Aspects and Annotations for Controlling the Roles Application Classes Play for Design Patterns

Giunta, Rosario; Pappalardo, Giuseppe; Tramontana, Emiliano

doi:10.1109/apsec.2011.53

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…For the future work we are developing a modular [9], [10], [11], [4], [33] custom analyser based on swarm intelligence approaches [25], [36] that can process a huge amount of data [19], [27], [30] by offloading to cloud resources [35], [3] the computational demand. For further works will be explored solutions based also on alternative approaches such as flexible neuro-fuzzy models [6], self organizing maps, UCT-based [39] and statistical decision threes based [31] approaches.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Environmental Surveillance Against Organised Crime with Radial Basis Neural Networks

Napoli

Tramontana

Woźniak

2015

2015 IEEE Symposium Series on Computational Intelligence

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A huge amount of data concerning the position of individual is often gathered in surveillance scenarios, to prevent crimes or to collect evidence of unlawful behaviour. Given the abundance of data available, detectives need advanced analysis means in order to set apart the interesting locations. This paper proposes a solution that makes use of radial basis neural networks to find the points of interests, i.e. locations that have been used for meetings, by surveilled people whose paths have been traced. In our solution, newly gathered data will be analysed in order to find the points of interest, and will also be given to our neural network for further training. Our results show that the proposed approach is accurate enough and can improve the unaided search for meeting points between observed individuals.

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Section: Discussionmentioning

confidence: 99%

Enhancing Environmental Surveillance Against Organised Crime with Radial Basis Neural Networks

Napoli

Tramontana

Woźniak

2015

2015 IEEE Symposium Series on Computational Intelligence

Self Cite

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“…The proposed approach can be easily embedded on a P2P BitTorrent system, while preserving modularity and separation of concerns (Bannò et al, 2010;Giunta et al, 2011;Calvagna and Tramontana, 2013;Tramontana, 2013), since the computational cost due to prediction and modelling is essentially up to the tracker itself, hence freeing peers of the burden. This choice would tap into a resource, the tracker, which is an existing component that peers have to connect to.…”

Section: Discussionmentioning

confidence: 99%

A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent

Napoli

Pappalardo

Tramontana

2016

International Journal of Applied Mathematics and Computer Science

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BitTorrent splits the files that are shared on a P2P network into fragments and then spreads these by giving the highest priority to the rarest fragment. We propose a mathematical model that takes into account several factors such as the peer distance, communication delays, and file fragment availability in a future period also by using a neural network module designed to model the behaviour of the peers. The ensemble comprising the proposed mathematical model and a neural network provides a solution for choosing the file fragments that have to be spread first, in order to ensure their continuous availability, taking into account that some peers will disconnect.

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“…[35,7,20,24,27,37,5,28], and assisting in the identification of configuration errors [2,26,23,31,13,14,4].…”

Section: Related Workmentioning

confidence: 99%

Combining static and dynamic data flow analysis

Mongiovì

Giannone

Fornaia

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Protecting sensitive data requires controlling the behavior of third part software. Static and dynamic data flow analysis can aid, however both of them have limits. Static analysis often detects false data leaks, whereas the more precise dynamic analysis introduces a significant overhead. This paper proposes a novel hybrid approach that combines static and dynamic data flow analysis for detecting data leaks in Java applications. Our approach minimizes the overhead by computing a minimal set of "application points" that need to be monitored and injects control code on the target application. Our method has no loss in quality with respect to dynamic analysis. We show the feasibility of our approach by providing a tool and presenting a case study on a sample application.

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Aspects and Annotations for Controlling the Roles Application Classes Play for Design Patterns

Cited by 15 publications

References 10 publications

Enhancing Environmental Surveillance Against Organised Crime with Radial Basis Neural Networks

Enhancing Environmental Surveillance Against Organised Crime with Radial Basis Neural Networks

A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent

Combining static and dynamic data flow analysis

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