Cette étude est une analyse dialectométrique des parlers berbères de Kabylie. Le présent travail inclut un échantillon de 168 parlers kabyles répartis sur tout le territoire kabylophone. Le corpus analysé compte 130 entrées (lexèmes et syntagmes) recueillies dans chacune des variétés prises en compte. Nous avons opté pour la méthode Levenshtein afin de calculer la distance entre les variantes. Nous avons choisi l’algorithme de Ward’s Method pour regrouper les variétés. Nous avons testé trois méthodes pour calculer la distance entre les sons : la méthode binaire, la distance d’Euclide et la distance de Manhattan. L’analyse des résultats nous a permis de montrer le continuum dialectal en Kabylie et de classifier les parlers kabyles en cinq zones infradialectales principales. / This study is a dialectometric analysis of Berber dialects of Kabylia. This work includes a sample of 168 Kabyle dialects spread across the Kabyle territory. The analyzed corpus includes 130 entries (lexemes and phrases) collected in each of the varieties considered. We opted for the Levenshtein method to calculate the distance between the variants. We chose the algorithm of Ward's Method for grouping varieties. We tested three methods to calculate the distance between the sounds: the binary method, the Euclidean distance and the Manhattan distance. The analysis of the results allowed us to show the dialect continuum in Kabylia and classify Kabyle dialects into five mains areas.

Målet med detta projekt var att utforma en kommunikation och databassystem för ett multiplayer ordbaserat spel åt Tweakers HB. Tweakers HB är ett produktbolag som utvecklar mobila applikationer där Wordhunch är en utav deras produkter. Systemet är en webbaserad API som stöds av en databas system för att kunna hantera olika aspekter av ett multiplayer ordbaserat spel. API: et kommer att användas av en Android applikation som utvecklas av andra utvecklare på Tweakers HB. Kommunikationssystemet har tagits fram med Extreme Programming samt testdriven utveckling. Utvecklingen skedde med välkända och beprövade metoder för att kunna säkerställa att ett system med hög kvalitet levereras. Båda system utnyttjar gamla och vältestade verktyg så som MySQL för databashantering medan REST-arkitektur används för kommunikationssystem. I slutet av utvecklingen uppfylldes alla ställda krav. System klarar av prestandakravet enligt de tester som utfördes. Under testning har flera komplikationer uppstått som inte var kända under utvecklingen eller före som t.ex. ta fram det korrekta ord när ett felstavat ord har skickats in. / The goal of the project was to design a communication and database system for a multiplayer word based game for Tweakers HB. Tweakers HB is a company that develops mobile applications, where WordHunch is one of their products. The product itself is a web based API which is supported by a database system for handling different areas of a multiplayer based mobile game. The API will be used by the other developers of Tweakers HB for communication between Server and multiple Android and iOS mobiles. The system is a fully accomplished system. The system has been produced using Extreme programming. Development was done with well-known and proven methods to ensure that a system of high-quality was delivered. Both systems utilizes old and well tested products like MySQL for database management while REST architecture style for API system. At the end of the project all requirements were met. The system passed all the required tests conducted and will be able to handle large quantity of incoming traffic from Android and iOS mobile devices, according to tests. During testing multiple issues came up which were neither known before the production or under production for ex. retrieving the correct word when a misspelled word has been submitted.

Inom ramen för Världshälsoorganisationens (WHO:s) internationella biverkningsprogram rapporterar sjukvårdspersonal och patienter misstänkta läkemedelsbiverkningar i form av spontana biverkningsrapporter som via nationella myndigheter skickas till Uppsala Monitoring Centre (UMC). Hos UMC lagras rapporterna i VigiBase, WHO:s biverkningsdatabas. Rapporterna i VigiBase analyseras med hjälp av statistiska metoder för att hitta potentiella samband mellan läkemedel och biverkningar. Funna samband utvärderas i flera steg där ett tidigt steg i utvärderingen är att studera den medicinska litteraturen för att se om sambandet redan är känt sedan tidigare (tidigare kända samband filtreras bort från fortsatt analys). Att manuellt leta efter samband mellan ett visst läkemedel och en viss biverkan är tidskrävande. I den här studien har vi utvecklat ett verktyg för att automatiskt leta efter medicinska biverkningstermer i medicinsk litteratur och spara funna samband i ett strukturerat format. I verktyget har vi implementerat och integrerat funktionalitet för att söka efter medicinska biverkningar på olika sätt (utnyttja synonymer,ta bort ändelser på ord, ta bort ord som saknar betydelse, godtycklig ordföljd och stavfel). Verktygets prestanda har utvärderats på manuellt extraherade medicinska termer från SPC-texter (texter från läkemedels bipacksedlar) och på biverkningstexter från Martindale (medicinsk referenslitteratur för information om läkemedel och substanser) där WHO-ART- och MedDRA-terminologierna har använts som källa för biverkningstermer. Studien visar att sofistikerad textextraktion avsevärt kan förbättra identifieringen av biverkningstermer i biverkningstexter jämfört med en ordagrann extraktion. / Background: Initial review of potential safety issues related to the use of medicines involves reading and searching existing medical literature sources for known associations of drug and adverse drug reactions (ADRs), so that they can be excluded from further analysis. The task is labor demanding and time consuming. Objective: To develop a text extraction tool to automatically identify ADR information from medical adverse effects texts. Evaluate the performance of the tool’s underlying text extraction algorithm and identify what parts of the algorithm contributed to the performance. Method: A text extraction tool was implemented on the .NET platform with functionality for preprocessing text (removal of stop words, Porter stemming and use of synonyms) and matching medical terms using permutations of words and spelling variations (Soundex, Levenshtein distance and Longest common subsequence distance). Its performance was evaluated on both manually extracted medical terms (semi-structuredtexts) from summary of product characteristics (SPC) texts and unstructured adverse effects texts from Martindale (i.e. a medical reference for information about drugs andmedicines) using the WHO-ART and MedDRA medical term dictionaries. Results: For the SPC data set, a verbatim match identified 72% of the SPC terms. The text extraction tool correctly matched 87% of the SPC terms while producing one false positive match using removal of stop words, Porter stemming, synonyms and permutations. The use of the full MedDRA hierarchy contributed the most to performance. Sophisticated text algorithms together contributed roughly equally to the performance. Phonetic codes (i.e. Soundex) is evidently inferior to string distance measures (i.e. Levenshtein distance and Longest common subsequence distance) for fuzzy matching in our implementation. The string distance measures increased the number of matched SPC terms, but at the expense of generating false positive matches. Results from Martindaleshow that 90% of the identified medical terms were correct. The majority of false positive matches were caused by extracting medical terms not describing ADRs. Conclusion: Sophisticated text extraction can considerably improve the identification of ADR information from adverse effects texts compared to a verbatim extraction.

The society today is dependent on information technology and with the help of the technologymakes it easier to access information. Due to the constantly growing network environment,various techniques of accessing and handling information have developed. One of the mostused solutions to access and protect information is by using a password. The purpose of apassword is to protect sensitive and important data from unauthorized users who intentionallyor accidentally access the system. This can lead to unsolicited modifications of the original dataas well as unauthorized access of confidential information. Humans are those who design theinformation security, but at the same time the ones who are the weakest link in the securitychain. To prevent unauthorized access it is important to have a strong and tamper proofpassword. A good password should be easy to remember, hard to guess by others and bedifficult to predict by a person or software. The goal in this study is to find a good balancebetween a memorable and a secured password. The study will compare three types ofconstructions for password, own set, modified dictionary and association against each other tofind the one which is the strongest and the most memorable.

The goal of this master thesis was to design an algorithm that will be able to measure the difference between two programs based on their behavioral description. For the algorithm needs, the Levenshtein distance method between two strings and NCD method, were used. Both methods have their implementation approach and test result described. This term also discusses various methods of program analysis in virtual machine environment, as well as explanation of some basic concepts regarding malware analysis.

Efficiency in algorithms is important, especially in terms of execution time, as it directly impacts user experience. For example, when a customer visits a website, even a mere one-second delay can significantly reduce their patience, and the likelihood of them abandoning the site increases. This principle applies to search algorithms as well. This project is about implementing a time-efficient tree-based search algorithm that focuses on finding similarities between search input and stored data. The objective is to achieve an execution time as close to O(1) regardless of the data size. The implemented algorithm will be compared with a linear search algorithm, which has an execution time that grows along with the data size. By measuring the executiontimes of both search methods, the project aims to demonstrate the superiority of the tree-based search algorithm in terms of time efficiency.

<p>This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology&nbsp / resented can be integrated with the researchers&rsquo / own knowledge, experimentation and observations for optimal progression of scientific research.</p>

<p>This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology&nbsp / resented can be integrated with the researchers&rsquo / own knowledge, experimentation and observations for optimal progression of scientific research.</p>

In this study, the plant layout problem of a worldwide Printed Circuit Board (PCB) producer company is analyzed. Machines are grouped into cells using grouping methodologies of Tabular Algorithm, K-means clustering algorithm, and Hierarchical grouping with Levenshtein distances. Production plant layouts, which are formed by using different techniques, are evaluated using technical and economical indicators.

Philosophiae Doctor - PhD / This thesis describes and introduces novel methods for knowledge discovery and presents a software system that is able to extract information from biomedical literature, review interesting connections between various biomedical concepts and in so doing, generates new hypotheses. The experimental results obtained by using methods described in this thesis, are compared to currently published results obtained by other methods and a number of case studies are described. This thesis shows how the technology, resented can be integrated with the researchers own knowledge, experimentation and observations for optimal progression of scientific research. / South Africa