Pasquale De Michele

IoT-based collaborative reputation system for associating visitors and artworks in a cultural scenario

We design a CRS to establish the people reputation within Cultural spaces.The system is able to classify visitor behaviours.Our approach is suitable for both nonprofit and business oriented organizations. In this paper, starting from a comprehensive mathematical model of a Collaborative Reputation Systems (CRSes), we present a research study within the Cultural Heritage domain. The main goal of this study has been the evaluation and classification of the visitors behaviour during a cultural event. By means of mobile technological instruments, opportunely deployed within the environment, it is possible to collect data representing the knowledge to be inferred and give a reliable rate for both visitors and exposed artworks. Discussed results, confirm the reliability and the usefulness of CRSes for deeply understand dynamics related to people visiting styles.

3D data denoising via Nonlocal Means filter by using parallel GPU strategies.

Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising.

A Regularized MRI Image Reconstruction based on Hessian Penalty Term on CPU/GPU Systems

In this paper we investigate an inverse reconstruction problem of Magnetic Resonance Imaging with few acquired body scanner samples. The missing information in the Fourier domain causes image artefacts, therefore iterative computationally expensive recovery techniques are needed. We propose a regularization approach based on second order derivative of both simulated and real images with highly undersampled data, obtaining a good reconstruction accuracy. Moreover, an accelerated regularization algorithm, by using a projection technique combined with an implementation on Graphics Processing Unit (GPU) computing environment, is presented. The numerical experiments give clinically-feasible reconstruction runtimes with an increase in speed and accuracy of the MRI dataset reconstructions.

Visiting Styles in an Art Exhibition Supported by a Digital Fruition System

We investigate the user dynamics related to the interaction with artworks in an exhibition. In a first step, we characterize visitors in a cultural heritage scenario and after, we study how these interact with available technologies. Accordingly with the fact that the technology plays a crucial role in supporting spectators and enhancing their experiences, the starting point of this research is the analysis of real data coming from visitors of the art exhibition named The Beauty or the Truth that was located in Naples, Italy. The event was equipped with several technological tools arranged within the halls of the exhibition, with the aim to create a novel metaphor that stimulates the user enjoyment and the knowledge diffusion. The collected log files from a suitable expert software system are used in a flexible framework in order to analyse how the supporting pervasive technology influence and modify behaviours and visiting styles. Finally, we carried out some experiments to exploit the clustering facilities for finding groups that reflect visiting styles. The obtained results have revealed interesting issues also to understand hidden aspects in the data and unattended in the analysis.

Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit.

The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory‐related pathologies like Alzheimers Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long‐Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down‐regulated during AD, possibly degrading memory functions. Therefore, the concept of CREB‐based memory enhancers, i.e. drugs that would boost memory by activation of CREB, has emerged. Here, using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD‐like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB‐based therapies could provide a new approach to treat AD.

Visitor Dynamics in a Cultural Heritage Scenario

We propose a biologically inspired mathematical model to simulate the personalized interactions of users with cultural heritage objects and spaces in the real case of an exhibition. The main idea is to measure the interests of a spectator with respect to an artwork by means of a model able to describe the users behavioural dynamics. In our approach, the user is assimilated to a computational neuron, and its interests are deduced by counting potential spike trains, generated by external currents. As an effort, we relies on an huge amount of log files that store visitors movements and interactions within a beautiful art exhibition named The Beauty or the Truth located in Naples, Italy. The technological tools deployed within the exhibition aim to create a novel metaphor stimulating user enjoyment and knowledge diffusion and the collected log files are useful data to analyse how such technology an influence and modify user behaviours. We also performed an experimental analysis exploiting clustering facilities to discover natural groups that reflect visiting styles. This is particularly suitable to provide the tuning of a heuristic classifier. The obtained results revealed to be particularly interesting also to understand other important aspects hidden in the data and unattended in our first analysis.

A GPU parallel implementation of the Local Principal Component Analysis overcomplete method for DW image denoising

We focus on the Overcomplete Local Principal Component Analysis (OLPCA) method, which is widely adopted as denoising filter. We propose a programming approach resorting to Graphic Processor Units (GPUs), in order to massively parallelize some heavy computational tasks of the method. In our approach, we design and implement a parallel version of the OLPCA, by using a suitable mapping of the tasks on a GPU architecture with the aim to investigate the performance and the denoising features of the algorithm. The experimental results show improvements in terms of GFlops and memory throughput.

A parallel PDE-based numerical algorithm for computing the Optical Flow in hybrid systems

Abstract In this paper, we propose a fine-to-coarse parallelization strategy in order to exploit, in a case study, a parallel hybrid architecture. We consider the Optical Flow numerical problem, modelled by partial differential equations, and implement a parallel multilevel software. Our hybrid software solution is a smart combination between codes on Graphic Processor Units (GPUs) and standard scientific parallel computing libraries on a cluster. Numerical experiments, on real satellite image sequences coming from a large dataset in a big data scenario, together with application profiling, highlight good results in terms of performance for the proposed approach.

A Stochastic Method for Financial IoT Data

The extraction of information from IoT data plays a fundamental role in many fields. In this paper we focus our attention on financial data and we use them to describe derivatives in the Black-Scholes model. This model lets us obtain an expression of the price of a derivative in a complete market with no possibility of arbitrage portfolios. Traders can sell amounts of assets even if they do not own them (i.e., short sellings are allowed) and must pay no frictional costs.

Classify Visitor Behaviours in a Cultural Heritage Exhibition

Classify the dynamic of users in a cultural heritage exhibition in order to infer information about the event fruition is a very interesting research field. In this paper, starting from real data, we investigate the user dynamics related to the interaction with artworks and how a spectator interacts with available technologies. Accordingly with the fact that the technology plays a crucial role in supporting spectators and enhancing their experiences, the starting point of this research has been the art exhibition named The Beauty or the Truth that was located in Naples (Italy), where event was equipped with several technological tools. Here, the collected log files, stored in a suitable expert software system, are used in a flexible framework in order to analyse how the supporting pervasive technology influence and modify behaviours and visiting styles. Finally, we carried out some experiments to exploit the clustering facilities for finding groups that reflect visiting styles. The obtained results have revealed interesting issues also to understand hidden aspects in the data and unattended in the analysis.