Special Section Guest Editorial: Machine Learning In Optics,
2020
Old Dominion University
Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin
Electrical & Computer Engineering Faculty Publications
This guest editorial summarizes the Special Section on Machine Learning in Optics.
Generative Adversarial Networks For Visible To Infrared Video Conversion,
2020
Old Dominion University
Generative Adversarial Networks For Visible To Infrared Video Conversion, Mohammad Shahab Uddin, Jiang Li, Chiman Kwan (Ed.)
Electrical & Computer Engineering Faculty Publications
Deep learning models are data driven. For example, the most popular convolutional neural network (CNN) model used for image classification or object detection requires large labeled databases for training to achieve competitive performances. This requirement is not difficult to be satisfied in the visible domain since there are lots of labeled video and image databases available nowadays. However, given the less popularity of infrared (IR) camera, the availability of labeled infrared videos or image databases is limited. Therefore, training deep learning models in infrared domain is still challenging. In this chapter, we applied the pix2pix generative adversarial network (Pix2Pix GAN) …
A Mathematical Analysis Of The Game Of Santorini,
2020
The College of Wooster
A Mathematical Analysis Of The Game Of Santorini, Carson Clyde Geissler
Senior Independent Study Theses
Santorini is a two player combinatorial board game. Santorini bears resemblance to the graph theory game of Geography, a game of moving and deleting vertices on a graph. We explore Santorini with game theory, complexity theory, and artificial intelligence. We present David Lichtenstein’s proof that Geography is PSPACE-hard and adapt the proof for generalized forms of Santorini. Last, we discuss the development of an AI built for a software implementation of Santorini and present a number of improvements to that AI.
Cheat Detection Using Machine Learning Within Counter-Strike: Global Offensive,
2020
The College of Wooster
Cheat Detection Using Machine Learning Within Counter-Strike: Global Offensive, Harry Dunham
Senior Independent Study Theses
Deep learning is becoming a steadfast means of solving complex problems that do not have a single concrete or simple solution. One complex problem that fits this description and that has also begun to appear at the forefront of society is cheating, specifically within video games. Therefore, this paper presents a means of developing a deep learning framework that successfully identifies cheaters within the video game CounterStrike: Global Offensive. This approach yields predictive accuracy metrics that range between 80-90% depending on the exact neural network architecture that is employed. This approach is easily scalable and applicable to all types of …
Computer Vision Gesture Recognition For Rock Paper Scissors,
2020
The College of Wooster
Computer Vision Gesture Recognition For Rock Paper Scissors, Nicholas Hunter
Senior Independent Study Theses
This project implements a human versus computer game of rock-paper-scissors using machine learning and computer vision. Player’s hand gestures are detected using single images with the YOLOv3 object detection system. This provides a generalized detection method which can recognize player moves without the need for a special background or lighting setup. Additionally, past moves are examined in context to predict the most probable next move of the system’s opponent. In this way, the system achieves higher win rates against human opponents than by using a purely random strategy.
Multi-Evidence Learning For Medical Diagnosis,
2020
Faculty of Engineering
Multi-Evidence Learning For Medical Diagnosis, Tongjai Yampaka
Chulalongkorn University Theses and Dissertations (Chula ETD)
In recent years, a great many approaches for learning from multiple sources by considering the diversity of different views have been proposed. The most interesting field is medical diagnosis. For example, breast cancer screening normally employs two views of mammography (Cranio-Caudal and Medio-Lateral-Oblique) or two modes of ultrasound (B-mode and Doppler mode) breast images. This study proposes a multi-evidence learning model that combines the multiple evidences of breast images to improve diagnosis. Two views mammography and two modes of ultrasound were used. Our proposed model consists of four stages. First, feature extraction using Convolutional Neuron Networks was operated to extract …
Artificial Intelligence And Copyright Law In Singapore A Study On The Protection Of Compilations And Databases Arranged By Ai-Systems,
2020
Faculty of Law
Artificial Intelligence And Copyright Law In Singapore A Study On The Protection Of Compilations And Databases Arranged By Ai-Systems, Sella Say
Chulalongkorn University Theses and Dissertations (Chula ETD)
While the capability of artificial intelligence ("AI") gains remarkable momentum in creating copyrightable materials – the questions regarding the eligibility of these new creations, at the moment, are broadly discussed and posed challenges to the regime. The problem of how we fit the conventional notion of authorship and the condition of originality for AI-generated works remains a controversial topic. Some might suggest that subject matter created by AI should not be granted copyright protection on the presumption that AI is not a human who could treat as authors of works. At the same time, other supportive claims that the first …
A Robust System For Core Thai Natural Language Processing Technologies,
2020
Faculty of Engineering
A Robust System For Core Thai Natural Language Processing Technologies, Can Udomcharoenchaikit
Chulalongkorn University Theses and Dissertations (Chula ETD)
As the amount of unstructured textual data grows, it becomes increasingly important to build an intelligent system that can process it. Natural Language Processing (NLP) is a technology that allows a computer to exploit human languages to perform tasks. Deep learning models have shown excellent results across fundamental tasks in NLP, such as word segmentation, part-of-speech tagging, and named-entity recognition. However, in many situations, these proposed methods fail to perform well. For an NLP system to be robust, it must address issues such as out-of-vocabulary and spelling-mistakes. This thesis's research goal is to develop NLP models that can handle malformed …
Semi-Supervised Thai Sentence Segmentation Using Local And Distant Word Representations,
2020
Faculty of Engineering
Semi-Supervised Thai Sentence Segmentation Using Local And Distant Word Representations, Chanatip Saetia
Chulalongkorn University Theses and Dissertations (Chula ETD)
A sentence is typically treated as the minimal syntactic unit used for extracting valuable information from a longer piece of text. However, in written Thai, there are no explicit sentence markers. We proposed a deep learning model for the task of sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, …
Deep Sequential Real Estate Recommendation Approach For Solving Item Cold Start Problem,
2020
Faculty of Engineering
Deep Sequential Real Estate Recommendation Approach For Solving Item Cold Start Problem, Jirut Polohakul
Chulalongkorn University Theses and Dissertations (Chula ETD)
The item cold-start problem occurs when a recommendation system cannot recommend new items owing to record deficiencies and new listing omissions. When searching for real estate, users can register a concurrent interest in recent and prior projects. Thus, an approach to recommend cold-start and warm-start items simultaneously must be determined. Furthermore, unrequired membership and stop-by behavior cause real estate recommendations to have many cold-start and new users. This characteristic encourages the use of a content-based approach and a session-based recommendation system. Herein, we propose a real estate recommendation approach for solving the item cold-start problem with acceptable warm-start item recommendations …
A Real Estate Valuation Model Using Boosted Feature Selection,
2020
Faculty of Engineering
A Real Estate Valuation Model Using Boosted Feature Selection, Kankawee Chanasit
Chulalongkorn University Theses and Dissertations (Chula ETD)
To estimate real estate values, a complex valuation model based on artificial neural network (ANN) has been established as a successful means in modern machine learning research, specifically when high-dimensional data are available. Unfortunately, the real estate data in many locations, such as Thailand, are quite limited in terms of features. Hence, it becomes mandatory to reduce the complexity using feature selection techniques. These techniques aim to improve performance by identifying significant factors and help decrease the computational overload and model construction. However, due to the lack of explicability and interpretability in ANNs, the analysis of input factors cannot be …
Detection Of Wagyu Beef Sources With Image Classification Using Convolutional Neural Network,
2020
Faculty of Engineering
Detection Of Wagyu Beef Sources With Image Classification Using Convolutional Neural Network, Nattakorn Kointarangkul
Chulalongkorn University Theses and Dissertations (Chula ETD)
Wagyu beef originated in Japan. However, there are many types of Wagyu beef in the market around the globe. Primary sources include Australia, USA, Canada and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor and tenderness. Observing that there are differences in flavor, texture, and quality between distinct sources of Wagyu. This research presents an AI-based approach to identify Wagyu beef sources with image classification. The input images were collected from reliable sources on the internet and augmented with DCGAN. Deep neural networks, CNN, was constructed to detect the marbled fat …
Using Automatic Speech Recognition To Assess Thai Speech Language Fluency In Montreal Cognitive Assessment (Moca),
2020
Faculty of Engineering
Using Automatic Speech Recognition To Assess Thai Speech Language Fluency In Montreal Cognitive Assessment (Moca), Pimarn Kantithammakorn
Chulalongkorn University Theses and Dissertations (Chula ETD)
The Montreal Cognitive Assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning where scores are based on the number of unique correct words produced by the test-taker. However, with different languages, it is possible that unique words may be counted differently. This study focuses on Thai as a language that differs from English in its type of word combination. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the language fluency test of the MoCA with Thai language support. The …
Accurate Surface Ultraviolet Radiation Forecasting For Clinical Applications With Deep Neural Network,
2020
Faculty of Engineering
Accurate Surface Ultraviolet Radiation Forecasting For Clinical Applications With Deep Neural Network, Raksit Raksasat
Chulalongkorn University Theses and Dissertations (Chula ETD)
Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280-360 nm, maximum 320 nm), or narrow-band UV B irradiation (main emission spectrum 310-315nm, maximum 311nm). For patients who cannot access phototherapy centers, sun-bathing, or heliotherapy, can be a safe and effective treatment alternative. However, as sunlight contains the full range of UV radiation (290-400 nm), careful sun-bathing supervised by photodermatologist based on accurate UV radiation forecast is vital to minimize potential adverse effects. Here, using 10-year UV radiation …
Invariance And Invertibility In Deep Neural Networks,
2020
Virginia Commonwealth University
Invariance And Invertibility In Deep Neural Networks, Han Zhang
Theses and Dissertations
Machine learning is concerned with computer systems that learn from data instead of being explicitly programmed to solve a particular task. One of the main approaches behind recent advances in machine learning involves neural networks with a large number of layers, often referred to as deep learning. In this dissertation, we study how to equip deep neural networks with two useful properties: invariance and invertibility. The first part of our work is focused on constructing neural networks that are invariant to certain transformations in the input, that is, some outputs of the network stay the same even if the input …
Sparsity And Weak Supervision In Quantum Machine Learning,
2020
Virginia Commonwealth University
Sparsity And Weak Supervision In Quantum Machine Learning, Seyran Saeedi
Theses and Dissertations
Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics, and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. As building quantum computers is nearing the era of commercialization and quantum supremacy, it is essential to think of potential applications that we might benefit from. Among many applications of quantum computation, one of the emerging fields is quantum machine learning. We focus on predictive models for binary classification and variants of Support Vector Machines that we expect to be …
Opening Books And The National Corpus Of Graduate Research,
2020
Virginia Polytechnic Institute and State University
Opening Books And The National Corpus Of Graduate Research, William A. Ingram, Edward A. Fox, Jian Wu
Computer Science Faculty Publications
Virginia Tech University Libraries, in collaboration with Virginia Tech Department of Computer Science and Old Dominion University Department of Computer Science, request $505,214 in grant funding for a 3-year project, the goal of which is to bring computational access to book-length documents, demonstrating that with Electronic Theses and Dissertations (ETDs). The project is motivated by the following library and community needs. (1) Despite huge volumes of book-length documents in digital libraries, there is a lack of models offering effective and efficient computational access to these long documents. (2) Nationwide open access services for ETDs generally function at the metadata level. …
Teacher-Student Networks With Multiple Decoders For Solving Math Word Problem,
2020
University of Electronic Science and Technology of China
Teacher-Student Networks With Multiple Decoders For Solving Math Word Problem, Jipeng Zhang, Roy Ka-Wei Lee, Ee-Peng Lim, Wei Qin, Lei Wang, Jie Shao, Qianru Sun
Research Collection School Of Computing and Information Systems
Math word problem (MWP) is challenging due to the limitation in training data where only one “standard” solution is available. MWP models often simply fit this solution rather than truly understand or solve the problem. The generalization of models (to diverse word scenarios) is thus limited. To address this problem, this paper proposes a novel approach, TSN-MD, by leveraging the teacher network to integrate the knowledge of equivalent solution expressions and then to regularize the learning behavior of the student network. In addition, we introduce the multiple-decoder student network to generate multiple candidate solution expressions by which the final answer …
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 …
Intelligent Cinematic Camera Control For Real-Time Graphics Applications,
2020
California Polytechnic State University, San Luis Obispo
Intelligent Cinematic Camera Control For Real-Time Graphics Applications, Ian Harris Meeder
Master's Theses
E-sports is currently estimated to be a billion dollar industry which is only growing in size from year to year. However the cinematography of spectated games leaves much to be desired. In most cases, the spectator either gets to control their own freely-moving camera or they get to see the view that a specific player sees. This thesis presents a system for the generation of cinematically-pleasing views for spectating real-time graphics applications. A custom real-time engine has been built to demonstrate the effect of this system on several different game modes with varying visual cinematic constraints, such as the rule …
