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Work Measurement and Ergonomics Open Textbook
No full textThis open textbook for Work Measurement & Ergonomics was created under an Affordable Materials Grant. Topics include operation process charts, flow process charts, manual work, workflow and yield, manual assembly lines, service processes, motion economy, performance rating, and direct time study
Impact of Information Security Awareness Training on Knowledge, Attitude, and Behavior: A K-12 Case Study
Get PDFAbstract— Information security breaches remain a serious threat across all sectors, often exploiting human factors rather than technical flaws. This study examines how a structured Information Security Awareness (ISA) training program influences employees’ knowledge of security policies, attitudes towards those policies, and self-reported security behaviors within a K-12 educational environment. A quantitative pre-test/post-test design was employed with 201 staff members (administrators, teachers, and support personnel) in a public school district. Participants completed the Human Aspects of Information Security Questionnaire (HAIS-Q) before and after undergoing an interactive cybersecurity training program. Statistical analysis revealed a significant improvement in information security knowledge, attitudes, and behavior following the training (p \u3c 0.001 for all domains). Additionally, strong positive correlations were observed among knowledge, attitude, and behavior both before and after training. These results indicate that well-designed ISA training can effectively enhance the human aspects of cybersecurity, reinforcing employees’ understanding of policies and encouraging safer security practices. While conducted in a single K-12 district, the findings suggest the potential applicability of the KAB model and interactive training approaches to other educational contexts and organizational sectors, though results may vary depending on organizational culture and demographic factors. The study also highlights that the improvements were measured in the short term; future research should evaluate long-term retention and behavioral maintenance. Implications for broader organizational practices and recommendations for addressing demographic and contextual differences in future studies are discussed
Uncalibrated Visual Servoing for Spatial Under-Constrained Cable-Driven Parallel Robots
Get PDFCable-driven parallel robots (CDPRs) offer large workspaces with minimal infrastructure, but their control becomes difficult when the platform is under-constrained and sensing is limited. This paper investigates uncalibrated visual servoing (UVS) with a single monocular camera, asking whether simple global static Jacobians (GSJ) can be sufficient and how an adaptive Jacobian estimator behaves. Two platforms are evaluated: a three-cable (3C) platform and a redundant six-cable du-al-plane platform (RC). Motion-capture (MoCap) validation shows that redundancy improves stability and tracking by reducing platform tilt and making image errors correspond more directly to Cartesian motions. Across static and low-speed tracking tasks, GSJ proved reliable, while a baseline recursive least-squares (RLS) estimator without safety triggers was often unstable. These findings suggest that improving mechanical conditioning may be as important as adding algorithmic complexity, and that carefully estimated global models can suffice in practice. Limitations include the use of a single camera, laboratory conditions, and a baseline RLS variant; future work will evaluate event-triggered adaptation and higher-speed trajectories
Validating the fundamental cybersecurity competency index (FCCI) through expert evaluation for human-generative artificial intelligence (GenAI) teaming
No full textThe increasing volume of cyber threats, combined with a critical shortage of skilled professionals and rising burnout among practitioners, highlights the urgent need for innovative solutions in cybersecurity operations. Generative Artificial Intelligence (GenAI) offers promising potential to augment human analysts in cybersecurity, but its integration requires rigorous validation of the fundamental competencies that enable effective collaboration of human-GenAI teams. Fundamental cybersecurity competencies, encompassing essential cybersecurity Knowledge, Skills, and Tasks completion (KSTs). Competency is defined as the ability to complete tasks within a work role. In this research study, we employed a mixed-methods research approach designed to evaluate human-GenAI teams, emphasizing the role of expert consensus in shaping the experimental assessment of the Fundamental Cybersecurity Competency Index (FCCI) in a commercial cyber range. Selecting a commercial cyber range allowed us to identify the specific KSTs from the United States (U.S.) Department of Defense (DoD) Cyber Workforce Framework (DCWF) and measure them at the KSTs level. The specific commercial cyber range we assessed enables the extraction of the users’ performance at the KST level. To validate the proposed experimental assessment of the FCCI and confirm the relevance of the selected cybersecurity KSTs, a panel of 20 Subject Matter Experts (SMEs) was engaged to evaluate and validate the proposed competency measures. The expert panel refined the cybersecurity scenarios and experimental procedures used in the commercial cyber range hands-on scenarios, ensuring alignment with DCWF. Our findings indicated that 46 of 47 fundamental cybersecurity KSTs were validated by the SMEs as essential components of the FCCI. Consensus levels of 85–90% confirmed strong expert support for incorporating GenAI (e.g., large language models such as ChatGPT) as a teammate or decision-support agent in these controlled experiments. The validated scenarios and experiments pave the way for future research on assessing cybersecurity competencies in commercial cyber range platforms with and without GenAI support (e.g., large language models such as ChatGPT). By establishing the baseline for competency assessment in this research, the SMEs’ feedback contributed to advancing cybersecurity workforce development and provided critical insights for integrating GenAI into collaborative cybersecurity human-GenAI teaming operations. The validated FCCI provides a robust mechanism to evaluate both human and human–GenAI team performance within realistic cybersecurity scenarios, while providing the needed metrics to measure cybersecurity competencies quantitatively. While this study achieved strong consensus, like any other research, several limitations were observed, including a relatively small SME panel size (n=20) and the absence of empirical testing with users. Future research will employ hands-on cyber range experiments to measure the FCCI by comparing KSTs measured across human-only and human–GenAI teams. Ultimately, this research advances cybersecurity workforce development by establishing a validated foundation for a quantitative assessment of cybersecurity competencies based on DCWF necessary for effective collaboration between humans and GenAI in defending against complex and evolving cyber threats
Integrating Vulnerability Assessments with Security Control Compliance
No full textInformation technology providers must implement security controls to protect client and partner data, as well as comply with government security requirements. The preparation of security compliance documentation is a slow process due to the manual efforts involved. We present SSP Manager, a framework that streamlines compliance and supports the building and maintenance of System Security Plans. The tool integrates vulnerability assessment, program analysis, and control monitoring by i) implementing a security control prioritization strategy that outputs NIST SP 800-53 controls to mitigate MITRE ATT\&CK techniques, ii) by incorporating reachability analysis of Python dependencies to filter out false positives from vulnerability scan results, and iii) by providing compliance monitoring of functionality based on Chef’s InSpec testing framework and the Open Policy Agent policy engine. Test reports are generated in a machine-readable format for easy integration into automated compliance pipelines. Our work bridges the gap between vulnerability assessment and security compliance. Moreover, it reduces the manual overhead in security workflows
SIMULATED HUMAN-AI TEAMING FRAMEWORK FOR ML MODEL DEVELOPMENT
No full textDeveloping effective and efficient AI systems to solve complex tasks requires human
expert involvement, such as judging AI decisions, identifying critical contexts, etc.
Expert cognitive factors significantly affect the building and decision-making of this
AI model. This thesis presents a novel Simulated Human-AI Teaming (HAT) frame-
work for enhancing machine learning model development, focusing on misinforma-
tion classification. Traditional AI systems often overlook human cognitive factors like
trust, cognitive load, and decision difficulty, leading to inefficiencies and low trust. To
address this, the study leverages large language models (LLMs) to simulate human
cognitive states, generating synthetic datasets enriched with behavioral metadata.
The framework integrates these factors into model training and decision-making,
employing adaptive weighting and K-means clustering to prioritize high-reliability
inputs.
Experimentalresultsdemonstratesignificantimprovements: theGatedHFJoint-
Model achieved 86.23% accuracy and 86.87% F1 score, outperforming baseline models
by 9.07% and 22.06%, respectively. The system activated human intervention for only
39.47% of cases, optimizing resource use. Analysis revealed that high cognitive load
(\u3e0.7) reduced accuracy by 15-20%, while low trust (\u3c 0.4) increased error rates by
14%. Clustering identified three reliability tiers, with 28.6% of samples requiring hu-
man review due to low reliability (Cluster 0), 43% benefiting from hybrid processing
(Cluster 1), and 28.4% suitable for full automation (Cluster 2).
The framework’s adaptive gating mechanism improved accuracy by 8.67% over
majority voting (0.762 → 0.828) and reduced false positives by 39%. Human-AI
collaboration corrected 1,203 cases where AI missed contextual cues, while 65.7% of
decisions achieved partial agreement. This work advances trustworthy AI systems
by bridging human intuition and algorithmic precision, offering a scalable, ethical
alternative to traditional human-in-the-loop approaches
Landscape and Environmental Characteristics that Shape Bat Assemblage Activity Across Varying Levels of Development in North America
No full textThis study investigates how environmental and landscape characteristics influence bat activity across varying levels of development in South Carolina. Acoustic monitoring was used to passively survey at five sites: Brays Island, Nemours Plantation, Palmetto Bluff, Spring Island, and Sun City. For six months, twenty-four Wildlife Acoustics Song Meter Mini monitors recorded bat calls, which were then analyzed using Kaleidoscope Pro 5.6.8 and SPSS Statistics 29. There were four hypotheses that were tested regarding the relationship between bat activity and environmental factors: temperature, canopy cover, midstory, and artificial light at night (ALAN) to determine if they were correlated. It was found that bat activity is positively influenced by temperature, which is consistent with previous research. Differences in bat acoustic assemblage activity among locations was documented. Canopy cover and midstory were significant predictors of activity, but trends were not consistent between frequency groups. Higher levels of ALAN did not have a negative effect on groups and even increased activities for some acoustic groups in some locations. These data suggest that while some environmental factors influence bat activity, this can significantly vary from site to site. This has implications for conservation and development planning, emphasizing the necessity for strategies specific to that region or location
An Interview with Dan Leach, Author of Junah at the End of the World
Get PDFDan Leach Spartanburg: Hub City Press, 2025 ISBN: 9798885740494 217 p. $17.95 (Pbk