127 research outputs found
AMADEUS: Towards the AutoMAteD secUrity teSting
The proper configuration of systems has become a fundamental
factor to avoid cybersecurity risks. Thereby, the analysis of cyber security vulnerabilities is a mandatory task, but the number of vul nerabilities and system configurations that can be threatened is ex tremely high. In this paper, we propose a method that uses software
product line techniques to analyse the vulnerable configuration of
the systems. We propose a solution, entitled AMADEUS, to enable
and support the automatic analysis and testing of cybersecurity
vulnerabilities of configuration systems based on feature models.
AMADEUS is a holistic solution that is able to automate the analy sis of the specific infrastructures in the organisations, the existing
vulnerabilities, and the possible configurations extracted from the
vulnerability repositories. By using this information, AMADEUS
generates automatically the feature models, that are used for rea soning capabilities to extract knowledge, such as to determine
attack vectors with certain features. AMADEUS has been validated
by demonstrating the capacities of feature models to support the
threat scenario, in which a wide variety of vulnerabilities extracted
from a real repository are involved. Furthermore, we open the door
to new applications where software product line engineering and
cybersecurity can be empowered.Ministerio de Ciencia, Innovación y Universidades RTI2018-094283-B-C33 (ECLIPSE)Junta de Andalucía P20-01224 (COPERNICA)Junta de Andalucía US-1381375 (METAMORFOSIS
A Practical Entity Linking System for Tables in Scientific Literature
Entity linking is an important step towards constructing knowledge graphs
that facilitate advanced question answering over scientific documents,
including the retrieval of relevant information included in tables within these
documents. This paper introduces a general-purpose system for linking entities
to items in the Wikidata knowledge base. It describes how we adapt this system
for linking domain-specific entities, especially for those entities embedded
within tables drawn from COVID-19-related scientific literature. We describe
the setup of an efficient offline instance of the system that enables our
entity-linking approach to be more feasible in practice. As part of a broader
approach to infer the semantic meaning of scientific tables, we leverage the
structural and semantic characteristics of the tables to improve overall entity
linking performance
8-Chloro-5,5-dimethyl-5,6-dihydrotetrazolo[1,5-c]quinazoline
In the title compound, C10H10ClN5, the tetrazole ring and the phenyl ring make a dihedral angle of 7.7 (2)°. The hexahydropyrimidine ring adopts a screw-boat conformation. In the crystal, intermolecular bifurcated N—H⋯(N,N) hydrogen bonds link the molecules into [001] chains
Extracting novel facts from tables for Knowledge Graph completion
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Other distinctive features are the lack of assumptions about the underlying KG and the enabling of a fine-grained tuning of the precision/recall trade-off of extracted facts. Our experiments show that our approach has a higher recall during the interpretation process than the state-of-the-art, and is more resistant against the bias observed in extracting mostly redundant facts since it produces more novel extractions
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Using ontologies to enhance human understandability of global post-hoc explanations of black-box models
The interest in explainable artificial intelligence has grown strongly in recent years because of the need to convey safety and trust in the ‘how’ and ‘why’ of automated decision-making to users. While a plethora of approaches has been developed, only a few focus on how to use domain knowledge and how this influences the understanding of explanations by users. In this paper, we show that by using ontologies we can improve the human understandability of global post-hoc explanations, presented in the form of decision trees. In particular, we introduce Trepan Reloaded, which builds on Trepan, an algorithm that extracts surrogate decision trees from black-box models. Trepan Reloaded includes ontologies, that model domain knowledge, in the process of extracting explanations to improve their understandability. We tested the understandability of the extracted explanations by humans in a user study with four different tasks. We evaluate the results in terms of response times and correctness, subjective ease of understanding and confidence, and similarity of free text responses. The results show that decision trees generated with Trepan Reloaded, taking into account domain knowledge, are significantly more understandable throughout than those generated by standard Trepan. The enhanced understandability of post-hoc explanations is achieved with little compromise on the accuracy with which the surrogate decision trees replicate the behaviour of the original neural network models
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