Computer Sciences Commons^™

On Learning Psycholinguistics Tools For English-Based Creole Languages Using Social Media Data, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The Linguistic Inquiry and Word Count (LIWC) tool is a psycholinguistics tool that has been widely used in both psychology and sociology research, and the LIWC scores derived from user-generated content are known to be good features for personality prediction [1], [2]. LIWC, however, is language specific as it relies on counting the percentage of predefined dictionary words occurring in the content. For content written in English Creoles which are languages based on English, the original English LIWC may not perform optimally due to its lack of words which are only used in the English Creoles. In this paper, we …

An Architectural Design And Evaluation Of An Affective Tutoring System For Novice Programmers, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Affect is prevalent in learning and it influences students’ learning achievement. This paper details the design and evaluation of an Affective Tutoring System (ATS) that tutors student in computer programming. Although most ATSs are purpose built for a specific domain, making adaptation to another domain difficult, this ATS is architected for adaptability and extensibility. This study also addresses a lack of research exploring the theories and methods of integrating affect and learning within the learning process by proposing methods of regulating the negative affect of students. Both quantitative and qualitative techniques were used for evaluation of the effectiveness of the …

Preprocess-Then-Ntt Technique And Its Applications To Kyber And Newhope, Shuai Zhou, Haiyang Xue, Daode Zhang, Kunpeng Wang, Xianhui Lu, Bao Li, Jingnan He

Research Collection School Of Computing and Information Systems

The Number Theoretic Transform (NTT) provides efficient algorithm for multiplying large degree polynomials. It is commonly used in cryptographic schemes that are based on the hardness of the Ring Learning With Errors problem (RLWE), which is a popular basis for post-quantum key exchange, encryption and digital signature.To apply NTT, modulus q should satisfy that , RLWE-based schemes have to choose an oversized modulus, which leads to excessive bandwidth. In this work, we present “Preprocess-then-NTT (PtNTT)” technique which weakens the limitation of modulus q, i.e., we only require or . Based on this technique, we provide new parameter settings for KYBER …

Material Identification And Target Imaging With Rfids [Iot Connection], Ju Wang, Xiaojiang Chen, Dingyi Fang, Jie Xiong, Hongbo Jiang, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

TagScan is a system that determines the material type and shape of an object with inexpensive commercial RFID technology. Real-world experiments show that TagScan can identify 10 common liquids with accuracy greater than 94%.

Authorized Function Homomorphic Signature, Qingwen Guo, Qiong Huang, Guomin Yang

Research Collection School Of Computing and Information Systems

Homomorphic signature (HS) is a novel primitive that allows an agency to carry out arbitrary (polynomial time) computation f on the signed data (m) over right arrow and accordingly gain a signature sigma(h) for the computation result f ((m) over right arrow) with respect to f on behalf of the data owner (DO). However, since DO lacks control of the agency's behavior, receivers would believe that DO did authenticate the computation result even if the agency misbehaves and applies a function that the DO does not want. To address the problem above, in this paper we introduce a new primitive …

Improving Accuracy Of The Edgebox Approach, Kamna Yadav

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Object region detection plays a vital role in many domains ranging from self-driving cars to lane detection, which heavily involves the task of object detection. Improving the performance of object region detection approaches is of great importance and therefore is an active ongoing research in Computer Vision. Traditional sliding window paradigm has been widely used to identify hundreds of thousands of windows (covering different scales, angles, and aspect ratios for objects) before the classification step. However, it is not only computationally expensive but also produces relatively low accuracy in terms of the classifier output by providing many negative samples. Object …

Sheaf Theory As A Foundation For Heterogeneous Data Fusion, Seyed M-H Mansourbeigi

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A major impediment to scientific progress in many fields is the inability to make sense of the huge amounts of data that have been collected via experiment or computer simulation. This dissertation provides tools to visualize, represent, and analyze the collection of sensors and data all at once in a single combinatorial geometric object. Encoding and translating heterogeneous data into common language are modeled by supporting objects. In this methodology, the behavior of the system based on the detection of noise in the system, possible failure in data exchange and recognition of the redundant or complimentary sensors are studied via …

Collection And Analysis Of Digital Forensic Data From Devices In The Internet Of Things, Raed Alharbi

Theses and Dissertations

Despite the abundance articles that have been written about the Internet of Things (IoT), little attention has been given to how digital forensics approaches can be utilized to direct advanced investigations in IoT-based frameworks. As of yet, IoT has not completely adjusted to digital forensic strategies given the fact that current digital forensic tools and functions are not ready to tackle the complexity of IoT frameworks for the purpose of collecting, analyzing, and testing potential evidence from IoT environments that might be utilized as permissible evidence in a court. Hence, the issue addressed is that; currently, there is no accepted …

Toward Real-Time Flip Fluid Simulation Through Machine Learning Approximations, Javid Kennon Pack

Theses and Dissertations

Fluids in computer generated imagery can add an impressive amount of realism to a scene, but are particularly time-consuming to simulate. In an attempt to run fluid simulations in real-time, recent efforts have attempted to simulate fluids by using machine learning techniques to approximate the movement of fluids. We explore utilizing machine learning to simulate fluids while also integrating the Fluid-Implicit-Particle (FLIP) simulation method into machine learning fluid simulation approaches.

Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu

Master's Theses

Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …

Deploying, Improving And Evaluating Edge Bundling Methods For Visualizing Large Graphs, Jieting Wu

School of Computing: Dissertations, Theses, and Student Research

A tremendous increase in the scale of graphs has been witnessed in a wide range of fields, which demands efficient and effective visualization techniques to assist users in better understandings of large graphs. Conventional node-link diagrams are often used to visualize graphs, whereas excessive edge crossings can easily incur severe visual clutter in the node-link diagram of a large graph. Edge bundling can effectively remedy visual clutter and reveal high-level graph structures. Although significant efforts have been devoted to developing edge bundling, three challenging problems remain. First, edge bundling techniques are often computationally expensive and are not easy to deploy …

Evoalloy: An Evolutionary Approach For Analyzing Alloy Specifications, Jianghao Wang

School of Computing: Dissertations, Theses, and Student Research

Using mathematical notations and logical reasoning, formal methods precisely define a program’s specifications, from which we can instantiate valid instances of a system. With these techniques, we can perform a variety of analysis tasks to verify system dependability and rigorously prove the correctness of system properties. While there exist well-designed automated verification tools including ones considered lightweight, they still lack a strong adoption in practice. The essence of the problem is that when applied to large real world applications, they are not scalable and applicable due to the expense of thorough verification process. In this thesis, I present a new …

Controller Evolution And Divergence: A Software Perspective, Balaji Balasubramaniam

School of Computing: Dissertations, Theses, and Student Research

Successful controllers evolve as they are refined, extended, and adapted to new systems and contexts. This evolution occurs in the controller design and also in its software implementation. Model-based design and controller synthesis can help to synchronize this evolution of design and software, but such synchronization is rarely complete as software tends to also evolve in response to elements rarely present in a control model, leading to mismatches between the control design and the software.

In this thesis, we perform a first-of-its-kind study on the evolution of two popular open-source safety-critical autopilot control software -- ArduPilot, and Paparazzi, to better …

Design Of Virtual Interactive Simulations For Surgical Training, Doga Demirel

Theses and Dissertations

Design and development of a virtual reality based surgical simulation has many steps and the first and most important step is the comprehensive analysis of the surgery. We performed comprehensive analysis, hierarchical task analysis, which allowed steps and goals of the surgery to be understood while expressing the order of execution and hierarchical relations between the tasks of the surgery. Time and performance metrics derived from the comprehensive analysis provides detailed procedural feedback throughout the surgical simulation, which will help classify surgeon’s skill level. We developed quantitative performance metrics for arthroscopy-based rotator cuff surgery with the goal to establish objective …

Transfer Learning With Deep Recurrent Neural Networks For Remaining Useful Life Estimation, Ansi Zhang, Honglei Wang, Shaobo Li, Yuxin Cui, Guanci Yang, Jianjun Hu

Faculty Publications

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results …

Static Analysis Of Android Secure Application Development Process With Findsecuritybugs, Xianyong Meng

Master of Science in Computer Science Theses

Mobile devices have been growing more and more powerful in recent decades, evolving from a simple device for SMS messages and phone calls to a smart device that can install third party apps. People are becoming more heavily reliant on their mobile devices. Due to this increase in usage, security threats to mobile applications are also growing explosively. Mobile app flaws and security defects can provide opportunities for hackers to break into them and access sensitive information. Defensive coding needs to be an integral part of coding practices to improve the security of our code.

We need to consider data …

Virtual Reality As Navigation Tool: Creating Interactive Environments For Individuals With Visual Impairments, Nick Murphy

Master of Science in Computer Science Theses

Research into the creation of assistive technologies is increasingly incorporating the use of virtual reality experiments. One area of application is as an orientation and mobility assistance tool for people with visual impairments. Some of the challenges are developing useful knowledge of the user’s surroundings and effectively conveying that information to the user. This thesis examines the feasibility of using virtual environments conveyed via auditory feedback as part of an autonomous mobility assistance system. Two separate experiments were conducted to study key aspects of a potential system: navigation assistance and map generation. The results of this research include mesh models …

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, …

Pymol Plugin To Build Protein Structures Based On Natural Term Overlaps, Noah T. Paravicini

Dartmouth College Undergraduate Theses

This project is a continuation of the Grigoryan Lab's exploration of TERMs. A TERM is a tertiary structural motif, which is a fragment of a protein that includes the secondary, tertiary, and quaternary environments around a certain residue. As displayed in past publications discussing TERMs, they are a useful way of decomposing proteins into smaller components that help in understanding design and prediction of protein structures. The Grigoryan Lab developed a database that keeps track of naturally occurring overlaps between TERMs, which gives a user the information they would need to put these TERMs together into complex structures. These events …

Big Data And Sensor Network For Construction Material Testing, Yao Shi

Theses and Dissertations

Engineers and contractors need to have a precise understanding of the development progress of concrete strength in the natural environment, which helps to save project time and cost. However, current practice of concrete construction depends on published data, charts and curves from laboratory tests. The data would not show frequently changing environmental conditions in the real world, which can affect concrete quality significantly. The objective of this research is to design a reliable and accurate method to validate test data of the strength developments of concrete specimens in early stages. The approach includes the following tasks: (1) arrange sensors to …

Sequence Pattern Mining With Variables, James S. Okolica, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila

Faculty Publications

Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that all of the relevant differences between items are captured through creating distinct items, e.g., if color matters then the same item in two different colors would have two items created, one for each color. In some domains, that is unrealistic. This paper makes two contributions. The first extends SPM algorithms to allow item differentiation through attribute variables for domains with large numbers of items, e.g, by having one item with a variable with a color attribute rather than …

Multidimensional Feature Engineering For Post-Translational Modification Prediction Problems, Norman Mapes Jr.

Doctoral Dissertations

Protein sequence data has been produced at an astounding speed. This creates an opportunity to characterize these proteins for the treatment of illness. A crucial characterization of proteins is their post translational modifications (PTM). There are 20 amino acids coded by DNA after coding (translation) nearly every protein is modified at an amino acid level. We focus on three specific PTMs. First is the bonding formed between two cysteine amino acids, thus introducing a loop to the straight chain of a protein. Second, we predict which cysteines can generally be modified (oxidized). Finally, we predict which lysine amino acids are …

Enabling The Social Internet Of Things, Ahmed E. Khaled, Sumi Helal

Faculty Research and Creative Activities Symposium

No abstract provided.

A Study Of Text Simplification On Breast Cancer Information Targeting A Low-Health Literacy Population, Francisco D. Iacobelli, Xiwei Wang

Faculty Research and Creative Activities Symposium

No abstract provided.

A Parallelized Implementation Of Cut-And-Solve And A Streamlined Mixed-Integer Linear Programming Model For Finding Genetic Patterns Optimally Associated With Complex Diseases, Michael Yip-Hin Chan

Theses

With the advent of genetic sequencing, there was much hope of finding the inherited elements underlying complex diseases, such as late-onset Alzheimer’s disease (AD), but it has been a challenge to fully uncover the necessary information hidden in the data. A likely contributor to this failure is the fact that the pathogenesis of most complex diseases does not involve single markers working alone, but patterns of genetic markers interacting additively or epistatically. But as we move upwards beyond patterns of size two, it quickly becomes computationally infeasible to examine all combinations in the solution space. A common solution to solving …

Exploring Best Lossy Compression Strategy By Combining Sz With Spatiotemporal Decimation, Xin Liang, Sheng Di, Sihuan Li, Dingwen Tao, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

In today’s extreme-scale scientific simulations, vast volumes of data are being produced such that the data cannot be accommodated by the parallel file system or the data writing/ reading performance will be fairly low because of limited I/O bandwidth. In the past decade, many snapshot-based (or space-based) lossy compressors have been developed, most of which rely on the smoothness of the data in space. However, the simulation data may get more and more complicated in space over time steps, such that the compression ratios decrease significantly. In this paper, we propose a novel, hybrid lossy compression method by leveraging spatiotemporal …

Improving Strategic It Investment Decisions By Reducing Information Asymmetry, Thomas P. Stablein

USF Tampa Graduate Theses and Dissertations

The unprecedented ubiquity with which technological advancements, such as blockchain, the Internet of things (IoT), big data, machine learning, and artificial intelligence (AI), are impacting the world has forced large organizations to rethink their information technology roadmaps. Their decisions about how they invest in technology have become more important. It is against this backdrop that companies must decide how much to invest in their aging technologies versus these new potentially transformational ones. A decision is only as good as the information available to the decision-makers when they make it. This research project seeks to understand the effects that information asymmetry …

Improving Error-Bounded Compression For Cosmological Simulation, Sihuan Li, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Cosmological simulations may produce extremely large amount of data, such that its successful run depends on large storage capacity and huge I/O bandwidth, especially in the exascale computing scale. Effective error-bounded lossy compressors with both high compression ratios and low data distortion can significantly reduce the total data size while guaranteeing the data valid for post-analysis. In this poster, we propose a novel, efficient compression model for cosmological N-body simulation framework, by combining the advantages of both space-based compression and time-based compression. The evaluation with a well-known cosmological simulation code shows that our proposed solution can get much higher compression …

Predicting Cost Of Care In Total Hip Replacement, Cecily Corrine Froemke, Martin Zwick

Complex Systems Faculty Publications and Presentations

Legislative reforms aimed at slowing growth of US healthcare costs are focused on achieving greater value per dollar. To increase value healthcare providers must not only provide high quality care, but deliver this care at a sustainable cost. Predicting risks that may lead to poor outcomes and higher costs enable providers to augment decision making for optimizing patient care and inform the risk stratification necessary in emerging reimbursement models. Healthcare delivery systems are looking at their high volume service lines and identifying variation in cost and outcomes in order to determine the patient factors that are driving this variation and …

Constrained K-Means Clustering Validation Study, Nicholas Mcdaniel, Stephen Burgess, Jeremy Evert

Student Research

Machine Learning (ML) is a growing topic within Computer Science with applications in many fields. One open problem in ML is data separation, or data clustering. Our project is a validation study of, “Constrained K-means Clustering with Background Knowledge" by Wagstaff et. al. Our data validates the finding by Wagstaff et. al., which shows that a modified k-means clustering approach can outperform more general unsupervised learning algorithms when some domain information about the problem is available. Our data suggests that k-means clustering augmented with domain information can be a time efficient means for segmenting data sets. Our validation study focused …