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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 ...
Solver agent: Towards emotional and opponent-aware agent for human-robot negotiation
(International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2021)
Negotiation is one of the crucial processes for resolving conflicts between parties. In automated negotiation, agent designers mostly take opponent's offers and the remaining time into account while designing their strategies. ...
A decentralized token-based negotiation approach for multi-agent path finding
(Springer, 2021)
This paper introduces a negotiation approach to solve the Multi-Agent Path Finding problem. The approach aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, ...
Cost minimization for deploying serverless functions
(ACM, 2021-03)
The costs of serverless functions increase proportional to the amount of memory reserved on the deployed server. However, increasing the amount of memory decreases the function execution time, which is also a factor that ...
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 ...
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 ...
DPSec: A blockchain-based data plane authentication protocol for SDNs
(IEEE, 2020-11-02)
Software-Defined Networking (SDN) is a promising networking architecture that enables central management along with network programmability. However, SDN brings additional security threats due to untrusted control and data ...
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