Numerical Analysis and Scientific Computing Commons^™

Equilibrium Structures And Thermal Fluctuations In Interacting Monolayers, Emmanuel Rivera

Williams Honors College, Honors Research Projects

Coherency strains appear in interacting atomic monolayers due to differing bond lengths, which can arise from different materials or geometries. Examples include extended monolayers interacting with a substrate and the interacting walls of a multi-walled carbon nanotube. These strains can induce various equilibrium configurations, which we will analyze by means of a phenomenological model that incorporates forces from bond stretching and bending within each layer and the weak van der Waals interactions connecting the separate layers. We vary the strengths of each interaction to explore their effects on equilibrium structures, and the specific case of a two-walled carbon nanotube is …

Absorption Calculator: A Cross-Platform Application For Portable Data Analysis, Annmarie Kolbl

Williams Honors College, Honors Research Projects

Traditional spectrometers are expensive and non-portable, making them inaccessible to the public. This application will be used in conjunction with spectrometer hardware developed by Erie Open Systems. The hardware itself is 3D printed and, in addition to being portable, enables data to be collected easily. The purpose of this project is to create a cross-platform application capable of reading the output from the spectrometer hardware, calculating the absorbance levels of the sample against the control, and recording the data in tables stored on the cloud. The end result will be an application that runs on iOS and Android, and is …

Microarray Data Analysis And Classification Of Cancers, Grant Gates

Williams Honors College, Honors Research Projects

When it comes to cancer, there is no standardized approach for identifying new cancer classes nor is there a standardized approach for assigning cancer tumors to existing classes. These two ideas are known as class discovery and class prediction. For a cancer patient to receive proper treatment, it is important that the type of cancer be accurately identified. For my Senior Honors Project, I would like to use this opportunity to research a topic in bioinformatics. Bioinformatics incorporates a few different subjects into one including biology, computer science and statistics. An intricate method for class discovery and class prediction is …

Mpa-Ibm Project Safer: Sense-Making Analytics For Maritime Event Recognition, Gavin Yeo, Shiau Hong Lim, Laura Wynter, Hifaz Hassan

Research Collection School Of Computing and Information Systems

Project SAFER, a collaboration between the Singapore Maritime and PortAuthority and the IBM Research Singapore Laboratory, was established to conceptualize,develop, and test new analytics-based technologies to enhance port operations and cater tothe increasing growth in vessel traffic in Singapore. The SAFER system addresses areas inmaritime management that have historically required significant human effort. Through acommon set of machine learning–based models, the SAFER system is able to forecast vesselarrival timings and potential traffic hot spots within port waters as well as to detectunusual behavior of vessels, from illegal bunkering (i.e., transfer of marine fuel) to shipsflouting Singapore regulations. The SAFER project …

Automated Essay Evaluation Using Natural Language Processing And Machine Learning, Harshanthi Ghanta

Theses and Dissertations

The goal of automated essay evaluation is to assign grades to essays and provide feedback using computers. Automated evaluation is increasingly being used in classrooms and online exams. The aim of this project is to develop machine learning models for performing automated essay scoring and evaluate their performance. In this research, a publicly available essay data set was used to train and test the efficacy of the adopted techniques. Natural language processing techniques were used to extract features from essays in the dataset. Three different existing machine learning algorithms were used on the chosen dataset. The data was divided into …

Snap Scholar: The User Experience Of Engaging With Academic Research Through A Tappable Stories Medium, Ieva Burk

CMC Senior Theses

With the shift to learn and consume information through our mobile devices, most academic research is still only presented in long-form text. The Stanford Scholar Initiative has explored the segment of content creation and consumption of academic research through video. However, there has been another popular shift in presenting information from various social media platforms and media outlets in the past few years. Snapchat and Instagram have introduced the concept of tappable “Stories” that have gained popularity in the realm of content consumption.

To accelerate the growth of the creation of these research talks, I propose an alternative to video: …

Description Of Motor Control Using Inverse Models, Anton Sobinov

Graduate Theses, Dissertations, and Problem Reports (ETD)

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation …

Classification Of Stars From Redshifted Stellar Spectra Utilizing Machine Learning, Michael J. Brice

All Master's Theses

The classification of stellar spectra is a fundamental task in stellar astrophysics. There have been many explorations into the automated classification of stellar spectra but few that involve the Sloan Digital Sky Survey (SDSS). Stellar spectra from the SDSS are applied to standard classification methods such as K-Nearest Neighbors, Random Forest, and Support Vector Machine to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using standard Feature Selection methods such as Chi-Squared and Fisher score and with domain-specific astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify …

Online Burst Events Detection Oriented Real-Time Microblog Message Stream, Guozhong Dong, Jun Gao, Liang Huang, Chunlei Shi

Research Collection School Of Computing and Information Systems

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table. …

Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints …

Template-Based Math Word Problem Solvers With Recursive Neural Networks, Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, Heng Tao Shen

Research Collection School Of Computing and Information Systems

The design of automatic solvers to arithmetic math word problems has attracted considerable attention in recent years and a large number of datasets and methods have been published. Among them, Math23K is the largest data corpus that is very helpful to evaluate the generality and robustness of a proposed solution. The best performer in Math23K is a seq2seq model based on LSTM to generate the math expression. However, the model suffers from performance degradation in large space of target expressions. In this paper, we propose a template-based solution based on recursive neural network for math expression construction. More specifically, we …

Distributed Multi-Label Learning On Apache Spark, Jorge Gonzalez Lopez

Theses and Dissertations

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes …

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia

Research Collection School Of Computing and Information Systems

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …

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 …

Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior helps us gain useful insights about venues as well as the users visiting them. These insights are important in urban planning and recommender system applications. Since check-in behavior is the result of multiple factors, this paper focuses on studying two venue related factors, namely, area attraction and neighborhood competition. The former refers to the ability of a spatial area covering multiple venues to collectively attract check-ins from users, while the latter represents the extent to which a venue can compete with other venues in the same area for check-ins. We first embark on empirical studies to …

Automatic Slide Generation For Scientific Papers, Athar Sefid, Jian Wu, Prasenjit Mitra, C. Lee Giles

Computer Science Faculty Publications

We describe our approach for automatically generating presentation slides for scientific papers using deep neural networks. Such slides can help authors have a starting point for their slide generation process. Extractive summarization techniques are applied to rank and select important sentences from the original document. Previous work identified important sentences based only on a limited number of features that were extracted from the position and structure of sentences in the paper. Our method extends previous work by (1) extracting a more comprehensive list of surface features, (2) considering semantic or meaning of the sentence, and (3) using context around the …

Analysis Of Bus Ride Comfort Using Smartphone Sensor Data, Hoong-Chor Chin, Xingting Pang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point scale and recorded at 5-second …

A Method Of Evaluation Of High-Performance Computing Batch Schedulers, Jeremy Stephen Futral

UNF Graduate Theses and Dissertations

According to Sterling et al., a batch scheduler, also called workload management, is an application or set of services that provide a method to monitor and manage the flow of work through the system [Sterling01]. The purpose of this research was to develop a method to assess the execution speed of workloads that are submitted to a batch scheduler. While previous research exists, this research is different in that more complex jobs were devised that fully exercised the scheduler with established benchmarks. This research is important because the reduction of latency even if it is miniscule can lead to massive …

Feasible Form Parameter Design Of Complex Ship Hull Form Geometry, Thomas L. Mcculloch

LSU New Orleans Theses and Dissertations

This thesis introduces a new methodology for robust form parameter design of complex hull form geometry via constraint programming, automatic differentiation, interval arithmetic, and truncated hierarchical B- splines. To date, there has been no clearly stated methodology for assuring consistency of general (equality and inequality) constraints across an entire geometric form parameter ship hull design space. In contrast, the method to be given here can be used to produce guaranteed narrowing of the design space, such that infeasible portions are eliminated. Furthermore, we can guarantee that any set of form parameters generated by our method will be self consistent. It …

Pasnet: Pathway-Associated Sparse Deepneural Network For Prognosis Prediction From High-Throughput Data, Jie Hao, Youngsoon Kim, Tae-Kyung Kim, Mingon Kang

Faculty Articles

Background: Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis maybe caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data. Results: In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes …