Dronesig: Lightweight Digital Signature Protocol For Micro Aerial Vehicles,
2020
Marshall University
Dronesig: Lightweight Digital Signature Protocol For Micro Aerial Vehicles, Yucheng Li
Theses, Dissertations and Capstones
Micro aerial vehicles a.k.a. drones, have become an integral part of a variety of civilian and military application domains, including but not limited to aerial surveying and mapping, aerial surveillance and security, aerial inspection of infrastructure, and aerial delivery. Meanwhile, the cybersecurity of drones is gaining significant attention due to both financial and strategic information and value involved in aerial applications. As a result of the lack of security features in the communication protocol, an adversary can easily interfere with on-going communications or even seize control of the drone. In this thesis, we propose a lightweight digital signature protocol, also …
Using Natural Language Processing To Categorize Fictional Literature In An Unsupervised Manner,
2020
University of Denver
Using Natural Language Processing To Categorize Fictional Literature In An Unsupervised Manner, Dalton J. Crutchfield
Electronic Theses and Dissertations
When following a plot in a story, categorization is something that humans do without even thinking; whether this is simple classification like “This is science fiction” or more complex trope recognition like recognizing a Chekhov's gun or a rags to riches storyline, humans group stories with other similar stories. Research has been done to categorize basic plots and acknowledge common story tropes on the literary side, however, there is not a formula or set way to determine these plots in a story line automatically. This paper explores multiple natural language processing techniques in an attempt to automatically compare and cluster …
Deep Siamese Neural Networks For Facial Expression Recognition In The Wild,
2020
University of Denver
Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale
Electronic Theses and Dissertations
The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …
Facial Action Unit Detection With Deep Convolutional Neural Networks,
2020
University of Denver
Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal
Electronic Theses and Dissertations
The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …
Development Of Machine Learning Tutorials For R,
2020
Minnesota State University, Mankato
Development Of Machine Learning Tutorials For R, John Pintar
All Undergraduate Theses and Capstone Projects
Machine learning (ML) techniques developed in computer science have revolutionized nearly every sector of industry. Despite the prevalence and usefulness of ML, students outside of computer science rarely receive training in ML. Students frequently receive training in statistical analysis, often using the software package R, which is free, open source, and has additional downloadable modules. A popular module is the ML package caret, which contains 238 different ML algorithms, each with 0-9 hyperparameters. caret is powerful, flexible, and provides consistent syntax across algorithms. In the hands of an experienced practitioner, this tunability is welcomed and can increase accuracy. However, when …
Accessibility Of Deepfakes,
2020
Old Dominion University
Accessibility Of Deepfakes, Andrew L. Collings
Cybersecurity Undergraduate Research Showcase
The danger posed by falsified media, commonly referred to as deepfakes, has been well researched and documented. The software Faceswap to was used to swap the faces of two politician (Joe Biden and Donald Trump). The testing was performed using an affordable consumer GPU (an AMD Radeon RX 570) over 100,000 iterations. The process and results for the two attempts with the best results (and largest differences) were recorded. The result was ultimately unconvincing, while the software was able to recreate the facial structure the lighting and skin tone did not blend at all.
Multimodal Data Integration For Real-Time Indoor Navigation Using A Smartphone,
2020
CUNY City College
Multimodal Data Integration For Real-Time Indoor Navigation Using A Smartphone, Yaohua Chang
Dissertations and Theses
We propose an integrated solution of indoor navigation using a smartphone, especially for assisting people with special needs, such as the blind and visually impaired (BVI) individuals. The system consists of three components: hybrid modeling, real-time navigation, and client-server architecture. In the hybrid modeling component, the hybrid model of a building is created region by region and is organized in a graph structure with nodes as destinations and landmarks, and edges as traversal paths between nodes. A Wi-Fi/cellular-data connectivity map, a beacon signal strength map, a 3D visual model (with destinations and landmarks annotated) are collected while a modeler walks …
Automatic Distinction Between Twitter Bots And Humans,
2020
Minnesota State University, Mankato
Automatic Distinction Between Twitter Bots And Humans, Jeremiah Stubbs
All Undergraduate Theses and Capstone Projects
Weak artificial intelligence uses encoded functions of rules to process information. This kind of intelligence is competent, but lacks consciousness, and therefore cannot comprehend what it is doing. In another view, strong artificial intelligence has a mind of its own that resembles a human mind. Many of the bots on Twitter are only following a set of encoded rules. Previous studies have created machine learning algorithms to determine whether a Twitter account was being run by a human or a bot. Twitter bots are improving and some are even fooling humans. Creating a machine learning algorithm that differentiates a bot …
Gröbner Bases And Systems Of Polynomial Equations,
2020
Minnesota State University, Mankato
Gröbner Bases And Systems Of Polynomial Equations, Rachel Holmes
All Graduate Theses, Dissertations, and Other Capstone Projects
This paper will explore the use and construction of Gröbner bases through Buchberger's algorithm. Specifically, applications of such bases for solving systems of polynomial equations will be discussed. Furthermore, we relate many concepts in commutative algebra to ideas in computational algebraic geometry.
Leveraging Peer-To-Peer Energy Sharing For Resource Optimization In Mobile Social Networks,
2020
Virginia Commonwealth University
Leveraging Peer-To-Peer Energy Sharing For Resource Optimization In Mobile Social Networks, Aashish Dhungana
Theses and Dissertations
Mobile Opportunistic Networks (MSNs) enable the interaction of mobile users in the vicinity through various short-range wireless communication technologies (e.g., Bluetooth, WiFi) and let them discover and exchange information directly or in ad hoc manner. Despite their promise to enable many exciting applications, limited battery capacity of mobile devices has become the biggest impediment to these appli- cations. The recent breakthroughs in the areas of wireless power transfer (WPT) and rechargeable lithium batteries promise the use of peer-to-peer (P2P) energy sharing (i.e., the transfer of energy from the battery of one member of the mobile network to the battery of …
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm,
2020
Old Dominion University
Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe
Engineering Management & Systems Engineering Faculty Publications
Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …
Comparison Of The Tally Numbering System To Traditional Arithmetic Systems In Field Programmable Gate Arrays,
2020
Eastern Washington University
Comparison Of The Tally Numbering System To Traditional Arithmetic Systems In Field Programmable Gate Arrays, Robert Paul Shredow
EWU Masters Thesis Collection
This research explores the use of heterogeneous computing platforms for use in machine learning as well as different neural network architectures. These platforms and architectures can be used to accelerate the complex operations that are required for machine learning, more specifically neural networks. The use of different architectures, implementing different types of numbering and mathematics systems is explored in hopes of accelerating mathematical functions. The heterogeneous computing platform explored in this thesis is a Field Programmable Gate Arrays (FPGA), specifically a SoC/FPGA which is a ARM CPU and a FPGA in the same chip. FPGAs are unique because they are …
Quantum Computing: Principles And Applications,
2020
University of Alaska Anchorage
Quantum Computing: Principles And Applications, Yoshito Kanamori, Seong-Moo Yoo
Journal of International Technology and Information Management
The development of quantum computers over the past few years is probably one of the significant advancements in the history of quantum computing. D-Wave quantum computer has been available for more than eight years. IBM has made its quantum computer accessible via its cloud service. Also, Microsoft, Google, Intel, and NASA have been heavily investing in the development of quantum computers and their applications. The quantum computer seems to be no longer just for physicists and computer scientists but also for information system researchers. This paper introduces the basic concepts of quantum computing and describes well-known quantum applications for non-physicists. …
V-Slam And Sensor Fusion For Ground Robots,
2020
CUNY City College
V-Slam And Sensor Fusion For Ground Robots, Ejup Hoxha
Dissertations and Theses
In underground, underwater and indoor environments, a robot has to rely solely on its on-board sensors to sense and understand its surroundings. This is the main reason why SLAM gained the popularity it has today. In recent years, we have seen excellent improvement on accuracy of localization using cameras and combinations of different sensors, especially camera-IMU (VIO) fusion. Incorporating more sensors leads to improvement of accuracy,but also robustness of SLAM. However, while testing SLAM in our ground robots, we have seen a decrease in performance quality when using the same algorithms on flying vehicles.We have an additional sensor for ground …
Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment,
2020
University of New Hampshire
Landing Throttleable Hybrid Rockets With Hierarchical Reinforcement Learning In A Simulated Environment, Francesco Alessandro Stefano Mikulis-Borsoi
Honors Theses and Capstones
In this paper, I develop a hierarchical Markov Decision Process (MDP) structure for completing the task of vertical rocket landing. I start by covering the background of this problem, and formally defining its constraints. In order to reduce mistakes while formulating different MDPs, I define and develop the criteria for a standardized MDP definition format. I then decompose the problem into several sub-problems of vertical landing, namely velocity control and vertical stability control. By exploiting MDP coupling and symmetrical properties, I am able to significantly reduce the size of the state space compared to a unified MDP formulation. This paper …
Toward Efficient Automation Of Interpretable Machine Learning Boosting,
2020
Central Washington University
Toward Efficient Automation Of Interpretable Machine Learning Boosting, Nathan Neuhaus
All Master's Theses
Developing efficient automated methods for Interpretable Machine Learning (IML) is an important and long-term goal in the field of Artificial Intelligence. Currently the Machine Learning landscape is dominated by Neural Networks (NNs) and Support Vector Machines (SVMs), models which are often highly accurate. Despite high accuracy, such models are essentially “black boxes” and therefore are too risky for situations like healthcare where real lives are at stake. In such situations, so called “glass-box” models, such as Decision Trees (DTs), Bayesian Networks (BNs), and Logic Relational (LR) models are often preferred, however can succumb to accuracy limitations. Unfortunately, having to choose …
A Review Of Emotion Sensing: Categorization Models And Algorithms,
2020
Singapore Management University
A Review Of Emotion Sensing: Categorization Models And Algorithms, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Sentiment analysis consists in the identification of the sentiment polarity associated with a target object, such as a book, a movie or a phone. Sentiments reflect feelings and attitudes, while emotions provide a finer characterization of the sentiments involved. With the huge number of comments generated daily on the Internet, besides sentiment analysis, emotion identification has drawn keen interest from different researchers, businessmen and politicians for polling public opinions and attitudes. This paper reviews and discusses existing emotion categorization models for emotion analysis and proposes methods that enhance existing emotion research. We carried out emotion analysis by inviting experts from …
Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore,
2020
Singapore Management University
Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang
Research Collection School Of Computing and Information Systems
In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have …
Structure-Priority Image Restoration Through Genetic Algorithm Optimization,
2020
Singapore Management University
Structure-Priority Image Restoration Through Genetic Algorithm Optimization, Zhaoxia Wang, Haibo Pen, Ting Yang, Quan Wang
Research Collection School Of Computing and Information Systems
With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based …
Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification,
2020
Singapore Management University
Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin
Research Collection School Of Computing and Information Systems
Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …
