Research On Campus Epidemic Evolution Based On Multi-Scale Modeling And Simulation In Microscopic & Microscopic View, Mingwei Hu, Wenjie Yang

Journal of System Simulation

Abstract: High density of population leads to high possibility of cross-infection. It is necessary to focus on campus epidemic prevention and control. Basing on existing studies in macroscopic or microscopic view, this paper proposed a multi-scale means to analyze a short-term evolution of Corona virus disease 2019 (COVID-19) on campus and estimated the efficiency of prevention strategies. Macroscopic model was based on the susceptible-exposed-infections-recovered(SEIR) model, which exported the time curve of the number of asymptomatic patients and symptomatic patients. Microscopic model combined discrete event simulation modeling and agent-based modeling to simulate the behavior of campus students and the state evolution …

Heterogeneous Multi-Ant Colony Algorithm Combining Competitive Interaction Strategy And Eliminatingreconstructing Mechanism, Chen Feng, Xiaoming You, Sheng Liu

Journal of System Simulation

Abstract: The traditional ant colony algorithm has many problems in convergence and diversity when solving the traveling salesman problem (TSP). Therefore, this paper proposes a heterogeneous multi-ant colony algorithm that combines the competitive interaction strategy and the eliminating-reconstructing mechanism (CEACO) to overcome these shortcomings. Firstly, the algorithm uses a competitive interaction strategy, which adjusts the interaction period adaptively according to the Hamming distance of different groups in different periods. Competition coefficients are adopted to differentiate matching interaction objects for interaction. The matched objects interact with each other through the optimal solution and pheromone matrix. This mechanism achieves a balance between …

Multi-Model Soft Sensor Modeling Under Help-Training Strategy, Luosuyang He, Weili Xiong

Journal of System Simulation

Abstract: Due to the strong nonlinearity, multi-stage coupling, and the small number of labeled samples in complex industrial processes, it is difficult for traditional global soft sensor models to accurately describe the whole process. Therefore, a multi-model soft sensor modeling method under the helptraining strategy is proposed. This method uses a fuzzy C-means (FMC) clustering algorithm to mine similar samples in the sample set and build several sub-models. By introducing the help-training strategy, a collaborative training framework based on main and auxiliary learners is formed, and a confidence evaluation mechanism is designed to eliminate error samples and expand the modeling …

Strategy Optimization Method Of Multi-Dimension Projection Based On Deep Reinforcement Learning, Jing An, Guangya Si, Lei Zhang

Journal of System Simulation

Abstract: Based on the perfect performance of deep reinforcement learning (DRL) in strategy optimization, this paper proposes a strategy optimization method of action taking the multi-dimension projection action as the main research object. The method combines the simulation experiment method with the DRL method. After analyzing the current situation of strategy optimization research, the deep learning framework is selected according to the research problems, and a DRL multi-dimension projection strategy model based on the asynchronous advantage actor-critic (A3C) algorithm is constructed. Through simulation experiments, the interactive learning between the DRL model and the simulation of "out of the loop" is …

A Simulation Method Based On Multi-Source Sensors For Aircraft Type Identification, Shaozhu Gu, Yuxin Ying, Huajie Zhang, Yiqi Tong

Journal of System Simulation

Abstract: Existing simulation methods for aircraft type identification mainly focus on a single sensor and a single target. They do not consider the joint acquisition of aircraft parameters by various sensor devices such as optoelectronics, radar, and electronic detection in real scenarios, leading to the simple simulation scenarios. This paper proposes a simulation platform based on multi-source sensors. Specifically, the platform includes an infrared image simulator that uses a cycleGAN network to generate infrared images of the aircraft, a flight simulator that adopts the three-degree-of-freedom flight control method to generate the movement trajectory of the aircraft, a radar simulator, that …

Securing Edge Computing: A Hierarchical Iot Service Framework, Sajan Poudel, Nishar Miya, Rasib Khan

Posters-at-the-Capitol

Title: Securing Edge Computing: A Hierarchical IoT Service Framework

Authors: Nishar Miya, Sajan Poudel, Faculty Advisor: Rasib Khan, Ph.D.

Department: School of Computing and Analytics, College of Informatics, Northern Kentucky University

Abstract:

Edge computing, a paradigm shift in data processing, faces a critical challenge: ensuring security in a landscape marked by decentralization, distributed nodes, and a myriad of devices. These factors make traditional security measures inadequate, as they cannot effectively address the unique vulnerabilities of edge environments. Our research introduces a hierarchical framework that excels in securing IoT-based edge services against these inherent risks.

Our secure by design approach prioritizes …

Motivations Driving Video Research Podcasts: Impact On Value And Creation Of Research Video Presentations, My Doan, Anh Tran, Na Le

Posters-at-the-Capitol

Abstract

Purpose: The purpose of the study is to better understand the role and impact of video research podcasts in bridging the gap between academia and the general public, especially concerning the challenges of accessibility and comprehension of scholarly research.

Methods: A 10-question survey was administered to evaluate the effectiveness, utility, and acceptance of video recordings in research presentations. The survey also aimed to gather insights into the motivations, challenges, and benefits of using video podcasts for research dissemination. Results were then analyzed using the Unified Theory of Acceptance and Use of Technology (UTAUT) model.

Results: There were 102 respondents …

Electron Effective Mass In Gan Revisited: New Insights From Terahertz And Mid-Infrared Optical Hall Effect, Nerijus Armakavicius, Sean Knight, Philipp Kühne, Vallery Stanishev, Dat Q. Tran, Steffen Richter, Alexis Papamichail, Megan Stokey, Preston Sorensen, Ufuk Kilic, Mathias Schubert, Plamen P. Paskov, Vanya Darakchieva

Department of Electrical and Computer Engineering: Faculty Publications

Electron effective mass is a fundamental material parameter defining the free charge carrier transport properties, but it is very challenging to be experimentally determined at high temperatures relevant to device operation. In this work, we obtain the electron effective mass parameters in a Si-doped GaN bulk substrate and epitaxial layers from terahertz (THz) and mid-infrared (MIR) optical Hall effect (OHE) measurements in the temperature range of 38–340 K. The OHE data are analyzed using the well-accepted Drude model to account for the free charge carrier contributions. A strong temperature dependence of the electron effective mass parameter in both bulk and …

Digital Phobia: An Inquiry For Mapping The Unseen Dimension Of New Digital Anxiety, The ‘Digiphobia’, Amarjit Kumar Singh ,Library Assistant, Md. Arshad Ali , Professional Assistant, Dr. Pankaj Mathur, Deputy Librarian,

Library Philosophy and Practice (e-journal)

Background: As technology continues to advance, individuals' interactions with digital platforms have become integral to daily life. Amidst this technological evolution, a novel concern emerges—Digital Phobia, hereafter referred to as “Digiphobia.” This phenomenon, although not previously explored in scholarly literature, necessitates an in-depth investigation due to its potential impact on individuals' well-being. Our research employs a two-step methodology to investigate its existence, implications, and manifestations.

Introduction: This research paper introduces and proposes the term "Digiphobia" as a comprehensive conceptualization of anxiety arising from interactions with digital spaces, applications, and environments. The proliferation of digital technologies has led to the emergence …

Parallel Algorithm For Testing The Singularity Of An N-Th Order Matrix, Ehab Alasadi

Al-Bahir

Analyze the possibilities of implementing a parallel algorithm to test the singularity of the N-th order matrix. Design and implement in ( C/C++) a solution based on sending messages between nodes using the PVM system library. Distribute the load among the nodes such that the computation time is as small as possible. Find out how the execution time and calculation acceleration depend on the number of nodes and the size of the problem (indicate the table and graphs). Based on the results, estimate the communication latency, for what size the task is (well) scalable on the given architecture, and what …

Alice In Cyberspace 2024, Stanley Mierzwa

Center for Cybersecurity

‘Alice in Cyberspace’ Conference Nurtures Women’s Interest, Representation in Cybersecurity

Unrealvision: A Synthetic Dataset Generator For Human-Pose Estimation And Behavior Analysis, Thinh Lu

Master's Theses

For over a decade, computer vision (CV) has become an indispensable component of numerous camera surveillance applications as well as intelligent autonomous systems. Thanks to new advances in AI, Edge Computing, and IoT technologies, there is now a rapidly growing number of smart camera devices, industrial and consumer robots that are using computer vision for various applications - from human tracking and analysis, object classification, to visual inspection and anomaly detection. For most common use cases, building vision-based applications can be a straightforward and affordable task thanks to the increasing number of publicly accessible datasets and research publications. However, it …

Embedded Deep Learning To Improve The Performance Of Approaches For Extinct Heritage Images Denoising, Ali Salim Rasheed, Alaa Hamza Omran

Iraqi Journal for Computer Science and Mathematics

Many advanced deep convolutional neural network (DCNN) methods have proven their efficacy in reconstructing the texture of super-resolution images (SR) from low-resolution images (LR). Nevertheless, the objective of achieving super-resolution (SR) reconstruction using Deep Convolutional Neural Networks (DCNN) becomes difficult when the input image is distorted by noise. Photographs captured at the inception of the camera are presently regarded as acultural heritage that chronicles an important periodin human history; however, they are marred by low resolution and noise as a result of obsolescence and the primitive nature of the technology that captured them, in contrast to the technological advances that …

An Integrative Computational Intelligence For Robust Anomaly Detection In Social Networks, Helina Rajini Suresh, K R. Harsavarthini, R Mageswaran, Hirald Dwaraka Praveena, C Gnanaprakasam, C.Sakthi Lakshmi Priya

Iraqi Journal for Computer Science and Mathematics

Anomaly detection is one of the most important tasks for maintaining the integrity, security, and trustworthiness of online communities in a social network. This paper proposes AdaptoDetect, which represents a new framework; it discusses a new anomaly detection approach called Pufferfish Optimization Technique for feature selection, together with a Graph Embedding Autoencoder for identifying anomalies. What makes AdaptoDetect special is that, with the use of POT, it has a distinctive capability in dynamic adaptation against network changes by selecting only the most relevant features in social network data. The technique for optimization underlines the important attributes for anomaly detection so …

Improving Security In The 5g-Based Medicalinternet Of Things Toimprove The Qualityofpatient Services, Israa Ibraheem Al_Barazanchi, Kholood J. Moulood, Muneer Sameer Gheni Mansoor, Jamal Fadhil Tawfeq

Iraqi Journal for Computer Science and Mathematics

The Internet of Medical Things (IoMT) is like a tech upgrade that benefits patients by reducing healthcare costs, making medical care more accessible, and improving the quality of treatment. To make IoMT devices smart and capable, they need super-fast 5G support. However, there are security concerns when using IoMT devices that can put a patient's data and privacy at risk. For instance, someone could eavesdrop on your medical data due to weak network access management and data encryption.Many systems use encryption methods to protect data, but these methods often fall short when it comes to the high security standards required …

Automatic Temperature Control System Using African Vultures Optimization Algorithm, Mostafa Abdulghafoor Mohammed, Muntadher Khamees, Dina Hassan Abbas

Iraqi Journal for Computer Science and Mathematics

One of the most important tasks in control engineering is tuning a PID controller for maximum efficiency. However, without a great deal of practice, manual adjustment of PID settings might result in erroneous results. Using met heuristic algorithms is one method for tweaking the PID controller. These algorithms, which are inspired bythe laws of nature, can effectively find the sweet spot for the PID settings. Therefore, instead of manually tweaking the PID controller, using met heuristic methods can greatly enhance the system's performance while decreasing the related expenses. A reliable temperature control system is crucial to the production of a …

Immersive Framework For Designing Trajectories Using Augmented Reality, Joseph Anderson, Leo Materne, Karis Cooks, Michelle Aros, Jaia Huggins, Jesika Geliga-Torres, Kamden Kuykendall, David Canales, Barbara Chaparro

Publications

The intuitive interaction capabilities of augmented reality make it ideal for solving complex 3D problems that require complex spatial representations, which is key for astrodynamics and space mission planning. By implementing common and complex orbital mechanics algorithms in augmented reality, a hands-on method for designing orbit solutions and spacecraft missions is created. This effort explores the aforementioned implementation with the Microsoft Hololens 2 as well as its applications in industry and academia. Furthermore, a human-centered design process and study are utilized to ensure the tool is user-friendly while maintaining accuracy and applicability to higher-fidelity problems.

Uncovering Weaknesses In Autonomous Driving: A Formal Approach To Adversarial Scenario Generation And Falsification, Carlos O. Hernandez

Master's Theses

Autonomous vehicles utilize advanced safety features like proactive driving assistance and pre-collision alerts to minimize the risk of accidents. However, evaluating the correct functionality of these systems is complex. First, safety systems are highly sophisticated, integrating software, networking, and hardware components, many of which rely on advanced artificial intelligence and machine learning algorithms. Second, an array of dynamic factors, including numerous actors and physical variables, can influence the performance of safety mechanisms during critical scenarios. Each actor’s unique behavior introduces unpredictability, making it difficult to anticipate future states and outcomes. This thesis presents a comprehensive framework for testing autonomous vehicle …

Resource Allocation And Edge Computing For Dual Hop Communication In Satellite Assisted Uavs Enabled Vanets, Ahmed Adil Nafea, Mustafa Maad Hamdi, Sami Abduljabbar Rashid

Iraqi Journal for Computer Science and Mathematics

VANETs are highly attractive and isused in maximum of the applications of cross-regional communication. To increase the coverage of the vehicular network, Unmanned Arial Vehicles (UAVs) are introduced, and they getconnected with the satellite networks to perform heterogeneous communication. With the help of this connectivity,the communication quality of ground level to air medium is increased.Currently the vehicle usage is highly increased and as aresults of communication link failure, improper resource allocation are arises whither abruptly assumesa stability about a network with that increasesan energy consumption and communication delay in the heterogeneous networks. In these conditions, thus study is idea of …

A Review Of Optimization Techniques: Applications And Comparative Analysis, Ahmed Hasan Alridha, Fouad H. Abd Alsharify, Zahir Al-Khafaji

Iraqi Journal for Computer Science and Mathematics

Optimization algorithms exist to find solutions to various problems and then find out the optimal solutions. These algorithms are designed to reach desired goals with high accuracy and low error, as well as improve performance in various fields, including machine learning, operations research, physics, chemistry, and engineering. As technology continues to advance, optimization algorithms are increasingly needed to address complex real-world challenges and drive innovation across all disciplines. Quantitative leaps have been achieved in improving the efficiency of optimization algorithms through the diversity of sources of information feeding these algorithms according to the type of optimization problem, based on scientific …

The Sensory Accommodation Framework For Technology: Bridging Sensory Processing To Social Cognition, Louanne Boyd

Engineering Faculty Books and Book Chapters

This book provides a thorough introduction to the many facets of designing technologies for autism, with a particular focus on optimizing visual attention frameworks. This book is designed to provide a detailed overview of several aspects of technology for autism. Each Chapter illustrates different parts of the Sensory Accommodation Framework and provides examples of relevant available technologies. The books first discusses a variety of skills that make up human development as well as a history of autism as a diagnosis and the birth of the neurodiversity movement. It goes on to detail individual types of therapy and how they interact …

Identification Of Faulty Sensor Nodes In Wban Using Genetically Linked Artificial Neural Network, Haider Rasheed Abdulshaheed, Mayasa M. Abdulrahman, Israa Ibraheem Al_Barazanchi, Jamal Fadhil Tawfeq

Iraqi Journal for Computer Science and Mathematics

Wireless Body Area Networks (WBANs) have risen as a promising innovation for checking human physiological parameters in real time. Be that as it may, the unwavering quality and precision of WBANs depend on the right working of sensor hubs. The distinguishing proof of defective sensor hubs is vital for guaranteeing the quality of information collected by WBANs. In this paper, we propose a novel approach to recognizing faulty sensor hubs in WBAN employing a hereditarily linked artificial neural organize (GLANN). The GLANN is preparedto employa crossbreedfuzzy-genetic calculation to optimize its execution in distinguishing defective sensor hubs. The proposed approach is …

Personalised Feedback On Assessments In Computing Modules - Gender Equality Action In Context, Alina Berry

Academic Posters Collection

Personalised feedback in computing higher education has been known to positively influence retention of women. The issue of gender inequality in computing field is well known and one of the efforts to address it is the development of a gender equality toolkit (TechMate), with one initiative (action) in the toolkit being personalised feedback. While all actions in the toolkit are research-driven, the aim of this work was to evaluate the action on personalised feedback in a local context.

The study comprised of 10 semi-structured interviews with computing lecturers at TU Dublin who provide personalised feedback, a student survey with 68 …

A Framework-Based Cross-Institutional Cpd For Academic Staff In Gen-Ai Literacy, Critical Inquiry And Authentic Assessment, Roisin Donnelly, Ita Kennelly

Books/Book Chapters

Generative-Artificial Intelligence (Gen-AI) has emerged as a transformative force profoundly influencing, if not revolutionizing the way we now teach and how students learn in higher education (HE). Despite the initial flurry of early research studies following the raising of public awareness of Gen-AI (and in particular ChatGPT), enduring pragmatic questions remain for academic staff on how best to protect and promote student learning, how to meaningfully support assessment integrity from a curriculum perspective, and additionally how to effectively use Gen-AI technologies to aid learning and foster deeper critical thinking.

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu

Browse all Theses and Dissertations

Sickle Cell Disease (SCD) is one of the most prevalent genetic blood disorders affecting millions of people worldwide. It is often accompanied by acute and/or chronic pain leading to increased healthcare costs and adverse outcomes. Effective management of SCD requires an understanding of the diverse physiological profiles. This study employs unsupervised machine learning, specifically K-means clustering to categorize the patients suffering with SCD into different clusters based on their vital signs. The main aim is to identify the groups that reflect similarities in physiological and pain profiles, allowing an in-depth analysis to reveal distinctive features distinguishing patient clusters. The project …

The Trouble With Technology, John O'Connor

Conference Papers

In contemporary education, technology is either hailed as the panacea for affordable mass education or dreaded as a threat to our humanity. At the European Culture and Technology Laboratory, technology is understood in the context of the Ancient Greek origin of the word: technē—meaning a system or a method of making or doing, an art or a craft; a technique or a practice, even a way of thinking. The tools humans use are not merely a means of intervention in our environment but also a way of becoming human and thus, technology has a fundamental impact on our identity and …

Freyr⁺: Harvesting Idle Resources In Serverless Computing Via Deep Reinforcement Learning, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park

Computer Science Faculty Research & Creative Works

Serverless computing has revolutionized online service development and deployment with ease-to-use operations, auto-scaling, fine-grained resource allocation, and pay-as-you-go pricing. However, a gap remains in configuring serverless functions - the actual resource consumption may vary due to function types, dependencies, and input data sizes, thus mismatching the static resource configuration by users. Dynamic resource consumption against static configuration may lead to either poor function execution performance or low utilization. This paper proposes Freyr+, a novel resource manager (RM) that dynamically harvests idle resources from over-provisioned functions to accelerate under-provisioned functions for serverless platforms. Freyr+ monitors each function's resource utilization in real-time …

Bert-Based Detection Of Ai-Generated Text For Content Verification, Soham Biren Katlariwala

2024 REYES Proceedings

With advancements in AI-driven natural language generation, distinguishing between AI-generated and human-written text has become imperative for ensuring content authenticity across industries. This study explores the effectiveness of Bidirectional Encoder Representations from Transformers (BERT) in addressing this classification challenge. Utilizing a diverse dataset and robust preprocessing techniques, BERT achieved a peak F1-score of 0.94364, outperforming traditional models such as Logistic Regression and Support Vector Machines. The results underscore the potential of transformer-based models in addressing real-world con- tent verification problems. Future enhancements include fine-tuning and expanding datasets for greater generalizability.

Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub

2024 REYES Proceedings

Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …

Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi

Browse all Theses and Dissertations

Graph Neural Networks (GNNs) have increasingly gained popularity as tools for analyzing graph data in areas like biology, knowledge-graphs, social networks, biology, and recommendation systems. However, their vulnerability to adversarial attacks - small, targeted manipulations of graph structures or node features - raises serious concerns about their reliability in real-world applications. Existing defense strategies, such as adversarial training, edge filtering, low-rank approximations, and randomization-based methods, often suffer from high computational costs, scalability issues, or reduced clean-data performance. Unlike these methods, the proposed approach integrates multi-hop relationships, applies adaptive regularization, and maintains a balance between feature-based and structural embeddings, ensuring improved …