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On the Provenance of Software Systems: Automating Software Traceability with Knowledge Graph and Large Language Model Synergy
Get PDFThe present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life cycle provenance, must also be captured and maintained. Artifacts are often delivered without an explicit trace of their evolution. This is particularly unacceptable for critical systems, where requirements documents, codebases, and other meta-artifacts are revisited without a corresponding history of how or why they came to be, leading to confusion and rework.
While the software development life cycle (SDLC) incorporates meta-artifacts like traceability matrices to improve artifact provenance, these are typically informal, heavy with natural language, and lack structured explainability. This work proposes that each artifact should be attended by a machine-readable, human-interpretable, extensible provenance record, implemented in the form of a knowledge graph, backed by well-established ontologies. The developed system, ProvTracer, leverages structured knowledge via PROV-O and the Basic Formal Ontology alongside generative natural language capabilities via the Generative Pretrained Transformer (GPT) series of Multimodal Large Language Models (MLLMs), to create real-time, traceable, and explainable links between development activities and their resulting artifacts. By capturing these provenance trace links automatically through multimodal signals, e.g., screenshots, peripheral device input, etc., ProvTracer aims to bridge the gap between implicit processes and explicit traces, enabling developers to understand, query, maintain, integrate, and trust the evolution of their projects and systems.
The synergy between knowledge graphs and MLLMs enables a novel form of interactive, explainable software development. Natural language queries of provenance trace link knowledge graphs can reduce information overload, extract developer rationale and decision histories, support task assignment, and a range of project management activities. This aligns with a burgeoning trend in research demonstrating that structured knowledge improves machine learning trust, transparency and reproducibility. The present dissertation addresses the challenges of traceability and explainability in the SDLC by presenting a system that automatically captures artifact provenance and operationalizes it for practical use in real-world software development
Examining the Impact of Stress on Collegiate Flight Students
Get PDFStress may negatively affect the timely completion of a student pilot\u27s flight training. A student pilot influenced by stress may be subject to what is known as falling behind the aircraft, a condition that constantly requires minor interventions from their flight instructor. This lack of situational awareness and decreased performance may inadvertently cause the student pilot to repeat the flight lesson, increasing the time spent in the training course
Assessing the Effect of Traffic Incident Management Areas on Highway Capacity
No full textTraffic Incident Management Areas (TIMAs) are established to facilitate emergency response and restore traffic flow following roadway incidents. However, lane closures within TIMAs significantly impact roadway capacity, requiring an analysis of capacity reduction across different closure scenarios. This study examines the effects of one-lane, two-lane, and three-lane closures on traffic flow and capacity reduction. Data was collected from select locations along the I-4 and I-95 corridors, in Florida between January 2023 and June 2024. Observation locations were predominately on rural freeway segments. One-lane closures made the majority of observations with 41 incidents analyzed and an average duration of 26.5 minutes. Of these, 59 percent exhibited measurable impacts on traffic flow, while the remainder showed no significant speed/flow reduction or density increase. The capacity reduction results suggest that a 1-lane closure results in a capacity reduction between 50-60% on the segment. Two-lane closures had the highest observed variability in duration, with a median of 20 minutes but a range of 131 minutes. Due to a limited sample size (12 incidents), capacity reduction calculations for two-lane closures were inconclusive. Three-lane closures, where only the shoulder remained open, were rare; with only five observations recorded along I-4. Observed flow rates on the shoulder ranged from 72 to 924 veh/hr, averaging 583 veh/hr—lower than post-recovery flow rates. Key limitations include the restricted dataset, reliance on predominately rural, three-lane freeway segments, and the need for incidents to occur near detectors. Capacity reduction calculations based on the 15-minute recovery period flow rates proved unreliable. Future research should expand the dataset, explore urban settings, and utilize higher-resolution flow rates to refine capacity estimation methodologies
Conducting Background Checks in the Corporate Security Environment
Get PDFIn the current threat landscape, the background check has become a critical frontline defense in protecting corporate assets, brand integrity and personnel safety. But as any security leader will tell you, the traditional background check — focused on criminal history, employment verification and references — has limitations, especially in today’s regulatory environment.
Federal and state restrictions continue to erode the depth and scope of what can be legally reviewed, and meanwhile, insider threats and deceptive candidates are becoming more sophisticated. In this context, it\u27s no longer about whether to conduct background checks — it\u27s about how to conduct them effectively, legally and in alignment with operational risk management strategies.
This article outlines modern best practices in corporate background screening, with a focus on layered, risk-adjusted approaches and the growing role of behavioral screening tools in identifying concealed threats early
Exploring Bio-Inspired Agent Behaviors from Ants for Robotic Swarm Fault Resilience
Get PDFAs Multi-Agent Systems (MASs) become increasingly involved in every aspect of everyday life, the need to maintain reliability and resilience within these systems grows. However, there is a need for control schemes and agent behaviors that provide security against these threats while also avoiding large degradation in system performance as a tradeoff. Current research has covered a wide breadth of avenues and strategies that provide measurable resilience to faulted agents. However, these strategies often require group consensus, specialized observer agents, or identification of faulted agents, which can weaken overall system performance. This work presents an alternative solution through individual agent behaviors. These behaviors are taken from the strategies that insects use to resist fungal infections. Preliminary data shows these biologically inspired behaviors present emergent swarm resilience to faulted agents and malicious spreading faults
External Property Fire Prevention System
Get PDFWildfires are a significant threat worldwide causing immense property loss and environmental damage. To mitigate these impacts, we propose a dual-layered fire defense strategy: external property fire suppression and neighborhood-wide protection. Our External Property Fire Prevention System (EPFPS) is designed to safeguard buildings from external fire threats through an integrated network of water and foam sprinklers, heat sensors, and real-time fire tracking.
The system operates through strategically placed sprinklers, both rotary and stationary, activated by heat sensors and manual triggers. Real-time monitoring enhances situational awareness, allowing homeowners and emergency responders to make informed decisions. The EPFPS main objective is to prevent flames from reaching structures, reducing property damage and potential casualties. The proposed system is anticipated to significantly lower fire damage compared to those without protection. The system’s ability to act before a fire reaches critical levels ensures faster and more effective suppression ultimately saving lives while reducing financial losses. Beyond immediate fire prevention, the EPFPS will enhance long-term community resilience by minimizing recovery costs and supporting sustainable wildfire management. In conclusion, the External Property Fire Prevention System (EPFPS) offers a groundbreaking solution to significantly reduce wildfire damage and protect lives. Its adaptability to various environments makes it a viable solution for urban, suburban, and rural areas alike. By investing in proactive fire defense systems, communities worldwide can shift from reactive firefighting to preventive wildfire management, ensuring safer living conditions for future generations
Towards the Wearable Cardiorespiratory Sensors for Aerospace Applications
Get PDFIn safety-critical aviation operations, adaptive Human-Machine Interfaces (HMI) rely on accurate physiological monitoring to mitigate cognitive overload. While cardiorespiratory sensors are promising for real-time cognitive workload assessment, existing studies lack rigorous validation of consumer-grade devices in high-stress aviation contexts and fail to address measurement uncertainty propagation. This study evaluates the Zephyr BioHarness, a commercial wearable sensor, against clinical-grade equipment during arithmetic tasks simulating aviation cognitive demands. By integrating a neuro-fuzzy system with uncertainty propagation methods, we quantify the reliability of heart rate (HR) and breathing rate (BR) metrics for workload estimation. Results demonstrate moderate HR accuracy (RMSE: 4.85 bpm, CC: 0.66) but poor BR performance (RMSE: 9.73 bpm, CC: 0.09), attributed to inconsistent breath detection during cognitive strain. The novel uncertainty framework reveals workload prediction variances (σWL: 0.38–2.22) driven primarily by BR inaccuracies, emphasizing the need for improved respiratory sensing in adaptive HMI. This work pioneers the application of neuro-fuzzy systems for uncertainty analysis in aviation physiology, offering a validated methodology for sensor integration and highlighting critical limitations in current consumer-grade technologies. These findings advance the design of robust cognitive monitoring systems, ensuring safer and more efficient human-machine collaboration in aviation
Stairway To Heaven
Get PDFThe author in this narrated creative non fiction takes us on a journey of his hiking adventure of a stratovolcano while visiting the Mediterranaen island of Sicily