A P2P strategy for QoS discovery and SLA negotiation in Grid environment

In recent years, Grid systems and Peer to Peer networks are the most commonly-used solutions to achieve the same goal: the sharing of resources and services in heterogeneous, dynamic, distributed environments. Many studies have proposed hybrid approaches that try to conjugate the advantages of the two models. This paper proposes an architecture that integrates the P2P interaction model in Grid environments, so as to build an open cooperative model wherein Grid entities are composed in a decentralized way. In particular, this paper focuses on a QoS aware discovery algorithm for P2P Grid systems, analyzing protocol and explaining techniques used to improve its performance.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.     

Sensitive-by-distance: quasi-health data in the algorithmic era

This article offers a new perspective on the boundaries between health and non-health data in the age of ‘Quantified-Self’ apps: the ‘data-sensitiveness-by-computational-distance’ approach-or, more simply, the ‘sensitive-by-distance’ approach. This approach takes into account two variables: the intrinsic sensitiveness (a static variable) of personal data and the computational distance (a dynamic variable) between some kinds of personal data and pure health (or sensitive) data, which depends upon computational capacity. From an objective perspective, computational capacity depends on the level of development of data retrieval technologies at a certain moment, the availability of ‘accessory data’, and the applicable legal restraints on processing data. From a subjective perspective, computational capacity depends on the specific data mining efforts (or the ability to invest in them) taken by a given data controller: economic resources, human resources, and the use of accessory data. A direct consequence of the expansion of augmented humanity in collecting and inferring personal data is the increasing loss of health data processing ‘legibility’ for data subjects. In order to address this issue, we propose exploiting the existing legal tools in the General Data Protection Regulation to empower data subjects (the right to data access, the right to know the logic involved in automated decision-making, data portability, etc.).     

A study of co-occurrence based local features for camera model identification

Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.     

Authorship verification applied to detection of compromised accounts on online social networks

Compromising legitimate accounts has been the most used strategy to spread malicious content on OSN (Online Social Network). To address this problem, we propose a pure text mining approach to check if an account has been compromised based on its posts content. In the first step, the proposed approach extracts the writing style from the user account. The second step comprehends the k-Nearest Neighbors algorithm (k-NN) to evaluate the post content and identify the user. Finally, Baseline Updating (third step) consists of a continuous updating of the user baseline to support the current trends and seasonality issues of user’s posts. Experiments were carried out using a dataset from Twitter composed by tweets of 1000 users. All the three steps were individually evaluated, and the results show that the developed method is stable and can detect the compromised accounts. An important observation is the Baseline Updating contribution, which leads to an enhancement of accuracy superior of 60 %. Regarding average accuracy, the developed method achieved results over 93 %.     

Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution

Wireless Body Area Sensor Networks (WBASN) are an emerging technology enabling the design of natural human–computer interfaces (HCI). Automatic recognition of human motion, gestures, and activities is studied in several contexts. For example, mobile computing technology is being considered as a replacement of traditional input systems. Moreover, body posture and activity monitoring can be used for entertainment and health-care applications. However, until now, little work has been done to develop flexible and efficient WBASN solutions suitable for a wide range of applications. Their requirements pose new challenges for sensor network designs, such as optimizing traditional solutions for use as environmental monitoring-like applications and developing on-the-field stress tests. In this paper, we demonstrate the flexibility of a custom-designed WBASN called WiMoCA with respect to a wide range of posture and activity recognition applications by means of practical implementation and on-the-field testing. Nodes of the network mounted on different parts of the human body exploit tri-axial accelerometers to detect its movements. The advanced digital Micro-electro-mechanical system (MEMS) based inertial sensor has been chosen for WiMoCA because it demonstrated high flexibility of use in many different situations, providing the chance to exploit both static and dynamic acceleration components for different purposes. Furthermore, the sensibility and accuracy of the sensing element is perfectly adequate for monitoring human movement, while keeping cost low and size compact, thus meeting our requirements. We implemented three types of applications, stressing the WBASN in many aspects. In fact, they are characterized by different requirements in terms of accuracy, timeliness, and computation distributed on sensing nodes. For each application, we describe its implementation, and we discuss results about performance and power consumption.

Influence of temperature on the liver circadian clock in the ruin lizard Podarcis sicula

Reptiles represent an interesting animal model to investigate the influence of temperature on molecular circadian clocks. The ruin lizard Podarcis sicula lives in a continental climate and it is subjected to wide range of environmental temperatures during the course of the year. As consequence, ruin lizard daily activity pattern includes either the hibernation or periods of inactivity determined by hypothermia. Here we showed the rhythmic expression of two clock genes, lPer2 and lClock, in the liver of active lizards exposed to summer photo-thermoperiodic conditions. Interestingly, the exposition of lizards to hypothermic conditions, typical of winter season, induced a strong dampening of clock genes mRNA rhythmicity with a coincident decrease of levels. We also examined the qualitative and quantitative distribution of lPER2 and lCLOCK protein in different cellular compartments during the 24-h cycle. In the liver of active lizards both proteins showed a rhythmic expression profile in all cellular compartments. After 3 days at 6 degrees C, some temporal fluctuations of the lCLOCK and lPER2 are still detectable, although, with some marked modifications in respect to the values detected in the liver of active lizards. Besides demonstrating the influence of low temperature on the lizard liver circadian oscillators, present results could provide new essential information for comparative studies on the influence of temperature on the circadian system across vertebrate classes.     

Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing

This paper presents novel algorithmic and architectural solutions for real-time and power-efficient enhancement of images and video sequences. A programmable class of Retinex-like filters, based on the separation of the illumination and reflectance components, is proposed. The dynamic range of the input image is controlled by applying a suitable non-linear function to the illumination, while the details are enhanced by processing the reflectance. An innovative spatially recursive rational filter is used to estimate the illumination. Moreover, to improve the visual quality results of two-branch Retinex operators when applied to videos, a novel three-branch technique is proposed which exploits both spatial and temporal filtering. Real-time implementation is obtained by designing an Application Specific Instruction-set Processor (ASIP). Optimizations are addressed at algorithmic and architectural levels. The former involves arithmetic accuracy definition and linearization of non-linear operators; the latter includes customized instruction set, dedicated memory structure, adapted pipeline, bypasses, custom address generator, and special looping structures. The ASIP is synthesized in standard-cells CMOS technology and its performances are compared to known Digital signal processor (DSP) implementations of real-time Retinex filters. As a result of the comparison, the proposed algorithmic/architectural design outperforms state-of-art Retinex-like operators achieving the best trade-off between power consumption, flexibility, and visual quality.

Diagnostic and therapeutic update on primary duodenogastric reflux

The authors, after some remarks on the pathophysiology of primary alkaline gastritis, analyze the main methods, available at present, for diagnosis; they particularly consider the importance of endoscopy and histology, gastroesophageal 24-hours pH-metry and 99mTc HIDA scintigraphy. Successively, they present the criteria for the identification of patients who will predictably benefit from surgical treatment, and examine advantages and limits of the two main corrective surgical techniques for the duodenogastric reflux in patients not previously operated on the gastrointestinal tract: duodenal extramucosal myotomy according to Mattioli, relatively easy to perform, and the "duodenal switch" according to De Meester, technically more complex, but pathophysiologically more acceptable, completely eliminating the duodenogastric reflux.