Embodied Interactions with Adaptive Architecture

2016 ◽  
pp. 183-202 ◽  
Author(s):  
Nils Jäger ◽  
Holger Schnädelbach ◽  
Jonathan Hale
Keyword(s):  
Adaptive Architecture

Robust and adaptive architecture for multilingual spoken dialogue systems

2004 ◽  
Author(s):  
Markku Turunen ◽  
Esa-Pekka Salonen ◽  
Mikko Hartikainen ◽  
Jaakko Hakulinen
Keyword(s):  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue ◽  
Adaptive Architecture

Prediction, Detection, and Mitigation of DDoS Attacks Using HPCs

2021 ◽  
pp. 523-538
Author(s):  
Pablo Pessoa Do Nascimento ◽  
Isac F. A. F. Colares ◽  
Ronierison Maciel ◽  
Humberto Caetano Da Silva ◽  
Paulo Maciel
Keyword(s):  
Web Service ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Detection Accuracy ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Intrusion Prevention ◽  
Detection Systems ◽  
Use Of Data ◽  
Adaptive Architecture

Web service interruptions caused by DDoS (distributed denial of service) attacks have increased considerably over the years, and intrusion detection systems (IDS) are not enough to detect threats on the network, even when used together with intrusion prevention systems (IPS), taking into account the increase of assets in the traffic path, where it creates unique points of failure in the system, and also taking into account the use of data that contains information about normal traffic situations and attacks, where this comparison and analysis can cost a significant amount of host resources, to try to guarantee the prediction, detection, and mitigation of attacks in real-time or in time between detection and mitigation, being crucial in harm reduction. This chapter presents an adaptive architecture that combines techniques, methods, and tools from different segments to improve detection accuracy as well as the prediction and mitigation of these threats and to show that it is capable of implementing a powerful architecture against this type of threat, DDoS attacks.

A Semantic-Driven Adaptive Architecture for Large Scale P2P Networks

2010 ◽  
Vol 2 (4) ◽  
pp. 12-30 ◽  
Author(s):  
Athena Eftychiou ◽  
Bogdan Vrusias ◽  
Nick Antonopoulos
Keyword(s):  
Large Scale ◽  
Network Formation ◽  
Collective Intelligence ◽  
Semantic Knowledge ◽  
Resource Discovery ◽  
Online Information ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Adaptive Architecture

The increasing amount of online information demands effective, scalable, and accurate mechanisms to manage and search this information. Distributed semantic-enabled architectures, which enforce semantic web technologies for resource discovery, could satisfy these requirements. In this paper, a semantic-driven adaptive architecture is presented, which improves existing resource discovery processes. The P2P network is organised in a two-layered super-peer architecture. The network formation of super-peers is a conceptual representation of the network’s knowledge, shaped from the information provided by the nodes using collective intelligence methods. The authors focus on the creation of a dynamic hierarchical semantic-driven P2P topology using the network’s collective intelligence. The unmanageable amounts of data are transformed into a repository of semantic knowledge, transforming the network into an ontology of conceptually related entities of information collected from the resources located by peers. Appropriate experiments have been undertaken through a case study by simulating the proposed architecture and evaluating results.

scholarly journals Slim and Efficient Neural Network Design for Resource-Constrained SAR Target Recognition

2018 ◽  
Vol 10 (10) ◽  
pp. 1618 ◽  
Author(s):  
Hongyi Chen ◽  
Fan Zhang ◽  
Bo Tang ◽  
Qiang Yin ◽  
Xian Sun
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Target Recognition ◽  
Practical Implementation ◽  
Network Storage ◽  
Specific Situation ◽  
Resource Constrained ◽  
Adaptive Architecture ◽  
Deep Cnn

Deep convolutional neural networks (CNN) have been recently applied to synthetic aperture radar (SAR) for automatic target recognition (ATR) and have achieved state-of-the-art results with significantly improved recognition performance. However, the training period of deep CNN is long, and the size of the network is huge, sometimes reaching hundreds of megabytes. These two factors of deep CNN hinders its practical implementation and deployment in real-time SAR platforms that are typically resource-constrained. To address this challenge, this paper presents three strategies of network compression and acceleration to decrease computing and memory resource dependencies while maintaining a competitive accuracy. First, we introduce a new weight-based network pruning and adaptive architecture squeezing method to reduce the network storage and the time of inference and training process, meanwhile maintain a balance between compression ratio and classification accuracy. Then we employ weight quantization and coding to compress the network storage space. Due to the fact that the amount of calculation is mainly reflected in the convolution layer, a fast approach for pruned convolutional layers is proposed to reduce the number of multiplication by exploiting the sparsity in the activation inputs and weights. Experimental results show that the convolutional neural networks for SAR-ATR can be compressed by 40 × without loss of accuracy, and the number of multiplication can be reduced by 15 × . Combining these strategies, we can easily load the network in resource-constrained platforms, speed up the inference process to get the results in real-time or even retrain a more suitable network with new image data in a specific situation.

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