Artificial Intelligence and Robotics Commons^™

Sentiment Analysis For Airline Services On Twitter Using Deep Learning With Word Embedding, Nour El Daim El Khalifa Mawada Mohamed

Student Works (2020-2029)

The use of social media platform in the airline industries have increased rapidly to allow analysis introduce the quality and performance of the services. The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. This problem has led to low accuracy in …

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, 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 …

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.

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 …

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 …

Certified Functions For Mesh Generation, Andrey N. Chernikov

Chemistry & Biochemistry Faculty Publications

Formal methods allow for building correct-by-construction software with provable guarantees. The formal development presented here resulted in certified executable functions for mesh generation. The term certified means that their correctness is established via an artifact, or certificate, which is a statement of these functions in a formal language along with the proofs of their correctness. The term is meaningful only when qualified by a specific set of properties that are proven. This manuscript elaborates on the precise statements of the properties being proven and their role in an implementation of a version of the Isosurface Stuffing algorithm by Labelle and …

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 …

An Intra-Severity Classification And Adaptation Technique To Improve Dysarthric Speech Recognition Accuracy, Al-Qatab Bassam Ali Qasem

Student Works (2020-2029)

Dysarthria is a motor speech impairment at the neurological and/or muscular levels that caused difficulty in pronouncing words clearly. Automatic speech recognition (ASR) system is increasingly applied as assistive technology to aid an individual with physical disability particularly the speech impaired community such as dysarthria speakers. However, the development of an effective ASR system is hindered by the data sparsity, either in the coverage of the language or the size of the existing speech databases. The speaker adaptation (SA) technique is one of the solutions to overcome the data sparsity issue of ASR for dysarthric speakers. Our proposed method introduces …

The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses, Shirkhorshidi Ali Seyed

Student Works (2020-2029)

Having a significant amount of data is not useful unless the data can be processed for extracting knowledge and information. One of the elementary steps in crunching data is to break it down into groups. When the data is small and collected in a controlled manner, and when the training data is appropriately labelled, the trivial approach is to use supervised learning to perform the grouping. Supervised methods need training data and information about groups beforehand; however, in the current reality, with an avalanche of data, this information is not available. Nevertheless, the need for grouping data remains. Clustering, as …

Satellite Constellation Deployment And Management, Joseph Ryan Kopacz

Electronic Theses and Dissertations

This paper will review results and discuss a new method to address the deployment and management of a satellite constellation. The first two chapters will explorer the use of small satellites, and some of the advances in technology that have enabled small spacecraft to maintain modern performance requirements in incredibly small packages.

The third chapter will address the multiple-objective optimization problem for a global persistent coverage constellation of communications spacecraft in Low Earth Orbit. A genetic algorithm was implemented in MATLAB to explore the design space – 288 trillion possibilities – utilizing the Satellite Tool Kit (STK) software developers kit. …

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.

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, 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 …

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 …

Searching For Needles In The Cosmic Haystack, Thomas Ryan Devine

Graduate Theses, Dissertations, and Problem Reports (ETD)

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best …

Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong

Electrical Engineering Theses and Dissertations

Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.

Adversarial Attack On Neural Machine Translation System, Abijith K. P.

Master’s Dissertations

Nowadays Deep Neural Network based solutions are deployed to solve numerous tasks. Thus, it has become absolutely important to study the robustness of these systems. Machine Translation is one of the popular applications of Deep Neural Networks. This thesis studies the robustness of Neural Machine Translation systems by generating adversarial examples with the objective to fool the model. Whenever there is a change in the source, i.e. when a word in the input sentence is replaced by an unrelated word, the translation system is supposed to reflect the changes while doing translation. These unwanted invariance learned by the model is …

Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed

Dissertations

Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project …

Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen

Research Collection School Of Computing and Information Systems

Vehicle Routing Problem (VRP) is a combinatorial problem where a certain set of nodes must be visited within a certain amount of time as well as the vehicle’s capacity. There are numerous variants of VRP such as VRP with time windows, where each node has opening and closing time, therefore, the visiting time must be during that interval. Another variant takes time-dependent constraint into account. This variant fits real-world scenarios, where at different period of time, the speed on the road varies depending on the traffic congestion. In this study, three objectives – total traveling time, total traveling distance, and …

Emotion-Aware Chat Machine: Automatic Emotional Response Generation For Human-Like Emotional Interaction, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu

Research Collection School Of Computing and Information Systems

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms …

Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon

Theses

The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …

Texture-Based Deep Neural Network For Histopathology Cancer Whole Slide Image (Wsi) Classification, Nelson Zange Tsaku

Master of Science in Computer Science Theses

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological …

Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan

Research Collection School Of Computing and Information Systems

Due to the recent cyber attacks, cybersecurity is becoming more critical in modern society. A single attack (e.g., WannaCry ransomware attack) can cause as much as $4 billion in damage. However, the cybersecurity investment by companies is far from satisfactory. Therefore, governments (e.g., in the UK) launch grants and subsidies to help companies to boost their cybersecurity to create a safer national cyber environment. The allocation problem is hard due to limited subsidies and the interdependence between self-interested companies and the presence of a strategic cyber attacker. To tackle the government's allocation problem, we introduce a Stackelberg game-theoretic model where …

Applications Of The Op, Pieter Vansteenwegen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In recent years, we observe from literature that the VRP and OP, including their variants, have been used to model many different planning and scheduling problems from practice, such as the routing of technicians, athlete recruitment, or military applications. Recently, other practical applications, such as the tourist trip design problem, the mobile crowdsourcing problem, the smuggler search problem, the wildfire routing problem, and the integration of vehicle routing, inventory management, and customer selection problems, have been studied and use the OP as a basic model. In this chapter, various practical applications will be discussed in more detail. We will describe …

A Review On Swarm Intelligence And Evolutionary Algorithms For Solving Flexible Job Shop Scheduling Problems, Kaizhou Gao, Zhiguang Cao, Le Zhang, Zhenghua Chen, Yuyan Han, Quanke Pan

Research Collection School Of Computing and Information Systems

Flexible job shop scheduling problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population …

Correlated Learning For Aggregation Systems, Tanvi Verma, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Aggregation systems (e.g., Uber, Lyft, FoodPanda, Deliveroo) have been increasingly used to improve efficiency in numerous environments, including in transportation, logistics, food and grocery delivery. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. Due to optimizing a metric of importance to the centralized entity, the interests of individuals (e.g., drivers, delivery boys) can be sacrificed. Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals in the presence …

Simulated Annealing For The Single-Vehicle Cyclic Inventory Routing Problem, Aldy Gunawan, Vincent F. Yu, Audrey T. Widjaja, Pieter. Vansteenwegen

Research Collection School Of Computing and Information Systems

This paper studies the Single-Vehicle Cyclic Inventory Routing Problem (SV-CIRP) with the objective of simultaneously minimizing distribution and inventory costs for the customers and maximizing the collected rewards. A subset of customers is selected for the vehicle, including the quantity to be delivered to them. Simulated Annealing (SA) is proposed for solving the problem. Experimental results on 50 benchmark instances show that SA is comparable to the state-of-the-art algorithms. It is able to obtain 12 new best known solutions.

Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari

Civil & Environmental Engineering Theses & Dissertations

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident …

Can Algorithms Help Us Decide Who To Trust?, David De Cremer, Jack Mcguire, Yorck Hesselbarth, Ke M Mai

Research Collection Lee Kong Chian School Of Business

The use of artificial intelligence (AI) and algorithms is increasing within organizations to manage business processes, hire employees, and automate routine organizational decision making. This comes as no surprise, since the application of simple linear algorithms have been shown to outperform human judgment in the accuracy of many administrative tasks. A 2017 Accenture survey also revealed that 85% of executives want to invest more extensively in AI-related technologies over the next three years.