A combined approach for hiding partial information in RSA

Partial information leakage in deterministic public-key cryptosystems refers to a problem that arises when information about either the plaintext or the key is leaked in subtle ways. Quite a common case is where there are a small number of possible messages that may be sent. An attacker may be able to crack the scheme simply by enumerating all the possible ciphertexts. Two methods are proposed for facing the partial information leakage problem in RSA that incorporate a random element into the encrypted message to increase the number of possible ciphertexts. The resulting scheme is, effectively, an RSA-like cryptosystem which exhibits probabilistic encryption. The first method involves encrypting several similar messages with RSA and then using the Quadratic Residuosity Problem (QRP) to mark the intended one. In this way, an adversary who has correctly guessed two or more of the ciphertexts is still in doubt about which message is the intended one. The cryptographic strength of the combined system is equal to the computational difficulty of factorising a large integer; ideally, this should be feasible. The second scheme uses error-correcting codes for accommodating the random component. The plaintext is processed with an error-correcting code and deliberately corrupted before encryption. The introduced corruption lies within the error-correcting ability of the code, so as to enable the recovery of the original message. The random corruption offers a vast number of possible ciphertexts corresponding to a given plaintext; hence an attacker cannot deduce any useful information from it. The proposed systems are compared to other cryptosystems sharing similar characteristics, in terms of execution time and ciphertext size, so as to determine their practical utility. Finally, parameters which determine the characteristics of the proposed schemes are also examined.

A computational model of prosody for Yorùbá text-to-speech synthesis

This work examines prosody modelling for the Standard Yorùbá (SY) language in the context of computer text-to-speech synthesis applications. The thesis of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combines acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. Our prosody model is conceptualised around a modular holistic framework. The framework is implemented using the Relational Tree (R-Tree) techniques (Ehrich and Foith, 1976). R-Tree is a sophisticated data structure that provides a multi-dimensional description of a waveform. A Skeletal Tree (S-Tree) is first generated using algorithms based on the tone phonological rules of SY. Subsequent steps update the S-Tree by computing the numerical values of the prosody dimensions. To implement the intonation dimension, fuzzy control rules where developed based on data from native speakers of Yorùbá. The Classification And Regression Tree (CART) and the Fuzzy Decision Tree (FDT) techniques were tested in modelling the duration dimension. The FDT was selected based on its better performance. An important feature of our R-Tree framework is its flexibility in that it facilitates the independent implementation of the different dimensions of prosody, i.e. duration and intonation, using different techniques and their subsequent integration. Our approach provides us with a flexible and extendible model that can also be used to implement, study and explain the theory behind aspects of the phenomena observed in speech prosody.

Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102:a bioinformatic approach to the prediction of new epitopes

Predictive models of peptide-Major Histocompatibility Complex (MHC) binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.

Palette-colouring: a belief propagation approach

We consider a variation of the prototype combinatorial optimization problem known as graph colouring. Our optimization goal is to colour the vertices of a graph with a fixed number of colours, in a way to maximize the number of different colours present in the set of nearest neighbours of each given vertex. This problem, which we pictorially call palette-colouring, has been recently addressed as a basic example of a problem arising in the context of distributed data storage. Even though it has not been proved to be NP-complete, random search algorithms find the problem hard to solve. Heuristics based on a naive belief propagation algorithm are observed to work quite well in certain conditions. In this paper, we build upon the mentioned result, working out the correct belief propagation algorithm, which needs to take into account the many-body nature of the constraints present in this problem. This method improves the naive belief propagation approach at the cost of increased computational effort. We also investigate the emergence of a satisfiable-to-unsatisfiable 'phase transition' as a function of the vertex mean degree, for different ensembles of sparse random graphs in the large size ('thermodynamic') limit.

A modular holistic approach to prosody modelling for Standard Yorùbá speech synthesis

This paper presents a novel prosody model in the context of computer text-to-speech synthesis applications for tone languages. We have demonstrated its applicability using the Standard Yorùbá (SY) language. Our approach is motivated by the theory that abstract and realised forms of various prosody dimensions should be modelled within a modular and unified framework [Coleman, J.S., 1994. Polysyllabic words in the YorkTalk synthesis system. In: Keating, P.A. (Ed.), Phonological Structure and Forms: Papers in Laboratory Phonology III, Cambridge University Press, Cambridge, pp. 293–324]. We have implemented this framework using the Relational Tree (R-Tree) technique. R-Tree is a sophisticated data structure for representing a multi-dimensional waveform in the form of a tree. The underlying assumption of this research is that it is possible to develop a practical prosody model by using appropriate computational tools and techniques which combine acoustic data with an encoding of the phonological and phonetic knowledge provided by experts. To implement the intonation dimension, fuzzy logic based rules were developed using speech data from native speakers of Yorùbá. The Fuzzy Decision Tree (FDT) and the Classification and Regression Tree (CART) techniques were tested in modelling the duration dimension. For practical reasons, we have selected the FDT for implementing the duration dimension of our prosody model. To establish the effectiveness of our prosody model, we have also developed a Stem-ML prosody model for SY. We have performed both quantitative and qualitative evaluations on our implemented prosody models. The results suggest that, although the R-Tree model does not predict the numerical speech prosody data as accurately as the Stem-ML model, it produces synthetic speech prosody with better intelligibility and naturalness. The R-Tree model is particularly suitable for speech prosody modelling for languages with limited language resources and expertise, e.g. African languages. Furthermore, the R-Tree model is easy to implement, interpret and analyse.

“I didn’t mean to steal someone else’s words!”:a forensic linguistic approach to detecting intentional plagiarism

The concept of plagiarism is not uncommonly associated with the concept of intellectual property, both for historical and legal reasons: the approach to the ownership of ‘moral’, nonmaterial goods has evolved to the right to individual property, and consequently a need was raised to establish a legal framework to cope with the infringement of those rights. The solution to plagiarism therefore falls most often under two categories: ethical and legal. On the ethical side, education and intercultural studies have addressed plagiarism critically, not only as a means to improve academic ethics policies (PlagiarismAdvice.org, 2008), but mainly to demonstrate that if anything the concept of plagiarism is far from being universal (Howard & Robillard, 2008). Even if differently, Howard (1995) and Scollon (1994, 1995) argued, and Angèlil-Carter (2000) and Pecorari (2008) later emphasised that the concept of plagiarism cannot be studied on the grounds that one definition is clearly understandable by everyone. Scollon (1994, 1995), for example, claimed that authorship attribution is particularly a problem in non-native writing in English, and so did Pecorari (2008) in her comprehensive analysis of academic plagiarism. If among higher education students plagiarism is often a problem of literacy, with prior, conflicting social discourses that may interfere with academic discourse, as Angèlil-Carter (2000) demonstrates, we then have to aver that a distinction should be made between intentional and inadvertent plagiarism: plagiarism should be prosecuted when intentional, but if it is part of the learning process and results from the plagiarist’s unfamiliarity with the text or topic it should be considered ‘positive plagiarism’ (Howard, 1995: 796) and hence not an offense. Determining the intention behind the instances of plagiarism therefore determines the nature of the disciplinary action adopted. Unfortunately, in order to demonstrate the intention to deceive and charge students with accusations of plagiarism, teachers necessarily have to position themselves as ‘plagiarism police’, although it has been argued otherwise (Robillard, 2008). Practice demonstrates that in their daily activities teachers will find themselves being required a command of investigative skills and tools that they most often lack. We thus claim that the ‘intention to deceive’ cannot inevitably be dissociated from plagiarism as a legal issue, even if Garner (2009) asserts that generally plagiarism is immoral but not illegal, and Goldstein (2003) makes the same severance. However, these claims, and the claim that only cases of copyright infringement tend to go to court, have recently been challenged, mainly by forensic linguists, who have been actively involved in cases of plagiarism. Turell (2008), for instance, demonstrated that plagiarism is often connoted with an illegal appropriation of ideas. Previously, she (Turell, 2004) had demonstrated by comparison of four translations of Shakespeare’s Julius Caesar to Spanish that the use of linguistic evidence is able to demonstrate instances of plagiarism. This challenge is also reinforced by practice in international organisations, such as the IEEE, to whom plagiarism potentially has ‘severe ethical and legal consequences’ (IEEE, 2006: 57). What plagiarism definitions used by publishers and organisations have in common – and which the academia usually lacks – is their focus on the legal nature. We speculate that this is due to the relation they intentionally establish with copyright laws, whereas in education the focus tends to shift from the legal to the ethical aspects. However, the number of plagiarism cases taken to court is very small, and jurisprudence is still being developed on the topic. In countries within the Civil Law tradition, Turell (2008) claims, (forensic) linguists are seldom called upon as expert witnesses in cases of plagiarism, either because plagiarists are rarely taken to court or because there is little tradition of accepting linguistic evidence. In spite of the investigative and evidential potential of forensic linguistics to demonstrate the plagiarist’s intention or otherwise, this potential is restricted by the ability to identify a text as being suspect of plagiarism. In an era with such a massive textual production, ‘policing’ plagiarism thus becomes an extraordinarily difficult task without the assistance of plagiarism detection systems. Although plagiarism detection has attracted the attention of computer engineers and software developers for years, a lot of research is still needed. Given the investigative nature of academic plagiarism, plagiarism detection has of necessity to consider not only concepts of education and computational linguistics, but also forensic linguistics. Especially, if intended to counter claims of being a ‘simplistic response’ (Robillard & Howard, 2008). In this paper, we use a corpus of essays written by university students who were accused of plagiarism, to demonstrate that a forensic linguistic analysis of improper paraphrasing in suspect texts has the potential to identify and provide evidence of intention. A linguistic analysis of the corpus texts shows that the plagiarist acts on the paradigmatic axis to replace relevant lexical items with a related word from the same semantic field, i.e. a synonym, a subordinate, a superordinate, etc. In other words, relevant lexical items were replaced with related, but not identical, ones. Additionally, the analysis demonstrates that the word order is often changed intentionally to disguise the borrowing. On the other hand, the linguistic analysis of linking and explanatory verbs (i.e. referencing verbs) and prepositions shows that these have the potential to discriminate instances of ‘patchwriting’ and instances of plagiarism. This research demonstrates that the referencing verbs are borrowed from the original in an attempt to construct the new text cohesively when the plagiarism is inadvertent, and that the plagiarist has made an effort to prevent the reader from identifying the text as plagiarism, when it is intentional. In some of these cases, the referencing elements prove being able to identify direct quotations and thus ‘betray’ and denounce plagiarism. Finally, we demonstrate that a forensic linguistic analysis of these verbs is critical to allow detection software to identify them as proper paraphrasing and not – mistakenly and simplistically – as plagiarism.

On the numerical solution of a Cauchy problem for the Laplace equation via a direct integral equation approach

We investigate the problem of determining the stationary temperature field on an inclusion from given Cauchy data on an accessible exterior boundary. On this accessible part the temperature (or the heat flux) is known, and, additionally, on a portion of this exterior boundary the heat flux (or temperature) is also given. We propose a direct boundary integral approach in combination with Tikhonov regularization for the stable determination of the temperature and flux on the inclusion. To determine these quantities on the inclusion, boundary integral equations are derived using Green’s functions, and properties of these equations are shown in an L2-setting. An effective way of discretizing these boundary integral equations based on the Nystr¨om method and trigonometric approximations, is outlined. Numerical examples are included, both with exact and noisy data, showing that accurate approximations can be obtained with small computational effort, and the accuracy is increasing with the length of the portion of the boundary where the additionally data is given.

An alternating potential based approach for a Cauchy problem for the Laplace equation in a planar domain with a cut

We consider a Cauchy problem for the Laplace equation in a bounded region containing a cut, where the region is formed by removing a sufficiently smooth arc (the cut) from a bounded simply connected domain D. The aim is to reconstruct the solution on the cut from the values of the solution and its normal derivative on the boundary of the domain D. We propose an alternating iterative method which involves solving direct mixed problems for the Laplace operator in the same region. These mixed problems have either a Dirichlet or a Neumann boundary condition imposed on the cut and are solved by a potential approach. Each of these mixed problems is reduced to a system of integral equations of the first kind with logarithmic and hypersingular kernels and at most a square root singularity in the densities at the endpoints of the cut. The full discretization of the direct problems is realized by a trigonometric quadrature method which has super-algebraic convergence. The numerical examples presented illustrate the feasibility of the proposed method.

A simple Bayesian modelling approach to event extraction from Twitter

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.

Complexity and chirality indices for molecular informatics::differential geometry approach

Novel molecular complexity measures are designed based on the quantum molecular kinematics. The Hamiltonian matrix constructed in a quasi-topological approximation describes the temporal evolution of the modelled electronic system and determined the time derivatives for the dynamic quantities. This allows to define the average quantum kinematic characteristics closely related to the curvatures of the electron paths, particularly, the torsion reflecting the chirality of the dynamic system. A special attention has been given to the computational scheme for this chirality measure. The calculations on realistic molecular systems demonstrate reasonable behaviour of the proposed molecular complexity indices.

Alpha helical trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.

Beta barrel trans-membrane proteins:enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address beta-barrel topology prediction. The beta-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.

Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Genetic Algorithm Approach for Solving the Task Assignment Problem

This research was partially supported by the Serbian Ministry of Science and Ecology under project 144007. The authors are grateful to Ivana Ljubić for help in testing and to Vladimir Filipović for useful suggestions and comments.

Model Construction and Research using System Compositional Approach on Natural Hierarchical Neural Networks. Development of Computer Toolbox

System compositional approach to model construction and research of informational processes, which take place in biological hierarchical neural networks, is being discussed. A computer toolbox has been successfully developed for solution of tasks from this scientific sphere. A series of computational experiments investigating the work of this toolbox on olfactory bulb model has been carried out. The well-known psychophysical phenomena have been reproduced in experiments.