492,373 research outputs found
SiamVGG: Visual Tracking using Deeper Siamese Networks
Recently, we have seen a rapid development of Deep Neural Network (DNN) based
visual tracking solutions. Some trackers combine the DNN-based solutions with
Discriminative Correlation Filters (DCF) to extract semantic features and
successfully deliver the state-of-the-art tracking accuracy. However, these
solutions are highly compute-intensive, which require long processing time,
resulting unsecured real-time performance. To deliver both high accuracy and
reliable real-time performance, we propose a novel tracker called SiamVGG. It
combines a Convolutional Neural Network (CNN) backbone and a cross-correlation
operator, and takes advantage of the features from exemplary images for more
accurate object tracking.
The architecture of SiamVGG is customized from VGG-16, with the parameters
shared by both exemplary images and desired input video frames.
We demonstrate the proposed SiamVGG on OTB-2013/50/100 and VOT 2015/2016/2017
datasets with the state-of-the-art accuracy while maintaining a decent
real-time performance of 50 FPS running on a GTX 1080Ti. Our design can achieve
2% higher Expected Average Overlap (EAO) compared to the ECO and C-COT in
VOT2017 Challenge
High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection
Precision experimental determination of photon correlation requires the
massive amounts of data and extensive measurement time. We present a technique
to monitor second-order photon correlation of amplified quantum
noise based on wideband balanced homodyne detection and deep-learning
acceleration. The quantum noise is effectively amplified by an injection of
weak chaotic laser and the of the amplified quantum noise is
measured with a real-time sample rate of 1.4 GHz. We also exploit a photon
correlation convolutional neural network accelerating correlation data using a
few quadrature fluctuations to perform a parallel processing of the
for various chaos injection intensities and effective bandwidths.
The deep-learning method accelerates the experimental acquisition
with a high accuracy, estimating 6107 sets of photon correlation data with a
mean square error of 0.002 in 22 seconds and achieving a three orders of
magnitude acceleration in data acquisition time. This technique contributes to
a high-speed and precision coherence evaluation of entropy source in secure
communication and quantum imaging.Comment: 6 pages, 6 figure
Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis
With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle
Gender classification: a convolutional neural network approach
An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition
Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services
The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over 96% of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is 54% lower than a centralized framework based on deep-Q networks
Partitioned Graph Convolution Using Adversarial and Regression Networks for Road Travel Speed Prediction
Access to quality travel time information for roads in a road network has
become increasingly important with the rising demand for real-time travel time
estimation for paths within road networks. In the context of the Danish road
network (DRN) dataset used in this paper, the data coverage is sparse and
skewed towards arterial roads, with a coverage of 23.88% across 850,980 road
segments, which makes travel time estimation difficult. Existing solutions for
graph-based data processing often neglect the size of the graph, which is an
apparent problem for road networks with a large amount of connected road
segments. To this end, we propose a framework for predicting road segment
travel speed histograms for dataless edges, based on a latent representation
generated by an adversarially regularized convolutional network. We apply a
partitioning algorithm to divide the graph into dense subgraphs, and then train
a model for each subgraph to predict speed histograms for the nodes. The
framework achieves an accuracy of 71.5% intersection and 78.5% correlation on
predicting travel speed histograms using the DRN dataset. Furthermore,
experiments show that partitioning the dataset into clusters increases the
performance of the framework. Specifically, partitioning the road network
dataset into 100 clusters, with approximately 500 road segments in each
cluster, achieves a better performance than when using 10 and 20 clusters.Comment: This thesis was completed 2020-06-12 and defended 2020-06-2
A monitoring and threat detection system using stream processing as a virtual function for big data
The late detection of security threats causes a significant increase in the risk of irreparable damages, disabling any defense attempt. As a consequence, fast realtime threat detection is mandatory for security guarantees. In addition, Network Function Virtualization (NFV) provides new opportunities for efficient and low-cost security solutions. We propose a fast and efficient threat detection system based on stream processing and machine learning algorithms. The main contributions of this work are i) a novel monitoring threat detection system based on stream processing; ii) two datasets, first a dataset of synthetic security data containing both legitimate and malicious traffic, and the second, a week of real traffic of a telecommunications operator in Rio de Janeiro, Brazil; iii) a data pre-processing algorithm, a normalizing algorithm and an algorithm for fast feature selection based on the correlation between variables; iv) a virtualized network function in an open-source platform for providing a real-time threat detection service; v) near-optimal placement of sensors through a proposed heuristic for strategically positioning sensors in the network infrastructure, with a minimum number of sensors; and, finally, vi) a greedy algorithm that allocates on demand a sequence of virtual network functions.A detecção tardia de ameaças de segurança causa um significante aumento no risco de danos irreparáveis, impossibilitando qualquer tentativa de defesa. Como consequência, a detecção rápida de ameaças em tempo real é essencial para a administração de segurança. Além disso, A tecnologia de virtualização de funções de rede (Network Function Virtualization - NFV) oferece novas oportunidades para soluções de segurança eficazes e de baixo custo. Propomos um sistema de detecção de ameaças rápido e eficiente, baseado em algoritmos de processamento de fluxo e de aprendizado de máquina. As principais contribuições deste trabalho são: i) um novo sistema de monitoramento e detecção de ameaças baseado no processamento de fluxo; ii) dois conjuntos de dados, o primeiro ´e um conjunto de dados sintético de segurança contendo tráfego suspeito e malicioso, e o segundo corresponde a uma semana de tráfego real de um operador de telecomunicações no Rio de Janeiro, Brasil; iii) um algoritmo de pré-processamento de dados composto por um algoritmo de normalização e um algoritmo para seleção rápida de características com base na correlação entre variáveis; iv) uma função de rede virtualizada em uma plataforma de código aberto para fornecer um serviço de detecção de ameaças em tempo real; v) posicionamento quase perfeito de sensores através de uma heurística proposta para posicionamento estratégico de sensores na infraestrutura de rede, com um número mínimo de sensores; e, finalmente, vi) um algoritmo guloso que aloca sob demanda uma sequencia de funções de rede virtual
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