A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy

In this paper we propose a query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy, storing and classifying the tags used to label a set of available resources. Our approach builds and maintains a profile for each user. When he submits a query (consisting of a set of tags) on this folksonomy to retrieve a set of resources of his interest, it automatically finds further “authoritative” tags to enrich his query and proposes them to him. All “authoritative” tags considered interesting by the user are exploited to refine his query and, along with those tags directly specified by him, are stored in his profile in such a way to enrich it. The expansion of user queries and the enrichment of user profiles allow any content-based recommender system operating on the folksonomy to retrieve and suggest a high number of resources matching with user needs and desires. Moreover, enriched user profiles can guide any collaborative filtering recommender system to proactively discover and suggest to a user many resources relevant to him, even if he has not explicitly searched for them.     

Blind maximum likelihood estimation of traffic matrices under long-range dependent traffic

A new method, based on the maximum likelihood principle, through the numerical Expectation–Maximization algorithm, is proposed to estimate traffic matrices when traffic exhibits long-range dependence. The methods proposed so far in the literature do not account for long-range dependence. The method proposed in the present paper also provides an estimate of the Hurst parameter. Simulation results show that: (i) the estimate of the traffic matrix is more efficient than those obtained via existing techniques; (ii) the estimation error of the traffic matrix is lower for larger values of the true traffic intensity; (iii) the estimate of the Hurst parameter is slightly negatively biased.     

Enhancing structural software coverage by incrementally computing branch executability

Structural code coverage criteria have been studied since the early seventies, and now they are well supported by commercial and open-source tools and are commonly embedded in several advanced industrial processes. Most industrial applications still refer to simple criteria, like statement and branch coverage, and consider complex criteria, like modified condition decision coverage, only rarely and often driven by the requirements of certification agencies. The industrial value of structural criteria is limited by the difficulty of achieving high coverage, due to both the complexity of deriving test cases that execute specific uncovered elements and the presence of many infeasible elements in the code. In this paper, we propose a technique that both generates test cases that execute yet uncovered branches and identifies infeasible branches that can be eliminated from the computation of the branch coverage. In this way, we can increase branch coverage to closely approximate full coverage, thus improving its industrial value. The algorithm combines symbolic analysis, abstraction refinement, and a novel technique named coarsening, to execute unexplored branches, identify infeasible ones, and mitigate the state space explosion problem. In the paper, we present the technique and illustrate its effectiveness through a set of experimental results obtained with a prototype implementation.     

Comparing sensitivities of differently oriented multi-walled carbon nanotubes integrated on silicon wafer for electrochemical biosensors

In this study, we report on multi-walled carbon nanotubes fabricated on silicon substrate with four different orientations via chemical vapor deposition. It is well-known that chemical treatments improve the nanotube electrochemical reactivity by creating edge-like defects on their exposed sidewalls. Before use, we performed an acid treatment on carbon nanotubes. To prove the effect of the treatment on these nanostructured electrodes, contact angles were measured. Then, sensitivities and detection limits were evaluated performing cyclic voltammetry. Two target molecules were used: potassium ferricyanide, an inorganic electroactive molecule, and hydrogen peroxide that is a product of reactions catalyzed by many enzymes, such as oxidases and peroxidases. Carbon nanotubes with tilted tips become hydrophilic after the treatment showing a contact angle of 22° ± 2°. This kind of electrode has shown also the best electrochemical performance. Sensitivity and detection limit values are 110.0 ± 0.5 μA/(mM cm²) and 8 μM for potassium ferricyanide solutions and 16.4 ± 0.1 μA/(mM cm²) and 24 μM using hydrogen peroxide as target compound. Considering the results of wettability and voltammetric measurements, nanotubes with tilted tips-based electrodes are found to be the most promising for future biosensing applications.     

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.     

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.     

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.