Digital Commons Network^™

A Systematic Mapping Study On Gamification Applications For Undergraduate Cybersecurity Education, Sherri Weitl-Harms, Adam Spanier, John Hastings, Matthew Rokusek

Journal of Cybersecurity Education, Research and Practice

Gamification in education presents a number of benefits that can theoretically facilitate higher engagement and motivation among students when learning complex, technical concepts. As an innovative, high-potential educational tool, many educators and researchers are attempting to implement more effective gamification into undergraduate coursework. Cyber Security Operations (CSO) education is no exception. CSO education traditionally requires comprehension of complex concepts requiring a high level of technical and abstract thinking. By properly applying gamification to complex CSO concepts, engagement in students should see an increase. While an increase is expected, no comprehensive study of CSO gamification applications (GA) has yet been undertaken …

Pathogen Emergence As Complex Biological Invasion: Lessons From Dynamical Systems Modeling, Sudam Surasinghe, Marisabel Rodriguez, Victor Meszaros, Jane Molofsky, Salvador Almagro-Moreno, Brandon Ogbunugafor

Northeast Journal of Complex Systems (NEJCS)

Infectious disease emergence has become the target of cross-disciplinary efforts
that aim to understand and predict the shape of outbreaks. The many challenges
involved with the prediction of disease emergence events is a characteristic that in-
fectious diseases share with biological invasions in many subfields of ecology (e.g.,
how certain plants are able to successfully invade a new niche). Like infectious
diseases, biological invasions by plants and animals involve interactions between
agents (pathogens and plants in their respective cases) and a recipient niche. In
this study, we examine the problem of pathogen emergence through the lens of a
framework first …

Isolation Of Bovine Milk-Derived Exosomes Via A Polyester Capillary- Channeled Polymer (C-Cp) Fiber Stationary Phase, Jerisa Pimentel, Carolina Mata, R. Kenneth Marcus

Chemistry Summer Research Program

No abstract provided.

Bertnet: Harvesting Knowledge Graphs With Arbitrary Relations From Pretrained Language Models, Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu

Machine Learning Faculty Publications

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of …

Fast Rates For Maximum Entropy Exploration, Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Éric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Machine Learning Faculty Publications

We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards. In this work, we study the maximum entropy exploration problem of two different types. The first type is visitation entropy maximization previously considered by Hazan et al. (2019) in the discounted setting. For this type of exploration, we propose a game-theoretic algorithm that has Oe(H3S2A/ε2) sample complexity thus improving the ε-dependence upon existing results, where S is a number of states, A is a number of actions, H is an episode length, and ε is a desired …

Coco: A Coupled Contrastive Framework For Unsupervised Domain Adaptive Graph Classification, Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian Sheng Hua, Xiao Luo

Machine Learning Faculty Publications

Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, how to apply GNNs to domain adaptation remains unsolved owing to the insufficient exploration of graph topology and the significant domain discrepancy. In this paper, we propose Coupled Contrastive Graph Representation Learning (CoCo), which extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning. CoCo contains a graph convolutional network …

Detecting Out-Of-Distribution Data Through In-Distribution Class Prior, Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

Machine Learning Faculty Publications

Given a pre-trained in-distribution (ID) model, the inference-time out-of-distribution (OOD) detection aims to recognize OOD data during the inference stage. However, some representative methods share an unproven assumption that the probability that OOD data belong to every ID class should be the same, i.e., these OOD-to-ID probabilities actually form a uniform distribution. In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and …

Reef Benthic Fauna And Sediment Characterization, Robert M. Cerrato, Matthew Sclafani

School of Marine & Atmospheric Sciences Faculty Publications

High-resolution backscatter and bathymetric maps created by multibeam sonar surveys were used to identify different seafloor bottom types within existing, potentially expanded, and newly proposed reef areas in New York waters. Existing sites included Smithtown in Long Island Sound (LIS), and Rockaway, Atlantic Beach, Hempstead, Yellowbar, Kismet, Fire Island, Twelve Mile along the South Shore. Potential expansions are proposed on the South Shore for McAllister, Moriches, and Shinnecock reefs in addition to a new site called Sixteen Fathom. In Long Island Sound, new sites are proposed for Huntington/Oyster Bay, Port Jefferson/Mount Sinai, and Mattituck. Grab samples were collected within these …

Gouldian Finch Monitoring: Foraging Activity And Grass Phenology - Goomig Project 2022 To 2023, J Graff

Irrigated Agricultural Development in the North

The Goomig Project (Weaber Plains Development Project) is an irrigated agricultural development located approximately 30 km north of Kununurra, which involves clearing of approximately 9,260 ha of vegetation for agriculture. Approximately 11,470 ha of native vegetation surrounding, or remaining between, the cleared areas is designated as a buffer area (the study area) to be managed to protect surrounding conservation reserves and watercourses (Strategen 2014). Monitoring of Gouldian Finch wet season foraging activity and phenology and productivity of grasses important for foraging in the buffer area during ongoing operation are required as conditions of the approval for the project, and is …

Norfolk Integrated Waste-Energy Framework, Emma Kurtz, Abigail Schroeder, Jonah Mcdowell

Project Energy Nebraska: Student Projects

Environmental Engagement in the Community projects, spring 2023 A poster titled Norfolk Integrated Waste-Energy Framework for client City of Norfolk, Nebraska and Nebraska Public Power District.

Faculty sponsor: David Gosselin

Enhancing And Securing Wireless Medical Technology For Diagnosis And Treatment Of Lower Urinary Tract Dysfunction, Farhath Zareen

USF Tampa Graduate Theses and Dissertations

Lower urinary tract dysfunction (LUTD) is a debilitating medical condition that affects millions of individuals worldwide. Urodynamics is the current gold standard for diagnosing LUTD but uses non-physiologically fast, retrograde cystometric filling to obtain a brief snapshot of bladder function. Current state-of-the-art research in bladder monitoring includes ambulatory urodynamics using wireless implantable devices to evaluate bladder function during natural filling for long-term monitoring. However, there are various challenges and limitations to this multi-sensor approach. This research focuses on developing frameworks for automated event detection, data analysis, and optimization of long-term bladder recordingsto improve the diagnosis and treatment of LUTD. In …

The Effects Of Temperature And Flooding Duration On The Structure And Magnitude Of A Floodplain Prey Subsidy, Ryan J. Rezek, Jordan A. Massie, James A. Nelson, Rolando O. Santos, Natasha M. Viadero, W. Ryan James, Ross E. Boucek, Jennifer S. Rehage

Marine Science

Riverine floodplains serve as an important link between terrestrial and aquatic systems, as the rising and falling of water drive spatial food web subsidies that are critical to the functioning and stability of ecosystems. As these systems are increasingly impacted by hydrological alterations and climate change, it is important to understand how floodplain spatial food web subsidies may respond to changing environmental conditions.

Here, we examine the interannual variation in the structure of a sunfish (Lepomis spp.) prey subsidy from freshwater marshes into the mangrove-lined creeks of Rookery Branch in the Florida Coastal Everglades that occurs during seasonal dry downs. …

Suspicious Behavior Detection With Temporal Feature Extraction And Time-Series Classification For Shoplifting Crime Prevention, Amril Nazir, Rohan Mitra, Hana Sulieman, Firuz Kamalov

All Works

The rise in crime rates in many parts of the world, coupled with advancements in computer vision, has increased the need for automated crime detection services. To address this issue, we propose a new approach for detecting suspicious behavior as a means of preventing shoplifting. Existing methods are based on the use of convolutional neural networks that rely on extracting spatial features from pixel values. In contrast, our proposed method employs object detection based on YOLOv5 with Deep Sort to track people through a video, using the resulting bounding box coordinates as temporal features. The extracted temporal features are then …

Security Of Text To Image Conversions, Zobaida Alssadi

Theses and Dissertations

The use of images and icons to represent news or narratives has grown in popularity. Still, one critical problem is that they are not equivalent to language, making them vulnerable to adversary attacks. This study examines the impact of image-poisoning attacks based on polysemantic words and of image attacks based on cultural differences when converting text to images. Such attacks can lead to the loss of important information and create confusion and incorrect interpretations of the intended meaning, misinforming the general public. The study specifically focuses on possible effects in a news and story context. This study highlights the significance …

Optimizing Convolutional Neural Networks For Transient Detection In Optical Astronomy With Augmented Datasets, Wendy Mendoza

Theses and Dissertations

We present a technique for optical transient detection using artificial neural networks, particularly a Convolutional Neural Network (CNN), a deep learning algorithm. This method analyzes images of the same area of the sky captured by several telescopes, with one image serving as a reference for a probable transient’s epoch and the other as an image from a previous epoch. We train the CNN on simulated sources and test it on actual image data samples using data from the Dr. Cristina V. Torres Memorial Astronomical Observatory and Sloan Digital Sky Survey. This autonomous detection method replaces the standard procedure, which involves …

Generation Of Cardiac Chamber Models Using Interpretable Generative Neural Networks For Electrophysiology Studies, Sunil Mathew

Dissertations (1934 -)

An Electrophysiology study is conducted to diagnose and treat heart rhythm disorders, such as arrhythmias (abnormal heartbeat) like atrial fibrillation. A catheter is inserted into the chamber of interest to acquire 3D location and electrical information to create an electroanatomical map. This dissertation explores the design of a mapping system based on interpretable generative neural networks for generating patient specific cardiac models. Chapter 1 provides an introduction to electroanatomical mapping, the need for interpretability in neural networks and other relevant topics that are discussed in detail in the chapters that follow. Neural networks are often very large models with millions …

Dissolved Oxygen Dynamics And Nutrient Cycling In Marine And Lacustrine Benthic Communities, Corrianna R. Boucher

Theses and Dissertations

The delivery of biodetritus to the sediment water interface supports a complex series of biogeochemical reactions collectively referred to as early diagenesis. From the abyssal ocean to littoral freshwater ecosystems, this work explores the dynamics of these reactions in deep ocean detrital aggregates and lacustrine benthic algal mats. In the ocean, periods of increased export flux to abyssal depths increase the organic carbon export as aggregates to the seafloor while in littoral freshwater, proliferations of benthic algae such as Microseira wollei have grown to fill much of the shallow water column they inhabit.

Data collected at Station M (Sta. M) …

Identification Of New Disinfection Byproducts In Drinking Water And Impacts Of Algae, Md. Tareq Aziz

Theses and Dissertations

The disinfection of water has been hailed as one of the most important triumphs for public health in the 20th century. Drinking water treatment plants produce safe drinking water by inactivating microorganisms through the use of disinfectants, including chlorine, chloramine, chlorine dioxide, UV irradiation, and ozone. However, these disinfectants also produce toxic disinfection by-products (DBPs), through reactions with natural organic matter and anthropogenic pollutants, as well as bromide and iodide present in source waters. The presence of bromide and iodide results in the formation of bromo- and iodo-DBPs which are much more toxic than DBPs containing chlorine. DBPs are always …

Cadmium And Salinity Stressor Antagonism On Vallisneria Neotropicalis, Christopher P. Mikolaitis

Graduate Theses and Dissertations (2019 - present)

Submerged macrophytes form the foundation of freshwater ecosystems. These organisms are sessile and are very susceptible to shifts in their environment. Heavy metals are of particular concern as they can be sequestered indefinitely in sediments and are readily taken up by rooted vegetation. In the presence of saltwater intrusions, these metals can interact with salt ions potentially changing their availability to submerged vegetation. In this study a freshwater macrophyte, Vallisneria neotropicalis, was used as a test species for interactive effects between Cd, a non-essential heavy metal, and salt stress. The metrics used to establish the individual as well as …

State Of The Fisheries: Status Reports And Aquatic Resources Of Western Australia 2021/22, Department Of Primary Industries And Regional Development, Western Australia, S J. Newman, B S. Wise, K G. Santaro, D J. Gaughan

Status Reports of the Fisheries and Aquatic Resources

Aquatic resources within Western Australia (WA) are in good condition, and this has positioned WA as a global leader in sustainable fisheries management. The sustainable fisheries of WA continue to support our strong economy and regional communities. Nonetheless, the lack of a consistent approach to build in the knowledge of Traditional Owners remains a gap in our longer-term fisheries science in Western Australia.

Climate change and climate variability continues to impact fish stocks, challenging our ability to effectively monitor, assess, and manage fish stocks. We are continually working with our stakeholders, and the broader community to be adaptive, responsive, and …

Mapping, Intensities And Future Prediction Of Land Use/Land Cover Dynamics Using Google Earth Engine And Ca- Artificial Neural Network Model, Maysoon A.A. Osman, Elfatih M. Abdel-Rahman, Joshua Orungo Onono, Lydia A. Olaka, Muna M. Elhag, Marian Adan, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Mapping of land use/ land cover (LULC) dynamics has gained significant attention in the past decades. This is due to the role played by LULC change in assessing climate, various ecosystem functions, natural resource activities and livelihoods in general. In Gedaref landscape of Eastern Sudan, there is limited or no knowledge of LULC structure and size, degree of change, transition, intensity and future outlook. Therefore, the aims of the current study were to (1) evaluate LULC changes in the Gedaref state, Sudan for the past thirty years (1988–2018) using Landsat imageries and the random forest classifier, (2) determine the underlying …

Authenticated Public Key Elliptic Curve Based On Deep Convolutional Neural Network For Cybersecurity Image Encryption Application, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek M. Hassan

Research outputs 2022 to 2026

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the (Formula presented.) parameters between two parties by using the EC-DHKE …

Crosssections, Summer 2023, University Of Northern Iowa. Department Of Physics.

CrossSections

Contents:

A Message from the Department Head, Dr. Paul Shand --- 1
Department Happenings --- 2
Faculty Profile: Andrew Stollenwerk --- 7
Student Profile: Lukas Stuelke --- 8
Student Focus: Jenna Heinen --- 11
Physics Education --- 13
Alumni Profile: Sam Prophet --- 14
Alumni News: Shawn Poellet --- 15
New Physics: Quantum Mechanics Must Be Complex --- 17

Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu

Research Collection School Of Computing and Information Systems

Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related …

Modularized Zero-Shot Vqa With Pre-Trained Models, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Large-scale pre-trained models (PTMs) show great zero-shot capabilities. In this paper, we study how to leverage them for zero-shot visual question answering (VQA).Our approach is motivated by a few observations. First, VQA questions often require multiple steps of reasoning, which is still a capability that most PTMs lack. Second, different steps in VQA reasoning chains require different skills such as object detection and relational reasoning, but a single PTM may not possess all these skills. Third, recent work on zero-shot VQA does not explicitly consider multi-step reasoning chains, which makes them less interpretable compared with a decomposition-based approach. We propose …

Singapore's Ai Applications In The Public Sector: Six Examples, Steven M. Miller

Research Collection School Of Computing and Information Systems

Steven M. Miller describes six instances in which Singapore has applied AI in the public sector, illustrating different ways of improving its engagement with the public by making government services more accessible, anywhere, anytime, and speeding its responses to public processes and feedback. He illustrates how its leaders made the city a living lab for AI use, and what they learned.

Theme Park Visitors Prefer Human-Like Robots In Customer Service Interactions, Ady Milman, Asli D.A. Tasci

Rosen Research Review

Service robots are becoming increasingly popular in many industries and social settings, including education, childcare, elderly therapy centers, and even theme parks. Tourism and hospitality industries are adopting robots enthusiastically and are being closely studied to observe guest engagement and reaction to robotic services. Service robots are becoming increasingly popular in many industries and social settings, including education, childcare, elderly therapy centers, and even theme parks. Tourism and hospitality industries are adopting robots enthusiastically and are being closely studied to observe guest engagement and reaction to robotic services. UCF Rosen College of Hospitality Management researchers, Dr. Ady Milman and Dr. …

Number And Viability Of Seeds Recovered From Faeces Of Ruminant Animals, Alaba O. Jolaosho, Olufemi S. Onifade, O. M. Arigbede, Jimoh A. Olanite, T. O. Akinola

IGC Proceedings (1977-2023)

Ruminants play a significant role in the dissemination of plant seeds as a result of ingested seeds during grazing on pasture escaping digestion that are voided with the faeces and returned to the seed bank (Russi et al., 1992).

Adsorption Performance Of Mg0.33ni0.33co0.33fe2o4 Nanoparticles Doped With Gadolinium And Lanthanum For Lead (Ii) Removal, Mariam Rabaa, Amani Aridi, Ghassan Younes, Ramadan Awad

BAU Journal - Science and Technology

The issue of water pollution has become a major concern in recent times, and the need for effective strategies for treating contaminated water sources has become more urgent. One promising approach that has been gaining attention in the field of wastewater treatment is the use of nano-ferrites. In this regard, novel Mg_0.33Ni_0.33Co_0.33La_xFe_2-xO₄ and Mg_0.33Ni_0.33Co_0.33Gd_xFe_2-xO₄ nanoparticles (NPs), where x = 0.00, 0.01 and 0.08, were synthesized to test their adsorption performance for the removal of Pb (II). The structural …

On The Use Of Wireless Technologies For Wildlife Monitoring: Wireless Sensor Network Routing Protocols, Lilian Mutalemwa

Tanzania Journal of Engineering and Technology (TJET)

Traditional methods for wildlife monitoring are labor-intensive and time-consuming. Therefore, advanced technologies and remote monitoring methods are becoming increasingly popular. This paper presents a study on wireless technologies for wildlife monitoring. In the study, a review of the literature was done to identify the most commonly used wireless technologies. Various technologies were explored including unmanned aerial vehicles (UAVs), Internet of Things (IoT), wireless sensor networks (WSNs), artificial intelligence (AI), global positioning system (GPS), and very high frequency (VHF) radio. Then, a more detailed study was done on WSN technology. Investigations were done to observe the performance of routing protocols in …