic learning from demonstration technique. This technique is capable of playing back newly learned motions in real-time, as well as combining multiple learned motions together in a configurable way, either to reduce trajectory error or to generate entirely …
