Digital Commons Network^™

A Deep Learning Model To Predict Traumatic Brain Injury Severity And Outcome From Mr Images, Dacosta Yeboah, Hung Nguyen, Daniel B. Hier, Gayla R. Olbricht, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

For Many Neurological Disorders, Including Traumatic Brain Injury (TBI), Neuroimaging Information Plays a Crucial Role Determining Diagnosis and Prognosis. TBI is a Heterogeneous Disorder that Can Result in Lasting Physical, Emotional and Cognitive Impairments. Magnetic Resonance Imaging (MRI) is a Non-Invasive Technique that Uses Radio Waves to Reveal Fine Details of Brain Anatomy and Pathology. Although MRIs Are Interpreted by Radiologists, Advances Are Being Made in the Use of Deep Learning for MRI Interpretation. This Work Evaluates a Deep Learning Model based on a Residual Learning Convolutional Neural Network that Predicts TBI Severity from MR Images. the Model Achieved a …

Subsumption Reduces Dataset Dimensionality Without Decreasing Performance Of A Machine Learning Classifier, Donald C. Wunsch, Daniel B. Hier

Chemistry Faculty Research & Creative Works

When Features in a High Dimension Dataset Are Organized Hierarchically, There is an Inherent Opportunity to Reduce Dimensionality. Since More Specific Concepts Are Subsumed by More General Concepts, Subsumption Can Be Applied Successively to Reduce Dimensionality. We Tested Whether Sub-Sumption Could Reduce the Dimensionality of a Disease Dataset Without Impairing Classification Accuracy. We Started with a Dataset that Had 168 Neurological Patients, 14 Diagnoses, and 293 Unique Features. We Applied Subsumption Repeatedly to Create Eight Successively Smaller Datasets, Ranging from 293 Dimensions in the Largest Dataset to 11 Dimensions in the Smallest Dataset. We Tested a MLP Classifier on All …

Swill-Tac: Skill-Oriented Dynamic Task Allocation With Willingness For Complex Job In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Allocating tasks to the best-fit candidates is a classical problem in crowdsourcing (CS). Most of the existing approaches assume that the task and candidate knowledge is known in advance and ignore the effect of enrolled candidates' willingness on the CS system's selection decision. For instance, an unwilling candidate assigned to a task may quit without completing it, thus depreciating the utility of the CS platform. In practice, a task or candidate may arrive or leave the CS system dynamically. Moreover, a complex task may be broken into smaller sub-tasks, each requiring a variety of computations and expertise. To overcome these …

Nodesense2vec: Spatiotemporal Context-Aware Network Embedding For Heterogeneous Urban Mobility Data, Dakshak Keerthi Chandra, Jennifer Leopold, Yanjie Fu

Computer Science Faculty Research & Creative Works

The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In this paper, we propose a spatiotemporal context-aware network embedding framework that jointly captures the spatial regularities between objects and the sequential transition patterns of human mobility. First, we model the heterogeneous urban mobility data collected …

Detection Dns Tunneling Botnets, Bohdan Savenko, Sergii Lysenko, Kira Bobrovnikova, Oleg Savenko, George Markowsky

Computer Science Faculty Research & Creative Works

Botnets are often used in cyberattacks on network services and individual users, so the ability to detect botnets is very important. Botnets use DNS tunneling to send malicious command-and-control (CC) commands to victims' hosts. Unfortunately, DNS tunneling attacks are very hard to detect. The paper presents a new approach for DNS tunneling botnet detection, which considers all the features and architectural characteristics of botnets. The technique described in this paper is highly efficient at detecting DNS tunneling attacks.

Efficient Route Selection For Drone-Based Delivery Under Time-Varying Dynamics, Arindam Khanda, Federico Coro, Francesco Betti Sorbelli, Cristina M. Pinotti, Sajal K. Das

Computer Science Faculty Research & Creative Works

The use of drones can be a valuable solution for the problem of delivering goods for many reasons. In fact, they can be efficiently employed in time-critical situations when there is a traffic jam on the roads, to serve customers in hard-to-reach places, or simply to expand the business. However, due to limited battery capacities and the fact that drones can serve a single customer at a time, a drone-based delivery system (DBDS) aims to minimize the drones' energy usage for completing a route from the depot to the customer and go back to the depot for new deliveries. In …

Exploring Autoencoder-Based Error-Bounded Compression For Scientific Data, Jinyang Liu, Sheng Di, Kai Zhao, Sian Jin, Dingwen Tao, Xin Liang, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide …

Improving Lossy Compression For Sz By Exploring The Best-Fit Lossless Compression Techniques, Jinyang Liu, Sihuan Li, Sheng Di, Xin Liang, Kai Zhao, Dingwen Tao, Zizhong Chen, Franck Cappello

Computer Science Faculty Research & Creative Works

In the past decades, various lossy compressors have been studied broadly due to the ever-increasing volume of data being produced by today's scientific applications. SZ has been one of the best error-bounded lossy compressors ever raised, and it has a flexible framework that includes four adjustable steps: prediction, quantization, variable-length encoding, and lossless compression. In this paper, we improve the lossy compression performances of the SZ compression model by exploring different existing lossless compression techniques using the Squash data compression benchmark. Specifically, we first characterize the bytes outputted by the first three steps in SZ, then we investigate the best …

Optimizing Error-Bounded Lossy Compression For Scientific Data On Gpus, Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

Computer Science Faculty Research & Creative Works

Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or low compression ratios. To this end, we propose CUSZ+ to target both high compression ratios and throughputs. We identify that data sparsity and data smoothness are key factors for high compression throughputs. Our key contributions in this work are fourfold: (1) We propose an efficient compression workflow to adaptively perform run-length encoding and/or variable-length encoding. (2) We derive Lorenzo reconstruction in …

Connectionless Edge-Cache Servers For Reducing Cellular Bandwidth Usage In Vehicular Networks, Rui Wang, Jayanthi Rao, Ce Zhou, Subir Biswas

Computer Science Faculty Research & Creative Works

This paper presents a novel caching mechanism based on Connectionless Edge Cache Servers in vehicular networks. The goal is to intelligently cache content within the vehicles and the edge servers so that majority of the vehiclerequested content can be obtained from those caches, thus minimizing the amount of cellular network usage needed for fetching content from a central server. A notable feature of the cache servers in this work is that they do not have backhaul connectivity. This makes the connectionless servers to be relatively less expensive compared to the usual Roadside Service Units (RSUs), and potentially moveable in response …

Free Water In T2 Flair Whitematter Hyperintensity Lesions, Xiaowei Yu, Norman Scheel, Lu Zhang, David C. Zhu, Rong Zhang, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Background: White matter (WM) free water (FW) is likely associated with cerebral small vessel disease (CSVD). FWis the fraction of unconstrained water within an image voxel, which can be estimated from diffusion-weighted images. T2-weighted Fluid- Attenuated Inversion Recovery (FLAIR) whitematter hyperintensity (WMH)is awidely used index to assess the damages caused by CSVD. It is critical to characterize howFW content is altered inWMHlesions. In this work, we proposed a data processing framework to assess FW distributions in WMH and normal-appearingWM as well as in differentWMfiber tracts. Method: Single-shell diffusion-weighted image (SS-DWI) and T2 FLAIR image data of 133 cognitively normal (CN) …

Viability Of A Proposed Transparent Fluid Model For Analog Metal Casting Filling Process, Andres Segura Irazoqui

Dissertations and Theses @ UNI

This research work was conducted to develop a proposed transparent fluid model to mimic dross formation and dispersion in metal castings during the mold filling process. The major cause for dross formation includes the air entrainment, surface turbulence, and bubbles, which create oxide films inside the molten metal. The oxide films are trapped inside the casting after solidification causing detrimental effects on the mechanical properties of the casting.

The objective of the investigation was to explore the feasibility using phenolic urethane as a transparent fluid material to investigate air entrainment mechanism. The experimentation consists of the following materials: the phenolic …

Activity Recognition Based On Thermopile Imaging Array Sensors, Qingshan Liang

Dissertations and Theses @ UNI

With aging population, the importance of caring for elderly people is getting more and more attention. In this paper, a low resolution thermopile array sensor is used to develop an activity recognition system for elderly people. The sensor is composed of a 32x32 thermopile array with the corresponding 33° × 33° field of view. The outputs of the sensor are sequential images in which each pixel contains a temperature value. According to the thermopile images, the activity recognition system first determines whether the target is within the tracking area; if the target is within the tracking area, the location of …

Dehors The Record: A Correction Of A Final Jeopardy Question, Thomas E. Baker

FIU Law Review

No abstract provided.

Laboratory Schools Annual Report, 2020-2021, Laboratory Schools, Illinois State University

Annual Reports – Laboratory Schools

Illinois State University Laboratory Schools annual report

Analysis Of Assessments On Secondary Students' Development And Interpretation Of Models, Alaina L. Appley

Graduate Research Papers

As districts are making the shift to three-dimensional learning the development of a coherent set of high-quality task-based assessments has been a challenge. For this research I collected and analyzed twelve of my district's assessments over the scientific skill of modeling from grades seven through 12. The analysis involved two tools developed by NGSS and Achieve.org to determine the extent to which the assessments ask students to perform tasks that are driven by phenomena and use the three-dimensional in service of sense-making, the Task Screener and the Framework to Evaluate Cognitive Complexity in Science Assessments. The findings support what …

Multimodal Learning For Dyslexic Musicians: Practical Applications For Adults, Melissa Mikucki

Theses : Honours

Dyslexia affects 15 to 20% of the population according to the International Dyslexia Association. Multimodal media, such as smartphones and tablets, which are capable of presenting varied modes of information (for example, visual, aural, and kinetic), have been shown to aid learning in dyslexic children. Music has been identified as a useful multisensory tool to help educators improve literacy skills in children. However, little research has been done on the impact of dyslexia on a child or adult’s ability to learn and perform music. Few studies have been undertaken that focus solely on dyslexia’s effect on musical ability in children; …

Imputation, Modelling And Optimal Sampling Design For Digital Camera Data In Recreational Fisheries Monitoring, Ebenezer Afrifa-Yamoah

Theses: Doctorates and Masters

Digital camera monitoring has evolved as an active application-oriented scheme to help address questions in areas such as fisheries, ecology, computer vision, artificial intelligence, and criminology. In recreational fisheries research, digital camera monitoring has become a viable option for probability-based survey methods, and is also used for corroborative and validation purposes. In comparison to onsite surveys (e.g. boat ramp surveys), digital cameras provide a cost-effective method of monitoring boating activity and fishing effort, including night-time fishing activities. However, there are challenges in the use of digital camera monitoring that need to be resolved. Notably, missing data problems and the cost …

Investigating Self-Efficacy: Early Childhood Teachers’ Understanding Of Self-Efficacy, Dimity Franks

Theses: Doctorates and Masters

Students are experiencing an increased pressure to perform academically at a younger age with reports of the narrowing of curriculum and student disengagement. Current research literature suggests curricula should reflect the increased pressures students are facing. A focus on the social and emotional skills to support student learning is recommended to increase student engagement and enrichment and prepare students for their future. Self-efficacy is one element of social and emotional learning that demands attention. Self-efficacy is considered important for teachers to understand as it can predict how students approach their tasks as well as influence their levels of motivation and …

An Investigation Into The Efficacy Of Url Content Filtering Systems, Brett Ronald Turner

Theses: Doctorates and Masters

Content filters are used to restrict to restrict minors from accessing to online content deemed inappropriate. While much research and evaluation has been done on the efficiency of content filters, there is little in the way of empirical research as to their efficacy. The accessing of inappropriate material by minors, and the role content filtering systems can play in preventing the accessing of inappropriate material, is largely assumed with little or no evidence. This thesis investigates if a content filter implemented with the stated aim of restricting specific Internet content from high school students achieved the goal of stopping students …

Soil Carbon Stocks Vary Across Geomorphic Settings In Australian Temperate Tidal Marsh Ecosystems, Connor Gorham, Paul Lavery, Jeffrey J. Kelleway, Cristian Salinas, Oscar Serrano

Research outputs 2014 to 2021

Tidal marshes rank among the ecosystems with the highest capacity to sequester and store organic carbon (C_org) on earth. To inform conservation of coastal vegetated ecosystems for climate change mitigation, this study investigated the factors driving variability in carbon storage. We estimated soil C_org stocks in tidal marshes across temperate Western Australia and assessed differences among geomorphic settings (marine and fluvial deltas, and mid-estuary) and vegetation type (Sarcocornia quinqueflora and Juncus kraussii) linked to soil biogeochemistry. Soil C_org stocks within fluvial and mid-estuary settings were significantly higher (209 ± 14 and 211 ± 20 …

Real-Time Monitoring As A Supplementary Security Component Of Vigilantism In Modern Network Environments, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan

Research outputs 2014 to 2021

© 2020, The Author(s). The phenomenon of network vigilantism is autonomously attributed to how anomalies and obscure activities from adversaries can be tracked in real-time. Needless to say, in today’s dynamic, virtualized, and complex network environments, it has become undeniably necessary for network administrators, analysts as well as engineers to practice network vigilantism, on traffic as well as other network events in real-time. The reason is to understand the exact security posture of an organization’s network environment at any given time. This is driven by the fact that modern network environments do, not only present new opportunities to organizations but …

Communications In The Time Of A Pandemic: The Readability Of Documents For Public Consumption, Catherine Ferguson, Margaret Merga, Stephen Winn

Research outputs 2014 to 2021

© 2021 The Authors Objective: Government communications in a crisis can influence public health outcomes. This research aimed to investigate if written communications of the most commonly sought sources of COVID-19 information available on the internet have readability levels commensurate with those of the general public. Methods: Online documents from the World Health Organization (WHO), and the governments of Australia, the UK and the US were assessed for readability using an online instrument that calculated scores for the Flesch Reading Ease Score, the SMOG Index and the Readability Consensus Grade Level. Results: Similar to the previous research, most documents assessed …

Identifying Differences In Nutrition-Related Learning Interests Of Adolescent Students, Margaret Miller, Donna Barwood, Michael Furfaro, Julie Boston, Sandra Smith, Martin Masek

Research outputs 2014 to 2021

© 2021 The Authors. Journal of School Health published by Wiley Periodicals LLC. on behalf of American School Health Association. BACKGROUND: In this study, we examined nutrition-related topics among adolescent students attending schools with different Indices of Community Socio-Educational Advantage (ICSEA). METHOD: Participating students (N = 206) from 5 schools in Western Australia completed a paper-based questionnaire on nutrition-related topics. Frequencies and independent chi-square tests were used to identify differences between sexes and school ICSEA. RESULTS: Of the participating students, 75% were interested in learning about 8 of 16 topics. We found statistically significant differences by sex (p < .01) for “Eating the right foods for preventing illness and disease,” “Eating the right food for being active” among girls, and “Reducing food waste” among boys. We also noted differences by school ICSEA. CONCLUSION: Acknowledging sex and socioeducational differences in teaching and learning may help teachers to engage adolescent students in nutrition education.

An Energy-Efficient And Secure Data Inference Framework For Internet Of Health Things: A Pilot Study, James Jin Kang, Mahdi Dibaei, Gang Luo, Wencheng Yang, Paul Haskell-Dowland, Xi Zheng

Research outputs 2014 to 2021

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption …

A Secured Privacy-Preserving Multi-Level Blockchain Framework For Cluster Based Vanet, A. F.M.Suaib Akhter, Mohiuddin Ahmed, A. F.M.Shahen Shah, Adnan Anwar, Ahmet Zengin

Research outputs 2014 to 2021

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Existing research shows that Cluster-based Medium Access Control (CB-MAC) protocols perform well in controlling and managing Vehicular Ad hoc Network (VANET), but requires ensuring improved security and privacy preserving authentication mechanism. To this end, we propose a multi-level blockchain-based privacy-preserving authentication protocol. The paper thoroughly explains the formation of the authentication centers, vehicles registration, and key generation processes. In the proposed architecture, a global authentication center (GAC) is responsible for storing all vehicle information, while Local Authentication Center (LAC) maintains a blockchain to enable quick handover between internal clusters of vehicle. …

School Programming For Homelessness: Perspectives Of Families And Local Homeless Liaisons, Jazlynn J. Paige

All-Inclusive List of Electronic Theses and Dissertations

Thisdissertation focuses on children experiencing homelessness and the effects homelessness has on their academic achievement from the perspective of the children, their parents, and local homeless liaisons. More importantly, this research explores what educators can do to create a positive, supportive, and stable environment at school for children experiencing homelessness by implementing an effective program or a number of support services. Data for the current study were gathered through phone and face-to-face interviews. The participants were 10 parent and child dyads living in a shelter and three school homeless liaisons. The semi-structured interviews focused on the resources, or lack thereof, …

Design And Catastrophe: 51 Scientists Explore Evidence In Nature, Gordon J. Atkins

Faculty Publications

Review of

L. James Gibson, Ronny Nalin, and Humberto M. Rasi, eds. Design and Catastrophe: 51 Scientists Explore Evidence in Nature (Berrien Springs, Mich.: Andrews University Press, 2021). ISBN: 9781940980305 (Print). 240 pages (Hardback), US$24.99.

A Static Code Analysis And Pattern Recognition Algorithm-Driven, Quantitative, Mathematical Model-Oriented Risk Assessment Framework Of Cloud-Based Health Information Applications, Dennis B. Park

All-Inclusive List of Electronic Theses and Dissertations

According to a survey, the healthcare industry is one of the least cloud-adopting industries. The low adoption reflects the healthcare industry's ongoing concerns about the security of the cloud. Business applications, according to another survey, are among the most vulnerable components of business information systems. Many risk assessment frameworks available today, particularly for health information applications, require significant customization before they can be used. This study created a new framework to assess cloud risks specifically for their health information applications, utilizing data-driven risk assessment methodologies to avoid surveys, interviews, and meetings for data collection. For the feasibility study, the open-source …

Genetic And Ecological Consequences Of Recent Habitat Fragmentation In A Narrow Endemic Plant Species Within An Urban Context, Nicola Delnevo, Andrea Piotti, Michele Carbognani, Eddie J. Van Etten, William D. Stock, David L. Field, Margaret Byrne

Research outputs 2014 to 2021

Understanding the timescales that shape spatial genetic structure is pivotal to ascertain the impact of habitat fragmentation on the genetic diversity and reproductive viability of long-lived plant populations. Combining genetic and ecological information with current and past fragmentation conditions allows the identification of the main drivers important in shaping population structure and declines in reproduction, which is crucial for informing conservation strategies. Using historic aerial photographs, we defined the past fragmentation conditions for the shrub Conospermum undulatum, a species now completely embedded in an urban area. We explored the impact of current and past conditions on its genetic layout …