In spite of the ever-growing technology in information security, organizations are still vulnerable to security attacks due to mistakes made by their employees. To evaluate organizational security vulnerability and keep organizations alert on their security situation, in this dissertation, we developed a framework for measuring the security vulnerability of organizations based on online behaviours analysis of their employees. In this framework, the behavioural data of employees for their online privacy are taken as input, and the personal vulnerability profiles of them are generated and represented as confusion matrices. Then, by incorporating the personal vulnerability data into the local social network of interpersonal security influence in the workplace, the overall security vulnerability of each organization is evaluated and rated as a percentile value representing its position to all other organizations. Through evaluation with real-world data and simulation, this framework is verified to be both effective and efficient in estimating the actual security vulnerability status of organizations. Besides, a demo application is developed to illustrate the feasibility of this framework in the practice of improving information security for organizations. / Graduate

High levels of speech recognition have been obtained with cochlear implant users in quiet conditions. In noisy environments, speech recognition deteriorates considerably, especially in speech-like noise. The aim of this study was to determine what underlies measured speech recognition in cochlear implantees, and furthermore, what underlies perception of speech in noise. Vowel and consonant recognition was determined in ten normal-hearing listeners using acoustic simulations. An acoustic model was developed in order to process vowels and consonants in quiet and noisy conditions; multi-talker babble and speech-like noise were added to the speech segments for the noisy conditions. A total of seven conditions were simulated acoustically; namely for recognition in quiet and as a function of signal-to-noise ratio (0 dB, 20 dB and 40 dB speech-like noise and 0 dB, 20 dB and 40 dB multi-talker babble). An eight- channel SPEAK processor was modelled and used to process the speech segments. A number of biophysical interactions between simulated nerve fibres and the cochlear implant were simulated by including models of these interactions in the acoustic model. Biophysical characteristics that were modelled included dynamic range compression and current spread in the cochlea. Recognition scores deteriorated with increasing noise levels, as expected. Vowel recognition was better than consonant recognition in general. In quiet conditions, the features transmitted most efficiently for recognition of speech segments were duration and F2 for vowels and burst and affrication for consonants. In noisy conditions, listeners mainly depended on the duration of vowels for recognition and the burst of consonants. As the SNR decreased, the number of features used to recognise speech segments also became fewer. This suggests that the addition of noise reduces the number of acoustic features available for recognition. Efforts to improve the transmission of important speech features m cochlear implants should improve recognition of speech in noisy conditions. / Dissertation (MEng (Bio-Engineering))--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted

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Context. Reports suggest that defects in code cost the US in excess of $50billion per year to put right. Defect Prediction is an important part of Software Engineering. It allows developers to prioritise the code that needs to be inspected when trying to reduce the number of defects in code. A small change in the number of defects found will have a significant impact on the cost of producing software. Aims. The aim of this dissertation is to investigate the factors which a ect the performance of defect prediction models. Identifying the causes of variation in the way that variables are computed should help to improve the precision of defect prediction models and hence improve the cost e ectiveness of defect prediction. Methods. This dissertation is by published work. The first three papers examine variation in the independent variables (code metrics) and the dependent variable (number/location of defects). The fourth and fifth papers investigate the e ect that di erent learners and datasets have on the predictive performance of defect prediction models. The final paper investigates the reported use of di erent machine learning approaches in studies published between 2000 and 2010. Results. The first and second papers show that independent variables are sensitive to the measurement protocol used, this suggests that the way data is collected a ects the performance of defect prediction. The third paper shows that dependent variable data may be untrustworthy as there is no reliable method for labelling a unit of code as defective or not. The fourth and fifth papers show that the dataset and learner used when producing defect prediction models have an e ect on the performance of the models. The final paper shows that the approaches used by researchers to build defect prediction models is variable, with good practices being ignored in many papers. Conclusions. The measurement protocols for independent and dependent variables used for defect prediction need to be clearly described so that results can be compared like with like. It is possible that the predictive results of one research group have a higher performance value than another research group because of the way that they calculated the metrics rather than the method of building the model used to predict the defect prone modules. The machine learning approaches used by researchers need to be clearly reported in order to be able to improve the quality of defect prediction studies and allow a larger corpus of reliable results to be gathered.

Made available in DSpace on 2017-07-10T19:24:53Z (GMT). No. of bitstreams: 1 Franciele_texto.pdf: 972546 bytes, checksum: 4159555de632249d0c83764a3aecc74c (MD5) Previous issue date: 2011-07-07 / Studies on spatial variability of soybeans yield are of great importance for the development of new technologies that improve the world agricultural production. One of methods that allows this study is geostatistics. The geostatistical analysis makes possible the predictions of results and one of its products are thematic maps. Thus, this trial describes some techniques to draw and compare thematic maps using kriging. The analysis was based on data from soybean yield in t ha−1 according to harvest year 2004/2005 in an experimental area with sampling grades whose distances were: 25x25 m, 50x50 m, 75x75 m and 100x100 m plus a harvest monitor. The maps were compared using error matrix and confusion matrix. In addition, there was a better accuracy of the spatial variability maps that were drawn, while the analysis of coefficients of accuracy allows a better planning of sampling mesh for future studies. The measures of accuracy that were obtained by error matrix are significant options to make comparison among thematic maps, once they provide global indices and also by classes. / Com o aumento da produção agrícola mundial, o processo de produção agrícola tornou-se alvo do estudo de diversos pesquisadores. Estudos sobre a variabilidade espacial da produtividade da soja são de grande importância para o desenvolvimento de novas tecnologias, que beneficiam a agricultura. A análise geoestatística torna possível realizar previsões dos resultados, tendo como um de seus produtos os mapas temáticos. Este trabalho descreve algumas técnicas para a construção e comparação de mapas temáticos, utilizando a krigagem. A análise foi realizada com dados da produtividade de soja em t ha−1 do ano agrícola 2004/2005 numa área experimental com grades de amostragem com distâncias de 25x25 m, 50x50 m, 75x75 m, 100x100 m e monitor de colheita, comparando-se os mapas, utilizando a matriz de erros e a matriz de confusão. Além de uma melhor precisão dos mapas de variabilidade espacial gerados, a análise dos índices de acurácia possibilita um melhor planejamento das malhas amostrais para futuros estudos. As medidas de acurácia obtidas por meio da matriz de erros são opções significativas para realizar a comparação entre mapas temáticos, uma vez que fornecem índices globais e também por classes.

Made available in DSpace on 2017-05-12T14:48:16Z (GMT). No. of bitstreams: 1 Franciele_texto.pdf: 972546 bytes, checksum: 4159555de632249d0c83764a3aecc74c (MD5) Previous issue date: 2011-07-07 / Studies on spatial variability of soybeans yield are of great importance for the development of new technologies that improve the world agricultural production. One of methods that allows this study is geostatistics. The geostatistical analysis makes possible the predictions of results and one of its products are thematic maps. Thus, this trial describes some techniques to draw and compare thematic maps using kriging. The analysis was based on data from soybean yield in t ha−1 according to harvest year 2004/2005 in an experimental area with sampling grades whose distances were: 25x25 m, 50x50 m, 75x75 m and 100x100 m plus a harvest monitor. The maps were compared using error matrix and confusion matrix. In addition, there was a better accuracy of the spatial variability maps that were drawn, while the analysis of coefficients of accuracy allows a better planning of sampling mesh for future studies. The measures of accuracy that were obtained by error matrix are significant options to make comparison among thematic maps, once they provide global indices and also by classes. / Com o aumento da produção agrícola mundial, o processo de produção agrícola tornou-se alvo do estudo de diversos pesquisadores. Estudos sobre a variabilidade espacial da produtividade da soja são de grande importância para o desenvolvimento de novas tecnologias, que beneficiam a agricultura. A análise geoestatística torna possível realizar previsões dos resultados, tendo como um de seus produtos os mapas temáticos. Este trabalho descreve algumas técnicas para a construção e comparação de mapas temáticos, utilizando a krigagem. A análise foi realizada com dados da produtividade de soja em t ha−1 do ano agrícola 2004/2005 numa área experimental com grades de amostragem com distâncias de 25x25 m, 50x50 m, 75x75 m, 100x100 m e monitor de colheita, comparando-se os mapas, utilizando a matriz de erros e a matriz de confusão. Além de uma melhor precisão dos mapas de variabilidade espacial gerados, a análise dos índices de acurácia possibilita um melhor planejamento das malhas amostrais para futuros estudos. As medidas de acurácia obtidas por meio da matriz de erros são opções significativas para realizar a comparação entre mapas temáticos, uma vez que fornecem índices globais e também por classes.

The main aim of the master thesis is to compare of different methodologies of financial health models and bankruptcy prediction models and their cause to company classification. The work deals with the applicability of models on the sample of 45 prosperous companies and 45 companies that were initiating in insolvency process. Sample contain about 33 % companies from building industry, 33 % retail, 16,7 % manufacturing industry and 16,7 % of the other industries mainly services. The special kind of contingency table - the confusion matrix - is used in the methodology to calculate sensitivity, specificity, negative predictive, false positive rate, accuracy, error and other classification statistics. Overall model accuracy is obtained as a difference between accuracy and error. Dependencies of models are acquired based on Pearson´s correlation coefficient. The changes (removing of grey zone and testing new cut-off points) in models are tested in the sensitivity analysis. In practise part there are about 12 financial models calculated (Altman Z´, Altman Z´´, Index IN99, IN01 and IN05, Kralicek Quicktest, Zmijewski model, Taffler model and its modification, Index Creditworthiness, Grunwald Site Index, Doucha´s Analysis). Only two financial indicators (ROA and Sales / Assets) in results were important as crucial part for more than one model. Then are classifications of companies in models determined. It shows that the best models according to overall accuracy are Zmijewski and Altman´s Z´´. On the other hand the worst models are index IN99 and both versions of Taffler´s model. The classification is not caused excessively by extreme values, year of the model creation or country of the origin (hypothesis 1). Based on results it is suggested that the bankruptcy prediction is an accurate forecaster of failure up to three years prior to bankruptcy in most examined models (hypothesis 2). It is observed that the type of model and industry influence the classification of models. In the end, the changes based on sensitivity analysis in the worst companies are made. All of three changes have increased overall classification accuracy of models.

Data Ductus, a multination IT-consulting company, wants to develop an AI that monitors a logistic system and looks for errors. Once trained enough, this AI will suggest a correction and automatically right issues if they arise. This project presents how one works with machine learning problems and provides a deeper insight into how cross-validation and regularisation, among other techniques, are used to improve the performance of machine learning algorithms on the defined problem. Three techniques are tested and evaluated in our logistic system on three different machine learning algorithms, namely Naïve Bayes, Logistic Regression and Random Forest. The evaluation of the algorithms leads us to conclude that Random Forest, using cross-validated parameters, gives the best performance on our specific problems, with the other two falling behind in each tested category. It became clear to us that cross-validation is a simple, yet powerful tool for increasing the performance of machine learning algorithms. / Data Ductus, ett multinationellt IT-konsultföretag vill utveckla en AI som övervakar ett logistiksystem och uppmärksammar fel. När denna AI är tillräckligt upplärd ska den föreslå korrigering eller automatiskt korrigera problem som uppstår. Detta projekt presenterar hur man arbetar med maskininlärningsproblem och ger en djupare inblick i hur kors-validering och regularisering, bland andra tekniker, används för att förbättra prestandan av maskininlärningsalgoritmer på det definierade problemet. Dessa tekniker testas och utvärderas i vårt logistiksystem på tre olika maskininlärnings algoritmer, nämligen Naïve Bayes, Logistic Regression och Random Forest. Utvärderingen av algoritmerna leder oss till att slutsatsen är att Random Forest, som använder korsvaliderade parametrar, ger bästa prestanda på våra specifika problem, medan de andra två faller bakom i varje testad kategori. Det blev klart för oss att kors-validering är ett enkelt, men kraftfullt verktyg för att öka prestanda hos maskininlärningsalgoritmer.

For years, intrusion detection has been considered a key component of many organizations’ network defense capabilities. Although a number of approaches to intrusion detection have been tried, few have been capable of providing security personnel responsible for the protection of a network with sufficient information to make adjustments and respond to attacks in real-time. Because intrusion detection systems rarely have complete information, false negatives and false positives are extremely common, and thus valuable resources are wasted responding to irrelevant events. In order to provide better actionable information for security personnel, a mechanism for quantifying the confidence level in predictions is needed. This work presents an approach which seeks to combine a primary prediction model with a novel secondary confidence level model which provides a measurement of the confidence in a given attack prediction being made. The ability to accurately identify an attack and quantify the confidence level in the prediction could serve as the basis for a new generation of intrusion detection devices, devices that provide earlier and better alerts for administrators and allow more proactive response to events as they are occurring.

The goal of this study was to investigate the methodology in designing a vowel intelligibility model that can objectively predict the outcome of a vowel confusion test performed with normal hearing individuals listening to a cochlear implant acoustic model. The model attempts to mimic vowel perception of a cochlear implantee mathematically. The output of the model is the calculated probability of correct identification of vowel tokens and the probability of specific vowel confusions in a subjective vowel confusion test. In such a manner, the model can be used to aid cochlear implant research by complementing subjective listening tests. The model may also be used to test hypotheses concerning the use and relationship of acoustic cues in vowel identification. The objective vowel intelligibility model consists of two parts: the speech processing component (used to extract the acoustic cues which allow vowels to be identified) and the decision component (simulation of the decision making that takes place in the brain). Acoustic cues were extracted from the vowel sounds and used to calculate probabilities of identifying or confusing specific vowels. The confusion matrices produces by the objective vowel perception model were compared with results from subjective tests performed with normal hearing listeners listening to an acoustic cochlear implant model. The most frequent confusions could be predicted using the first two formant frequencies and the vowel duration as acoustic cues. The model could predict the deterioration of vowel recognition when noise was added to the speech being evaluated. The model provided a first approximation of vowel intelligibility and requires further4 development to completely predict speech perception of cochlear implantees. / Dissertation (ME)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted