Truth Values in t-norm based Systems Many-valued FUZZY Logic

In t-norm based systems many-valued logic, valuations of propositions form a non-countable set: interval [0,1]. In addition, we are given a set E of truth values p, subject to certain conditions, the valuation v is v=V(p), V reciprocal application of E on [0,1]. The general propositional algebra of t-norm based many-valued logic is then constructed from seven axioms. It contains classical logic (not many-valued) as a special case. It is first applied to the case where E=[0,1] and V is the identity. The result is a t-norm based many-valued logic in which contradiction can have a nonzero degree of truth but cannot be true; for this reason, this logic is called quasi-paraconsistent.

Evaluating knowledge-based approaches to the multilingual extension of a temporal expression normalizer

The extension to new languages is a well known bottleneck for rule-based systems. Considerable human effort, which typically consists in re-writing from scratch huge amounts of rules, is in fact required to transfer the knowledge available to the system from one language to a new one. Provided sufficient annotated data, machine learning algorithms allow to minimize the costs of such knowledge transfer but, up to date, proved to be ineffective for some specific tasks. Among these, the recognition and normalization of temporal expressions still remains out of their reach. Focusing on this task, and still adhering to the rule-based framework, this paper presents a bunch of experiments on the automatic porting to Italian of a system originally developed for Spanish. Different automatic rule translation strategies are evaluated and discussed, providing a comprehensive overview of the challenge.

A Hardware-Software Approach for On-Line Soft Error Mitigation in Interrupt-Driven Applications

Integrity assurance of configuration data has a significant impact on microcontroller-based systems reliability. This is especially true when running applications driven by events which behavior is tightly coupled to this kind of data. This work proposes a new hybrid technique that combines hardware and software resources for detecting and recovering soft-errors in system configuration data. Our approach is based on the utilization of a common built-in microcontroller resource (timer) that works jointly with a software-based technique, which is responsible to periodically refresh the configuration data. The experiments demonstrate that non-destructive single event effects can be effectively mitigated with reduced overheads. Results show an important increase in fault coverage for SEUs and SETs, about one order of magnitude.

Application-Based Analysis of Register File Criticality for Reliability Assessment in Embedded Microprocessors

There is an increasing concern to reduce the cost and overheads during the development of reliable systems. Selective protection of most critical parts of the systems represents a viable solution to obtain a high level of reliability at a fraction of the cost. In particular to design a selective fault mitigation strategy for processor-based systems, it is mandatory to identify and prioritize the most vulnerable registers in the register file as best candidates to be protected (hardened). This paper presents an application-based metric to estimate the criticality of each register from the microprocessor register file in microprocessor-based systems. The proposed metric relies on the combination of three different criteria based on common features of executed applications. The applicability and accuracy of our proposal have been evaluated in a set of applications running in different microprocessors. Results show a significant improvement in accuracy compared to previous approaches and regardless of the underlying architecture.

Hydrogenated graphene nanoribbons for spintronics

We show how hydrogenation of graphene nanoribbons at small concentrations can open venues toward carbon-based spintronics applications regardless of any specific edge termination or passivation of the nanoribbons. Density-functional theory calculations show that an adsorbed H atom induces a spin density on the surrounding π orbitals whose symmetry and degree of localization depends on the distance to the edges of the nanoribbon. As expected for graphene-based systems, these induced magnetic moments interact ferromagnetically or antiferromagnetically depending on the relative adsorption graphene sublattice, but the magnitude of the interactions are found to strongly vary with the position of the H atoms relative to the edges. We also calculate, with the help of the Hubbard model, the transport properties of hydrogenated armchair semiconducting graphene nanoribbons in the diluted regime and show how the exchange coupling between H atoms can be exploited in the design of novel magnetoresistive devices.

Technologies for Anatomical and Geometric Characterization of the Corneal Structure and Anterior Segment: A Review

Corneal and anterior segment imaging techniques have become a crucial tool in the clinical practice of ophthalmology, with a great variety of applications, such as corneal curvature and pachymetric analysis, detection of ectatic corneal conditions, anatomical study of the anterior segment prior to phakic intraocular lens implantation, or densitometric analysis of the crystalline lens. From the Placido-based systems that allow only a characterization of the geometry of the anterior corneal surface to the Scheimpflug photography-based systems that provide a characterization of the cornea, anterior chamber, and crystalline lens, there is a great variety of devices with the capability of analyzing different anatomical parameters with very high precision. To date, Scheimpflug photography-based systems are the devices providing the more complete analysis of the anterior segment in a non-invasive way. More developments are required in anterior segment imaging technologies in order to improve the analysis of the crystalline lens structure as well as the ocular structures behind the iris in a non-invasive way when the pupil is not dilated.

Preliminary interpretation of shaking-table response of a full-scale 3-storey building composed of thin reinforced concrete sandwich walls

In the last few decades, the use of cast in situ reinforced concrete sandwich panels for the construction of low- to mid-rise buildings has become more and more widespread due to several interesting properties of this construction technique, such as fast construction and high thermal and acoustic performances. Nonetheless the level of knowledge of the structural behavior of systems made of squat reinforced concrete sandwich panels is still not so consolidated, especially with reference to the seismic response, due to the lack of experimental studies. In recent years, while various experimental tests have been conducted on single panels aimed at assessing their seismic capacity, only few tests have been carried out on more complex structural systems. In this paper, the experimental results of a series of shaking-table tests performed on a full-scale 3-storey building are presented in detail. The main goal is to give to the scientific community the possibility of develop independent interpretation of these experimental results. An in-depth interpretation of the discrepancies between the analytical predictions and the experimental results is beyond the objective of this paper and is still under development. Nonetheless, preliminary interpretations indicate that both the stiffness and the strength of the building under dynamic excitation appear quite superior with respect to those expected from the results of previous pseudo-static cyclic tests conducted on simple specimens.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Efficient Mitigation of Data and Control Flow Errors in Microprocessors

The use of microprocessor-based systems is gaining importance in application domains where safety is a must. For this reason, there is a growing concern about the mitigation of SEU and SET effects. This paper presents a new hybrid technique aimed to protect both the data and the control-flow of embedded applications running on microprocessors. On one hand, the approach is based on software redundancy techniques for correcting errors produced in the data. On the other hand, control-flow errors can be detected by reusing the on-chip debug interface, existing in most modern microprocessors. Experimental results show an important increase in the system reliability even superior to two orders of magnitude, in terms of mitigation of both SEUs and SETs. Furthermore, the overheads incurred by our technique can be perfectly assumable in low-cost systems.

S-SETA: Selective Software-Only Error-Detection Technique Using Assertions

Software-based techniques offer several advantages to increase the reliability of processor-based systems at very low cost, but they cause performance degradation and an increase of the code size. To meet constraints in performance and memory, we propose SETA, a new control-flow software-only technique that uses assertions to detect errors affecting the program flow. SETA is an independent technique, but it was conceived to work together with previously proposed data-flow techniques that aim at reducing performance and memory overheads. Thus, SETA is combined with such data-flow techniques and submitted to a fault injection campaign. Simulation and neutron induced SEE tests show high fault coverage at performance and memory overheads inferior to the state-of-the-art.