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Articles 6481 - 6510 of 63011

Full-Text Articles in Computer Sciences

Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo Sep 2024

Edge Surveillance Task Offloading And Resource Allocation Algorithm Based On Drl, Chao Li, Jiabao Li, Caichang Ding, Zhiwei Ye, Fangwei Zuo

Journal of System Simulation

Abstract: For the resource limitation of intensive surveillance tasks in edge computing, a surveillance task offloading and resource allocation algorithm based on DRL is proposed. With the optimization objectives of surveillance task delay and recognition accuracy, the joint decision objective optimization solution of task offloading, wireless channel allocation, and image compression rate was modeled as a Markov decision process. To address the problem of slow and unstable algorithm convergence due to the high volatility of training samples caused by the dynamic nature of wireless channels and the randomness of surveillance tasks, an attention mechanism is used to jointly encode channel …

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Study On Invulnerability Of Urban Rail Network Considering Sum Of The Neighbors Degree, Shuqing Li, Yixiao Song, Guojian Zhong Sep 2024

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 …

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Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel, Chao Yuan, Yao Zhang, Yadong Zhao, Dawei Xu, Jing Yuan, Yongjie Zhai Sep 2024

Modeling And Simulation Of Pipeline Cable Inspection Robot Based On Omnidirectional Wheel, Chao Yuan, Yao Zhang, Yadong Zhao, Dawei Xu, Jing Yuan, Yongjie Zhai

Journal of System Simulation

Abstract: Aiming at the problem that the inner space of underground pipeline cable is narrow and closed, which cannot be inspected by humans, and the existing pipeline robot cannot adapt to the special environment of pipeline cable, a miniaturized, compact pipeline cable inspection robot is designed. This robot is capable of operating within the underground pipeline where cables have already been laid to inspect the inner wall of the pipeline and the working condition of the cables. According to the requirements of the working conditions, the whole three-dimensional model of the robot has been established. The mapping relationship between the …

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Multi-Step Information Aided Q-Learning Path Planning Algorithm, Yuelong Wang, Songyan Wang, Tao Chao Sep 2024

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 …

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Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds, Hanlin Huo, Xiangjun Zou, Yan Chen, Xinzhao Zhou, Mingyou Chen, Chengen Li, Yaoqiang Pan, Yunchao Tang Sep 2024

Visual Robot Obstacle Avoidance Planning And Simulation Using Mapped Point Clouds, Hanlin Huo, Xiangjun Zou, Yan Chen, Xinzhao Zhou, Mingyou Chen, Chengen Li, Yaoqiang Pan, Yunchao Tang

Journal of System Simulation

Abstract: In response to the large and complex data volume and high redundancy of visual point cloud obstacle recognition in complex unstructured orchard environments, which severely impacts the real-time performance and efficiency of harvesting operations, a point cloud compression algorithm is proposed based on point cloud segmentation to enhance the efficiency of point cloud obstacle recognition and environmental adaptability. An Informed RRT* based approach is used combined with an inverse projection algorithm, mapping-based informed RRT*(M-Informed RRT*) to solve the harvesting path problem. By constructing a highly real-time and robust integrated robot system for sampling, perception, and obstacle avoidance, efficient obstacle …

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The Synchronous Grasping Method Of Virtual-Real Assembly Robot Based On Digital Twin, Jian Xu, Gaofeng Liu, Yijian Zhao, Zili Zheng, Huanying Yan Sep 2024

The Synchronous Grasping Method Of Virtual-Real Assembly Robot Based On Digital Twin, Jian Xu, Gaofeng Liu, Yijian Zhao, Zili Zheng, Huanying Yan

Journal of System Simulation

Abstract: A method based on digital twin for assembly robot virtual-real synchronization and grasping is proposed to address the issues of poor intelligent grasping accuracy and difficult data processing in assembly tasks for industrial robots. Based on the digital twin, a digital twin assembly robot virtual-real synchronization and grasping architecture is designed. The OPC UA information model is built by classifying multi-source heterogeneous data, and the OPC UA communication protocol is used as a bridge for data communication of the assembly robot, achieving virtual-real synchronization. The convolutional neural network is further trained using the virtual robot to improve the grasping …

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Deep Learning Approach In Melanoma Stage Classification, Frank Lemba Lemba, Clopas Kwenda Sep 2024

Deep Learning Approach In Melanoma Stage Classification, Frank Lemba Lemba, Clopas Kwenda

African Conference on Information Systems and Technology

Accurate and efficient classification of melanoma stages is crucial for effective treatment planning and improved patient outcomes. Traditional diagnostic methods are often time-consuming and subjective, highlighting the need for advanced computational approaches. This study proposes a self-supervised learning framework combined with a convolutional neural network (CNN) to classify melanoma stages more effectively. Initially, features are extracted from unlabeled skin images using pre-trained VGG16 and ResNet50 models. These features are combined and reduced in dimensionality using Principal Component Analysis (PCA). Subsequently, K-means and DBSCAN clustering is applied to pseudo-label the data, providing a foundation for pre-training a CNN model. This pre-trained …

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Trends From 20 Years Of Artificial Intelligence In Financial Services In Africa, Nthabiseng Moela, Lerato Matlala, Jackie Ma, Dipuo Maphutha, Hossana Twinomurinzi Sep 2024

Trends From 20 Years Of Artificial Intelligence In Financial Services In Africa, Nthabiseng Moela, Lerato Matlala, Jackie Ma, Dipuo Maphutha, Hossana Twinomurinzi

African Conference on Information Systems and Technology

The need for financial inclusion in Africa, particularly for marginalised groups like women and small businesses, highlights the importance of leveraging Artificial Intelligence (AI). This study provides a bibliometric analysis of AI's integration into African financial services from 2003 to 2023. The key results show a significant increase in AI use, particularly in fraud detection, credit risk prediction, and stock market volatility forecasting, with 49% of the research coming from South Africa, Nigeria, and Tunisia. However, areas like financial development management, inflation control, and gender disparities in loan access remain underexplored. The emphasis has been on the technical implementation of …

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Some Studies On Mathematical Morphology In Remotely Sensed Data Analysis, Geetika Barman Sep 2024

Some Studies On Mathematical Morphology In Remotely Sensed Data Analysis, Geetika Barman

Doctoral Theses

The application of Mathematical Morphology (MM) techniques has proven to be beneficial in the extraction of shapebased and texture-based features during remote sensing image analysis. The characteristics of these techniques, such as nonlinear adaptability and comprehensive lattice structure, make them useful for contextual spatial feature analysis. Despite the advancements, there are still persistent challenges, including the curse of dimensionality, maintaining spatial correlation, and the adaptability of morphological operators in higher dimensions. The focus of this thesis is to explore the potential of MM-based methods to analyse spatial features in addressing these challenges, specifically in the context of spatialcontextual feature analysis …

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Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra Sep 2024

Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra

Physical Therapy Faculty Articles and Research

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …

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Institutional Data Repositories Are Vital, Jen Darragh, Mikala R. Narlock, Halle Burns, Peter A. Cerda, Wind Cowles, Leslie M. Delserone, Seth Erickson, Joel Herndon, Heidi Imker, Lisa R. Johnston, Sherry Lake, Michael Lenard, Alicia Hofelich Mohr, Jennifer Moore, Jonathan Petters, Brandie Pullen, Shawna Taylor, Briana Wham Sep 2024

Institutional Data Repositories Are Vital, Jen Darragh, Mikala R. Narlock, Halle Burns, Peter A. Cerda, Wind Cowles, Leslie M. Delserone, Seth Erickson, Joel Herndon, Heidi Imker, Lisa R. Johnston, Sherry Lake, Michael Lenard, Alicia Hofelich Mohr, Jennifer Moore, Jonathan Petters, Brandie Pullen, Shawna Taylor, Briana Wham

University of Nebraska-Lincoln Libraries: Faculty Publications

As funding agencies and publishers reiterate research data sharing expectations (1), many higher-education institutions have demonstrated their commitment to the long-term stewardship of research data by connecting researchers to local infrastructure, with dedicated staffing, that eases the burden of data sharing. Institutional repositories are an example of this investment (2). They provide support for researchers in sharing data that might otherwise be lost: data without a disciplinary repository, data from projects with limited funding, or data that are too large to sustainably store elsewhere. The staffing and technical infrastructure provided by institutional repositories ensures responsible access to information while considering …

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The Use Of Social Media For Organisational Learning: A Systematic Review Of Literature, Harry Moongela, Marie Hattingh Sep 2024

The Use Of Social Media For Organisational Learning: A Systematic Review Of Literature, Harry Moongela, Marie Hattingh

African Conference on Information Systems and Technology

Social media (categorised as Web 2.0 platforms) has been reported to significantly impact and improve organisational learning (OL), but there seems to be a lack of systematic literature reviews (SLR) covering the use of social media to facilitate OL. Thus, this article conducted a SLR for both social media and OL by presenting the significant concepts, models and frameworks from literature. A total number of 48 articles were found for the analysis method and the findings reveal important concepts such as types of OL, levels of OL and frameworks incorporating both social media and OL. The results also show that …

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Use Your Own Device (Uyod): Framework For Building A Human Firewall, Tapiwa Gundu, Kevin Kativu Sep 2024

Use Your Own Device (Uyod): Framework For Building A Human Firewall, Tapiwa Gundu, Kevin Kativu

African Conference on Information Systems and Technology

The Use Your Own Device (UYOD) paradigm is increasingly common in modern digital workplaces, offering benefits like flexibility and cost savings but also introducing significant cybersecurity risks. This study develops a comprehensive framework for constructing a robust human firewall aimed at mitigating these risks. The research employs a systematic literature review (SLR) methodology, starting with a corpus of 198 articles, from which 17 high-quality studies were selected for in-depth analysis. Key components identified include cybersecurity awareness training, regular simulated attacks, clear policies and procedures, technology competence training, and fostering a security-first culture. Findings reveal that integrating these components into an …

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Ai Bioelectricity Management System, Fungai Jacqueline Kiwa, Tawanda Bundukutu, Thoko Matnell Mawoyo, Batsiranai Linda Chiduku, Martin Muduva, Belinda Ndlovu Sep 2024

Ai Bioelectricity Management System, Fungai Jacqueline Kiwa, Tawanda Bundukutu, Thoko Matnell Mawoyo, Batsiranai Linda Chiduku, Martin Muduva, Belinda Ndlovu

African Conference on Information Systems and Technology

This document emphasizes on the generation of electricity from trees and its usability in all the sectors of Zimbabwe. The research focused on positively changing the lives of citizens through the provision of uninterrupted and reliable bioelectricity. The literature review was completely and accurately performed through finding out the current news associated with the use of trees in producing electricity and the use of AI to manage the flow. The Scrum’s development model was adopted and followed during the research project to address issues like transparency, early mitigation of risks and constant feedback. The Scrum-model is one of the best …

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Data Breach Mitigation In Hospital Database Management Systems – The Case Of A Hospital In South-South Nigeria, Leton Rebecca Nsereka, Irene Govender Sep 2024

Data Breach Mitigation In Hospital Database Management Systems – The Case Of A Hospital In South-South Nigeria, Leton Rebecca Nsereka, Irene Govender

African Conference on Information Systems and Technology

The damaging effects of data breaches result in the loss of sensitive data, operational downtime, financial losses, and, in extreme cases, legal action. This study investigates the Hospital Database Management systems (HDMS) in a selected hospital in South-South Nigeria for the mitigation of data breaches. The deployment of an Incident Response Framework for data breach mitigation on HDMS has yet to be fully researched, creating a gap literature. The research objectives were accomplished through mixed methods and design science research (DSR). About 180 participants including forty employees from the medical records unit and 140 patients/patient relatives, who interact with the …

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Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari Sep 2024

Neurosymbolic Cognitive Methods For Enhancing Foundation Model-Based Reasoning, Kaushik Roy, Siyu Wu, Alessandro Oltramari

Faculty Publications

Foundation models have emerged as powerful tools, exhibiting extraordinary performance across various tasks, such as language processing, visual recognition, code generation, and human-centered engagement. However, recent studies have highlighted their limitations when grounded, abstract, and generalized reasoning capabilities are required. Complex tasks often involve multiple hierarchical reasoning steps, which are typical features of human thinking processes. In fact, in this chapter we claim that cognitively-inspired computational models, such as the so-called Common Model of Cognition, are key to enable complex reasoning within foundation model-based artificial intelligence (AI) systems. We investigate neurosymbolic approaches for mapping AI system components to those of …

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Exploring Artificial Intelligence: A Collaborative Small Group Analysis And Application, Ellamarie Powell Sep 2024

Exploring Artificial Intelligence: A Collaborative Small Group Analysis And Application, Ellamarie Powell

AI Assignment Library

In this small group project, students will collaborate to explore the principles and applications of Artificial Intelligence (AI). Each group will research, analyze, and present on a specific AI topic, highlighting its real-world implications and ethical considerations. The project involves team members contributing to various roles, including research, technical analysis, and presentation. The final deliverable will be a video presentation integrating individual contributions, showcasing a comprehensive understanding of AI and its impact on society. This assignment fosters teamwork, critical thinking, and effective communication skills.

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A Primer On How Al Algorithms Control You, Russell Fulmer Sep 2024

A Primer On How Al Algorithms Control You, Russell Fulmer

Journal of Technology in Counselor Education and Supervision

Artificial intelligence (AI) algorithms can control you by exerting heavy influence on your worldview. Your worldview is akin to your personal philosophy, which affects how you perceive and label social systems and structures, groups of people, and politics. Algorithms impact your decision-making, beliefs, mood, relationships, and more. My rhetoric is intentionally strong when discussing algorithms, and I invite you to assess its merit by reviewing related literature and thinking critically.

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Developing Decision Support Tools To Optimize Crop Production Using Uav-Based Multispectral Imagery, Fared Farag Sep 2024

Developing Decision Support Tools To Optimize Crop Production Using Uav-Based Multispectral Imagery, Fared Farag

Student Theses and Dissertations

Multispectral imagery collected through unmanned aerial vehicles (UAVs) holds promise for optimizing crop production to meet the rising market demands. This study aimed to enhance the main two facets behind the UAV data pipeline: data collection and data analysis. Chapter II addresses data collection challenges by presenting a software tool designed to assist UAV operators in validating and identifying potential errors in the collected images. Through beta testing, the software effectively reduced operational costs and laid a solid foundation for subsequent data analysis. Chapter III delves into the data analysis facet to develop advanced modeling approaches for accurate and robust …

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Interoperability In Deep Learning: A User Survey And Failure Analysis Of Onnx Model Converters, Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George Thiruvathukal, James C. Davis Sep 2024

Interoperability In Deep Learning: A User Survey And Failure Analysis Of Onnx Model Converters, Purvish Jajal, Wenxin Jiang, Arav Tewari, Erik Kocinare, Joseph Woo, Anusha Sarraf, Yung-Hsiang Lu, George Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models between frameworks and to runtime environments. Conversion errors compromise model quality and disrupt deployment. However, the failure characteristics of DL model converters are unknown, adding risk when using DL interoperability technologies. This paper analyzes failures in DL model converters. We survey software engineers about DL interoperability tools, use cases, and pain points (N=92). Then, we characterize failures in model converters associated with the main interoperability tool, ONNX (N=200 issues in PyTorch and TensorFlow). Finally, we formulate …

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Malware Classification Through Abstract Syntax Trees And L-Moments, Anthony J. Rose, Christine M. Schubert Kabban, Scott R. Graham, Wayne C. Henry, Christopher M. Rondeau Sep 2024

Malware Classification Through Abstract Syntax Trees And L-Moments, Anthony J. Rose, Christine M. Schubert Kabban, Scott R. Graham, Wayne C. Henry, Christopher M. Rondeau

Faculty Publications

The ongoing evolution of malware presents a formidable challenge to cybersecurity: identifying unknown threats. Traditional detection methods, such as signatures and various forms of static analysis, inherently lag behind these evolving threats. This research introduces a novel approach to malware detection by leveraging the robust statistical capabilities of L-moments and the structural insights provided by Abstract Syntax Trees (ASTs) and applying them to PowerShell. L-moments, recognized for their resilience to outliers and adaptability to diverse distributional shapes, are extracted from network analysis measures like degree centrality, betweenness centrality, and closeness centrality of ASTs. These measures provide a detailed structural representation …

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Exploring The Impact Of Conceptual Bottlenecks On Adversarial Robustness Of Deep Neural Networks, Bader Rasheed, Mohamed Abdelhamid, Adil Khan, Igor Menezes, Asad Masood Khatak Sep 2024

Exploring The Impact Of Conceptual Bottlenecks On Adversarial Robustness Of Deep Neural Networks, Bader Rasheed, Mohamed Abdelhamid, Adil Khan, Igor Menezes, Asad Masood Khatak

All Works

Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise enhanced interpretability. This study delves into the robustness of Concept Bottleneck Models (CBMs) against adversarial attacks, comparing their original and adversarial performance with standard Convolutional Neural Networks (CNNs). The premise is that CBMs prioritize conceptual integrity and data compression, enabling them to maintain high performance under adversarial conditions by filtering out non-essential variations in input data. Our extensive evaluations across different datasets and adversarial attacks confirm that CBMs …

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Evaluating The Cost Of Classifier Discrimination Choices For Iot Sensor Attack Detection, Mathew Nicho, Brian Cusack, Shini Girija, Nalin Arachchilage Sep 2024

Evaluating The Cost Of Classifier Discrimination Choices For Iot Sensor Attack Detection, Mathew Nicho, Brian Cusack, Shini Girija, Nalin Arachchilage

All Works

The intrusion detection of IoT devices through the classification of malicious traffic packets have become more complex and resource intensive as algorithm design and the scope of the problems have changed. In this research, we compare the cost of a traditional supervised pattern recognition algorithm (k-Nearest Neighbor (KNN)), with the cost of a current deep learning (DL) unsupervised algorithm (Convolutional Neural Network (CNN)) in their simplest forms. The classifier costs are calculated based on the attributes of design, computation, scope, training, use, and retirement. We find that the DL algorithm is applicable to a wider range of problem-solving tasks, but …

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Domain-Based Nucleic-Acid Minimum Free Energy: Algorithmic Hardness And Parameterized Bounds, Erik D. Demaine, Elize Grizzell, Jayson Lynch, Ahmed Shalaby, Timothy Gomez, Markus Hecher, Robert Schweller, Damien Woods Sep 2024

Domain-Based Nucleic-Acid Minimum Free Energy: Algorithmic Hardness And Parameterized Bounds, Erik D. Demaine, Elize Grizzell, Jayson Lynch, Ahmed Shalaby, Timothy Gomez, Markus Hecher, Robert Schweller, Damien Woods

Computer Science Faculty Publications

Molecular programmers and nanostructure engineers use domain-level design to abstract away messy DNA/RNA sequence, chemical and geometric details. Such domain-level abstractions are enforced by sequence design principles and provide a key principle that allows scaling up of complex multistranded DNA/RNA programs and structures. Determining the most favoured secondary structure, or Minimum Free Energy (MFE), of a set of strands, is typically studied at the sequence level but has seen limited domain-level work. We analyse the computational complexity of MFE for multistranded systems in a simple setting were we allow only 1 or 2 domains per strand. On the one hand, …

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Analyzing Student Prompts And Their Effect On Chatgpt’S Performance, Ghadeer Sawalha, Imran Taj, Abdulhadi Shoufan Sep 2024

Analyzing Student Prompts And Their Effect On Chatgpt’S Performance, Ghadeer Sawalha, Imran Taj, Abdulhadi Shoufan

All Works

Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link between these strategies and the model’s response accuracy, the existence of individual prompting tendencies, and the impact of gender in this context. Our students used ChatGPT to solve five problems related to embedded systems and provided the solutions and the conversations with this model. We analyzed the …

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Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning, Benjamin F. Akers, Kristina O. F. Williams Sep 2024

Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning, Benjamin F. Akers, Kristina O. F. Williams

Faculty Publications

A numerical method for evolving the nonlinear Schrödinger equation on a coarse spatial grid is developed. This trains a neural network to generate the optimal stencil weights to discretize the second derivative of solutions to the nonlinear Schrödinger equation. The neural network is embedded in a symmetric matrix to control the scheme’s eigenvalues, ensuring stability. The machine-learned method can outperform both its parent finite difference method and a Fourier spectral method. The trained scheme has the same asymptotic operation cost as its parent finite difference method after training. Unlike traditional methods, the performance depends on how close the initial data …

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A Visual Guide To Balancing Latency And Bandwidth In Mpi All-To-All, Naeris Netterville Sep 2024

A Visual Guide To Balancing Latency And Bandwidth In Mpi All-To-All, Naeris Netterville

All ETDs from UAB

The complex nature of parallel algorithms in the Message Passing Interface (MPI) standard challenges the ability of trivially visualizing them. Within MPI libaries (e.g., MPICH, Open-MPI), there are many collectives that offer different solutions for communicating data between processes. The collective MPI_Alltoall uses various techniques such as the spread-out and Bruck algorithms to have processes transfer data between all processes. Each technique within MPI_Alltoall is best suited for different scenarios. The spread-out algorithm uses a linear number of iterations, in process count P, while the Bruck algorithm is logarithmic. The Bruck algorithm transfers more data overall, but with fewer communication …

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Adaptive Worlds: Generative Ai In Game Design And Future Of Gaming, And Interactive Media, Jay Ratican, James Hutson Sep 2024

Adaptive Worlds: Generative Ai In Game Design And Future Of Gaming, And Interactive Media, Jay Ratican, James Hutson

Faculty Scholarship

Generative AI is revolutionizing the field of game design, introducing unprecedented adaptability and personalization in gameplay. The latest advancements in AI-driven engines enable real-time content creation, offering dynamic, player-driven experiences that diverge from traditional pre-programmed narratives. This shift marks a transition toward "choose your own adventure" formats, with an unlimited number of variations in levels, enemies, collectibles, and weaponry, tailored to each player's decisions. Google's GameNGen, for example, showcases AI's capacity to recreate classic games like DOOM, learning and generating gameplay in real time. These innovations are not restricted to gaming alone; they extend to edutainment, television, and film, where …

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Designing Cybersecurity Escape Rooms: A Gamified Approach To Undergraduate Learning, Thitima Srivatanakul Sep 2024

Designing Cybersecurity Escape Rooms: A Gamified Approach To Undergraduate Learning, Thitima Srivatanakul

Journal of Cybersecurity Education, Research and Practice

Gamification, including game-based learning (GBL), is a widely recognized pedagogical approach used for imparting and reinforcing cybersecurity knowledge and skills to learners. One innovative form of GBL gaining popularity across various educational levels, from secondary schools to professional development, is escape room-style education. This study is centered on the development and design of escape room activities tailored for teaching cybersecurity concepts, with a particular focus on web and software security, to undergraduate students at York College. The primary objectives of this research are twofold: firstly, to evaluate the effectiveness of educational escape room activities in reinforcing cybersecurity concepts taught in …

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Saas Application Maturity Assessment Model, Saiqa Aleem, Rabia Batool, Shayma Alkobaisi, Faheem Ahmed, Asad Masood Khattak Sep 2024

Saas Application Maturity Assessment Model, Saiqa Aleem, Rabia Batool, Shayma Alkobaisi, Faheem Ahmed, Asad Masood Khattak

All Works

Software-as-a-service (SaaS), as a software delivery model, has received substantial attention from software providers and users alike. In recent years, it has become one of the most promising service delivery models in cloud computing. Many existing companies are transferring their business into the SaaS delivery model. Network vendors also migrate to a SaaS business model by offering on-demand remote IT support. This increasingly competitive landscape and the variety in markets have imposed many challenges for SaaS developers and vendors and made it difficult to find a consensus on the factors contributing to the positive performance of SaaS businesses. This paper …

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