L'objectif de cette recherche est la construction manuelle de ressources lexicales pour les noms composés grecs qui sont définis par la structure morphosyntaxique : Nom (E+Déterminant au génitif) Nom au génitif, notés N (E+DET:G) N:G (e.g. ζώνη ασφαλείας/ceinture de sécurité). Les ressources élaborées peuvent être utilisées pour leur reconnaissance lexicale automatique dans les textes écrits et dans d'autres applications du TAL. Notre travail s'inscrit dans la perspective de l'élaboration du lexique-grammaire général du grec moderne en vue de l'analyse automatique des textes écrits. Le cadre théorique et méthodologique de cette étude est celui du lexique-grammaire (M. Gross 1975, 1977), qui s'appuie sur la grammaire transformationnelle harisienne.Notre travail s'organise en cinq parties. Dans la première partie, nous délimitons l'objet de notre travail tout en essayant de définir la notion fondamentale qui régit notre étude, à savoir celle de figement. Dans la deuxième partie, nous présentons la méthodologie utilisée pour le recensement de nos données lexicales et nous étudions les phénomènes de variation observés au sein des noms composés de type N (E+DET:G) N:G. La troisième partie est consacrée à la présentation des différentes sous-catégories des N (E+DET:G) N:G identifiées lors de l'étape du recensement et à l'étude de leur structure lexicale interne. La quatrième partie porte sur l'étude syntaxico-sémantique des N (E+DET:G) N:G. Enfin, dans la cinquième partie, nous présentons les différentes méthodes de représentation formalisée que nous proposons pour nos données lexicales en vue de leur reconnaissance lexicale automatique dans les textes écrits. Des échantillons représentatifs des ressources élaborées sont présentés en Annexe / The object of this research is the manual construction of lexical resources for the Greek compound nouns defined by the following morphosyntactic structure : Noun (E+Determiner in genitive) Noun in genitive, (N (E+DET:G) N:G) (e.g. ζώνη ασφαλείας/safety belt). The elaborated resources may be used for their automatic recognition in written texts and other NLP applications. Our study is part of the general lexicon-grammar for Modern Greek in view of automatic processing of written texts. Our theoretical and methodological framework is that of lexicon-grammar (M. Gross 1975, 1977), based on the Transformational Grammar principles defined by Z. S. Harris. Our study is organised into five parts. In the first part, we give an overview of the core notion governing our research : the notions of (fixed) multiword expression (MWE). In the second part, we present the methodology used to collect our lexical data and we study the variation phenomena observed within the framework of the N (E+DET:G) N:G. The third part is dedicated to the presentation of the different N (E+DET:G) N:G categories identified in the listing phase qnd to the study of their lexical composition. The fourth concerns the syntactical and semantic study of the N (E+DET:G) N:G. Finally, the fifth part deals with the formal representation methods we propose for our lexical data in view of their lexical recognition in Greek written texts. Representative samples of the elaborated resources are illustrated in Appendix

Many language instructors focus on vocabulary word by word, neglecting common phrases. The result is that English as a Second Language students do not learn to speak idiomatic English (i.e. they make collocation errors). This study of the English compositions of National Tainan Second Senior High School students in Taiwan examined collocation errors, categorizing them according to Benson, Benson and Ilson's Collocation Classification System. An examination was then made of the error types as correlated with general English proficiency.

Master thesis focuses on wireless network security. The thesis is divided in two parts. First part describes today’s used standards and their components, topology and security methods as stealth SSID, MAC addresses filtration, WEP, WPA and WPA2. The last three methods are described in detail. In second part there are realized attacks on above described methods of security. There are described attacks on WEP as KoreK chopchop attack, fragment attack, attack FMS, KoreK and attack PTW. Then is described the dictionary attack on passphrase by WPA/WPA2 with PreShared Key authentication obtaining, precomputed hash tables for faster passphrase finding and for using more core procesors during dictionary browsing. The last attack describes obtaining of keystream used for encrypting of frames by WPATKIP and then sending custom data to client. It is described how to carry out each attack and how to protect against them.

The goal of the thesis is being familiar with methods a techniques used in speech processing. Describe the current state of research and development of speech technology. Project and implement server speech recognizer that uses BSAPI. Integrate client that will use server for speech recognition to mobile dictionaries of Lingea company.

(EN) Valency Dictionary of Arabic Verbs PhDr. Viktor Bielický This dissertation serves as a theoretical background for creating a valency dictionary of verbs in Modern Standard Arabic (MSA) which is primarily in the form of an electronic database. The Functional Generative Description is used as a theoretical framework for the description of verbal valency in Arabic. Chapter 1 introduces the verbal system of MSA with an emphasis on semantic relations within the system of verbal morpho- semantic patterns (verbal stems). Chapter 2 outlines the current research status of valency in Arabic including various research projects focused on creating electronic databases for Arabic verbs. Chapter 3 presents an overview of a valency theory developed within the Funtional Generative Description and it also provides a brief description of two broad coverage valency dictionaries for the Czech language - VALLEX and PDT-Vallex. Chapter 4, the core of the study, analyses semantics and the surface realization of different types of valency complemens in MSA, i.e. actants, quasi-valency and free (adverbial) complements. The final chapter, Chapter 5, describes the methodology and the structure of the valency dictionary the printed version of which is attached to the thesis. Key words: verbal valency, Modern Standard...

Detta examensarbete är en utvärdering av nätverkssäkerheten hos J BiL AB, både på social och teknisk nivå. Företaget är beroende av säkra Internet-anslutningar för att nå externa tjänster och interna servrar lokaliserade på olika geografiska platser. Företaget har ingen IT-ansvarig som aktivt underhåller och övervakar nätverket, utan konsulterar ett externt dataföretag. Syftet med examensarbetet är att utvärdera säkerheten, upptäcka brister, ge förbättringsförslag och till viss del implementera lösningar. För att undersöka säkerheten har observationer och intervjuer med personalen gjorts och ett flertal attacker mot nätverket har utförts. Utifrån den data som samlats in kunde slutsatsen dras att företaget har brister vad gäller IT-säkerheten. Framförallt den sociala säkerheten visade sig ha stora luckor vilket till stor del beror på att de anställda varken har blivit utbildade eller fått någon information om hur de ska hantera lösenord, datorer och IT-frågor i allmänt. Förbättringsförslag har getts och viss implementation har genomförts för att eliminera bristerna. De anställda har även med hjälp av en IT-policy och föreläsning blivit utbildade i hur de ska agera och tänka kring IT-relaterade säkerhetsfrågor. / The aim of this project is to evaluate the network security at J Bil AB. The focus will be on both social and technical issues. For the employees to be able to con-nect to remote servers and external services and perform their daily work tasks, secure connections is needed. J Bil Ab has no IT manager who actively maintains and monitors the network; rather they consult a computer company when changes and implementations are required. The projects’ goal is to identify gaps, come up with suggestions for improvement and to some extent implement so-lutions. To do this, an observation of the employees hav been made, an inter-view have been held, and several attacks on the network have been performed. Based on the data collected, it was concluded that the company has shortcom-ings in IT security. Above all, the social security appeared to have major gaps in it and that is mainly because the lack of knowledge among the employees and they have never been informed of how to manage their passwords, computers and IT issues in general. Suggestions for improvement have been given and some implementations have been performed to eliminate the deficiencies.

In large antenna systems, traditional channel estimation is costly and infeasible in some situations. Compressive sensing was proposed to estimate the channel with fewer measurements. Most of the previous work uses a predefined discrete Fourier transform matrix or overcomplete Fourier transform matrix to approximate the channel. Then, a learned dictionary trained by K-singular value decomposition (K-SVD) was proposed and was proved superiority using orthogonal matching pursuit (OMP) to reconstruct the sparse channel. However, with the development of compressive sensing, there are plenty of dictionary learning algorithms and sparse recovery algorithms. It is important to identify the effect and the performance of different algorithms when transforming the high dimensional channel vectors to low dimensional representations. In this thesis, we use a spatial channel model to generate channel vectors. Dictionaries are trained by K-SVD and method of optimal directions (MOD). Several sparse recovery algorithms are used to find the sparse approximation of the channel like OMP and gradient descent with sparsification (GraDeS). We present simulation results and discuss the performance of the various algorithms in terms of accuracy, sparsity, and complexity. We find that predefined dictionaries works with most of the algorithms in sparse recovery but learned dictionaries only work with pursuit algorithms, and only show superiority when the algorithm coincides with the algorithm in the sparse coding stage. / I stora antennsystem är traditionell kanaluppskattning kostsam och omöjlig i vissa situationer. Kompressionsavkänning föreslogs för att uppskatta kanalen med färre mätningar. Det mesta av det tidigare arbetet använder en fördefinierad diskret Fourier transformmatris eller överkompletterad Fourier -transformmatris för att approximera kanalen. Därefter föreslogs en inlärd ordbok som utbildats av K-SVD och bevisades överlägsen med hjälp av OMP för att rekonstruera den glesa kanalen. Men med utvecklingen av komprimerad avkänning finns det gott om algoritmer för inlärning av ordlistor och glesa återställningsalgoritmer. Det är viktigt att identifiera effekten och prestandan hos olika algoritmer när de högdimensionella kanalvektorerna omvandlas till lågdimensionella representationer. I denna avhandling använder vi en rumslig kanalmodell för att generera kanalvektorer. Ordböcker tränas av K-SVD och MOD. Flera glesa återställningsalgoritmer används för att hitta den glesa approximationen av kanalen som OMP och GraDeS. Vi presenterar simuleringsresultat och diskuterar prestanda för de olika algoritmerna när det gäller noggrannhet, sparsamhet och komplexitet. Vi finner att fördefinierade ordböcker fungerar med de flesta algoritmerna i gles återhämtning, men inlärda ordböcker fungerar bara med jaktalgoritmer och visar bara överlägsenhet när algoritmen sammanfaller med algoritmen i det glesa kodningsstadiet.

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Machine learning can provide assistance to humans in making decisions, including linguistic decisions such as determining the part of speech of a word. Supervised machine learning methods derive patterns indicative of possible labels (decisions) from annotated example data. For many problems, including most language analysis problems, acquiring annotated data requires human annotators who are trained to understand the problem and to disambiguate among multiple possible labels. Hence, the availability of experts can limit the scope and quantity of annotated data. Machine-learned pre-annotation assistance, which suggests probable labels for unannotated items, can enable expert annotators to work more quickly and thus to produce broader and larger annotated resources more cost-efficiently. Yet, because annotated data is required to build the pre-annotation model, bootstrapping is an obstacle to utilizing pre-annotation assistance, especially for low-resource problems where little or no annotated data exists. Interactive pre-annotation assistance can mitigate bootstrapping costs, even for low-resource problems, by continually refining the pre-annotation model with new annotated examples as the annotators work. In practice, continually refining models has seldom been done except for the simplest of models which can be trained quickly. As a case study in developing sophisticated, interactive, machine-assisted annotation, this work employs the task of corpus-dictionary linkage (CDL), which is to link each word token in a corpus to its correct dictionary entry. CDL resources, such as machine-readable dictionaries and concordances, are essential aids in many tasks including language learning and corpus studies. We employ a pipeline model to provide CDL pre-annotations, with one model per CDL sub-task. We evaluate different models for lemmatization, the most significant CDL sub-task since many dictionary entry headwords are usually lemmas. The best performing lemmatization model is a hybrid which uses a maximum entropy Markov model (MEMM) to handle unknown (novel) word tokens and other component models to handle known word tokens. We extend the hybrid model design to the other CDL sub-tasks in the pipeline. We develop an incremental training algorithm for the MEMM which avoids wasting previous computation as would be done by simply retraining from scratch. The incremental training algorithm facilitates the addition of new dictionary entries over time (i.e., new labels) and also facilitates learning from partially annotated sentences which allows annotators to annotate words in any order. We validate that the hybrid model attains high accuracy and can be trained sufficiently quickly to provide interactive pre-annotation assistance by simulating CDL annotation on Quranic Arabic and classical Syriac data.