Computer Sciences Commons^™

A Review Of Methods For Detecting Rats At Low Densities, With Implications For Surveillance, Robert A. Davis, Philip J. Seddon, Michael D. Craig, James C. Russell

Research outputs 2022 to 2026

Invasive rats are the biggest threat to island biodiversity world-wide. Though the ecological impacts of rats on insular biota are well documented, introduced rats present a difficult problem for detection and management. In recent decades, improved approaches have allowed for island-wide eradications of invasive rats on small-medium sized islands and suppression on large islands, although both these still represent a formidable logistical and financial challenge. A key aspect of eradication or suppression and ongoing management is the ability to detect the presence of rats, especially at low densities. Here we review recent developments in the field of rat surveillance and …

Evaluating Staff Attitudes, Intentions, And Behaviors Related To Cyber Security In Large Australian Health Care Environments: Mixed Methods Study, Martin Dart, Mohiuddin Ahmed

Research outputs 2022 to 2026

Background: Previous studies have identified that the effective management of cyber security in large health care environments is likely to be significantly impacted by human and social factors, as well as by technical controls. However, there have been limited attempts to confirm this by using measured and integrated studies to identify specific user motivations and behaviors that can be managed to achieve improved outcomes.

Objective: This study aims to document and analyze survey and interview data from a diverse range of health care staff members, to determine the primary motivations and behaviors that influence their acceptance and application of cyber …

A Data Taxonomy For Adaptive Multifactor Authentication In The Internet Of Health Care Things, Tance Suleski, Mohiuddin Ahmed

Research outputs 2022 to 2026

The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as …

The Wacdt, A Modern Vigilance Task For Network Defense, Oliver A. Guidetti, Craig Speelman, Peter Bouhlas

Research outputs 2022 to 2026

Vigilance decrement refers to a psychophysiological decline in the capacity to sustain attention to monotonous tasks after prolonged periods. A plethora of experimental tasks exist for researchers to study vigilance decrement in classic domains such as driving and air traffic control and baggage security; however, the only cyber vigilance tasks reported in the research literature exist in the possession of the United States Air Force (USAF). Moreover, existent cyber vigilance tasks have not kept up with advances in real-world cyber security and consequently no longer accurately reflect the cognitive load associated with modern network defense. The Western Australian Cyber Defense …

Assessing The Performance Of A Particle Swarm Optimization Mobility Algorithm In A Hybrid Wi-Fi/Lora Flying Ad Hoc Network, William David Paredes

UNF Graduate Theses and Dissertations

Research on Flying Ad-Hoc Networks (FANETs) has increased due to the availability of Unmanned Aerial Vehicles (UAVs) and the electronic components that control and connect them. Many applications, such as 3D mapping, construction inspection, or emergency response operations could benefit from an application and adaptation of swarm intelligence-based deployments of multiple UAVs. Such groups of cooperating UAVs, through the use of local rules, could be seen as network nodes establishing an ad-hoc network for communication purposes.

One FANET application is to provide communication coverage over an area where communication infrastructure is unavailable. A crucial part of a FANET implementation is …

Evaluating Human Eye Features For Objective Measure Of Working Memory Capacity, Yasasi Abeysinghe, Enkelejda Kasneci (Ed.), Frederick Shic (Ed.), Mohamed Khamis (Ed.)

Computer Science Faculty Publications

Eye tracking measures can provide means to understand the underlying development of human working memory. In this study, we propose to develop machine learning algorithms to find an objective relationship between human eye movements via oculomotor plant and their working memory capacity, which determines subjective cognitive load. Here we evaluate oculomotor plant features extracted from saccadic eye movements, traditional positional gaze metrics, and advanced eye metrics such as ambient/focal coefficient , gaze transition entropy, low/high index of pupillary activity (LHIPA), and real-time index of pupillary activity (RIPA). This paper outlines the proposed approach of evaluating eye movements for obtaining an …

Vehicle-Based Disconnected Data Distribution, Aditya Singhania

Master's Projects

The world today is highly connected and there is an immense dependency on this connectivity to accomplish basic everyday tasks. However much of the world lacks connectivity. Even in well-connected locations, natural disasters can cause infrastructure disruption. To combat these situations, Delay Tolerant Networks

(DTNs) employ to store and forward techniques along with intermittently connected transports to provide data connectivity. DTNs focus on intermittently connected networks however what if the regions are never connected? For example, Region A - is never connected to the internet, and Region B – has internet connectivity. Using a vehicle that travels between the two …

Application Of Knowledge Graph Techniques On Textbooks, Yutong Yao

Master's Projects

Textbooks are written and organized in a way that facilitates learning and understanding. Sections like glossary terms at the end of a textbook provide guidance on the topic of interest. However, it takes manual effort to create the index terms in the glossary that highlight the key referenced terminologies and related terms. Knowledge graphs, which have been used to represent and even reason over data and knowledge, can potentially capture textbook’s important terms, concepts, and their relations. Popular since the initial introduction by Google Knowledge Graphs (KGs), they combine graph and data to capture and model enormous amounts of relational …

A Novel Efficient Deep Learning Framework For Facial Inpainting - Face Reconstruction From Masked Images, Akshay Ravi

Master's Projects

The use of face masks due to the covid-19 pandemic has made surveillance of people very difficult. Since a mask covers most of the facial components, security cameras are rendered of little to no use in the identification of criminals. In order to realize what a face looks like behind a mask, we have to construct the facial features in the masked region. On a higher level, this falls under the field of image inpainting, i.e. filling missing regions of images or correcting irregularities in images. Current research on image inpainting shows promising results on images that have missing/incorrect patches …

Eye Movements Behaviors In A Driving Simulator During Simple And Complex Distractions, Pradeep Narayana

Master's Projects

Road accidents occur frequently due to driving distractions all around the world. A driving simulator has been created to explore the cognitive effects of distractions while driving in order to address this problem. The purpose of this study is to discover the distraction-causing elements and how they affect driving performance. The simulator offers a secure and regulated setting for carrying out tests while being distracted by different visual distractions, such as solving mathematical equations and number memorizations.

Several trials have been conducted in the studies, which were carried out under varied circumstances like varying driving sceneries and by displaying different …

Twitter Bot Detection Using Nlp And Graph Classification, Warada Jayant Kulkarni

Master's Projects

Social media platforms are one of the primary resources for information as it is easily accessible, low in cost, and provides a high rate of information spread. Online social media (OSM) have become the main source of news information around the world, but because of the distributed nature of the web, it has increased the risk of fake news spread. Fake news is misleading information that is published as real news. Therefore, identifying fake news and flagging them as such, as well as detecting sources that generate them is an ongoing task for researchers and OSM companies. Bots are artificial …

Insecure Deserialization Detection In Python, Aneesh Verma

Master's Projects

The importance of Cyber Security is increasing every single day. From the emergence of new ransomware to major data breaches, the online world is getting dangerous. A multinational non- profit group devoted to online application security is called OWASP, or the Open Web Application Security Project. The OWASP Top 10 is a frequently updated report that highlights the ten most important vulnerabilities to web application security. Among these 10 vulnerabilities, there exists a vulnerability called Software and Data Integrity Failures. A subset of this vulnerability is Insecure Deserialization. An object is transformed into a stream of bytes through the serialization …

Dynamic Data Sample Selection And Scheduling In Edge Federated Learning, Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi

All Works

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (IoT), and data streaming, challenges such as monitoring and management remain unresolved. Edge IoT devices produce and stream huge amounts of sample sources, which can incur significant processing, computation, and storage costs during local updates using all data samples. Many research initiatives have improved the algorithm for FL in homogeneous networks. However, in the typical distributed learning application scenario, data is generated …

Computational Intelligence And Soft Computing Paradigm For Cheating Detection In Online Examinations, Sanaa Kaddoura, Shweta Vincent, D. Jude Hemanth

All Works

Covid-19 has been a life-changer in the sphere of online education. With complete lockdown in various countries, there has been a tumultuous increase in the need for providing online education, and hence, it has become mandatory for examiners to ensure that a fair methodology is followed for evaluation, and academic integrity is met. A plethora of literature is available related to methods to mitigate cheating during online examinations. A systematic literature review (SLR) has been followed in our article which aims at introducing the research gap in terms of the usage of soft computing techniques to combat cheating during online …

Application Of A Gene Modular Approach For Clinical Phenotype Genotype Association And Sepsis Prediction Using Machine Learning In Meningococcal Sepsis, Asrar Rashid, Arif R. Anwary, Feras Al-Obeidat, Joe Brierley, Mohammed Uddin, Hoda Alkhzaimi, Amrita Sarpal, Mohammed Toufiq, Zainab A. Malik, Raziya Kadwa, Praveen Khilnani, M. Guftar Shaikh, Govind Benakatti, Javed Sharief, Syed Ahmed Zaki, Abdulrahman Zeyada, Ahmed Al-Dubai, Wael Hafez, Amir Hussain

All Works

Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition …

Fuzzy Logic-Based Approach For Location Identification And Routing In The Outdoor Environment, Saravjeet Singh, Jaiteg Singh, Sukhjit Singh Sehra, Babar Shah, Farman Ali, Daehan Kwak

All Works

Finding the precise and accurate location of devices on road networks is challenging in remote areas with poor internet connectivity and Global Positioning System coverage. Navigation applications that completely depend on the internet and reference spatial data for location identification and mapping do not perform well in case of frequent internet disconnection. These reference spatial data sources have many associated challenges like large size, errors in data, and restricted access. To address these challenges, this paper provides an approach for localization and routing using self-generated reference data using likelihood estimation. According to the proposed approach, the trajectory information is used …

Of Stances, Themes, And Anomalies In Covid-19 Mask-Wearing Tweets, Jwen Fai Low, Benjamin C.M. Fung, Farkhund Iqbal, Ebrahim Bagheri

All Works

COVID-19 is an opportunity to study public acceptance of a ‘‘new’’ healthcare intervention, universal masking, which unlike vaccination, is mostly alien to the Anglosphere public despite being practiced in ages past. Using a collection of over two million tweets, we studied the ways in which proponents and opponents of masking vied for influence as well as the themes driving the discourse. Pro-mask tweets encouraging others to mask up dominated Twitter early in the pandemic though its continued dominance has been eroded by anti-mask tweets criticizing others for their masking behavior. Engagement, represented by the counts of likes, retweets, and replies, …

Design And Delivery Of National Housing In The Uae: An Alternative Approach, Basem Eid Mohamed, Mohamed Elkaftangui, Rana Zureikat, Rund Hiyasat

All Works

The provision of national housing to citizens in the United Arab Emirates (UAE) is considered a crucial topic. Over the past four decades, the process of developing national housing has emerged into multiple housing programs and schemes, all with the same aim of offering affordable and high-quality housing to citizens, in addition to meeting the needs of local families regarding spatial configurations while maintaining cultural values. However, despite all these efforts, the question has always remained: are the offered housing practices suited for family needs, socioeconomic trends, and environmental challenges? This study aims to offer an alternative approach for the …

Overhead Based Cluster Scheduling Of Mixed Criticality Systems On Multicore Platform, Amjad Ali, Asad Masood Khattak, Shahid Iqbal, Omar Alfandi, Bashir Hayat, Muhammad Hameed Siddiqi, Adil Khan

All Works

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling …

Need For Affect, Problematic Social Media Use And The Mediating Role Of Fear Of Missing Out In European And Arab Samples, Areej Babiker, Mohamed Basel Almourad, Constantina Panourgia, Sameha Alshakhsi, Christian Montag, Raian Ali

All Works

Purpose: The growing awareness and concern about the excessive use of social media have led to an increasing number of studies investigating the underlying factors contributing to this behavior. In the literature, it is discussed that problematic social media use (PSMU) can impact individuals’ mental health and well-being. Drawing on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study aimed to examine the association between the need for affect (affect approach and affect avoidance) and PSMU (operationalized via the social media disorder scale), as well as the mediating role of fear of missing out (FoMO) in that relation. Participants and Methods: …

3d Indoor Modeling And Game Theory Based Navigation For Pre And Post Covid-19 Situation, Jaiteg Singh, Noopur Tyagi, Saravjeet Singh, Babar Shah, Farman Ali, Ahmad Ali Alzubi, Abdulrhman Alkhanifer

All Works

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural …

Evolution Of Winning Solutions In The 2021 Low-Power Computer Vision Challenge, Xiao Hu, Ziteng Jiao, Ayden Kocher, Zhenyu Wu, Junjie Liu, James C. Davis, George K. Thiruvathukal, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

Mobile and embedded devices are becoming ubiquitous. Applications such as rescue with autonomous robots and event analysis on traffic cameras rely on devices with limited power supply and computational sources. Thus, the demand for efficient computer vision algorithms increases. Since 2015, we have organized the IEEE Low-Power Computer Vision Challenge to advance the state of the art in low-power computer vision. We describe the competition organizing details including the challenge design, the reference solution, the dataset, the referee system, and the evolution of the solutions from two winning teams. We examine the winning teams’ development patterns and design decisions, focusing …

Understanding Influencers Of College Major Decision: The Uae Case, Mohammad Amin Kuhail, Joao Negreiros, Haseena Al Katheeri, Sana Khan, Shurooq Almutairi

All Works

This study aims to understand and analyze what influences female students to choose a college major in the United Arab Emirates (UAE). To accomplish our target, we conducted a survey with mostly female first-year undergraduate students (N = 496) at Zayed University to understand the personal, social, and financial factors influencing students’ major choices. Further, this study also asked students to specify their actions before deciding on their major and assessed the information that could be helpful for future students to decide on their majors. Last, the study investigated how Science, Technology, Engineering, and Mathematics (STEM) students differ from other …

Using Blockchain For Enabling Transparent, Traceable, And Trusted University Ranking Systems, Ammar Battah, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob, Ashraf Khalil

All Works

Ranking systems have proven to improve the quality of education and help build the reputation of academic institutions. Each of the current academic ranking systems is based on different methodologies, criteria, and standards of measurement. Academic and employer reputations are subjective indicators of some rankings determined through surveying that is neither transparent nor traceable. The current academic ranking systems fall short of providing transparency and traceability features for both subjective and objective indicators that are used to calculate the ranking. Also, the ranking systems are managed and controlled in a centralized manner by specific entities. This raises concerns about fairness …

Covid-19 Pandemic And The Cyberthreat Landscape: Research Challenges And Opportunities, Heba Saleous, Muhusina Ismail, Saleh H. Aldaajeh, Nisha Madathil, Saed Alrabaee, Kim Kwang Raymond Choo, Nabeel Al-Qirim

All Works

Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.

Forecasting Energy Consumption Demand Of Customers In Smart Grid Using Temporal Fusion Transformer (Tft), Amril Nazir, Abdul Khalique Shaikh, Abdul Salam Shah, Ashraf Khalil

All Works

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per …

Dataset For Gendered Language, Shweta Soundararajan

Datasets

Gendered language is the use of words that denote an individual’s gender. This can be explicit where the gender is evident in the actual word used, e.g. mother, she, man, but it can also be implicit where social roles or behaviours can signal an individual’s gender - for example, expectations that women display communal traits (e.g., affectionate, caring, gentle) and men display agentic traits (e.g., assertive, competitive, decisive). The use of gendered language in NLP systems can perpetuate gender stereotypes and bias. This paper proposes an approach to generating gendered language datasets using ChatGPT which will provide data for data-driven …

Synthetic Heart Sound Dataset, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Datasets

The repository contains synthetic heart sound recordings. The publication related to this dataset is "Exploring the impact of noise and degradations on heart sound classification models", Biomedical Signal Processing and Control journal.

Cooperative Deep Q -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Publications

In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dual-neural network (NN)-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation …

Light Auditor: Power Measurement Can Tell Private Data Leakage Through Iot Covert Channels, Woosub Jung, Kailai Cui, Kenneth Koltermann, Junjie Wang, Chunsheng Xin, Gang Zhou

Electrical & Computer Engineering Faculty Publications

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users' private data by exploiting the smart bulb's capability of infrared emission.

In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process …