Digital Commons Network^™

How State Universities Are Addressing The Shortage Of Cybersecurity Professionals In The United States, Gary Harris

Journal of Cybersecurity Education, Research and Practice

Cybersecurity threats have been a serious and growing problem for decades. In addition, a severe shortage of cybersecurity professionals has been proliferating for nearly as long. These problems exist in the United States and globally and are well documented in literature. This study examined what state universities are doing to help address the shortage of cybersecurity professionals since higher education institutions are a primary source to the workforce pipeline. It is suggested that the number of cybersecurity professionals entering the workforce is related to the number of available programs. Thus increasing the number of programs will increase the number of …

Ai In The Health Professions, Heidi Monroe, Carrie Fry, Phillip Baker, Erika Busz

AI and the Future of Work

The aim of this track is to provide health professionals, and those interested in mental health and healthcare careers with an understanding of key aspects of AI use in healthcare. Participants will explore advantages of some recent AI developments and evaluate how they may be effectively leveraged to improve patient care, while addressing potential challenges, limitations, and ethical considerations.

Educating With Ai, Grace Seo, David Wicks

AI and the Future of Work

This conference track explores the integration of AI within teaching and learning, with a focus on practical approaches that leverage AI technologies to optimize teaching practices and enhance students’ learning experience. The topics include the essential AI literacies in educational contexts, collaborative learning with AI, and the use of AI for enhanced learning assessments.

A Generalized Machine Learning Model For Long-Term Coral Reef Monitoring In The Red Sea, Justin J. Gapper, Surendra Maharjan, Wenzhao Li, Erik Linstead, Surya Prakash Tiwari, Mohamed A. Qurban, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000–2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground …

Attention-Based Load Forecasting With Bidirectional Finetuning, Firuz Kamalov, Inga Zicmane, Murodbek Safaraliev, Linda Smail, Mihail Senyuk, Pavel Matrenin

All Works

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our …

Time-Series Feature Selection For Solar Flare Forecasting, Yagnashree Velanki, Pouya Hosseinzadeh, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

Computer Science Student Research

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple …

A Limited-Preemption Scheduling Model Inspired By Security Considerations, Benjamin Standaert, Fatima Raadia, Marion Sudvarg, Sanjoy Baruah, Thidapat Chantem, Nathan Fisher, Christopher Gill

Computer Science and Engineering Faculty Research

Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness.

We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems. Under MPS, task phases reflect execution using different security mechanisms which each have associated execution time costs …

Designing A Haptic Boot For Space With Prompt Engineering: Process, Insights, And Implications, Mohammad Amin Kuhail, Jose Berengueres, Fatma Taher, Sana Khan, Ansah Siddiqui

All Works

The existing literature has highlighted the potential of Artificial Intelligence (AI) tools in enhancing ideation and optimizing functionality across various engineering disciplines. However, a comprehensive understanding of the impact of AI on the engineering design process, particularly in creating innovative and efficient designs, is currently lacking. This research specifically investigates the integration of AI in developing space-haptic boots by utilizing haptic technology for immersive virtual interactions. The study analyzes the role of an AI tool, ChatGPT-3.5, in the design process, starting from requirement gathering to prototyping and testing, to assess the effectiveness and challenges of AI in engineering design. We …

Advancing Emotional Health Assessments: A Hybrid Deep Learning Approach Using Physiological Signals For Robust Emotion Recognition, Amna Waheed Awan, Imran Taj, Shehzad Khalid, Syed Muhammad Usman, Ali Shariq Imran, Muhammad Usman Akram

All Works

Emotional health significantly impacts physical and psychological well-being, with emotional imbalances and cognitive disorders leading to various health issues. Timely diagnosis of mental illnesses is crucial for preventing severe disorders and enhancing medical care quality. Physiological signals, such as Electrocardiograms (ECG) and Electroencephalograms (EEG), which reflect cardiac and neuronal activities, are reliable for emotion recognition as they are less susceptible to manipulation than physical signals. Galvanic Skin Response (GSR) is also closely linked to emotional states. Researchers have developed various methods for classifying signals to detect emotions. However, these signals are susceptible to noise and are inherently non-stationary, meaning they …

Synthesis Of Zno: Zro2 Nanocomposites Using Green Method For Medical Applications, Mohammed J. Tuama, Maysoon F. Alias

Karbala International Journal of Modern Science

These days, nanocomposites are very popular, especially in medical applications. The spread of diseases in general, and those caused by microbes and cancerous diseases in particular, and the increased resistance of these diseases to antibiotics, have led to the need for the rapid, low-cost, and environmentally friendly production of nanocomposites. To create the chemical G-ZnO: ZrO2 and S-ZnO: ZrO2 (green technique), two different plant extracts were utilized: Z. officinal and S. aromaticum. The effective synthesis and acceptable properties features of the nanoparticles were confirmed using characterization techniques such as X-ray diffraction (XRD), Fourier transform infrared (FTIR) , diffuse reflectance spectroscopy …

Detecting Lgbtq+ Instances Of Cyberbullying, Arslan Bisharat, Manuel Madrigal, Mohammed Abuhamad, Deborah Hall, Yasin Silva

Computer Science: Faculty Publications and Other Works

Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. …

An Automated Machine Learning Approach To The Retrieval Of Daily Soil Moisture In South Korea Using Satellite Images, Meteorological Data, And Digital Elevation Model, Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence …

The Evaluation Of Machine Learning Techniques For Isotope Identification Contextualized By Training And Testing Spectral Similarity, Aaron P. Fjelsted, Tyler J. Morrow, Clayton D. Scott, Yilun Zhu, Darren E. Holland, Azaree T. Lintereur, Douglas E. Wolfe

Faculty Publications

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset …

Impact Of An Online Decision Support Tool For Ductal Carcinoma In Situ (Dcis) Using A Pre-Post Design (Aft-25), Elissa Ozanne, Kellyn Maves, Angela Tramontano, Thomas Lynch, Alastair Thompson, Ann Partridge, Elizabeth Frank, Deborah Collyar, Desiree Basila, Donna Pinto, Terry Hyslop, Marc Ryser, Shoshana Rosenberg, E. Shelley Hwang, Rinaa Punglia

Department of Pharmacology and Experimental Therapeutics Faculty Papers

BACKGROUND: The heterogeneous biology of ductal carcinoma in situ (DCIS), as well as the variable outcomes, in the setting of numerous treatment options have led to prognostic uncertainty. Consequently, making treatment decisions is challenging and necessitates involved communication between patient and provider about the risks and benefits. We developed and investigated an interactive decision support tool (DST) designed to improve communication of treatment options and related long-term risks for individuals diagnosed with DCIS.

FINDINGS: The DST was developed for use by individuals aged > 40 years with DCIS and is based on a disease simulation model that integrates empirical data and …

Systematic Review On Isolation, Purification, Characterization, And Industrial Applications Of Thermophilic Microbial Α- Amylases, Rugaiyah A. Arfah, Sarlan Sarlan, Abdul Karim, Anita Anita, Ahyar Ahmad, Paulina Taba, Harningsih Karim, Siti Halimah Larekeng, Dorothea Agnes Rampisela, Rusdina Bte Ladju

Karbala International Journal of Modern Science

The α-amylase enzyme, sourced from diverse organisms, including plants, animals, and bacteria, plays a crucial role in multiple industries, notably food processing sectors like cakes, fruit juices, and starch syrup. Research identifies thermophilic organisms as prime sources of this enzyme thriving at temperatures ranging from 41°C to 122°C. The enzyme purification was carried out using liquid-liquid extraction, which involved the exchange of substances between two liquid phases that were immiscible or partially soluble. The optimal temperature for α-amylase was 45 to 90°C. The best pH for bacterial and fungal α-amylases ranged from 5.0 to 10.5 and 5.0 to 9.0. Based …

The Aimag Project: Using Machine Learning To Predict Crustal Magnetic Anomaly Values, Xavier Gobble, Marlie Mollett, Dr. Dawn King, Dr. Cory Reed, Erin Knese

Undergraduate Research Symposium

A detailed model of the Earth’s total magnetic field is important for acquiring the means for GPS-alternative, magnetic anomaly-based navigation. The Earth’s total magnetic field is an amalgam of 5 mechanisms: the geodynamo generated by the rotation of the Earth’s molten iron core, the fields induced by the flows of electric current in the atmosphere and oceans, the disturbance of the ionosphere by solar wind, and local anomalies attributable to ferromagnetic minerals present in the crust; the lattermost compose the crustal magnetic field. The EMAG2v3 dataset comprises a compilation of satellite, shipborne, and airborne magnetic measurements differenced from the Comprehensive …

Cyber Victimization In The Healthcare Industry: Analyzing Offender Motivations And Target Characteristics Through Routine Activities Theory (Rat) And Cyber-Routine Activities Theory (Cyber-Rat), Yashna Praveen, Mijin Kim, Kyung-Shick Choi

International Journal of Cybersecurity Intelligence & Cybercrime

The integration of computer technology in healthcare has revolutionized patient care but has also introduced significant cyber risks. Despite the healthcare sector being a primary target for cyber-attacks, research on the dynamics of these threats and practical solutions remains limited. Understanding the complexities of cyberattacks in this sector is critical, as the impact extends beyond financial losses to directly affect patient care and the protection of sensitive information. This paper applies Routine Activities Theory (RAT) and Cyber Routine Activities Theory (C-RAT) to analyze high-tech cyber victimization case studies in healthcare. The analysis explores the motivations behind these attacks and identifies …

Understanding The Use Of Artificial Intelligence In Cybercrime, Sinyong Choi, Thomas Dearden, Katalin Parti

International Journal of Cybersecurity Intelligence & Cybercrime

Artificial intelligence is one of the newest innovations that offenders also exploit to satisfy their criminal desires. Although understanding cybercrimes associated with this relatively new technology is essential in developing proper preventive measures, little has been done to examine this area. Therefore, this paper provides an overview of the articles featured in the special issue of the International Journal of Cybersecurity Intelligence and Cybercrime, ranging from deepfake in the metaverse to social engineering attacks. This issue includes articles that were presented by the winners of the student paper competition at the 2024 International White Hat Conference.

Investigating The Intersection Of Ai And Cybercrime: Risks, Trends, And Countermeasures, Sanaika Shetty, Kyung-Shick Choi, Insun Park

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Integrated Model Of Cybercrime Dynamics: A Comprehensive Framework For Understanding Offending And Victimization In The Digital Realm, Troy Smith Phd

International Journal of Cybersecurity Intelligence & Cybercrime

This article introduces the Integrated Model of Cybercrime Dynamics (IMCD), a novel theoretical framework for examining the complex interplay between individual characteristics, online behavior, environmental factors, and outcomes related to cybercrime offending and victimization. The model incorporates key concepts from existing theories, empirical evidence, and interdisciplinary perspectives to provide a comprehensive framework. In contrast to traditional criminological theories, the proposed model integrates concepts from multiple disciplines to offer a holistic framework that captures the complexity of cybercrime and specifically caters for the uniqueness of cyberspace. The article will provide a detailed overview of the conceptual model, its theoretical underpinnings drawing …

Fostering Trust Through User Interface Design In Multi-Drone Search And Rescue, Johanna Ahlskog, Maria Theresa Bahodi, Artur Lugmayr, Timothy Merritt

Research outputs 2022 to 2026

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly used in search and rescue (SAR) missions, with pilots transitioning from manual control of single drones to more collaborative tasks orchestrating semi-autonomous fleets. Designing user interfaces to support UAV pilots effectively is crucial to improving the success of search missions. We developed two versions of a multi-drone SAR system prototype to simulate SAR missions and evaluated them with professional UAV SAR pilots in Sweden. Both versions showed the flight paths of the UAVs, yet in one version, a heatmap was overlayed to provide information from a lost person model. We evaluated situational …

On Signifiable Computability: Part I: Signification Of Real Numbers, Sequences, And Types, Vladimir A. Kulyukin

Computer Science Faculty and Staff Publications

Signifiable computability aims to separate what is theoretically computable from what is computable through performable processes on computers with finite amounts of memory. Real numbers and sequences thereof, data types, and instances are treated as finite texts, and memory limitations are made explicit through a requirement that the texts be stored in the available memory on the devices that manipulate them. In Part I of our investigation, we define the concepts of signification and reference of real numbers. We extend signification to number tuples, data types, and data instances and show that data structures representable as tuples of discretely finite …

Research On Simulation Resource Management Based On Graph Association Organization, Zewei Liu, Yishan Ding, Tingyu Lin, Mingxing Ke, Liqing Guo, Yingying Xiao, Zhilong Zhao, Yan Li, Xuan Lü

Journal of System Simulation

Abstract: The simulation test and evaluation of intelligent system of systems, systems and single equipment is a complex system engineering, which requires effective management of multi-source, heterogeneous and distributed massive simulation resources scattered in cloud test centers and test sites of various units; and good control of dynamically generated tasks, assumptions, configurations, results, evaluations and other data and files. The traditional way of managing and querying simulation resources by category is inefficient and difficult to meet the requirements of large-scale intelligent simulation test and evaluation activities. An overall framework for simulation resource management based on graph association organization, defines a …

Path Planning Based On Improved A* And Dynamic Window Approach, Peng Ji, Xinyuan Zhang, Shuaixuan Gao, Shuorang Wei

Journal of System Simulation

Abstract: In response to the low efficiency, redundant turning points, and collision issues of the traditional A* algorithm, a smart vehicle path planning algorithm that integrates an improved A* algorithm with a dynamic window approach has been proposed. The algorithm has enhanced the search point selection method, optimized the evaluation function, selected key turning points based on the slope values between turning points, and removed redundant turning points. Between every two optimized key turning points, a dynamic window approach that balances speed and safety is used for local obstacle avoidance. Experiments show that compared to the traditional A* algorithm, this …

Research On Digital Twin System Of Rockshaft Hoist, Baiting Zhao, Jianguo Shi, Xiaofen Jia

Journal of System Simulation

Abstract: In order to solve the problem of low intelligence and digitization of the current mine hoisting system, a method based on DT for digital modeling, 3D visualization, and virtual real interaction of shaft hoisting machines is proposed. Aiming at the rockshaft hoist system, based on the digital twin five dimensional model framework, we analyze the operating mechanism of the equipment, and model the fully physical digital system of the rockshaft hoist. By constructing multidimensional multi-scale models and multidimensional heterogeneous data models, twin digital scenes are constructed, and virtual real mapping technology is combined to achieve dynamic mapping and virtual …

A Multimodal Residual Spatial-Temporal Fusion Model Based On Automatic Sleep Classification, Yecai Guo, Shuang Tong

Journal of System Simulation

Abstract: Highly accurate sleep staging plays a crucial role in correctly assessing sleep conditions. Aiming at the problem that the existing convolutional network cannot obtain the topological characteristics of physiological signals, a sleep staging algorithm based on multi-modal residual spatio-temporal fusion is proposed. Time-frequency images and spatio-temporal images are obtained using short-time Fourier transform and adaptive map convolution, which are converted into high-dimensional feature vectors; lightweight interaction of feature information flow is realized through time-frequency feature and spatiotemporal feature extraction modules; the feature enhancement fusion module fuses feature information to outputs sleep staging results. The results show that the model …

Research On Autonomous Decision-Making In Air-Combat Based On Improved Proximal Policy Optimization, Dianwei Qian, Hongmin Qi, Zhen Liu, Zhiming Zho, Jianqiang Yi

Journal of System Simulation

Abstract: To address the problems of high information redundancy and slow convergence speed of traditional reinforcement learning in air-combat autonomous decision-making applications, a proximal policy optimization air-combat autonomous decision-making method, based on dual observation and composite reward is proposed. A dual observation space, which contains interaction information as the main information and individual feature information as a supplement, was designed to reduce the influence of redundant battlefield information on the training efficiency of the decision model. A composite reward function combining result reward and process reward was designed to improve convergence speed. The generalized advantage estimator was applied in the …

An Intelligent Adversaries Behavior Simulation Technology Based On Improved Behavior Trees, Fang Zhou, Bo Fan, Xiaoyi Liu, Yishan Ding, Ningxin Zhang, Yachao Shao, Xiaoyu Zhai

Journal of System Simulation

Abstract: Intelligent algorithm/intelligent platform/intelligent system intelligence capability testing and evaluation need to solve high-level intelligent opponent simulation problems, an intelligent opponent behavior simulation technology based on improved behavior tree is proposed. Four types of behavior tress nodes are designed, including behavior control, combat tasks, behavior actions, and execution condition node. Five atomic behavior actions and parameters are established, including maneuver, reconnaissance and early warning, command and decision-making, firepower strike, and electronic interference node. Five atomic condition nodes are provided, including target selection, weapon launch, and incoming weapon judgment node. The intelligent adversarial behavior simulation system is designed, including a behavior …

Study On Invulnerability Of Urban Rail Network Considering Sum Of The Neighbors Degree, Shuqing Li, Yixiao Song, Guojian Zhong

Journal of System Simulation

Abstract: In order to solve the problem of network cascade paralysis caused by urban rail station or line failure, considering the influence of the first-order neighborhood of network nodes, the load distribution impedance coefficient is proposed based on the nonlinear capacity load model, and a nonlinear capacity load optimization model considering the sum of the neighbors degree is constructed. By optimizing load structure, the alternative probability of nodes during load redistribution is adjusted to reduce the number of node failures in the cascading process, thereby the rail network invulnerability is improved. Taking Chongqing rail network as an example, the rail …

Multi-Step Information Aided Q-Learning Path Planning Algorithm, Yuelong Wang, Songyan Wang, Tao Chao

Journal of System Simulation

Abstract: To improve the path planning capability of mobile robots in a static environment and solve the problem of slow convergence of the traditional Q-learning algorithm in path planning, this paper proposes a multi-step information-aided Q-learning improvement algorithm. Using the multi-step information of greedy action in ε -greedy strategy and length of the historical optimal path to update the eligibility traces, which makes the effective eligibility traces work continuously in the iteration of the algorithm and solves the loop traps that may fall into with the preserved multi-step information; using the local multiflower pollination algorithm to initialize the Q-value table …