261,608 research outputs found
Model the System from Adversary Viewpoint: Threats Identification and Modeling
Security attacks are hard to understand, often expressed with unfriendly and
limited details, making it difficult for security experts and for security
analysts to create intelligible security specifications. For instance, to
explain Why (attack objective), What (i.e., system assets, goals, etc.), and
How (attack method), adversary achieved his attack goals. We introduce in this
paper a security attack meta-model for our SysML-Sec framework, developed to
improve the threat identification and modeling through the explicit
representation of security concerns with knowledge representation techniques.
Our proposed meta-model enables the specification of these concerns through
ontological concepts which define the semantics of the security artifacts and
introduced using SysML-Sec diagrams. This meta-model also enables representing
the relationships that tie several such concepts together. This representation
is then used for reasoning about the knowledge introduced by system designers
as well as security experts through the graphical environment of the SysML-Sec
framework.Comment: In Proceedings AIDP 2014, arXiv:1410.322
The Wise Men Puzzle (Isabelle/HOL dataset)
The authors universal (meta-)logical reasoning approach is demonstrated and assessed with a prominent riddle in epistemic reasoning: the Wise Men Puzzle. The presented solution puts a particular emphasis on the adequate modeling of common knowledge and it illustrates the elegance and the practical relevance of the shallow semantical embedding approach when utilized within modern proof assistant systems such as Isabelle/HOL. The contributed dataset provides supporting evidence for claims made in the article “Universal (meta-)logical reasoning: Recent successes” (Benzmüller, 2019)
A cortical surface-based meta-analysis of human reasoning
Recent advances in neuroimaging have augmented numerous findings in the human reasoning process but have yielded varying results. One possibility for this inconsistency is that reasoning is such an intricate cognitive process, involving attention, memory, executive functions, symbolic processing, and fluid intelligence, whereby various brain regions are inevitably implicated in orchestrating the process. Therefore, researchers have used meta-analyses for a better understanding of neural mechanisms of reasoning. However, previous meta-analysis techniques include weaknesses such as an inadequate representation of the cortical surface’s highly folded geometry. Accordingly, we developed a new meta-analysis method called Bayesian meta-analysis of the cortical surface (BMACS). BMACS offers a fast, accurate, and accessible inference of the spatial patterns of cognitive processes from peak brain activations across studies by applying spatial point processes to the cortical surface. Using BMACS, we found that the common pattern of activations from inductive and deductive reasoning was colocalized with the multiple-demand system, indicating that reasoning is a high-level convergence of complex cognitive processes. We hope surface-based meta-analysis will be facilitated by BMACS, bringing more profound knowledge of various cognitive processes
Meta-Reasoning: Semantics-Symbol Deconstruction For Large Language Models
Neural-symbolic methods have shown their effectiveness in enhancing the
reasoning abilities of large language models (LLMs). However, existing methods
primarily rely on mapping natural languages to more syntactically complete
formal languages (e.g., Python and SQL). Those approaches necessitate that
reasoning tasks be convertible into programs, which cater more to the computer
execution mindset and deviate from human reasoning habits. To expand the
real-world applicability and flexibility of symbolic methods, we propose
Meta-Reasoning from the scope of linguistics itself. This method empowers LLMs
to deconstruct questions and effectively capture more generalized knowledge
autonomously. We find that Meta-Reasoning achieves improved in-context learning
efficiency, reasoning accuracy, and output stability in six arithmetic and
symbolic reasoning tasks. In particular, when applied to symbolic reasoning
tasks such as Tracking Shuffled Objects, GPT-3 (text-davinci-002) surpasses the
few-shot Chain-of-Thought prompting approach (+37.7%), with 99% accuracy after
a single demonstration of Meta-Reasoning.Comment: Work in progres
Virtual Simulation to Enhance Clinical Reasoning in Nursing: A Systematic Review and Meta-analysis.
Background: The COVID-19 pandemic has given rise to more virtual simulation training. This study aimed to review the effectiveness of virtual simulations and their design features in developing clinical reasoning skills among nurses and nursing students. Method: A systematic search in CINAHL, PubMed, Cochrane Library, Embase, ProQuest, PsycINFO, and Scopus was conducted. The PRISMA guidelines, Cochrane's risk of bias, and GRADE was used to assess the articles. Meta-analyses and random-effects meta-regression were performed. Results: The search retrieved 11,105 articles, and 12 randomized controlled trials (RCTs) were included. Meta-analysis demonstrated a significant improvement in clinical reasoning based on applied knowledge and clinical performance among learners in the virtual simulation group compared with the control group. Meta-regression did not identify any significant covariates. Subgroup analyses revealed that virtual simulations with patient management contents, using multiple scenarios with nonimmersive experiences, conducted more than 30-minutes and postscenario feedback were more effective. Conclusions: Virtual simulations can improve clinical reasoning skill. This study may inform nurse educators on how virtual simulation should be designed to optimize the development of clinical reasoning
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