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Towards automated aircraft maintenance inspection. A use case of detecting aircraft dents using mask r-cnn
(American Institute of Aeronautics and Astronautics Inc, AIAA, 2020)
Deep learning can be used to automate aircraft maintenance visual inspection. This can help increase the accuracy of damage detection, reduce aircraft downtime, and help prevent inspection accidents. The objective of this ...
ANAC 2018: Repeated multilateral negotiation league
(Springer, 2020)
This is an extension from a selected paper from JSAI2019. There are a number of research challenges in the field of Automated Negotiation. The Ninth International Automated Negotiating Agent Competition encourages participants ...
SeCaS: Secure capability sharing framework for IoT devices in a structured P2P network
(The ACM Digital Library, 2020-03-16)
The emergence of the internet of Things (IoT) has resulted in the possession of a continuously increasing number of highly heterogeneous connected devices by the same owner. To make full use of the potential of a personal ...
TruSD: Trust framework for service discovery among IoT devices
(Elsevier, 2020-09-04)
IoT provides an environment which enables access to a plethora of different services. In order to reach these services, devices need to decide if the providers are trustable or not. The decision to trust a node with whom ...
SUTSEC: SDN Utilized trust based secure clustering in IoT
(Elsevier, 2020-09-04)
Internet of Things (IoT) technology consists of huge number of heterogeneous devices that create enormous amount of data. Providing a robust communication for billions of devices is one of the most significant challenges ...
A benchmark for inpainting of clothing images with irregular holes
(Springer, 2020)
Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts on intelligent fashion analysis systems, clothing image inpainting has ...
Uncertainty-aware deep classifiers using generative models
(Association for the Advancement of Artificial Intelligence, 2020)
Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to ...
ACNMP: skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
(ML Research Press, 2020)
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). ...
Sampling-free variational inference of bayesian neural networks by variance backpropagation
(ML Research Press, 2020)
We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ...
Language inference with multi-head automata through reinforcement learning
(IEEE, 2020)
The purpose of this paper is to use reinforcement learning to model learning agents which can recognize formal languages. Agents are modeled as simple multi-head automaton, a new model of finite automaton that uses multiple ...
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