Crosswalk of Human Reliability Methods for Offshore Oil Incidents

PresentationHuman reliability analysis (HRA) has long been employed in nuclear power applications to account for the human contribution to safety. HRA is used qualitatively to identify and model sources of human error and quantitatively to calculate the human error probabilities of particular tasks. The nuclear power emphasis of HRA has helped ensure safe practices and risk-informed decision making in the international nuclear industry. This emphasis has also tended to result in a methodological focus on control room operations that are very specific to nuclear power, thereby potentially limiting the applicability of the methods for other safety critical domains. In recent years, there has been interest to explore HRA in other domains, including aerospace, defense, transportation, mining, and oil and gas. Following several high profile events in the oil and gas industry, notably the Macondo well kick event in the U.S., there has been a move to use HRA to model and reduce risk in future oil drilling and production activities. Organizations like the Bureau of Safety and Environmental Enforcement are adapting the risk framework of the U.S. Nuclear Regulatory Commission for offshore purposes. In this paper, we present recent work to apply HRA methods to the analysis of offshore activities. We present the results of retrospective analyses using three popular HRA methods: SPAR-H, Petro-HRA, and CREAM. With the exception of Petro- HRA, these HRA methods were developed primarily for nuclear power event analysis. We present a comparison of the findings of these methods and a discussion of lessons learned in applying the methods to offshore events. The objective of this paper is to demonstrate the suitability of HRA methods for oil and gas risk analysis but also to identify topics where future research would be warranted to tailor these HRA methods

pp-Poisson surface reconstruction in curl-free flow from point clouds

The aim of this paper is the reconstruction of a smooth surface from an unorganized point cloud sampled by a closed surface, with the preservation of geometric shapes, without any further information other than the point cloud. Implicit neural representations (INRs) have recently emerged as a promising approach to surface reconstruction. However, the reconstruction quality of existing methods relies on ground truth implicit function values or surface normal vectors. In this paper, we show that proper supervision of partial differential equations and fundamental properties of differential vector fields are sufficient to robustly reconstruct high-quality surfaces. We cast the $p$-Poisson equation to learn a signed distance function (SDF) and the reconstructed surface is implicitly represented by the zero-level set of the SDF. For efficient training, we develop a variable splitting structure by introducing a gradient of the SDF as an auxiliary variable and impose the $p$-Poisson equation directly on the auxiliary variable as a hard constraint. Based on the curl-free property of the gradient field, we impose a curl-free constraint on the auxiliary variable, which leads to a more faithful reconstruction. Experiments on standard benchmark datasets show that the proposed INR provides a superior and robust reconstruction. The code is available at \url{https://github.com/Yebbi/PINC}.Comment: 21 pages, accepted for Advances in Neural Information Processing Systems, 202

Authenticated Key Exchange Secure under the Computational Diffie-Hellman Assumption

In this paper, we present a new authenticated key exchange(AKE) protocol and prove its security under the random oracle assumption and the computational Diffie-Hellman(CDH) assumption. In the extended Canetti-Krawczyk model, there has been no known AKE protocol based on the CDH assumption. Our protocol, called NAXOS+, is obtained by slightly modifying the NAXOS protocol proposed by LaMacchia, Lauter and Mityagin. We establish a formal security proof of NAXOS+ in the extended Canetti-Krawczyk model using as a main tool the trapdoor test presented by Cash, Kiltz and Shoup

KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services

With the growth of online services, the need for advanced text classification algorithms, such as sentiment analysis and biased text detection, has become increasingly evident. The anonymous nature of online services often leads to the presence of biased and harmful language, posing challenges to maintaining the health of online communities. This phenomenon is especially relevant in South Korea, where large-scale hate speech detection algorithms have not yet been broadly explored. In this paper, we introduce "KoMultiText", a new comprehensive, large-scale dataset collected from a well-known South Korean SNS platform. Our proposed dataset provides annotations including (1) Preferences, (2) Profanities, and (3) Nine types of Bias for the text samples, enabling multi-task learning for simultaneous classification of user-generated texts. Leveraging state-of-the-art BERT-based language models, our approach surpasses human-level accuracy across diverse classification tasks, as measured by various metrics. Beyond academic contributions, our work can provide practical solutions for real-world hate speech and bias mitigation, contributing directly to the improvement of online community health. Our work provides a robust foundation for future research aiming to improve the quality of online discourse and foster societal well-being. All source codes and datasets are publicly accessible at https://github.com/Dasol-Choi/KoMultiText.Comment: Accepted to the NeurIPS 2023 Workshop on Socially Responsible Language Modelling Research (SoLaR

Mindfully Aware and Open: Mitigating Subjective and Objective Financial Vulnerability via Mindfulness Practices

Our research presents mindfulness as a potential intervention to mitigate financial vulnerability, defined as the ability to handle unexpected future financial setbacks. As potential interventions to mitigate consumer financial vulnerability, we provide a conceptual framework on how two types of mindfulness practices (i.e., non-judgmental awareness and openness to experience) can mitigate the subjective and objective financial vulnerability differently. We suggest ways to manipulate the two types of mindfulness and discuss the results of our initial pilot study, focusing on lower-income consumers. In addition, we propose fruitful avenues for future research and provide recommendations for managers and policymakers to better address consumer financial vulnerability and enhance consumer welfare via mindfulness practiceThis research has been supported by Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research" (SOCANET-CM-UC3M) and in the context of the V PRICIT Regional Programme of Research and Technological Innovation. Proyectos Interdisciplinares Jóvenes Doctores (2020/00031/002), and Ministerio de Ciencia, Innovación y Universidades, España (2019/00405/001)

Hydrodynamic Study on the “Stop-and-Acceleration” Pattern of Refilling Flow at Perforation Plates by Using a Xylem-Inspired Channel

Porous structures, such as perforation plates and pit membranes, have attracted considerable attention due to their hydraulic regulation of water flow through vascular plant networks. However, limited information is available regarding the hydraulic functions of such structures during water-refilling and embolism repair because of difficulties in simultaneous in vivo measurements of refilling flow and pressure variations in xylem vessels. In this study, we developed a xylem-inspired microchannel with a porous mesh for systematic investigation on the hydraulic contribution of perforation plates on water-refilling. In particular, the “stop-and-acceleration” phenomenon of the water meniscus at the porous mesh structure was carefully examined in macroscopic and microscopic views. This distinctive phenomenon usually occurs in the xylem vessels of vascular plants during embolism repair. Based on the experimental results, we established a theoretical model of the flow characteristics and pressure variations around the porous structure inside the microchannel. Perforation plates could be speculated to be a pressure-modulated flow controller that facilitates embolism recovery. Furthermore, the proposed xylem-inspired channel can be used to investigate the hydraulic functions of porous structures for water management in plants