Ce travail illustre comment les tendances actuelles d'utilisation dominantes sur Internet motivent la recherche sur des architectures futures de réseau plus orientées vers le contenu. Parmi les architectures émergentes pour l'Internet du futur, le paradigme du Information-Centric Networking (ICN) est présenté. ICN vise à redéfinir les protocoles de base d'Internet afin d'y introduire un changement sémantique des hôtes vers les contenus. Parmi les architectures ICN, le Named-Data Networking (NDN) prévoit que les demandes nommées de contenus des utilisateurs soient transmises par leur nom dans les routeurs le long du chemin d'un consommateur à une ou plusieurs sources de contenus. Ces demandes de contenus laissent des traces dans les routeurs traversés qui sont ensuite suivis par les paquets de contenus demandés. La table d'intérêt en attente (PIT) est le composant du plan de données de l'NDN qui enregistre temporairement les demandes de contenus acheminés dans les routeurs. D'une part, ce travail explique que le mécanisme d'acheminement à états de la PIT permet des propriétés comme l'agrégation de requêtes, le multicast de réponses et le contrôle natif de flux hop-by-hop. D'autre part, ce travail illustre comment l'acheminement à états de la PIT peut facilement être mal utilisé par des attaquants pour monter des attaques de déni de service distribué (DDoS) disruptives, appelées Interest Flooding Attacks (IFAs). Dans les IFAs, des botnets vaguement coordonnés peuvent inonder le réseau d'une grande quantité de demandes difficiles à satisfaire dans le but de surcharger soit l'infrastructure du réseau soit les producteurs de contenus. Ce travail de thèse prouve que bien que des contre-mesures contre les IFAs aient été proposées, il manque une compréhension complète de leur efficacité réelle puisque celles-ci ont été testées sous des hypothèses simplistes sur les scénarios d'évaluation. Dans l'ensemble, le travail présenté dans ce manuscrit permet de mieux comprendre les implications des IFAs et les opportunités d'améliorer les mécanismes de défense existants contre ces attaques. Les principales contributions de ce travail de thèse tournent autour d'une analyse de sécurité du plan d'acheminement dans l'architecture NDN. En particulier, ce travail définit un modèle d'attaquant plus robuste pour les IFAs à travers l'identification des failles dans les contre-mesures IFA existantes. Ce travail introduit un nouvel ensemble d'IFAs basé sur le modèle d'attaquant proposé. Les nouveaux IFAs sont utilisés pour réévaluer les plus efficaces contre-mesures IFA existantes. Les résultats de cette évaluation réfutent l'efficacité universelle des mécanismes de défense existants contre l'IFA et, par conséquent, appellent à différentes contre-mesures pour protéger le NDN contre cette menace de sécurité. Pour surmonter le problème révélé, ce travail définit également des contre-mesures proactives contre l'IFA, qui sont de nouveaux mécanismes de défense contre les IFA inspirés par les problèmes rencontrés dans l'état de l'art. Ce travail présente Charon, une nouvelle contre-mesure proactive contre l'IFA, et la teste contre les nouvelles IFAs. Ce travail montre que Charon est plus efficace que les contre-mesures IFA réactives existantes. Enfin, ce travail illustre la conception NDN.p4, c'est-à-dire la première implémentation d'un protocole ICN écrit dans le langage de haut niveau pour les processeurs de paquets P4. Le travail NDN.p4 est la première tentative dans la littérature visant à tirer parti des nouveaux techniques de réseaux programmables pour tester et évaluer différentes conceptions de plan de données NDN. Cette dernière contribution classe également les mécanismes alternatifs d'acheminement par rapport à un ensemble de propriétés cardinales de la PIT. Le travail souligne qu'il vaut la peine d'explorer d'autres mécanismes d'acheminement visant à concevoir un plan de données NDN moins vulnérable à la menace IFA / This work illustrates how today's Internet dominant usage trends motivate research on more content-oriented future network architectures. Among the emerging future Internet proposals, the promising Information-Centric Networking (ICN) research paradigm is presented. ICN aims to redesign Internet's core protocols to promote a shift in focus from hosts to contents. Among the ICN architectures, the Named-Data Networking (NDN) envisions users' named content requests to be forwarded by their names in routers along the path from one consumer to 1-or-many sources. NDN's requests leave trails in traversed routers which are then followed backwards by the requested contents. The Pending Interest Table (PIT) is the NDN's data-plane component which temporarily records forwarded content requests in routers. On one hand, this work explains that the PIT stateful mechanism enables properties like requests aggregation, multicast responses delivery and native hop-by-hop control flow. On the other hand, this work illustrates how the PIT stateful forwarding behavior can be easily abused by malicious users to mount disruptive distributed denial of service attacks (DDoS), named Interest Flooding Attacks (IFAs). In IFAs, loosely coordinated botnets can flood the network with a large amount of hard to satisfy requests with the aim to overload both the network infrastructure and the content producers. This work proves that although countermeasures against IFAs have been proposed, a fair understanding of their real efficacy is missing since those have been tested under simplistic assumptions about the evaluation scenarios. Overall, the work presented in this manuscript shapes a better understanding of both the implications of IFAs and the possibilities of improving the state-of-the-art defense mechanisms against these attacks. The main contributions of this work revolves around a security analysis of the NDN's forwarding plane. In particular, this work defines a more robust attacker model for IFAs by identifying flaws in the state-of-the-art IFA countermeasures. This work introduces a new set of IFAs built upon the proposed attacker model. The novel IFAs are used to re-assess the most effective existing IFA countermeasures. Results of this evaluation disproves the universal efficacy of the state-of-the-art IFA defense mechanisms and so, call for different countermeasures to protect the NDN against this threat. To overcome the revealed issue, this work also defines proactive IFA countermeasures, which are novel defense mechanisms against IFAs inspired by the issues with the state-of-the-art ones. This work introduces Charon, a novel proactive IFA countermeasure, and tests it against the novel IFA attacks. This work shows Charon counteracts latest stealthy IFAs better than the state-of-the-art reactive countermeasures. Finally, this work illustrates the NDN.p4 design, that is, the first implementation of an ICN protocol written in the high-level language for packet processors P4. The NDN.p4 work is the first attempt in the related literature to leverage novel programmable-networks technologies to test and evaluate different NDN forwarding plane designs. This last contribution also classifies existing alternative forwarding mechanisms with respect to a set of PIT cardinal properties. The work outlines that it is worth to explore alternative forwarding mechanisms aiming to design an NDN forwarding plane less vulnerable to the IFA threat

Au cours des dernières années, les réseaux de type (NDN) sont devenus une des architectures réseau les plus prometteuses. Pour être adopté à l'échelle d'Internet, NDN doit résoudre les problèmes inhérents à l'Internet actuel. La fuite d’informations fait partie de ces problèmes, et il est très important d’évaluer ce risque pour les réseaux de type NDN. La thèse se propose d'évaluer ce risque. En supposant (i) qu'un ordinateur appartient au réseau d'une entreprise basée sur une architecture NDN, (ii) que l'ordinateur a déjà été compromis par un support malveillant, et (iii) que la société installe un pare-feu, la thèse évalue la situation dans laquelle l’ordinateur infecté tente de divulguer des données à un attaquant externe à l'entreprise. Les contributions de cette thèse sont au nombre de cinq. Tout d'abord, cette thèse propose une attaque par fuite d'informations via un paquet donné et un paquet intérêt propres à NDN. Deuxièmement, afin de remédier à l'attaque fuite d'informations, cette thèse propose un pare-feu basé sur l'utilisation d'une liste blanche et d'une liste noire afin de surveiller et traiter le trafic NDN provenant des consommateurs. Troisièmement, cette thèse propose un filtre de noms NDN pour classifier un nom dans un paquet d'intérêt comme étant légitime ou non. Le filtre de noms peut ainsi réduire le débit par paquet d'intérêt. Cependant, pour adapter la vitesse de l'attaque, les logiciels malveillants peuvent envoyer de nombreux intérêts en très peu de temps. De même, le logiciel malveillant peut exploiter un intérêt avec une information explicite dans le nom (comme peut le faire un message véhiculé par un POST sur HTTP). Cela dépasse alors la portée du filtre de nom proposé et rend le filtre inefficace. Pour prendre en compte le flux de trafic analysé par le pare-feu NDN, cette thèse propose comme quatrième contribution la surveillance du flux NDN à travers le pare-feu. Enfin, afin de traiter les inconvénients du filtre de noms NDN, cette thèse propose un filtre de flux NDN permettant de classer un flux comme légitime ou non. L'évaluation des performances montre que le filtre de flux complète de manière tout à fait performante le filtre de nom et réduit considérablement le débit de fuite d'informations / In recent years, Named Data Networking (NDN) has emerged as one of the most promising future networking architectures. To be adopted at Internet scale, NDN needs to resolve the inherent issues of the current Internet. Since information leakage from an enterprise is one of the big issues even in the Internet and it is very crucial to assess the risk before replacing the Internet with NDN completely, this thesis investigates whether a new security threat causing the information leakage can happen in NDN. Assuming that (i) a computer is located in the enterprise network that is based on an NDN architecture, (ii) the computer has already been compromised by suspicious media such as a malicious email, and (iii) the company installs a firewall connected to the NDN-based future Internet, this thesis focuses on a situation that the compromised computer (i.e., malware) attempts to send leaked data to the outside attacker. The contributions of this thesis are fivefold. Firstly, this thesis proposes an information leakage attack through a Data and through an Interest in NDN. Secondly, in order to address the information leakage attack, this thesis proposes an NDN firewall which monitors and processes the NDN traffic coming from the consumers with the whitelist and blacklist. Thirdly, this thesis proposes an NDN name filter to classify a name in the Interest as legitimate or not. The name filter can, indeed, reduce the throughput per Interest, but to ameliorate the speed of this attack, malware can send numerous Interests within a short period of time. Moreover, the malware can even exploit an Interest with an explicit payload in the name (like an HTTP POST message in the Internet), which is out of scope in the proposed name filter and can increase the information leakage throughput by adopting a longer payload. To take traffic flow to the NDN firewall from the consumer into account, fourthly, this thesis proposes an NDN flow monitored at an NDN firewall. Fifthly, in order to deal with the drawbacks of the NDN name filter, this thesis proposes an NDN flow filter to classify a flow as legitimate or not. The performance evaluation shows that the flow filter complements the name filter and greatly chokes the information leakage throughput

La reconnaissance des entités nommées et une discipline cruciale du domaine du TAL. Elle sert à l'extraction de relations entre entités nommées, ce qui permet la construction d'une base de connaissance (Surdeanu and Ji, 2014), le résumé automatique (Nobata et al., 2002), etc... Nous nous intéressons ici aux phénomènes de structurations qui les entourent.Nous distinguons ici deux types d'éléments structurels dans une entité nommée. Les premiers sont des sous-chaînes récurrentes, que nous appelerons les affixes caractéristiques d'une entité nommée. Le second type d'éléments est les tokens ayant un fort pouvoir discriminant, appelés des tokens déclencheurs. Nous détaillerons l'algorithme que nous avons mis en place pour extraire les affixes caractéristiques, que nous comparerons à Morfessor (Creutz and Lagus, 2005b). Nous appliquerons ensuite notre méthode pour extraire les tokens déclencheurs, utilisés pour l'extraction d'entités nommées du Français et d'adresses postales.Une autre forme de structuration pour les entités nommées est de nature syntaxique, qui suit généralement une structure d'imbrications ou arborée. Nous proposons un type de cascade d'étiqueteurs linéaires qui n'avait jusqu'à présent jamais été utilisé pour la reconnaissance d'entités nommées, généralisant les approches précédentes qui ne sont capables de reconnaître des entités de profondeur finie ou ne pouvant modéliser certaines particularités des entités nommées structurées.Tout au long de cette thèse, nous comparons deux méthodes par apprentissage automatique, à savoir les CRF et les réseaux de neurones, dont nous présenterons les avantages et inconvénients de chacune des méthodes. / Named entity recognition is a crucial discipline of NLP. It is used to extract relations between named entities, which allows the construction of knowledge bases (Surdeanu and Ji, 2014), automatic summary (Nobata et al., 2002) and so on. Our interest in this thesis revolves around structuration phenomena that surround them.We distinguish here two kinds of structural elements in named entities. The first one are recurrent substrings, that we will call the caracteristic affixes of a named entity. The second type of element is tokens with a good discriminative power, which we call trigger tokens of named entities. We will explain here the algorithm we provided to extract such affixes, which we will compare to Morfessor (Creutz and Lagus, 2005b). We will then apply the same algorithm to extract trigger tokens, which we will use for French named entity recognition and postal address extraction.Another form of structuration for named entities is of a syntactic nature. It follows an overlapping or tree structure. We propose a novel kind of linear tagger cascade which have not been used before for structured named entity recognition, generalising other previous methods that are only able to recognise named entities of a fixed depth or being unable to model certain characteristics of the structure. Ours, however, can do both.Throughout this thesis, we compare two machine learning methods, CRFs and neural networks, for which we will compare respective advantages and drawbacks.

There are two major, well-known operas based on plays of Tennessee Williams. He refused many times throughout his life to give permission for his play, A Streetcar Named Desire, to be set as an opera. It was not until the 1960s that he granted permission for Lee Hoiby to choose any of his plays as a basis for a new opera. Hoiby chose Summer and Smoke, a play which was written at approximately the same time as Streetcar. Lanford Wilson created the libretto for the opera which was given its premier in 1971 by the St. Paul Opera Association. In 1994 representatives of the Williams estate granted permission to the San Francisco Opera to commission an opera based on A Streetcar Named Desire. With a libretto by Philip Littell, the opera was composed by André Previn and given its premier in 1998. These two plays share common themes, character types, character relationships, and literary symbols due in part to the autobiographical nature of Williams's writings. The plays exhibit a cinematic nature and possess common dramatic elements such as the symbolic use of sets, props, and musical leitmotifs as a result of his attempts to create a new "plastic" style of theatre. The purpose of this thesis is to examine how each composer has captured the essence of Williams's dramatic style in these well known plays while dealing with stylistic elements that by nature could interfere in operatic composition. A brief biography of Williams is included to show the familial basis of his character types. Illustrations of his style serve as the basis for a comparison of the librettos to the plays. The musical analysis focuses on the composers' choices in dealing with Williams's poetic southern language, use of music, cinematic techniques, and complex characterizations.

Fakturor är en viktig källa av information för företag. Två exempel på viktiga fält i en faktura kan vara, hur mycket pengar som ska betalas och faktura id. På grund av olika format och innehåll i fakturor som skiljer sig åt är extraktionen av information från dessa fakturor ofta en manuell process som kräver mycket tid. För att kunna spara viktig information från semi-strukturerade dokument som fakturor så måste vissa företag lägga ner mycket manuellt arbete. Detta arbete inkluderar att behöva förstå fakturan och därefter veta vilket innehåll som är av intresse för företaget. Detta arbete kan ta mycket tid och därför hade en automatisering av denna process varit av stort intresse. I denna forskningen används named entity recognition för att lösa problemet. De frågor som forskningen besvarar är: Hur effektiv named entity recognition är för klassificering av rubriker i fakturor, samt hur mycket effektiviteten kan öka vid komplettering av ytterligare komponenter. Named entity recognition används för att kategorisera entiteter som i detta fallet är rubriker för fält i fakturor. Modellen som skapas ska avgöra om rubriker i fakturan kan kategoriseras under någon av kategorierna: Invoice number, invoice date, due date, customer number, total amount, vat code, vat amount eller currency. Forskningen försöker endast göra en proof of concept för att se om denna algoritm kan användas för att minska tiden av manuellt arbete. Produktionsmodellen som skapas evalueras med måttet f1-score. Den får med denna metod resultatet 79 av 100. Detta resultatet antyder på att named entity recognition kan användas i ett verkligt scenario för att identifiera rubriker av intresse i en faktura. Men för att få så bra resultat som möjligt så bör modellen kombineras med en lösning som identifierar fält med hjälp av dess data. / Invoices are an important source of information for businesses. Two examples of important fields in an invoice could be the amount of money to be paid and the invoice Id. Due to the different formats and content of invoices, the extraction of information from these is often a manual and time consuming process. In order to save important information from semi-structured documents such as invoices, some companies have to put in a lot of manual work. This work includes understanding the invoice and then knowing what content is of interest to the company. This work can take a lot of time and therefore an automation of this process would be of great interest. In this research named entity recognition is used to solve the mentioned problem. The topics for this research are: How effective named entity recognition is for classification of headers in invoices, as well as how much the efficiency can be improved by complementing with further components. Named entity recognition is used to categorize entities. In this case the entities are the headings of the invoice. The model that is created must determine whether headings in the invoice can be categorized under one of the following categories: Invoice number, invoice date, due date, customer number, total amount, vat code, vat amount or currency. This research tries to make a proof of concept to discover if this algorithm can be used to reduce the time spent on manual work. The production model that is created is evaluated with the f1-score measurement. With this method, it gets a result of 79 out of 100. This result indicates that named entity recognition can be used by companies in real-world scenarios to identify headings in invoices. But to get the best results possible, the model should also be combined with a solution that identifies fields using its corresponding data.

Hantering av ostrukturerade och fysiska dokument inom vissa områden, såsom finansiell rapportering, medför betydande ineffektivitet i dagsläget. Detta examensarbete fokuserar på utmaningen att extrahera data från ostrukturerade finansiella dokument, specifikt balansräkningar i svenska årsredovisningar, genom att använda en AI-driven pipeline. Syftet är att utveckla en metod för att automatisera datautvinning och möjliggöra förbättrad dataanalys. Projektet fokuserade på att automatisera utvinning av finansiella poster från balansräkningar genom en kombination av Optical Character Recognition (OCR) och en modell för Named Entity Recognition (NER). TesseractOCR användes för att konvertera skannade dokument till digital text, medan en BERT-baserad NER-modell tränades för att identifiera och klassificera relevanta finansiella poster. Ett Python-skript användes för att extrahera de numeriska värdena som är associerade med dessa poster. Projektet fann att NER-modellen uppnådde hög prestanda, med ett F1-score på 0,95, vilket visar dess effektivitet i att identifiera finansiella poster. Den fullständiga pipelinen lyckades extrahera över 99% av posterna från balansräkningar med en träffsäkerhet på cirka 90% för numerisk data. Projektet drar slutsatsen att kombinationen av OCR och NER är en lovande lösning för att automatisera datautvinning från ostrukturerade dokument med liknande attribut som årsredovisningar. Framtida arbeten kan utforska att förbättra träffsäkerheten i OCR och utvidga utvinningen till andra sektioner av olika typer av ostrukturerade dokument. / The persistence of unstructured and physical document management in fields such as financial reporting presents notable inefficiencies. This thesis addresses the challenge of extracting valuable data from unstructured financial documents, specifically balance sheets in Swedish annual reports, using an AI-driven pipeline. The objective is to develop a method to automate data extraction, enabling enhanced data analysis capabilities. The project focused on automating the extraction of financial posts from balance sheets using a combination of Optical Character Recognition (OCR) and a Named Entity Recognition (NER) model. TesseractOCR was used to convert scanned documents into digital text, while a fine-tuned BERT-based NER model was trained to identify and classify relevant financial features. A Python script was employed to extract the numerical values associated with these features. The study found that the NER model achieved high performance metrics, with an F1-score of 0.95, demonstrating its effectiveness in identifying financial entities. The full pipeline successfully extracted over 99% of features from balance sheets with an accuracy of about 90% for numerical data. The project concludes that combining OCR and NER technologies could be a promising solution for automating data extraction from unstructured documents with similar attributes to annual reports. Future work could explore enhancing OCR accuracy and extending the methodology to other sections of different types of unstructured documents.

Master of Science / Department of Computing and Information Sciences / William H. Hsu / Global epidemic surveillance is an essential task for national biosecurity management and bioterrorism prevention. The main goal is to protect the public from major health threads. To perform this task effectively one requires reliable, timely and accurate medical information from a wide range of sources. Towards this goal, we present a framework for epidemiological analytics that can be used to extract and visualize infectious disease outbreaks from the variety of unstructured web sources automatically. More precisely, in this thesis, we consider several research tasks including document relevance classification, entity extraction and animal disease-related event recognition in the veterinary epidemiology domain. First, we crawl web sources and classify collected documents by topical relevance using supervised learning algorithms. Next, we propose a novel approach for automated ontology construction in the veterinary medicine domain. Our approach is based on semantic relationship discovery using syntactic patterns. We then apply our automatically-constructed ontology for the domain-specific entity extraction task. Moreover, we compare our ontology-based entity extraction results with an alternative sequence labeling approach. We introduce a sequence labeling method for the entity tagging that relies on syntactic feature extraction using a sliding window. Finally, we present our novel sentence-based event recognition approach that includes three main steps: entity extraction of animal diseases, species, locations, dates and the confirmation status n-grams; event-related sentence classification into two categories - suspected or confirmed; automated event tuple generation and aggregation. We show that our document relevance classification results as well as entity extraction and disease-related event recognition results are significantly better compared to the results reported by other animal disease surveillance systems.

LD2668 .T4 1966 W752 / Master of Science

Σήμερα η επιστήμη υπόσχεται νέες ανακαλύψεις που θα μας επιτρέπουν να ζούμε όχι μόνο περισσότερο αλλά και καλύτερα. Πρωτεύοντα ρόλο σε αυτή τη διαπίστωση κατέχει η φαρμακοβιομηχανία, όπου μέσω της συνεχούς έρευνας και ανάπτυξης παράγει νέα, καινοτόμα, πρωτότυπα φάρμακα. Τα πρωτότυπα φάρμακα εξακολουθούν να κατέχουν ηγετική θέση στην παγκόσμια φαρμακευτική αγορά, παρόλο που μετά τη λήξη της πατέντας τους δέχονται έντονες πιέσεις από την παραγωγή των αντίστοιχων γενοσήμων φαρμάκων. Είναι απαραίτητο να τονιστεί ο σημαντικός ρόλος των πρωτοτύπων στην προστασία της δημόσιας υγείας, όταν μάλιστα στην ελληνική αγορά έχουν εισέλθει μια πλειάδα γενόσημων φαρμάκων, υπό το μανδύα της οικονομικής κρίσης και της περιστολής των φαρμακευτικών δαπανών. Μέσα σε αυτό το πλαίσιο, η παρούσα διπλωματική εργασία προσπαθεί να αναζητήσει τους λόγους και τα κίνητρα της συνταγογράφησης των πρωτότυπων φαρμάκων από τους Έλληνες γιατρούς, μέσα σε ένα ταραχώδες συνεχώς μεταβαλλόμενο εξωτερικό περιβάλλον. Ουσιαστικά λοιπόν αναζητήσαμε ποιοι είναι αυτοί οι παράγοντες που κάνουν τους γιατρούς να συνταγογραφούν τα πρωτότυπα φάρμακα και πως αυτοί σχετίζονται με μια σειρά εξωτερικών παραγόντων. Τα ευρήματα της μελέτης προέκυψαν από τις απαντήσεις που έδωσαν 83 γιατροί διαφόρων ειδικοτήτων. Για την επιλογή των γιατρών δόθηκε έμφαση στη συμμετοχή ειδικοτήτων που έχουν υψηλή συνταγογράφηση, με κύριες ειδικότητες τους παθολόγους και τους ορθοπεδικούς. Η έρευνα πραγματοποιήθηκε σε γιατρούς των περιοχών των νομών Αχαΐας, Ηλείας και Κεφαλονιάς. Τα σημαντικότερα συμπεράσματα που προέκυψαν μετά τη στατιστική ανάλυση των απαντήσεων που δόθηκαν ήταν: 1) η πολύ μεγάλη αποδοχή που εξακολουθούν να έχουν τα πρωτότυπα φάρμακα από την ελληνική ιατρική κοινότητα, ακόμα και όταν υπάρχουν αντίστοιχα γενόσημα, 2) το ευρύτερο πλαίσιο των παραγόντων που επηρεάζουν τη συνταγογραφία των γιατρών είναι: α) η αποτελεσματικότητα, β) το κύρος/όνομα της φαρμακευτικής εταιρίας, γ) η τιμή του φαρμάκου, δ) η καινοτομία, ε) η ασφάλεια/ανεπιθύμητες ενέργειες, 3) οι εταιρίες με πρωτότυπα φάρμακα επηρεάζουν τη συνταγογραφία των γιατρών ανάλογα με το κύρος και το όνομά τους, 4) η άσχημη οικονομική κατάσταση που επικρατεί στη χώρα καθώς και τα μέτρα που έχουν ληφθεί στο χώρο της υγείας, δείχνουν να στρέφουν κάποιους γιατρούς από τα πρωτότυπα προς τα γενόσημα, 5) οι «διαμορφωτές γνώμης» επηρεάζουν σχετικά τη συνταγογραφία των Ελλήνων γιατρών, μόνο όμως όταν εμφανίζονται σε συνέδρια και συμπόσια ως ανεξάρτητοι επιστήμονες, ενώ δεν την επηρεάζουν καθόλου ως προσκεκλημένοι ομιλητές σε εταιρικές εκδηλώσεις, 6) οι πρακτικές marketing των εταιριών με πρωτότυπα υπερτερούν από τις αντίστοιχες των εταιριών με γενόσημα, 7) ο ιατρικός επισκέπτης επηρεάζει θετικά τη συνταγογραφία των γιατρών όταν είναι καλά ενημερωμένος για το φάρμακό του, 8) παρατηρείται τάση των γιατρών για χαμηλό ρίσκο στη συνταγογράφηση, 9) τα γενόσημα φάρμακα είναι πολύ πιο πιθανό να συνταγογραφηθούν σε αδύναμες οικονομικά και κοινωνικά ομάδες. Συμπερασματικά θα μπορούσαμε να πούμε πως οι Έλληνες γιατροί εξακολουθούν να εμπιστεύονται τόσο τα πρωτότυπα φάρμακα που προστατεύονται με πατέντα, όσο και τα πρωτότυπα εκτός πατέντας φαρμακευτικά σκευάσματα (σκευάσματα δηλαδή τα οποία αν και κυκλοφόρησαν ως πρωτότυπα έχουν απολέσει την προστασία της πατέντας τους). Παρόλο όμως που θεωρούν τα προαναφερθέντα φάρμακα αποτελεσματικά και αξιόπιστα, μια σειρά από εξωτερικούς παράγοντες τους δημιουργεί την τάση να κινηθούν προς την κατεύθυνση των γενοσήμων, περισσότερο από ότι στο πρόσφατο παρελθόν. / Today science promises new discoveries that will allow us to live not only longer but also better. The pharmaceutical industry plays a leading role to this fact where through continuous research and development, new, innovative, brand named drugs are produced. Brand named drugs continue to hold the leading place in the global pharmaceutical market, although after the expiration of their patent they face severe pressure from the production of their counterparts, generic drugs. It is essential to emphasize the important role that brand named drugs have to the protection of the public health, even though at the same time many unknown generic drugs have entered the Greek pharmaceutical market under the justification of the financial crisis and the containment of pharmaceutical expenditures. Under this prism, the present thesis tries to determine the reasons and the motivations of Greek physicians for prescribing brand names drugs, within a turbulent and constantly changing external environment. Our basic purpose was to discover the factors that make the physicians prescribebrand named drugs and how these factors were related with a number of external factors. The findings of this thesis are derived from the answers given by 83 doctors of different specialties. The specialties were those that have high prescription capability, with pathologists and orthopedics being the two most numerous specialties. The research was conducted with the participation of doctors from the counties of Achaia, Ilia and Cephalonia. The most important findings that came up after the statistical analysis of the answers given were: 1) The great acceptance that brand named drugs still have from the Greek medical community, even when their counterpart generics exist in the market 2) The main factors that influence the doctors prescriptions are: a) the efficacy of the medicine b) the prestige / name of the pharmaceutical company c) the price of the medicine d) the innovation of the medicine e) the safety / side effects 3) The pharmaceutical companies that produce brand named drugs influence the doctors prescriptions by their prestige and good name 4) The financial crisis in Greece and the measures that have been taken by the state for reducing the pharmaceutical expenditures seem to influence some doctors to prescribe more generics than they did in the past 5) “Opinion leaders” influence the prescription attitude of the Greek doctors only when they appear to congresses and forums as “independent” scientists. On the other hand they seem not to influence at all when they appear as speakers to any corporate event sponsored by a pharmaceutical company 6) Marketing practices of companies producing brand named drugs seem to be more effective than those that produce generic drugs 7) A medical representative that is well educated about his drug positively influences the prescription habits of the doctors 8) It is observed a tendency of doctors for taking low risk when they prescribe madicines to their patients 9) Generic drugs are more likely to be prescribed to low income and low social status groups of the population. In conclusion we can say that Greek doctors still trust not only the brand named drugs protected under patent, but also the off patent brand name drugs (brand named drugs that their patent has expired). Although they consider the aferementioned drugs effective and reliable, a number of external factors create a tendency for them to prescribe now more generic drugs than they used to prescribe in the recent past.

Through a comparison of Tennessee Williams’ Blanche in A Streetcar Named Desire and Neil LaBute’s Steph in reasons to be pretty, this thesis explores the ways in which the female position has both changed and remained relatively the same over the course of the last sixty years.