Computer Sciences Commons^™

Cyberbullying And Cybervictimization In Tanzanian Secondary Schools: Prevalence And Predictors, Hezron Zacharia Onditi, Jennifer Shapka

Journal of Humanities and Social Sciences

This study explored cyberbullying and cybervictimization, and the role of socio demographic and access to technology variables for Tanzanian adolescents. A self-report questionnaire was completed by secondary school students aged 14 to 18 (Form 1 to Form IV). Results provide evidence that online violence is increasingly becoming a problem of concern for Tanzanian adolescents. In particular, whereas 42% of the students reported to have cyberbullied others using electronic communication devices, 58% admitted having experienced cybervictimization. Also, results showed that students who spend more time online, share cellphones with others, and who access digital devices in a private location are more …

Autonomous Monocular Obstacle Detection For Avoidance In Quadrotor Uavs, Panos Valavanis

USF Tampa Graduate Theses and Dissertations

Unmanned Aircraft Systems (UAS) research and development and applications have witnessed unprecedented levels of growth in the past two decades. Although military applications have dominated the market, it is anticipated and expected that civilian and public domain applications will be dominant in the future. Consequently, gradual and timely integration of unmanned aviation into the National Airspace System (NAS) is a real challenge, and roadmaps towards achieving full integration are already in place in the US, Canada, Australia, South Africa and European Union (EU). However, before com- plete integration of manned-unmanned aviation, additional challenging problems need to be addressed and solved, …

Data Mining And Machine Learning To Improve Northern Florida’S Foster Care System, Daniel Oldham, Nathan Foster, Mihhail Berezovski

Beyond: Undergraduate Research Journal

The purpose of this research project is to use statistical analysis, data mining, and machine learning techniques to determine identifiable factors in child welfare service records that could lead to a child entering the foster care system multiple times. This would allow us the capability of accurately predicting a case’s outcome based on these factors. We were provided with eight years of data in the form of multiple spreadsheets from Partnership for Strong Families (PSF), a child welfare services organization based in Gainesville, Florida, who is contracted by the Florida Department for Children and Families (DCF). This data contained a …

Deepsz: A Novel Framework To Compress Deep Neural Networks By Using Error-Bounded Lossy Compression, Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello

Computer Science Faculty Research & Creative Works

Today's deep neural networks (DNNs) are becoming deeper and wider because of increasing demand on the analysis quality and more and more complex applications to resolve. The wide and deep DNNs, however, require large amounts of resources (such as memory, storage, and I/O), significantly restricting their utilization on resource-constrained platforms. Although some DNN simplification methods (such as weight quantization) have been proposed to address this issue, they suffer from either low compression ratios or high compression errors, which may introduce an expensive fine-tuning overhead (i.e., a costly retraining process for the target inference accuracy). In this paper, we propose DeepSZ: …

Authentication And Sql-Injection Prevention Techniques In Web Applications, Cagri Cetin

USF Tampa Graduate Theses and Dissertations

This dissertation addresses the top two “most critical web-application security risks” by combining two high-level contributions.

The first high-level contribution introduces and evaluates collaborative authentication, or coauthentication, a single-factor technique in which multiple registered devices work together to authenticate a user. Coauthentication provides security benefits similar to those of multi-factor techniques, such as mitigating theft of any one authentication secret, without some of the inconveniences of multi-factor techniques, such as having to enter passwords or biometrics. Coauthentication provides additional security benefits, including: preventing phishing, replay, and man-in-the-middle attacks; basing authentications on high-entropy secrets that can be generated and updated automatically; …

The Computer Science Professional's Hatchery, Amit Jain, Noah Salzman, Donald Winiecki

Computer Science Faculty Publications and Presentations

As a recipient of a National Science Foundation Revolutionizing Engineering and Computer Science Departments (RED) grant, the Computer Science Department at the Boise State University is building a Computer Science (CS) Professionals Hatchery. This paper is a summary to accompany the poster to be presented.

Raspbot - Raspberry Pi-Powered Robot Controlled By A Webapp, Julio Lopez, Eric Michel, Jhon Loiaza, Nancy Aguilera, Vilmarys Salgado

ICT

This project aims at designing a robot prototype made from Lego, which contains a Raspberry Pi device, that will be remotely controlled through a web application to allow the robot to perform certain movements which would be sent from the client’s interaction. The process began by researching several different technologies that we used as our starting point for designing both prototypes (hardware and software). Concurrently, we focused investigation on the IoT and were inspired to make a robot prototype that would implement the basic concepts and, at the same time, merge it with the previously learnt knowledge acquired during our …

E-Health Management System, Husnain Azeem Toor

ICT

Most of the reasons for implementing the EHMS (Electronic Health Management System) focus on improving medical care as a whole for Patient, Physicians and Doctors. However, achieving an excellent quality of best medical care through EMR (Electronic Medical Record) is neither low-cost nor easy. Based on our qualitative study on physician practices we have found that quality improvement depends heavily on doctors’ use of the EMRs, not use of papers for their daily tasks. I also identified Key barriers to physicians’ use of EMRs and also observed that EMR software becomes useless for doctors due to its complex interface. E-Health …

Supervised Machine Learning Models For Fake News Detection, Andrea Lopez, Adelo Vieira, Zafar Ahsan, Farooq Sabib, Shirley Marinho

ICT

Fake news or the distribution of disinformation has become one of the most challenging issues in society. News and information are churned out across online websites and platforms in real-time, with little or no way for the viewing public to determine what is real or manufactured. But an awareness of what we are consuming online is becoming apparent and efforts are underway to explore how we separate fake content from genuine and truthful information. The most challenging part of fake news is determining how to spot it. In technology, there are ways to help us do this. Supervised machine learning …

Orthogonal Array Sampling For Monte Carlo Based Rendering, Afnan Enayet

Dartmouth College Undergraduate Theses

In computer graphics (especially in offline rendering), the current state of the art rendering techniques utilize Monte Carlo integration to simulate light and calculate the value of each pixel in order to generate a realistic-looking image. Monte Carlo integration is a highly efficient method to estimate an integral that scales extremely well to a high number of dimensions, making it well suited for graphics, because generating images creates a high-dimensional integrand. The efficiency of these Monte Carlo integrations depends on the sampling techniques used, and using a more efficient sampling technique can make a Monte Carlo simulation converge to the …

A Study On Large-Scale Deep Learning In Bioinformatics And Biomedical Applications, Shayan Shams

LSU Doctoral Dissertations

Recent advances in Artificial Intelligence and deep learning have provided researchers in various fields insights into the analysis of multiple datasets. These applications include image analysis, text analysis, and many more. However, the effectiveness of deep learning in some areas, such as biomedical imaging and genomic research, has been overshadowed by the variance in the types and complexity of data. This is in addition to the expensive labeling process and the limited size of datasets in these fields. These challenges require advanced deep learning models capable of learning from a small dataset and also from a small number of labeled …

Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …

The Trust-Based Interactive Partially Observable Markov Decision Process, Richard S. Seymour

Theses and Dissertations

Cooperative agent and robot systems are designed so that each is working toward the same common good. The problem is that the software systems are extremely complex and can be subverted by an adversary to either break the system or potentially worse, create sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise. This research focuses on the ability of a group of agents to reason about the trustworthiness of each other and make decisions about whether to cooperate. A trust-based interactive partially observable Markov decision process (TI-POMDP) is …

Scheduling And Prefetching In Hadoop With Block Access Pattern Awareness And Global Memory Sharing With Load Balancing Scheme, Sai Suman

School of Computing: Dissertations, Theses, and Student Research

Although several scheduling and prefetching algorithms have been proposed to improve data locality in Hadoop, there has not been much research to increase cluster performance by targeting the issue of data locality while considering the 1) cluster memory, 2) data access patterns and 3) real-time scheduling issues together.

Firstly, considering the data access patterns is crucial because the computation might access some portion of the data in the cluster only once while the rest could be accessed multiple times. Blindly retaining data in memory might eventually lead to inefficient memory utilization.

Secondly, several studies found that the cluster memory goes …

Multi-Resolution Models For Learning Multilevel Abstract Representation With Application To Information Retrieval, Tolgahan Cakaloglu

Theses and Dissertations

Deep language models learning a hierarchical representation proved to be a powerful tool for natural language processing, text mining, and information retrieval tasks. However, more specifically, representations that perform well for ad-hoc retrieval must capture semantic meaning at different levels of abstraction or context-scopes. The primary goal of ad-hoc retrieval is to find relevant documents satisfying the information need posted in a natural language query. It requires a good understanding of the query and all the documents in a corpus, which is difficult because the meaning of natural language texts depends on the context, syntax, and semantics. In this dissertation, …

Unsupervised Deep Structured Semantic Models For Commonsense Reasoning, Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.

Enabling Multi-Hop Remote Method Invocation In Device-To-Device Networks, Minh Le, Stephen Clyde, Young‑Woo Kwon

Computer Science Faculty and Staff Publications

To avoid shrinking down the performance and preserve energy, low-end mobile devices can collaborate with the nearby ones by offloading computation intensive code. However, despite the long research history, code offloading is dilatory and unfit for applications that require rapidly consecutive requests per short period. Even though Remote Procedure Call (RPC) is apparently one possible approach that can address this problem, the RPC-based or message queue-based techniques are obsolete or unwieldy for mobile platforms. Moreover, the need of accessibility beyond the limit reach of the device-to-device (D2D) networks originates another problem. This article introduces a new software framework to overcome …

High-Performance Computing Frameworks For Large-Scale Genome Assembly, Sayan Goswami

LSU Doctoral Dissertations

Genome sequencing technology has witnessed tremendous progress in terms of throughput and cost per base pair, resulting in an explosion in the size of data. Typical de Bruijn graph-based assembly tools demand a lot of processing power and memory and cannot assemble big datasets unless running on a scaled-up server with terabytes of RAMs or scaled-out cluster with several dozens of nodes. In the first part of this work, we present a distributed next-generation sequence (NGS) assembler called Lazer, that achieves both scalability and memory efficiency by using partitioned de Bruijn graphs. By enhancing the memory-to-disk swapping and reducing the …

Using Big Data Analytics To Improve Hiv Medical Care Utilisation In South Carolina: A Study Protocol, Bankole Olatosi, Jiajia Zhang, Sharon Weissman, Jianjun Hu, Mohammad Rifat Haider, Xiaoming Li

Faculty Publications

Introduction Linkage and retention in HIV medical care remains problematic in the USA. Extensive health utilisation data collection through electronic health records (EHR) and claims data represent new opportunities for scientific discovery. Big data science (BDS) is a powerful tool for investigating HIV care utilisation patterns. The South Carolina (SC) office of Revenue and Fiscal Affairs (RFA) data warehouse captures individual-level longitudinal health utilisation data for persons living with HIV (PLWH). The data warehouse includes EHR, claims and data from private institutions, housing, prisons, mental health, Medicare, Medicaid, State Health Plan and the department of health and human services. The …

Context-Aware Wi-Fi Infrastructure-Based Indoor Positioning Systems, Huy Phuong Tran

Dissertations and Theses

Large enterprises are often interested in tracking objects and people within buildings to improve resource allocation and occupant experience. Infrastructure-based indoor positioning systems (IIPS) can provide this service at low-cost by leveraging already deployed Wi-Fi infrastructure. Typically, IIPS perform localization and tracking of devices by measuring only Wi-Fi signals at wireless access points and do not rely on inertial sensor data at mobile devices (e.g., smartphones), which would require explicit user consent and sensing capabilities of the devices.

Despite these advantages, building an economically viable cost-effective IIPS that can accurately and simultaneously track many devices over very large buildings is …

Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga

LSU Master's Theses

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different …

Weaving The Dark Web: Legitimacy On Freenet, Tor, And I2p, John Schriner

Publications and Research

This is a book review of Robert W. Gehl's Weaving the Dark Web: Legitimacy on Freenet, Tor, and I2P (2018). The book explores these anonymity networks and the concept of legitimacy throughout. Using a multidisciplinary approach and interviews with network-builders and users, Gehl helps to demystify the dark web and critically examine these networks and technologies.

Accessible And Responsive Website Design For Cal Poly Dbs Marine Education Program, Charles W. Alexander

Computer Engineering

In this project, I demonstrate how accessible and responsive designs are followed in order to implement a modern, multi-page website which both adapts to the size of the screen as well as has the logical, semantic structure needed for accessible technologies to accurately use the site. This website is designed for a marine education program, Dive Beneath the Surface, which hosts live streams of scientific divers as they interact in real time with students many miles away. Although this site will not be the streaming platform, it needs to host a repository of videos and lessons for those students and …

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

Decentralise Me, Paul Robert Griffin

MITB Thought Leadership Series

If you are in need of a reminder of the levels of hype surrounding blockchain, then look no further than Japan’s J-Pop sensation Kasotsuka Shojo, otherwise known as the Virtual Currency Girls who, with their debut track “The Moon and Cryptocurrencies and Me”, aim to educate fans about cryptocurrencies in an entertaining way.

In The Absence Of Information, 1/N Investment Makes Perfect Sense, Julio Urenda, Vladik Kreinovich

Departmental Technical Reports (CS)

When people have several possible investment instruments, people often invest equally into these instruments: in the case of n instruments, they invest 1/n of their money into each of these instruments. Of course, if additional information about each instrument is available, this 1/n investment strategy is not optimal. We show, however, that in the absence of reliable information, 1/n investment is indeed the best strategy.

Faster Quantum Alternative To Softmax Selection In Deep Learning And Deep Reinforcement Learning, Oscar Galindo, Christian Ayub, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

Deep learning and deep reinforcement learning are, at present, the best available machine learning tools for use in engineering problems. However, at present, the use of these tools is limited by the fact that they are very time-consuming, usually requiring the use of a high performance computer. It is therefore desirable to look for possible ways to speed up the corresponding computations. One of the time-consuming parts of these algorithms is softmax selection, when instead of selecting the alternative with the largest possible value of the corresponding objective function, we select all possible values, with probabilities increasing with the value …

Closing Banquet Eulogies, Russell Howell, C. Ray Rosentrater

ACMS Conference Proceedings 2019

A tribute to David Lay; A tribute to John Roe

Evaluating The Efficacy Of Magnetometer-Based Vehicle Sensors, Luke A. Hudspeth

Dartmouth College Undergraduate Theses

The issue of parking is more at the forefront of urban development than one might believe. In fact, academic studies have shown that roughly 30% of city traffic is due to drivers circling city blocks attempting to find an open spot. Due to such congestion people often avoid urban centers and downtown areas for shopping or dining because parking is such a hassle and assumed to be unavailable. If drivers knew where parking was available in real time, they could proceed directly to open spaces as opposed to their congestion-inducing attempts to park. A better solution would guide drivers to …

Bincor: An R Package For Estimating The Correlation Between Two Unevenly Spaced Time Series, Josue M. Polanco-Martinez, Martin A. Medina-Elizalde, Maria Fernanda Sanchez Goni, Manfred Mudelsee

The R Journal

This paper presents a computational program named BINCOR (BINned CORrelation) for estimating the correlation between two unevenly spaced time series. This program is also applicable to the situation of two evenly spaced time series not on the same time grid. BINCOR is based on a novel estimation approach proposed by Mudelsee (2010) for estimating the correlation between two climate time series with different timescales. The idea is that autocorrelation (e.g. an AR1 process) means that memory enables values obtained on different time points to be correlated. Binned correlation is performed by resampling the time series under study into time bins …