Computer Sciences Commons^™

Is The Selective Tuning Model Of Visual Attention Still Relevant?, John K. Tsotsos

MODVIS Workshop

No abstract provided.

Toward On-Demand Profile Hidden Markov Models For Genetic Barcode Identification, Jessica Sheu

Master's Projects

Genetic identification aims to solve the shortcomings of morphological identification. By using the cytochrome c oxidase subunit 1 (COI) gene as the Eukaryotic “barcode,” scientists hope to research species that may be morphologically ambiguous, elusive, or similarly difficult to visually identify. Current COI databases allow users to search only for existing database records. However, as the number of sequenced, potential COI genes increases, COI identification tools should ideally also be informative of novel, previously unreported sequences that may represent new species. If an unknown COI sequence does not represent a reported organism, an ideal identification tool would report taxonomic ranks …

Predictive Analysis For Cloud Infrastructure Metrics, Paridhi Agrawal

Master's Projects

In a cloud computing environment, enterprises have the flexibility to request resources according to their application demands. This elastic feature of cloud computing makes it an attractive option for enterprises to host their applications on the cloud. Cloud providers usually exploit this elasticity by auto-scaling the application resources for quality assurance. However, there is a setup-time delay that may take minutes between the demand for a new resource and it being prepared for utilization. This causes the static resource provisioning techniques, which request allocation of a new resource only when the application breaches a specific threshold, to be slow and …

Module: Robot Senses, Mohammad Azhar

Open Educational Resources

Learning Objectives:

Students will be able to:

• Describe the basics of Sensors

• Learn how to program the LEGO Robot to make decision using touch sensors

Module: Robot Locomotion Mini Hackathon, Mohammad Azhar

Open Educational Resources

Learning Objectives:

Students will be able to:

• Describe the basics of Robots.

• Describe basic hardware and software of the LEGO Robot.

• Write sequential code for LEGO Robot to move.

Self-Driving Cars: Evaluation Of Deep Learning Techniques For Object Detection In Different Driving Conditions, Ramesh Simhambhatla, Kevin Okiah, Shravan Kuchkula, Robert Slater

SMU Data Science Review

Deep Learning has revolutionized Computer Vision, and it is the core technology behind capabilities of a self-driving car. Convolutional Neural Networks (CNNs) are at the heart of this deep learning revolution for improving the task of object detection. A number of successful object detection systems have been proposed in recent years that are based on CNNs. In this paper, an empirical evaluation of three recent meta-architectures: SSD (Single Shot multi-box Detector), R-CNN (Region-based CNN) and R-FCN (Region-based Fully Convolutional Networks) was conducted to measure how fast and accurate they are in identifying objects on the road, such as vehicles, pedestrians, …

Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja

Honors Scholar Theses

Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In …

Assessing Code Obfuscation Of Metamorphic Javascript, Kaushik Murli

Master's Projects

Metamorphic malware is one of the biggest and most ubiquitous threats in the digital world. It can be used to morph the structure of the target code without changing the underlying functionality of the code, thus making it very difficult to detect using signature-based detection and heuristic analysis. The focus of this project is to analyze Metamorphic JavaScript malware and techniques that can be used to mutate the code in JavaScript. To assess the capabilities of the metamorphic engine, we performed experiments to visualize the degree of code morphing. Further, this project discusses potential methods that have been used to …

Species Classification Using Dna Barcoding And Profile Hidden Markov Models, Sphoorti Poojary

Master's Projects

Traditional classification systems for living organisms like the Linnaean taxonomy involved classification based on morphological features of species. This traditional system is being replaced by molecular approaches which involve using gene sequences. The COI gene, also known as the ”DNA barcode” since it is unique in every species, can be used to uniquely identify organisms and thus, classify them. Classifying using gene sequences has many advantages, including correct identification of cryptic species(individuals which appear similar but belong to different species) and species which are extremely small in size. In this project, I worked on classifying COI sequences of unknown species …

Image Compression Using Neural Networks, Kunal Rajan Deshmukh

Master's Projects

Image compression is a well-studied field of Computer Vision. Recently, many neural network based architectures have been proposed for image compression as well as enhancement. These networks are also put to use by frameworks such as end-to-end image compression.

In this project, we have explored the improvements that can be made over this framework to achieve better benchmarks in compressing images. Generative Adversarial Networks are used to generate new fake images which are very similar to original images. Single Image Super-Resolution Generative Adversarial Networks

(SI-SRGAN) can be employed to improve image quality. Our proposed architecture can be divided into four …

Worker Demographics And Earnings On Amazon Mechanical Turk: An Exploratory Analysis, Kotaro Hara, Kristy Milland, Benjamin V. Hanrahan, Chris Callison-Burch, Abigail Adams, Saiph Savage, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

Prior research reported that workers on Amazon Mechanical Turk (AMT) are underpaid, earning about $2/h. But the prior research did not investigate the difference in wage due to worker characteristics (e.g., country of residence). We present the first data-driven analysis on wage gap on AMT. Using work log data and demographic data collected via online survey, we analyse the gap in wage due to different factors. We show that there is indeed wage gap; for example, workers in the U.S. earn $3.01/h while those in India earn $1.41/h on average.

Nitrogenase Iron Protein Detection Using Neural Network, Ishan Shinde

Master's Projects

Nitrogenase Iron Protein (nifH) is the enzyme responsible for nitrogen fixation. Microbes with nifH gene are responsible for injecting reduced nitrogen into the biosphere, which is essential for all living things. Obtaining sequences from GenBank database is problematic due to annotation errors, nomenclature variation and paralogues. One possible solution could be to retrieve sequences from the GenBank database and use a sequence classifier to label the sequences. In this research, we convert sequences to images and build a nifH sequence classifier using image processing and convolutional neural network. We built a nifH classification model which can classify sequences with an …

Differential Estimation Of Audiograms Using Gaussian Process Active Model Selection, Trevor Larsen

McKelvey School of Engineering Graduate Student Theses & Dissertations

Classical methods for psychometric function estimation either require excessive resources to perform, as in the method of constants, or produce only a low resolution approximation of the target psychometric function, as in adaptive staircase or up-down procedures. This thesis makes two primary contributions to the estimation of the audiogram, a clinically relevant psychometric function estimated by querying a patient’s for audibility of a collection of tones. First, it covers the implementation of a Gaussian process model for learning an audiogram using another audiogram as a prior belief to speed up the learning procedure. Second, it implements a use case of …

Every Data Point Counts: Political Elections In The Age Of Digital Analytics, Julian Kehle, Samir Naimi

Honors Thesis

Synthesizing the investigative research and cautionary messages from experts in the fields of technology, political science, and behavioral science, this project explores the ways in which digital analytics has begun to influence the American political arena. Historically, political parties have constructed systems to target voters and win elections. However, rapid changes in the field of technology (such as big data, artificial intelligence, and the prevalence of social media) threaten to undermine the integrity of elections themselves. Future political campaigns will utilize profiling to micro-target individuals in order to manipulate and persuade them with hyper-personalized political content. Most dangerously, the average …

Identification And Classification Of Poultry Eggs: A Case Study Utilizing Computer Vision And Machine Learning, Jeremy Lubich, Kyle Thomas, Daniel W. Engels

SMU Data Science Review

We developed a method to identify, count, and classify chickens and eggs inside nesting boxes of a chicken coop. Utilizing an IoT AWS Deep Lens Camera for data capture and inferences, we trained and deployed a custom single-shot multibox (SSD) object detection and classification model. This allows us to monitor a complex environment with multiple chickens and eggs moving and appearing simultaneously within the video frames. The models can label video frames with classifications for 8 breeds of chickens and/or 4 colors of eggs, with 98% accuracy on chickens or eggs alone and 82.5% accuracy while detecting both types of …

Using Data Science To Detect Fake News, Eliza Shoemaker

Senior Honors Projects, 2010-2019

The purpose of this thesis is to assist in automating the detection of Fake News by identifying which features are more useful for different classifiers. The effectiveness of different extracted features for Fake News detection are going to be examined. When classifying text with machine learning algorithms features have to be extracted from the articles for the classifiers to be trained on. In this thesis, several different features are extracted: word counts, ngram counts, term frequency-inverse document frequency, sentiment analysis, lemmatization, and named entity recognition to train the classifiers. Two classifiers are used, a Random Forest classifier and a Naïve …

Machine Learning Pipeline For Exoplanet Classification, George Clayton Sturrock, Brychan Manry, Sohail Rafiqi

SMU Data Science Review

Planet identification has typically been a tasked performed exclusively by teams of astronomers and astrophysicists using methods and tools accessible only to those with years of academic education and training. NASA’s Exoplanet Exploration program has introduced modern satellites capable of capturing a vast array of data regarding celestial objects of interest to assist with researching these objects. The availability of satellite data has opened up the task of planet identification to individuals capable of writing and interpreting machine learning models. In this study, several classification models and datasets are utilized to assign a probability of an observation being an exoplanet. …

Leveraging Natural Language Processing Applications And Microblogging Platform For Increased Transparency In Crisis Areas, Ernesto Carrera-Ruvalcaba, Johnson Ekedum, Austin Hancock, Ben Brock

SMU Data Science Review

Through microblogging applications, such as Twitter, people actively document their lives even in times of natural disasters such as hurricanes and earthquakes. While first responders and crisis-teams are able to help people who call 911, or arrive at a designated shelter, there are vast amounts of information being exchanged online via Twitter that provide real-time, location-based alerts that are going unnoticed. To effectively use this information, the Tweets must be verified for authenticity and categorized to ensure that the proper authorities can be alerted. In this paper, we create a Crisis Message Corpus from geotagged Tweets occurring during 7 hurricanes …

Autonomous Watercraft Simulation And Programming, Nicholas J. Savino

Undergraduate Theses and Capstone Projects

Automation of various modes of transportation is thought to make travel more safe and efficient. Over the past several decades advances to semi-autonomous and autonomous vehicles have led to advanced autopilot systems on planes and boats and an increasing popularity of self-driving cars. We simulated the motion of an autonomous vehicle using computational models. The simulation models the motion of a small-scale watercraft, which can then be built and programmed using an Arduino Microcontroller. We examined different control methods for a simulated rescue craft to reach a target. We also examined the effects of different factors, such as various biases …

Managing The Power And Pitfalls Of Data In Ai, Singapore Management University

Perspectives@SMU

Ethics and education are crucial in maintaining data privacy and augmenting human ability

“It’s difficult to imagine the power that you’re going to have when so many different sorts of data are available.” – Tim Berners-Lee, father of the Internet

“Before Google, and long before Facebook, Bezos had realised that the greatest value of an online company lay in the consumer data it collected.” – George Packer, author for the New Yorker

The Future Robo-Advisor, Catalin Burlacu

MITB Thought Leadership Series

The accelerated digitalisation of both people and business around the world today is having a huge impact on the investment management and advisory space. The addition of new and vastly larger data sets, as well as exponentially more sophisticated analytical tools to turn that data into usable information is constantly changing the way investments are decided on, made and managed.

Fault Adaptive Workload Allocation For Complex Manufacturing Systems, Charlie B. Destefano

Graduate Theses and Dissertations

This research proposes novel fault adaptive workload allocation (FAWA) strategies for the health management of complex manufacturing systems. The primary goal of these strategies is to minimize maintenance costs and maximize production by strategically controlling when and where failures occur through condition-based workload allocation.

For complex systems that are capable of performing tasks a variety of different ways, such as an industrial robot arm that can move between locations using different joint angle configurations and path trajectories, each option, i.e. mission plan, will result in different degradation rates and life-expectancies. Consequently, this can make it difficult to predict when a …

Motor Control Systems Analysis, Design, And Optimization Strategies For A Lightweight Excavation Robot, Austin Jerold Crawford

Graduate Theses and Dissertations

This thesis entails motor control system analysis, design, and optimization for the University of Arkansas NASA Robotic Mining Competition robot. The open-loop system is to be modeled and simulated in order to achieve a desired rapid, yet smooth response to a change in input. The initial goal of this work is to find a repeatable, generalized step-by-step process that can be used to tune the gains of a PID controller for multiple different operating points. Then, sensors are to be modeled onto the robot within a feedback loop to develop an error signal and to make the control system self-corrective …

Clustering Of Multiple Instance Data., Andrew D. Karem

Electronic Theses and Dissertations

An emergent area of research in machine learning that aims to develop tools to analyze data where objects have multiple representations is Multiple Instance Learning (MIL). In MIL, each object is represented by a bag that includes a collection of feature vectors called instances. A bag is positive if it contains at least one positive instance, and negative if no instances are positive. One of the main objectives in MIL is to identify a region in the instance feature space with high correlation to instances from positive bags and low correlation to instances from negative bags -- this region is …

The Challenges Of Creating Engaging Content: Results From A Focus Group Study Of A Popular News Media Organization, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

The process of content creation for distribution via social media platforms is not a trivial one for social media editors as the goal of creating both serious and engaging content is challenging, with no clear or differing guidelines or rules across and between platforms. For creators of serious content, such as news organizations, advertisers, or educational institutions, engagement has a deeper meaning beyond likes, shares, etc. that is aimed at the audience actually processing the underlying content associated with a social media post. In this research, we report findings from a group study that aimed to understand the process and …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

A Bystander's Dilemma: Participatory Design Study Of Privacy Expectations For Smart Home Devices, Oriana Mcdonough

Renée Crown University Honors Thesis Projects - All

Traditional homes have become increasingly filled with Internet-connected devices, turning them into “smart homes.” Currently, research around privacy concerns with smart home devices has focused on the end users. The goal for our research is to understand the perceptions and desired privacy mechanisms from the perspective of a different stakeholder, i.e., the bystanders. Bystanders in this context are individuals who are not the owner or primary user of smart home devices but are potentially affected by the device usage, such as house guests or family members. In order to understand this, we conducted a focus group study with co-design activities …

Watersheds For Semi-Supervised Classification, Aditya Challa, Sravan Danda, B. S.Daya Sagar, Laurent Najman

Journal Articles

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting …

Analysis Literatures Of Machine Learning And Neural Networks For Real Time Scheduling, Phong Nguyenho, Mark Nguyen

Recent Advances in Real-Time Systems

Real time scheduling problems are present in every aspect of software development. An optimized real time scheduling scheme would determine the performance of an operating system. There are many different approaches that real time scheduling researchers developed to tackle scheduling problems in many computer systems that have great important roles in keeping our modern society running smoothly. Neural-network real time scheduling is one of those approaches that can solve many computer scheduling problems. As computing technology advanced, more and more real time scheduling problems arise that need new solutions to keep up with the demand of faster computer systems. In …

Designing A General Education Course On The Societal Impacts Of Artificial Intelligence, Vincent Rollins

Honors Theses

Most colleges, including UTC, already offer an artificial intelligence course (CPSC 4440) as part of their computer science curricula. Such courses are meant to explain the technology behind these elaborate systems, but these courses often neglect extensive coverage of the real-world impacts of the technology itself. UTC also offers a course entitled “Ethical and Social Issues in Computing” that does convey the importance behind the advances of computer technology and its impacts, but this course is practically available only to computer science majors. There is no generalized and widely available course that covers the technological, economic, cultural, philosophical/theological, and ethical …