21,233 research outputs found
A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity
Due to missing IP multicast support on an Internet scale, over-the-top media
streams are delivered with the help of overlays as used by content delivery
networks and their peer-to-peer (P2P) extensions. In this context,
mesh/pull-based swarming plays an important role either as pure streaming
approach or in combination with tree/push mechanisms. However, the impact of
realistic client populations with heterogeneous resources is not yet fully
understood. In this technical report, we contribute to closing this gap by
mathematically analysing the most basic scheduling mechanisms latest deadline
first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain
framework and combining them into a simple, yet powerful, mixed strategy to
leverage inherent differences in client resources. The main contributions are
twofold: (1) a mathematical framework for swarming on random graphs is proposed
with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed
strategy, named SchedMix, is proposed that leverages peer heterogeneity. The
proposed strategy, SchedMix is shown to outperform the other two strategies
using different abstractions: a mean-field theoretic analysis of buffer
probabilities, simulations of a stochastic model on random graphs, and a
full-stack implementation of a P2P streaming system.Comment: Technical report and supplementary material to
http://ieeexplore.ieee.org/document/7497234
Net:Geography Fieldwork Frequently Asked Questions
Abstract included in text
Simulation modelling and visualisation: toolkits for building artificial worlds
Simulations users at all levels make heavy use of compute resources to drive computational
simulations for greatly varying applications areas of research using different simulation
paradigms. Simulations are implemented in many software forms, ranging from highly standardised
and general models that run in proprietary software packages to ad hoc hand-crafted
simulations codes for very specific applications. Visualisation of the workings or results of a
simulation is another highly valuable capability for simulation developers and practitioners.
There are many different software libraries and methods available for creating a visualisation
layer for simulations, and it is often a difficult and time-consuming process to assemble a
toolkit of these libraries and other resources that best suits a particular simulation model. We
present here a break-down of the main simulation paradigms, and discuss differing toolkits and
approaches that different researchers have taken to tackle coupled simulation and visualisation
in each paradigm
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Ten virtues of structured graphs
This paper extends the invited talk by the first author about the virtues
of structured graphs. The motivation behind the talk and this paper relies on our
experience on the development of ADR, a formal approach for the design of styleconformant,
reconfigurable software systems. ADR is based on hierarchical graphs
with interfaces and it has been conceived in the attempt of reconciling software architectures
and process calculi by means of graphical methods. We have tried to
write an ADR agnostic paper where we raise some drawbacks of flat, unstructured
graphs for the design and analysis of software systems and we argue that hierarchical,
structured graphs can alleviate such drawbacks
The Meeting of Acquaintances: A Cost-efficient Authentication Scheme for Light-weight Objects with Transient Trust Level and Plurality Approach
Wireless sensor networks consist of a large number of distributed sensor
nodes so that potential risks are becoming more and more unpredictable. The new
entrants pose the potential risks when they move into the secure zone. To build
a door wall that provides safe and secured for the system, many recent research
works applied the initial authentication process. However, the majority of the
previous articles only focused on the Central Authority (CA) since this leads
to an increase in the computation cost and energy consumption for the specific
cases on the Internet of Things (IoT). Hence, in this article, we will lessen
the importance of these third parties through proposing an enhanced
authentication mechanism that includes key management and evaluation based on
the past interactions to assist the objects joining a secured area without any
nearby CA. We refer to a mobility dataset from CRAWDAD collected at the
University Politehnica of Bucharest and rebuild into a new random dataset
larger than the old one. The new one is an input for a simulated authenticating
algorithm to observe the communication cost and resource usage of devices. Our
proposal helps the authenticating flexible, being strict with unknown devices
into the secured zone. The threshold of maximum friends can modify based on the
optimization of the symmetric-key algorithm to diminish communication costs
(our experimental results compare to previous schemes less than 2000 bits) and
raise flexibility in resource-constrained environments.Comment: 27 page
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
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