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Doctor XAI: an ontology-based approach to black-box sequential data classification explanations
TLDR
We introduce Doctor XAI, a model-agnostic explainability technique able to deal with multi-labeled, sequential, ontology-linked data. Expand
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Multi-sorted Argumentation
TLDR
In the theory of abstract argumentation, the acceptance status of arguments is normally determined for the complete set of arguments at once, under a single semantics. Expand
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Rewriting Rules for the Computation of Goal-Oriented Changes in an Argumentation System
TLDR
We study the minimal changes or target sets on the current state of the debate that are required to achieve a goal, where changes are the addition and/or deletion of attacks among arguments. Expand
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Conditional Labelling for Abstract Argumentation
TLDR
Conditional labelling of abstract argumentation frameworks is based on argument labelling and assigns to each argument three propositional formulae. Expand
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Real-time behavioral DGA detection through machine learning
TLDR
In this paper, we report on an effective DGA-detection algorithm based on a single network monitoring. Expand
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Coalition Formation via Negotiation in Multiagent Systems with Voluntary Attacks
Argumentation networks are put forward by Dung considering only one kind of attack among arguments. In this paper, we propose to extend Dung’s argumentation framework with voluntary attacks in theExpand
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Learning and reasoning about norms using neural-symbolic systems
TLDR
In this paper we provide a neural-symbolic framework to model, reason about and learn norms in multi-agent systems. Expand
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Understanding Network Traffic States using Transfer Learning
TLDR
Large-scale network traffic analysis is crucial for many transport applications, ranging from estimation and prediction to control and planning. Expand
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Neural Networks for Runtime Verification
TLDR
A recent trend in High-Performance Computation is parallel computing, and the field of Neural Networks is showing impressive improvements in performance, especially with the use of GPU accelerators. Expand
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Runtime Verification Through Forward Chaining
TLDR
In this paper we present a novel rule-based approach for Runtime Verification of FLTL properties over finite but expanding traces. Expand
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