5 resultados para bigdata, data stream processing, dsp, apache storm, cyber security

em Research Open Access Repository of the University of East London.


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Background: Gamma-band oscillations are prominently impaired in schizophrenia, but the nature of the deficit and relationship to perceptual processes is unclear. Methods: 16 patients with chronic schizophrenia (ScZ) and 16 age-matched healthy controls completed a visual paradigm while magnetoencephalographic (MEG) data was recorded. Participants had to detect randomly occurring stimulus acceleration while viewing a concentric moving grating. MEG data were analyzed for spectral power (1-100 Hz) at sensorand source-level to examine the brain regions involved in aberrant rhythmic activity, and for contribution of differences in baseline activity towards the generation of low- and highfrequency power. Results: Our data show reduced gamma-band power at sensor level in schizophrenia patients during stimulus processing while alpha-band and baseline spectrum were intact. Differences in oscillatory activity correlated with reduced behavioral detection rates in the schizophrenia group and higher scores on the “Cognitive Factor” of the Positive and Negative Syndrome Scale. Source reconstruction revealed that extra-striate (fusiform/lingual gyrus), but not striate (cuneus), visual cortices contributed towards the reduced activity observed at sensorlevel in ScZ patients. Importantly, differences in stimulus-related activity were not due to differences in baseline activity. Conclusions: Our findings highlight that MEG-measured high-frequency oscillations during visual processing can be robustly identified in ScZ. Our data further suggest impairments that involve dysfunctions in ventral stream processing and a failure to increase gamma-band activity in a task-context. Implications of these findings are discussed in the context of current theories of cortical-subcortical circuit dysfunctions and perceptual processing in ScZ.

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Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

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Just as readers feel immersed when the story line adheres to their experiences, users will more easily feel immersed in a virtual environment if the behavior of the characters in that environment adheres to their expectations, based on their lifelong observations in the real world. This paper introduces a framework that allows authors to establish natural, human-like behavior, physical interaction and emotional engagement of characters living in a virtual environment. Represented by realistic virtual characters, this framework allows people to feel immersed in an Internet based virtual world in which they can meet and share experiences in a natural way as they can meet and share experiences in real life. Rather than just being visualized in a 3D space, the virtual characters (autonomous agents as well as avatars representing users) in the immersive environment facilitate social interaction and multi-party collaboration, mixing virtual with real.

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Individuals and corporate users are persistently considering cloud adoption due to its significant benefits compared to traditional computing environments. The data and applications in the cloud are stored in an environment that is separated, managed and maintained externally to the organisation. Therefore, it is essential for cloud providers to demonstrate and implement adequate security practices to protect the data and processes put under their stewardship. Security transparency in the cloud is likely to become the core theme that underpins the systematic disclosure of security designs and practices that enhance customer confidence in using cloud service and deployment models. In this paper, we present a framework that enables a detailed analysis of security transparency for cloud based systems. In particular, we consider security transparency from three different levels of abstraction, i.e., conceptual, organisation and technical levels, and identify the relevant concepts within these levels. This allows us to provide an elaboration of the essential concepts at the core of transparency and analyse the means for implementing them from a technical perspective. Finally, an example from a real world migration context is given to provide a solid discussion on the applicability of the proposed framework.

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Software protection is an essential aspect of information security to withstand malicious activities on software, and preserving software assets. However, software developers still lacks a methodology for the assessment of the deployed protections. To solve these issues, we present a novel attack simulation based software protection assessment method to assess and compare various protection solutions. Our solution relies on Petri Nets to specify and visualize attack models, and we developed a Monte Carlo based approach to simulate attacking processes and to deal with uncertainty. Then, based on this simulation and estimation, a novel protection comparison model is proposed to compare different protection solutions. Lastly, our attack simulation based software protection assessment method is presented. We illustrate our method by means of a software protection assessment process to demonstrate that our approach can provide a suitable software protection assessment for developers and software companies.