Abstract
This paper presents an overview regarding the synthesis of regular expressions targeting FPGAs. It describes current solutions and a number of open issues. Implementation of regular expressions can be very challenging when performance is critical. Software implementations may not be able to satisfy performance requirements and thus dedicated hardware engines have to be used. In the later case, automatic synthesis tools are of paramount importance to achieve fast prototyping of regular expression engines. As a case study, experimental results are presented, for FPGA implementations of the regular expressions included in the rule-set of a Network Intrusion Detection System (NIDS), Bleeding Edge, obtained using a state-of-the-art synthesis approach.

Abstract
Controlling the flow of materials inside job-shops involves several decisions such as the acceptance or rejection of an incoming order, the order's due date definition, the releasing and the dispatching of the job. This study applies a multiple decision-making scheme involving these four decision phases to examine the sensitivity of job-shop performance to different order release parameters. The performance criteria of shop workload and order delivery were collected to demonstrate the influence of the most significant order release parameters: the queue workload limit and the planning parameter of the latest release date. The influence of each parameter is evaluated by computational simulations. The way we compute the machine workload limit affects not only the workload but also delivery performance measures. However, surprisingly, the latest release date has not a significant impact on shop-floor performance measures. The effect of the queue workload limit in an input-output control. mechanism on delivery and workload related performance measures had not been studied up to date. Neither any analysis had investigated the influence of the latest release date calculus on the performance of the job-shop.

Abstract
Cost-sensitive learning is a key technique for addressing many real world data mining applications. Most existing research has been focused on classification problems. In this paper we propose a framework for evaluating regression models in applications with non-uniform costs and benefits across the domain of the continuous target variable. Namely, we describe two metrics for asserting the costs and benefits of the predictions of any model given a set of test cases. We illustrate the use of our metrics in the context of a specific type of applications where non-uniform costs are required: the prediction of rare extreme values of a continuous target variable. Our experiments provide clear evidence of the utility of the proposed framework for evaluating the merits of any model in this class of regression domains.

Abstract
Data-flow analyses, such as live variables analysis, available expressions analysis etc., are usefully specifiable as type systems. These are sound and, in the case of distributive analysis frameworks, complete wrt. appropriate natural semantics on abstract properties. Applications include certification of analyses and "optimization" of functional correctness proofs alongside programs. On the example of live variables analysis, we show that analysis type systems are applied versions of more foundational Hoare logics describing either the same abstract property semantics as the type system (liveness states) or a more concrete natural semantics on transition traces of a suitable kind (future defs and uses). The rules of the type system are derivable in the Hoare logic for the abstract property semantics and those in turn in the Hoare logic for the transition trace semantics. This reduction of the burden of trusting the certification vehicle can be compared to foundational proof-carrying code, where general-purpose program logics are preferred to special-purpose type systems and universal logic to program logics. We also look at conditional liveness analysis to see that the same foundational development is also possible for conditional data-flow analyses proceeding from type systems for combined "standard state and abstract property" semantics.

Abstract
This paper presents a new Bayesian approach to hyperspectral image segmentation that boosts the performance of the discriminative classifiers. This is achieved by combining class densities based on discriminative classifiers with a Multi-Level Logistic Markov-Gibs prior. This density favors neighbouring labels of the same class. The adopted discriminative classifier is the Fast Sparse Multinomial Regression. The discrete optimization problem one is led to is solved efficiently via graph cut tools. The effectiveness of the proposed method is evaluated, with simulated and real AVIRIS images, in two directions: 1) to improve the classification performance and 2) to decrease the size of the training sets.

Abstract
Dermoscopy is a non-invasive diagnostic technique for the in vivo observation of pigmented skin lesions used in dermatology. There is currently a great interest in the prospects of automatic image analysis methods for dermoscopy, both to provide quantitative information about a lesion, which can be of relevance for the clinician, and as a stand alone early warning tool. The effective implementation of such a tool could lead to a reduction in the number of cases selected for exeresis, with obvious benefits both to the patients and to the health care system. The standard approach in automatic dermoscopic image analysis has usually three stages: (i) image segmentation, (ii) feature extraction and feature selection, (iii) lesion classification. This paper presents a review of the dermoscopic image analysis systems currently available, and an evaluation of the performance of one such systems, the Tuebinger Mole Analyser, with 83 dermoscopic images of melanocitic nevus.