Artificial Intelligence and Robotics Commons^™

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …

Fine-Grained Detection Of Hate Speech Using Bertoxic, Yakoob Khan

Dartmouth College Undergraduate Theses

This thesis describes our approach towards the fine-grained detection of hate speech using deep learning. We leverage the transformer encoder architecture to propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text and utilizes additional post-processing steps to refine the prediction boundaries. The post-processing steps involve (1) labeling character offsets between consecutive toxic tokens as toxic and (2) assigning a toxic label to words that have at least one token labeled as toxic. Through experiments, we show that these two post-processing steps improve the performance of our model by 4.16% on …

Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg

Honors Projects

Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable.

Honors Symposium: ML is …

Statistical And Machine Learning Approaches To Depressive Disorders Among Adults In The United States: From Factor Discovery To Prediction Evaluation, Minhwa Lee

Senior Independent Study Theses

According to the National Institutes of Mental Health (NIMH), depressive disorders (or major depression) are considered one of the most common and serious health risks in the United States. Our study focuses on extracting non-medical factors of depressive disorders diagnosis, such as overall health states, health risk behaviors, demography, and healthcare access, using the Behavioral Risk Factor Surveillance System (BRFSS) data set collected by the Centers for Disease Control and Prevention (CDC) in 2018.

We set the two objectives of our study about depressive disorders diagnosis in the United States as follows. First, we aim to utilize machine learning algorithms …

The Application Of Machine Learning In Analyzing Organic Compounds From Nmr Spectral Data, Nicole Maia Powell

Senior Independent Study Theses

Nuclear magnetic resonance (NMR) is used in organic chemistry to identify unknown organic compounds. The data obtained from an NMR spectrometer are typically shown in the form of a spectrum, which is then analyzed by an analytical chemist. The action of analyzing a spectrum, especially one of a large and complex molecule, is a long and tedious process. In this project, Python is used to implement hierarchical clustering on NMR data obtained from an NMR spectrometer at the College of Wooster to explore its application in NMR analysis. MATLAB is used to build a decision tree from the same data, …

Tag: Automated Image Captioning, Nathan Funckes

McNair Scholars Manuscripts

Many websites remain non-ADA compliant, containing images which lack accompanying textual descriptions. This leaves sight-impaired individuals unable to fully enjoy the rich wonders of the web. To address this inequity, our research aims to create an autonomous system capable of generating semantically accurate descriptions of images. This problem involves two tasks: recognizing an image and linguistically describing it. Our solution uses state-of-the-art deep learning: employing a convolutional neural network that "learns" to understand images and extracts their salient features, and a recurrent neural network that learns to generate structured, coherent sentences. These two networks are merged to create a single …

A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad

Civil and Environmental Engineering Theses and Dissertations

Studying the growth pattern of cities/urban areas has received considerable attention during the past few decades. The goal is to identify directions and locations of potential growth, assess infrastructure and public service requirements, and ensure the integration of the new developments with the existing city structure. This dissertation presents a novel model for urban growth prediction using a novel machine learning model. The model treats successive historical satellite images of the urban area under consideration as a video for which future frames are predicted. A time-dependent convolutional encoder-decoder architecture is adopted. The model considers as an input a satellite image …

Cognition And Context-Aware Computing: Towards A Situation-Aware System With A Case Study In Aviation, Justin C. Wilson

Computer Science and Engineering Theses and Dissertations

In aviation, flight instructors seek to comprehend the intent and awareness of their students. With this awareness, derived from in-flight observation and post-flight examination, a human instructor can infer the internal contexts of their student aviators as they perform. It is this understanding that is fundamental for evaluating student development. Further, a well-understood construct for describing the state of knowledge about a dynamic environment is known as situational awareness (SA). Often pilot error is associated with SA [80], and it is fundamental to flight safety and mission execution. If these contexts can be automatically inferred, instructors and students can more …

Topics In Artifical Intelligence, Hunter Mcnichols, Nyc Tech-In-Residence Corps

Open Educational Resources

Syllabus for the course "CSC 59974: Special Topics in Artificial Intelligence" delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

Automated Change Detection In Privacy Policies, Andrick Adhikari

Electronic Theses and Dissertations

Privacy policies notify Internet users about the privacy practices of websites, mobile apps, and other products and services. However, users rarely read them and struggle to understand their contents. Also, the entities that provide these policies are sometimes unmotivated to make them comprehensible. Due to the complicated nature of these documents, it gets even harder for users to understand and take note of any changes of interest or concern when these policies are changed or revised.

With recent development of machine learning and natural language processing, tools that can automatically annotate sentences of policies have been developed. These annotations can …

Renewable Energy Integration In Distribution System With Artificial Intelligence, Yi Gu

Electronic Theses and Dissertations

With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power system operation and control.

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to feature out the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster calculation framework with Apache Spark is used to discover big data …

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, 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.

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.

Robot Simulation Analysis, Jacob Miller, Jeremy Evert

Student Research

• Simulate virtual robot for test and analysis

• Analyze SLAM solutions using ROS

• Assemble a functional Turtlebot

• Emphasize projects related to current research trajectories for NASA, and general robotics applications

Applied Machine Learning For Classification Of Musculoskeletal Inference Using Neural Networks And Component Analysis, Shaswat Sharma

Electronic Theses and Dissertations

Artificial Intelligence (AI) is acquiring more recognition than ever by researchers and machine learning practitioners. AI has found significance in many applications like biomedical research for cancer diagnosis using image analysis, pharmaceutical research, and, diagnosis and prognosis of diseases based on knowledge about patients' previous conditions. Due to the increased computational power of modern computers implementing AI, there has been an increase in the feasibility of performing more complex research.

Within the field of orthopedic biomechanics, this research considers complex time-series dataset of the "sit-to-stand" motion of 48 Total Hip Arthroplasty (THA) patients that was collected by the Human Dynamics …

Application Of Retrograde Analysis To Fighting Games, Kristen Yu

Electronic Theses and Dissertations

With the advent of the fighting game AI competition, there has been recent interest in two-player fighting games. Monte-Carlo Tree-Search approaches currently dominate the competition, but it is unclear if this is the best approach for all fighting games. In this thesis we study the design of two-player fighting games and the consequences of the game design on the types of AI that should be used for playing the game, as well as formally define the state space that fighting games are based on. Additionally, we also characterize how AI can solve the game given a simultaneous action game model, …

The D&D Sorting Hat: Predicting Dungeons And Dragons Characters From Textual Backstories, Joseph C. Macinnes

Senior Independent Study Theses

Dungeons and Dragons is a tabletop roleplaying game which focuses heavily on character interaction and creating narratives. The current state of the game's character creation process often bogs down new players in decisions related to game mechanics, not a character's identity and personality. This independent study investigates the use of machine learning and natural language processing to make these decisions for a player based on their character's backstory - the textual biography or description of a character. The study presents a collection of existing characters and uses these examples to create a family of models capable of predicting a character's …

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems

Computer Science and Engineering Theses and Dissertations

Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …

Developing An Affect-Aware Rear-Projected Robotic Agent, Ali Mollahosseini

Electronic Theses and Dissertations

Social (or Sociable) robots are designed to interact with people in a natural and interpersonal manner. They are becoming an integrated part of our daily lives and have achieved positive outcomes in several applications such as education, health care, quality of life, entertainment, etc. Despite significant progress towards the development of realistic social robotic agents, a number of problems remain to be solved. First, current social robots either lack enough ability to have deep social interaction with human, or they are very expensive to build and maintain. Second, current social robots have yet to reach the full emotional and social …

Rnn-Based Generation Of Polyphonic Music And Jazz Improvisation, Andrew Hannum

Electronic Theses and Dissertations

This paper presents techniques developed for algorithmic composition of both polyphonic music, and of simulated jazz improvisation, using multiple novel data sources and the character-based recurrent neural network architecture char-rnn. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.

Elephant 2000: A Programming Language For Remembering The Past And Building On It, Kerry J. Holmes

Williams Honors College, Honors Research Projects

Elephant 2000 is a programming language to specify programs that accept user speech as text inputs and outputs speech text. The inputs and outputs are based on Dialogue Act theory which describes several forms of speech outputs, such as requests, questions, and answers. The language also relies on Named Entity Recognition to determine what types of objects a user references. These entities include persons, locations, times and so on. Using these attributes of user speech, a program is able to perform simple rule matching and pattern recognition to respond to input. The result is a programming language with English like …

Sports Analytics With Computer Vision, Colby T. Jeffries

Senior Independent Study Theses

Computer vision in sports analytics is a relatively new development. With multi-million dollar systems like STATS’s SportVu, professional basketball teams are able to collect extremely fine-detailed data better than ever before. This concept can be scaled down to provide similar statistics collection to college and high school basketball teams. Here we investigate the creation of such a system using open-source technologies and less expensive hardware. In addition, using a similar technology, we examine basketball free throws to see whether a shooter’s form has a specific relationship to a shot’s outcome. A system that learns this relationship could be used to …

Isolation In Synchronized Drone Formations, Andrew P. Brunner

Electronic Theses and Dissertations

This paper expands on a theoretical model that is used for aerial robots that are working cooperatively to complete a task. In certain situations, such as when multiple robots have catastrophic failures, the surviving robots could become isolated so that they never again communicate with another robot. We prove some properties about isolated robots flying in a grid formation, and we present an algorithm that determines how many robots need to fail to isolate at least one robot. Finally, we propose a strategy that eliminates the possibility of isolation altogether.

Learning Emotions: A Software Engine For Simulating Realistic Emotion In Artificial Agents, Douglas Code

Senior Independent Study Theses

This paper outlines a software framework for the simulation of dynamic emotions in simulated agents. This framework acts as a domain-independent, black-box solution for giving actors in games or simulations realistic emotional reactions to events. The emotion management engine provided by the framework uses a modified Fuzzy Logic Adaptive Model of Emotions (FLAME) model, which lets it manage both appraisal of events in relation to an individual’s emotional state, and learning mechanisms through which an individual’s emotional responses to a particular event or object can change over time. In addition to the FLAME model, the engine draws on the design …

Toward Large-Scale Agent Guidance In An Urban Taxi Service, Agussurja Lucas, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Empty taxi cruising represents a wastage of resources in the context of urban taxi services. In this work, we seek to minimize such wastage. An analysis of a large trace of taxi operations reveals that the services’ inefficiency is caused by drivers’ greedy cruising behavior. We model the existing system as a continuous time Markov chain. To address the problem, we propose that each taxi be equipped with an intelligent agent that will guide the driver when cruising for passengers. Then, drawing from AI literature on multiagent planning, we explore two possible ways to compute such guidance. The first formulation …

Simulation, Application, And Resilience Of An Organic Neuromorphic Architecture, Made With Organic Bistable Devices And Organic Field Effect Transistors, Robert A. Nawrocki

Electronic Theses and Dissertations

This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at …