Regulating Data Exchange in Service Oriented Applications

We define a type system for COWS, a formalism for specifying and combining services, while modelling their dynamic behaviour. Our types permit to express policies constraining data exchanges in terms of sets of service partner names attachable to each single datum. Service programmers explicitly write only the annotations necessary to specify the wanted policies for communicable data, while a type inference system (statically) derives the minimal additional annotations that ensure consistency of services initial configuration. Then, the language dynamic semantics only performs very simple checks to authorize or block communication. We prove that the type system and the operational semantics are sound. As a consequence, we have the following data protection property: services always comply with the policies regulating the exchange of data among interacting services. We illustrate our approach through a simplified but realistic scenario for a service-based electronic marketplace

Digitisation Processing and Recognition of Old Greek Manuscipts (the D-SCRIBE Project)

After many years of scholar study, manuscript collections continue to be an important source of novel information for scholars, concerning both the history of earlier times as well as the development of cultural documentation over the centuries. D-SCRIBE project aims to support and facilitate current and future efforts in manuscript digitization and processing. It strives toward the creation of a comprehensive software product, which can assist the content holders in turning an archive of manuscripts into a digital collection using automated methods. In this paper, we focus on the problem of recognizing early Christian Greek manuscripts. We propose a novel digital image binarization scheme for low quality historical documents allowing further content exploitation in an efficient way. Based on the existence of closed cavity regions in the majority of characters and character ligatures in these scripts, we propose a novel, segmentation-free, fast and efficient technique that assists the recognition procedure by tracing and recognizing the most frequently appearing characters or character ligatures

Text Degradations and OCR Training

Printing and scanning of text documents introduces degradations to the characters which can be modeled. Interestingly, certain combinations of the parameters that govern the degradations introduced by the printing and scanning process affect characters in such a way that the degraded characters have a similar appearance, while other degradations leave the characters with an appearance that is very different. It is well known that (generally speaking) a test set that more closely matches a training set will be recognized with higher accuracy than one that matches the training set less well. Likewise, classifiers tend to perform better on data sets that have lower variance. This paper explores an analytical method that uses a formal printer/scanner degradation model to identify the similarity between groups of degraded characters. This similarity is shown to improve the recognition accuracy of a classifier through model directed choice of training set data

Feature Extraction Methods for Character Recognition

Not Include

Using Context and Interactions to Verify User-Intended Network Requests

Client-side malware can attack users by tampering with applications or user interfaces to generate requests that users did not intend. We propose Verified Intention (VInt), which ensures a network request, as received by a service, is user-intended. VInt is based on "seeing what the user sees" (context). VInt screenshots the user interface as the user interacts with a security-sensitive form. There are two main components. First, VInt ensures output integrity and authenticity by validating the context, ensuring the user sees correctly rendered information. Second, VInt extracts user-intended inputs from the on-screen user-provided inputs, with the assumption that a human user checks what they entered. Using the user-intended inputs, VInt deems a request to be user-intended if the request is generated properly from the user-intended inputs while the user is shown the correct information. VInt is implemented using image analysis and Optical Character Recognition (OCR). Our evaluation shows that VInt is accurate and efficient

Optimisation of a weightless neural network using particle swarms

Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This thesis is concerned with the design of weightless neural networks, which decompose a given pattern into several sets of n points, termed n-tuples. Considerable research has shown that by optimising the input connection mapping of such n-tuple networks classification performance can be improved significantly. In this thesis the application of a population-based stochastic optimisation technique, known as Particle Swarm Optimisation (PSO), to the optimisation of the connectivity pattern of such “n-tuple” classifiers is explored. The research was aimed at improving the discriminating power of the classifier in recognising handwritten characters by exploiting more efficient learning strategies. The proposed "learning" scheme searches for ‘good’ input connections of the n-tuples in the solution space and shrinks the search area step by step. It refines its search by attracting the particles to positions with good solutions in an iterative manner. Every iteration the performance or fitness of each input connection is evaluated, so a reward and punishment based fitness function was modelled for the task. The original PSO was refined by combining it with other bio-inspired approaches like Self-Organized Criticality and Nearest Neighbour Interactions. The hybrid algorithms were adapted for the n-tuple system and the performance was measured in selecting better connectivity patterns. The Genetic Algorithm (GA) has been shown to be accomplishing the same goals as the PSO, so the performances and convergence properties of the GA were compared against the PSO to optimise input connections. Experiments were conducted to evaluate the proposed methods by applying the trained classifiers to recognise handprinted digits from a widely used database. Results revealed the superiority of the particle swarm optimised training for the n-tuples over other algorithms including the GA. Low particle velocity in PSO was favourable for exploring more areas in the solution space and resulted in better recognition rates. Use of hybridisation was helpful and one of the versions of the hybrid PSO was found to be the best performing algorithm in finding the optimum set of input maps for the n-tuple network