Digital Commons Network^™

Quantization For A Set Of Discrete Distributions On The Set Of Natural Numbers, Juan Gomez, Haily Martinez, Mrinal Kanti Roychowdhury, Alexis Salazar, Daniel J. Vallez

School of Mathematical & Statistical Sciences Faculty Publications

The quantization scheme in probability theory deals with finding a best approximation of a given probability distribution by a probability distribution that is supported on finitely many points. In this paper, first we state and prove a theorem, and then give a conjecture. We verify the conjecture by a few examples. Assuming that the conjecture is true, for a set of discrete distributions on the set of natural numbers we have calculated the optimal sets of n-means and the nth quantization errors for all positive integers n. In addition, the quantization dimension is also calculated.

Note On Illuminating Constant Width Bodies, Alexey Glazyrin

School of Mathematical & Statistical Sciences Faculty Publications

Recently, Arman, Bondarenko, and Prymak constructed a constant width body in R n whose illumination number is exponential in n. In this note, we improve their bound by generalizing the construction. In particular, we construct a constant width body in R n whose illumination number is at least (τ + o(1))n, where τ ≈ 1.047.

Constrained Quantization For Probability Distributions, Megha Pandey, Mrinal Kanti Roychowdhury

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, for a Borel probability measure P on a Euclidean space Rk, we extend the definitions of nth unconstrained quantization error, unconstrained quantization dimension, and unconstrained quantization coefficient, which traditionally in the literature known as nth quantization error, quantization dimension, and quantization coefficient, to the definitions of nth constrained quantization error, constrained quantization dimension, and constrained quantization coefficient. The work in this paper extends the theory of quantization and opens a new area of research. In unconstrained quantization, the elements in an optimal set are the conditional expectations in their own Voronoi regions, and it is not true …

Furthering Usability And Efficiency Of Massively Parallel Gaussian Process Regression As Applied To Multi-Beam Sonar Data, Phillip M. Parisi

Open Access Master's Theses

Massively Parallel Gaussian Process Regression (MP-GPR) is a parallelized programmatic implementation of a machine learning algorithm used to process multi-beam sonar data and generate 2.5D terrain maps in close to real-time. This thesis furthered the capabilities of MP-GPR in two ways. The first introduced data reduction techniques to reduce the quantity of data inputted to the MP-GPR algorithm for computational efficiency. Various approaches were analyzed to determine an optimal data minimization strategy. The second informed the variances of regression estimates by considering beam angle and range uncertainty of the multi-beam sonar data being analyzed. In tandem, the presented methodologies decreased …

Power And Sample Size Calculations For Evaluating Spillover Effects In Networks With Non-Randomized Interventions, Ke Zhang

Open Access Master's Theses

Determining the appropriate sample size that is needed to achieve a desired level of statistical power is an integral aspect of study design, as it ensures that studies are adequately powered and that their results are reliable and meaningful. Adequate statistical power can also address some practical and ethical considerations. Various methodologies for power and sample size calculations have been developed for distinct types of studies. However, the implications for power when estimating spillover effects in sociometric network-based studies with non-randomized interventions (SNS-NRIs) remain inadequately explored. In this study, we first conducted a series of simulations to evaluate how the …

Recovering The Full Network Using A Sampling Bias Adjustment In Sampled Network, Ryan Humphreys

Open Access Master's Theses

This project aimed to investigate the differences in terms of network structural characteristics between full and sampled networks and to develop a new weighted generalized estimating equations model (WGEE) in order to mitigate the sampling bias and obtain less biased sample estimates for model coefficients and standard errors. The applications of such an approach are of interest in the field of network analysis and public health studies. The Transmission Reduction Intervention Project (TRIP) is an HIV study using risk networks and is a key motivator for this work. Two other motivating examples were constructed where it was found that sampled …

Examination Of Varying Pair Programming Strategies In Computer Science Laboratory Environments, Lily Sisouvong

Open Access Master's Theses

Pair programming is a technique within the computer science space in which two programmers are paired on one computer to solve a related programming task. This technique is often practiced in both the industry and the academic setting, as it has a consistent history of positively impacting working professionals and students regarding personal experience and performance in programming. As a result, there has been a large focus within the computer science education field on how and why pair programming is a successful technique. More specifically, what traits of pairing students together make pair programming successful. At the University of Rhode …

Analysis Of Turkey Reovirus Using Machine Learning, Maryam Kafikang

Open Access Master's Theses

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus (TERV) has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus tentatively named as turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV) are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, increased morbidity and mortality, and reduced marketability of commercial turkeys. To combat these issues, it …

Neural Network Based Generation Of Radar Heatmaps For Advanced Driver Assistance Systems (Adas), Angela Klassen

Open Access Master's Theses

Radar sensors are an essential component of Advanced Driver Assistance Systems (ADAS). They use radio waves to monitor the surroundings of a vehicle, capturing data that can be utilized to provide assistance to the driver. Major challenges of this procedure are the high complexity and the considerable power consumption that the transfer of radar data to subsequent processing units requires. This work addresses these issues by using deep learning architectures to compress the raw data captured by radar before it is being transmitted. As proceeding step so-called Range-Angle (RA) Heatmaps are reconstructed from the compressed data. These heatmaps present an …

Navigating The Void: Uncovering Research Gaps In The Detection Of Data Poisoning Attacks In Federated Learning-Based Big Data Processing: A Systematic Literature Review, Mohammad Aljanabi, Hijaz Ahmad

Mesopotamian Journal of Big Data

This systematic literature review scrutinizes the landscape of research at the intersection of federated learning, big data processing, and data poisoning attacks. Employing a meticulous search strategy across multiple databases, the studyunveils a surge in annual scientific production, emphasizing a growing interest in federated learning and related fields. However, a critical research gap becomes evident during the investigation of data poisoning attacks specifically in the context of federated learning when processing big data. The most relevant keywords and a visually compelling word cloud further illuminate the prevailing themes and emphases within the literature, emphasizing the lack of explicit focus on …

Design And Development Of Hybrid Spectrum Access Technique For Cr-Iot Network, Sandeep Singh

Mesopotamian Journal of Computer Science

The radio spectrum is an underutilized natural resource with significant untapped potential. The rapid proliferation of Internet of Things (IoT) devices is driving a dramatic increase in radio spectrum demand. These devices can utilize cognitive radio (CR) technology to access the bandwidth left unused by licensed spectrum users, also known as primary users (PUs), to meet end users spectrum requirements efficiently. However, because PUs are given priority, CR-enabled Internet of Things (CR-IoT) devices, also known as secondary users (SU), must frequently communicate with one another in an opportunistic manner or under stringent power constraints. This can complicate CR-IoT device communication, …

Role Of Chatgpt In Computer Programming., Som Biswas

Mesopotamian Journal of Computer Science

Purpose: The purpose of this abstract is to outline the role and capabilities of ChatGPT, a language model developed by OpenAI for computer programming. Methodology: ChatGPT is a large language model that has been trained on a diverse range of texts and can perform a variety of programming-related tasks. These tasks include code completion and correction, code snippet prediction and suggestion, automatic syntax error fixing, code optimization and refactoring suggestions, missing code generation, document generation, chatbot development, text-to-code generation, and answering technical queries. Results: ChatGPT can provide users with explanations, examples, and guidance to help them understand complex concepts and …

The Ethical Implications Of Dall-E: Opportunities And Challenges, Kai-Qing Zhou, Hatem Nabus

Mesopotamian Journal of Computer Science

Artificial intelligence (AI) images, like those produced by DALL-E, have seen explosive growth in the past several years and have the potential to disrupt numerous markets. While the technology offers exciting opportunities for creativity and innovation, it also raises important ethical considerations that must be addressed. These ethical implications include issues related to bias and discrimination, privacy, job displacement, and unintended consequences. To mitigate these challenges, a multi-disciplinary approach is needed, including the development of effective regulations and governance frameworks, the creation of unbiased algorithms, responsible data management practices, and educational and training programs. Additionally, encouraging ethical discussions and debates …

Infusing K-Means For Securing Iot Services In Edge Computing, Tam Sakirin, Iqra Asif

Mesopotamian Journal of Computer Science

Accurate, timely, and safe administration of data from IoT devices is made possible by intelligent computing. As the number of IoT devices proliferates, more and more data will be collected, adding depth and breadth to the existing range of IoT services. Technology that incorporates entire systems on a single integrated circuit has improved to the point where more and more consumer electronics can support full-fledged operating systems. It is impractical to use a single computing model for the entire planet since doing so would cause severe network congestion and security holes. In order to solve this problem, we present a …

Enhancing Hybrid Spectrum Access In Cr-Iot Networks: Reducing Sensing Time In Low Snr Environments, Nthatisi Margaret Hlapisi

Mesopotamian Journal of Computer Science

The current utilization of the licensed spectrum band is not optimal. the abundance of Internet of Things (IoT) gadgets could lead to congestion in the unlicensed spectrum band. A potential solution is to integrate cognitive radios into IoT devices, specifically by developing CR-IoT (cognitive-radio-enabled IoT) devices that leverage the hybrid spectrum access (HSA) technique to access the licensed spectrum band and employ energy detectors for spectrum sensing. While HSA can enable high data throughput for CR-IoT networks, environments with low signal-to-noise ratio (SNR) may experience reduced performance. Particularly, in low SNR environments with SNR values ranging from -20dB to -24dB, …

Predicting Carbon Dioxide Emissions With The Orange Application: An Empirical Analysis, Israa Ezzat, Alaa Wagih Abdulqader

Mesopotamian Journal of Computer Science

The effects of climate change, such as droughts, storms, and extreme weather, are increasingly being felt around the world. Greenhouse gases are the primary contributors to climate change, with carbon dioxide (CO2) being the most significant. In fact, CO2 accounts for a significant percentage of all greenhouse gas emissions. As a result, reducing CO2 emissions has become a critical priority for mitigating the impacts of climate change and preserving our planet for future generations. Based on simulation and data mining technologies that use historical data, CO2 is expected to continue to rise. Around the world, 80% of CO2 emissions come …

Student’S Rating System For Teachers: A Tool For Teacher Scheduling Consideration, Naomi Bajao, Jose Primo Bardoquillo, Jhay Concha, Mae Fatima Monsanto, Ma.Chrisfie Karen Mojar

Mesopotamian Journal of Computer Science

The end-of-term ratings are one of the most crucial components of improving educational institutions' ability to assess teachers' performance. The paper-based evaluation system for teachers at Cebu Technological University's Tuburan Campus is time-consuming and laborious.  During the flexible learning, the school uses Google Forms to create surveys to rate the teachers which also has shortcomings since Google Forms has limited functions. To solve these issues, the proponents created a web-based evaluation system that makes it simple for students to rate their professors and generates evaluation reports at ease. Additionally, the system gives the system administrator the ability to control colleges, …

The Evolving Role Of Artificial Intelligence In The Future Of Distance Learning: Exploring The Next Frontier, Maad M. Mijwil, Guma Ali, Emre Sadıkoğlu

Mesopotamian Journal of Computer Science

In recent years, education has become especially related to the applications provided by artificial intelligence technology through a digital environment that includes a set of tools that assist in processing and storing information. Artificial intelligence techniques contribute to the development of students' skills by providing them with advanced scientific content and building their mental capabilities faster. Moreover, these techniques support analysing student data and suggest suitable educational materials and activities for them. Artificial intelligence is a noteworthy tool for the growth of distance education, especially after the development of expert systems that have become a human advisor in many domains, …

Machine Learning Application On Employee ‎Promotion, Muhannad Ilwani, Ghalia Nassreddine, Joumana Younis

Mesopotamian Journal of Computer Science

Any company's most valuable asset, its workforce, is its employees. As a result, the company's primary goal should be to develop an excellent strategy for supporting and investing in its employees and staff by providing the best training and development. Employee Promotion denotes the advancement of an employee's rank. It raises the salary, position, duties, and benefits. It is a part of the job that propels employees to the highest commitment and loyalty to their organizations. Employee morale and loyalty are two critical components of any successful business. Employee promotion is the key to improving employee performance and engagement. It …

Clustering Algorithms And Comparisons In Vehicular Ad Hoc Networks, Radhakrishna Karne, Sreeja Tk

Mesopotamian Journal of Computer Science

Vehicular Ad hoc Network (VANET) is a new era in the transmission of dynamic information across communities. Intelligent Transportation Systems is only one of the many applications for VANET (ITS). The topology of VANET is extremely dynamic, and connections are irregular. These features cause information transmission in the VANET to be unreliable. Vehicle clustering is a successful strategy to increase the network's scalability and connection dependability. Characteristics of the VANET have an impact on clustering performance as well. An extensive explanation of VANET clustering algorithms is given in this article. A complete evaluation of clustering in VANETs is provided based …

Artificial Neural Networks: An Overview, Roheen Qamar, Baqar Ali Zardari

Mesopotamian Journal of Computer Science

Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by the human brain, and they replicate the way real neurons communicate with one another. Artificial neural networks (ANNs) are massively parallel systems comprised of a huge number of interconnected basic processors. This paper discuss about the artificial neural network and its basic types. This article explains the ANN and its basic outlines the fundamental neuron and the artificial computer model. It describes network structures …

Data-Integrity Aware Stochastic Model For Cascading Failures In Power Grids, Rezoan Ahmed Shuvro, Pankaz Das, Jamir Shariar Jyoti, Joana M. Abreu, Majeed M. Hayat

Electrical and Computer Engineering Faculty Research and Publications

The reliable operation of power grids during cascading failures is heavily dependent on the interdependencies between the power grid components and the supporting communications and control networks. Moreover, the system operators' expertise in dealing with cascading failures can play a pivotal role during contingencies. In this paper, a dynamical probabilistic model is developed based on Markov-chains, which captures the dynamics of cascading failures in the power grid. Specifically, a previously developed Markov-chain based model is extended to capture the trade-off between the benefits of having a robust communication infrastructure and its vulnerability from data integrity (e.g., cyber-attacks). State-space reduction of …

Hybrid Warfare And Disinformation: A Ukraine War Perspective, Sascha-Dominik Dov Bachmann, Dries Putter, Guy Duczynski

Research outputs 2022 to 2026

Misinformation, disinformation and mal information are part of the information disorder construct, dominating the information warfare domain. These are key enablers associated with grey zone operations, and an integral part of current adversaries' and competitors' hybrid warfare tool kit. Disinformation, in combination with influence operations, also plays an important role within the concept of hybrid warfare; both from a threat–and own resilience perspective. This article reflects on these information warfare tools and their application by Russia in the current Russo-Ukraine war, offering potentially considerable force multipliers in the information domain for the Russian aggressor. Hybrid warfare and associated threats, specifically …

Blue Carbon Opportunities: Seagrass Carbon Storage And Accumulation Rates At Trang, Thailand, Paul Lavery, Anna Lafratta, Rujinun Palahan, Maneewan Sanlee, Milica Stankovic, Janmanee Panyawai, Oscar Serrano, Pere Masque´

Research outputs 2022 to 2026

Report prepared as a contribution to the IKI Project “Conservation of biodiversity, seagrass ecosystems and their services – safeguarding food security and resilience in vulnerable coastal communities in a changing climate” funded through the International Klimate Initiative (IKI).

The IKI Project is a partnership between the CMS, Edith Cowan University, Project Seagrass, Seagrass Watch, Murdoch University, MRS, Blue Ventures, SAN, C3, ZSL, MareCet and Yapeka. The collaboration enhances the understanding of seagrass ecosystem services and the capacity to develop and deliver science-based policy solutions in seagrass conservation. It brings together scientists, policy experts, business development experts and conservation NGOs across …

Human In Vivo Medial Gastrocnemius Gear During Active And Passive Muscle Lengthening: Effect Of Inconsistent Methods And Nomenclature On Data Interpretation, Matheus Daros Pinto, Kazunori Nosaka, James M. Wakeling, Anthony J. Blazevich

Research outputs 2022 to 2026

‘Muscle gear’ is calculated as the ratio of fascicle-to-muscle length change, strain, or velocity. Inconsistencies in nomenclature and definitions of gear exist across disciplines partly due to differences in fascicle [curved (Lf) versus linear (Lf,straight)] and muscle [whole-muscle belly (Lb) versus belly segment (Lb,segment)] length calculation methods. We tested whether these differences affect gear magnitude during passive and active muscle lengthening of human medial gastrocnemius of young men (n=13, 26.3±5.0 years) using an isokinetic dynamometer. Lb, Lb,segment, Lf and Lf,straight were measured from motion analysis and ultrasound imaging data. Downshifts in belly gear but not belly segment gear occurred with …

An Integrative Review Of Project Portfolio Management Literature: Thematic Findings On Sustainability Mindset, Assessment, And Integration, Masoud Aghajani, Gesa Ruge, Kam Jugdev

Research outputs 2022 to 2026

Sustainability integration in project portfolio management helps shape strategic, organizational, and project-based contexts. The authors conducted a structured literature review from 2000 to 2021 and developed a novel integrative framework presenting a holistic view highlighting three substantive research themes: sustainability mindset, sustainability assessment, and sustainability integration in project portfolio processes. Noteworthy progress has been made at the strategic and portfolio levels toward framing a sustainability mindset (definition, values, and principles) and developing frameworks/tools for sustainability assessment and project portfolio selection. However, areas for more research include integrating sustainability into project portfolio processes, reporting, and organizational learning for portfolio improvement.

Preface : Fruit And Vegetable Waste Utilization And Sustainability, Sachin A. Mandavgane, Ipsita Chakravarty, Amit Jaiswal

Books/Book Chapters

Fruits and vegetables are an important source of nutrition and a key element of a healthy balanced diet. Several organizations such as the World Health Organization (WHO), Food and Agriculture Organization of United Nations (FAO), and Food Health England suggest consuming more than 400 g of fruits and vegetables per day (roughly five portions per day) to improve overall health and reduce the risk of certain cardiovascular diseases and cancer; subsequently, consumption and production of fruits and vegetables are on the rise. The increased consumption/production of fruits and vegetables has resulted in huge quantities of waste (approximately 42% of the …

Testing The Adequacy Of Formal Models Of An Irrational Learning Effect, Lenard Dome

School of Psychology Theses

The inverse base-rate effect (IBRE) is an irrational phenomenon in predictive learning characterized by as a preference for rare, unlikely outcomes in the face of ambiguity. This thesis investigates the adequacy of formal explanations for this puzzling phenomenon. In the first project, I will focus on mechanisms of learning that mathematical models posit underlie this preference. A class of attentional explanation produces a counter-intuitive prediction: the effect disappears under concurrent load. I confirm the prediction, but only when participants were under an obvious time constraint -- irrationality reduces under increased task demands. This suggests that multiple learning mechanisms operate independently …

Smoothed Particle Hydrodynamics For Modelling Cold-Water Coral Habitats In Changing Oceans, Konstantinos Georgoulas, Sebastian Hennige, Yeaw Chu Lee

School of Engineering, Computing and Mathematics

The importance of the growth, proliferation and longevity of reef-forming cold-water corals is paramount as they support various complex bio-diverse habitats and provide many essential ecosystem services. These cold-water coral reefs consist of layers of living coral tissue that grow on top of large masses of coral skeleton. Here, the Goldilocks Principle is used to simulate growth in optimal conditions and model how cold-water corals engineer their habitat to survive and prosper. A computational fluid dynamics model is created based on the Smoothed Particle Hydrodynamics method, a mesh-free Lagrangian numerical method. The SPH solver is written in the C++ programming …

An Analysis Of The Iranian Post-Earthquake Management And Structural Assessment System, Nirvan Razaghi-Kashani

School of Engineering, Computing and Mathematics Theses

This thesis investigated the theory and practice of structural assessment, as a component in a more extensive post-earthquake disaster management system. The research focused on Iran as a case study, and in particular the capital city of Iran (Tehran). This study aimed overview of the system used for post-earthquake disaster management and structural assessment in Tehran. This thesis examined three specific sub-components of the system, namely: The Earthquake Information Management System (EIMS), seismic hazard education programs, and initiatives to engage communities in the disaster management process. A blended phenomenological and ethnographic approach provided the basis for the analysis of collected …