Social networking has encouraged users to find new ways to create, post, search, collaborate and share information of various forms. Unfortunately there is a lot of data in social networks that is not well-managed, which makes the experience within these networks less than optimal. Therefore people generally need more and more time as well as advanced tools that are used for seeking relevant information. A new search paradigm is emerging, where the user perspective is completely reversed: from finding to being found. The aim of present thesis research is to evaluate two approaches of identifying content of interest: user-initiated and system-initiated. The most suitable approaches will be implemented. Various recommendation systems for system-initiated content recommendations will also be investigated, and the best suited ones implemented. The analysis that was performed demonstrated that the users have used all of the implemented approaches and have provided positive and negative comments for all of them, which reinforces the belief that the methods for the implementation were selected correctly. The results of the user testing of the methods were evaluated based on the amount of time it took the users to find the desirable content and on the correspondence of the result compared to the user expectations.

Submitted diploma thesis considers empirical comparison of knowledge discovery in databases systems. Basic terms and methods of knowledge discovery in databases domain are defined and criterions used to system comparison are determined. Tested software products are also shortly described in the thesis. Results of real task processing are brought out for each system. The comparison of individual systems according to previously determined criterions and comparison of competitiveness of commercial and non-commercial knowledge discovery in databases systems are performed within the framework of thesis.

The main focus of this paper is to improve the ServiceNow Discovery tool in two areas. The first one is the Application Dependency Mapping (ADM). ServiceNow is currently not able to determine the type of the relationship between two applications. As a solution, a new application was designed and implemented. It enables human element to define the type of relationship before it is found by the Discovery. This application also makes it posiible to define rules, which affect the behaviour of ADM. Second area, taken into account in this paper, is the discovery of application configuration. Weaknesses of Discovery are identified and solution is implemented.

Modern heterogeneous networks present a great challenge for network operators and engineers from a management and configuration perspective. The Tail-f Systems’ Network Control System (NCS) is a network management framework that addresses these challenges. NCS offers centralized network configuration management functionality, along with providing options for extending the framework with additional features. The devices managed by NCS are stored in its Configuration Database (CDB). However, currently there is no mechanism for automatically adding network devices to the configuration of NCS, thus each device’s management parameters have to be entered manually. The goal of this master’s thesis project is to develop a software module for NCS that simplifies the process of initial NCS configuration by allowing NCS to automatically add network devices to the NCS CDB. Apart from developing the software module for discovery, this project aims to summarize existing methods and to develop new methods for automated discovery of network devices with the main focus on differentiating between different types of devices. A credential-based device discovery method was developed and utilized to make advantage of known credentials to access devices, which allows for more precise discovery compared to some other existing methods. The selected methods were implemented as a component of NCS to provide device discovery functionality. Another focus of this master’s thesis project was the development of an approach to network topology discovery and its representation. The aim is to provide both a logical Internet Protocol (IP) network topology and a physical topology of device interconnections. The result is that we are able to automatically discover and store the topology representation as a data structure, and subsequently generate a visualization of the network topology. / Moderna heterogena nätverk utgör en stor utmaning för operatörer och ingenjörer att hantera och konfigurera. Tail-f Systems NCS produkt är ett ramverk för nätverks konfiguration som addresserar dessa utmaningar. NCS är ett centraliserat nätverks konfigurations verktyg. NCS är användbart som det är, men kan också byggas ut av användaren med ytterligare funktioner. De enheter som hanteras med NCS lagras i konfigurationsdatabasen (CDB). För närvarande finns det ingen automatiserad mekanism för att addera nätverksenheter till NCS, och varje enhets parametrar måste fyllas i manuellt. Detta examensarbetes mål är att utveckla en mjukvarumodul för NCS som förenklar NCS konfiguration genom att automatiskt lägga nätverksenheter till NCS CDB. Förutom att utveckla programvara för enhetsidentifiering, syftar detta projekt till att sammanfatta befintliga metoder och utveckla nya metoder för automatiserad nätverksenhetsidentifiering med huvudfokus på att skilja mellan olika typer av enheter. En metod baserad på förkonfigurerade autenticeringsuppgifter har utvecklats och den används för att precist kunna identifiera olika typer av nätverkselement. De valda metoderna har implementerats som en optionell modul till NCS som erbjuder enhetsidentifieringsfunktionalitet. Ytterligare ett fokus för detta examensarbete har varit att utveckla metoder för identifieraing av nätverkstopologin, och modeller för hur topologin ska representeras.  Vi har syftat till att identifiera både den logiska IP nätverkstopologin (L3) och den fysiska topologin av sammankopplade enheter (L2). Den viktigaste uppgiften har varit att identifiera och lagra topologi representation som en datastruktur, och dessutom generera en visualisering av nätverkstopologin.

A l'origine conçus pour protéger le protocole de Découverte de Voisins (Neighbor Discovery Protocol, NDP) en IPv6, les adresses générées de manière cryptographique (Cryptographically Generated Addresses, CGA) et le protocole SEND (Secure Neighbor Discovery) doivent maintenant s'adapter au contexte de mobilité et à ses nouvelles fonctionnalités. Cette mobilité revêt de nombreuses formes : mobilité du noeud (Mobile IPv6, MIPv6), mobilité des routeurs (Network Mobility, NEMO) ou encore mobilité gérée par le réseau (Proxy Mobile IPv6). De nombreux changements doivent être opérés dans le protocole SEND : les opérations cryptographiques doivent être allégées pour les terminaux à faible capacité de calcul, les incompatibilités entre le partage d'adresse dans les protocoles de mobilité et le mécanisme de protection d'adresses de SEND doivent être corrigés, etc. Dans une première partie de cette thèse, nous présentons le protocole de Découverte de Voisins, les adresses CGA et le protocole de sécurité SEND. Nous étudions leurs limitations et, afin d'améliorer les performances, nous proposons l'utilisation de la cryptographie basée sur les courbes elliptiques (ECC). À travers une série de tests, nous mesurons l'impact de notre proposition. Par la suite, nous modifions les spécifications du protocole SEND afin de supporter de nouveaux algorithmes crytpographiques. Dans une deuxième partie, nous résolvons les incompatibilités entre le protocole SEND et les protocoles de mobilité (par ex. MIPv6) et entre le protocole SEND et les adresses anycast. Dans une dernière partie, nous présentons plusieurs contributions basées sur une utilisation dérivée des adresses CGA et du protocole SEND. / Originally designed to protect the Neighbor Discovery Protocol (NDP) (part of the IPv6 protocol suite), the Cryptographically Generated Addresses (CGA) and the Secure Neighbor Discovery (SEND) now need to be adapted to the context of Mobility and extended to new functionalities. The term "Mobility" encompasses many aspects, among them : node mobility (Mobile IPv6, MIPv6), router mobility (Network Mobility, NEMO) and network-based mobility management (Proxy Mobile IPv6, PMIPv6). Numerous changes need to be operated on the SEND protocol in order to comply with the Mobility : the cryptographic operations need to be adapted to operate on low power mobile nodes, the incompatibilities between the address sharing model of the mobile protocol and the address protections offered by SEND need to be fixed, etc. Firstly, we present the Neighbor Discovery protocol, the CGA addresses and the SEND protocol. We study their limitations, and, in order to improve their performances, we propose to replace the signature algorithm used in SEND (RSA) by the elliptic curves cryptography (ECC). We then evaluate the performances of our proposal. Subsequently, we modify the SEND protocol to include a signature algorithm selection mechanism. Secondly, we solve incompatilities between the SEND protocol and the mobility protocols (e.g. MIPv6) and between the SEND protocol and the anycast addresses. Finally, we present our contributions containing a derivate use of the CGA addresses and the SEND protocol.

<p dir="ltr">Humans encode approximately 100 deubiquitinating enzymes (DUBs) which are categorized into seven distinct subfamilies. Each family and representative has a unique expression, function and binding topology to ubiquitin. In addition to human DUBs, parasites, bacteria, and viruses contain DUBs with unique structures and functions. One subfamily of DUBs, the ubiquitin C-terminal hydrolases (UCH), has four structurally similar human members and two known members within the <i>Plasmodium falciparum</i> genome. Human UCHL1 and UCHL3 are genetically validated targets in oncology and <i>Plasmodium falciparum</i><i> </i>UCHL3 (PfUCHL3) is a prospective target for antimalarial drug development. Though these three UCH enzymes have potential as therapeutic targets, there is a significant lack of quality small molecule chemical probes to understand the underlying biology and function of the enzymes, pharmacologically validate the targets, and serve as leads for drug development in oncology and malaria.</p><p dir="ltr">The UCH enzymes are cysteine proteases, which our lab has leveraged to identify novel covalent small molecule inhibitors of each enzyme. The workflow for each hit identification and optimization campaign is similar. Covalent fragment screening of electrophilic small molecule libraries against the respective recombinant enzyme was performed to identify chemical space around each enzyme. Subsequent medicinal chemistry hit-to-lead optimization was undertaken to improve upon the moderately potent hit molecules to provide improved small molecule inhibitors for each enzyme. Inhibitor identification and optimization for UCHL1 is described in Chapter 2, revealing a novel scaffold and a cocrystal structure reveals a unique binding pose for UCHL1 inhibitors. These molecules were also characterized in breast cancer cells to validate UCHL1 as a therapeutic target in breast cancer. First-in-class covalent inhibitors of UCHL3 are described in Chapter 3. Medicinal chemistry optimization along with a cocrystal structure of the initial hit has revealed the molecular interactions of this novel inhibitory scaffold. PfUCHL3 inhibitor identification is described in Chapter 4. Characterization of these molecules against Plasmodium falciparum is described along with a comparison to a recently identified reversible PfUCHL3 inhibitor. Finally, conclusions and future directions toward the development of potent, drug-like inhibitors of each UCH enzyme is presented in Chapter 5.</p>

This diploma thesis discusses a modern concept of information retrieval and search engines for libraries and other academic institutions. The concept named "web scale discovery" comprises of search engines, whose main characteristics lie in simplicity and user friendliness for the end users while maintaining all functional qualities of traditional research databases. Users can search in a wide variety of international research databases, and also in local sources of an institution that are combined within a large central index. The theoretical section presents definitions of web scale discovery, which conceptually set the new method of information retrieval within the field of information and library science. A graphic scheme of the basic functionality of a web scale discovery system is presented. Also discussed are requirements for a modern discovery system, an overview of the current situation in the Czech Republic, and a short characteristic of commercially available discovery systems. The theoretical part concludes with a literature review of selected foreign research, studying user satisfaction with the new solution, the impact on electronic and print resources in libraries, and usability testing. The practical part presents an original research study - usability testing of EBSCO Discovery...

La découverte de motifs qui caractérisent fortement une classe vis à vis d'une autre reste encore un problème difficile en fouille de données. La découverte de sous-groupes (Subgroup Discovery, SD) est une approche formelle de fouille de motifs qui permet la construction de classifieurs intelligibles mais surtout d'émettre des hypothèses sur les données. Cependant, cette approche fait encore face à deux problèmes majeurs : (i) comment définir des mesures de qualité appropriées pour caractériser l'intérêt d'un motif et (ii) comment sélectionner une méthode heuristique adaptée lorsqu’une énumération exhaustive de l'espace de recherche n'est pas réalisable. Le premier problème a été résolu par la fouille de modèles exceptionnels (Exceptional Model Mining, EMM) qui permet l'extraction de motifs couvrant des objets de la base de données pour lesquels le modèle induit sur les attributs de classe est significativement différent du modèle induit par l'ensemble des objets du jeu de données. Le second problème a été étudié en SD et EMM principalement avec la mise en place de méthodes heuristiques de type recherche en faisceau (beam-search) ou avec des algorithmes génétiques qui permettent la découverte de motifs non redondants, diversifiés et de bonne qualité. Dans cette thèse, nous soutenons que la nature gloutonne des méthodes d'énumération précédentes génère cependant des ensembles de motifs manquant de diversité. Nous définissons formellement la fouille de données comme un jeu que nous résolvons par l'utilisation de la recherche arborescente de Monte Carlo (Monte Carlo Tree Search, MCTS), une technique récente principalement utilisée pour la résolution de jeux et de problèmes de planning en intelligence artificielle. Contrairement aux méthodes traditionnelles d'échantillonnage, MCTS donne la possibilité d'obtenir une solution à tout instant sans qu'aucune hypothèse ne soit faite que ce soit sur la mesure de qualité ou sur les données. Cette méthode d'énumération converge vers une approche exhaustive si les budgets temps et mémoire disponibles sont suffisants. Le compromis entre l'exploration et l'exploitation que propose cette approche permet une augmentation significative de la diversité dans l'ensemble des motifs calculés. Nous montrons que la recherche arborescente de Monte Carlo appliquée à la fouille de motifs permet de trouver rapidement un ensemble de motifs diversifiés et de bonne qualité à l'aide d'expérimentations sur des jeux de données de référence et sur un jeu de données réel traitant de l'olfaction. Nous proposons et validons également une nouvelle mesure de qualité spécialement conçue pour des jeux de donnée multi labels présentant une grande variance de fréquences des labels. / The discovery of patterns that strongly distinguish one class label from another is still a challenging data-mining task. Subgroup Discovery (SD) is a formal pattern mining framework that enables the construction of intelligible classifiers, and, most importantly, to elicit interesting hypotheses from the data. However, SD still faces two major issues: (i) how to define appropriate quality measures to characterize the interestingness of a pattern; (ii) how to select an accurate heuristic search technique when exhaustive enumeration of the pattern space is unfeasible. The first issue has been tackled by Exceptional Model Mining (EMM) for discovering patterns that cover tuples that locally induce a model substantially different from the model of the whole dataset. The second issue has been studied in SD and EMM mainly with the use of beam-search strategies and genetic algorithms for discovering a pattern set that is non-redundant, diverse and of high quality. In this thesis, we argue that the greedy nature of most such previous approaches produces pattern sets that lack diversity. Consequently, we formally define pattern mining as a game and solve it with Monte Carlo Tree Search (MCTS), a recent technique mainly used for games and planning problems in artificial intelligence. Contrary to traditional sampling methods, MCTS leads to an any-time pattern mining approach without assumptions on either the quality measure or the data. It converges to an exhaustive search if given enough time and memory. The exploration/exploitation trade-off allows the diversity of the result set to be improved considerably compared to existing heuristics. We show that MCTS quickly finds a diverse pattern set of high quality in our application in neurosciences. We also propose and validate a new quality measure especially tuned for imbalanced multi-label data.

Recent years have seen the emergence of Mycobacterium abscessus, a highly drug-resistant non-tuberculous mycobacterium, which causes life-threatening infections in people with chronic lung conditions like cystic fibrosis. This opportunistic pathogen is refractory to treatment with standard anti-tuberculosis drugs and most currently available antibiotics, often resulting in accelerated lung function decline. This project aims to use a structure-guided fragment-based drug discovery approach to develop effective drugs to treat M. abscessus infections. During the early stage of the project, three bacterial targets were identified, based on analysis of the structural proteome of M. abscessus and prior knowledge of M. tuberculosis drug targets, followed by gene knockout studies to determine target essentiality for bacterial survival. The three targets from M. abscessus were then cloned, expressed and purified and suitable crystallization conditions were identified leading to the determination of high resolution structures. Further, a large number of starting fragments that hit the three target proteins were determined, using a combination of biophysical screening methods and by defining crystal structures of the complexes. For target 3, PPAT (Phosphopantethiene adenylyl transferase), a chemical linking of two fragments followed by iterative fragment elaboration was carried out to obtain two compounds with low micromolar affinities in vitro. However, these compounds afforded only low inhibitory activity on M. abscessus whole cell. All starting fragments of target 2, PurC (SAICAR synthase), occupied the ATP indole pocket. Efforts were then made to identify further fragment hits by screening diverse libraries. Sub-structure searches of these initial fragment hits and virtual screening helped to identify potential analogues amenable to further medicinal chemistry intervention. While fragment hits of target 1, TrmD (tRNA-(N1G37) methyl transferase), were prioritized, whereby two chemical series were developed using fragment growing and merging approaches. Iterative fragment elaboration cycle, aided by crystallography, biophysical and biochemical assays led to the development of several potential lead candidates having low nano-molar range of in vitro affinities. Two such compounds afforded moderate inhibition of M. abscessus and stronger inhibition of M. tuberculosis and S. aureus cultures. Further chemical modifications of these compounds as well as others are now being done, to optimize cellular and in vivo activities, to be ultimately presented as early stage clinical candidates.

文化的重要性，巳躍居為國家戰略級的地位。兩岸政府都將文化創意產業的發展列入下一波的新興產業，期許它能成為推動下一波經濟的新引擎。然而，文創產業除了強調文化在地性之價值，更重視與國際接軌，才有機會跨越國界，創造出如韓劇《大長今》遍及全球60國家的900億新台幣的週邊效益（超越750萬台EeePC的產值）。 八十年代起，各國政府對媒體解除管制，及新科技的推波助瀾，美國電視集團紛紛前進海外市場佈局。本研究針對Discovery傳播集團如何建立全球品牌，在國際化的過程將節目及行銷依不同市場選擇不同的在地化策略，最終將紀實影片的利基市場，從美國擴大到歐洲、亞洲、拉丁美洲等國際市場，成為全球性的紀實娛樂頻道的領導品牌。 Discovery傳播集團因其知識性與教育性的內容，天生具備「政府友善」(Government Friendly)的特質，迅速在美國成長。但其國際化初期，因節目版權、各國政府法令限制在地製作內容的比例、觀眾偏好不同等挑戰，進而推出尋求在地人才製作在地節目的《新銳導演計畫》，並舉辦紀實影片研討會廣納人才。隨後又與BBC合作推出大成本、大製作的紀實影片，推出「與世界同步觀賞」的世界級節目，建立全球一致的品牌形象。過程中，Discovery累積並發展出其核心能力，並複製到其他市場，是最終建立其全球品牌價值、優勢、及持續成長的重要關鍵。 透過四個個案的分析－《流言終結者》、《新銳導演計畫》、《聖母峰：攀越巔峰》及《瘋台灣》，探討Discovery「節目與行銷」vs.「標準化及在地化」四種策略的成效，進一步確認「在地化」因子，在國際化過程極為重要的關鍵。「在地化」架構一個平台，讓在地的觀眾、媒體、廣告主、政府能有進一步參與的角色，並達成以5R的綜效（Local Relevant、Rating、Relationship、Revenue、Recognition）。 關鍵詞：國際化、標準化與在地化、紀實娛樂、Discovery頻道。 / The huge success of Cool Britannia, Korean drama & K-pop, cultural and creative industries, aka soft power, are considered key drivers in booting economic growth by Taiwan & Mainland China governments. The key factor behind this success is internationalizing the unique local culture. The objective of this study is to reveal the internationalization history, the factor & the formula behind the World’s number one real-world entertainment media - Discovery Communications Inc. What challenges did Discovery confronted back to early 90s, and what competence the company built up through limited resources over the past 27 years, and how it adopted the success formula from Europe to Asia and make Discovery a global brand. The research analyzes four cases which include Mythbusters, First Time Filmmakers, Everest and Fun Taiwan by the framework of standardization and localization vs. programming & marketing. Through the process, this work reveals the key successful factor is localization either in programing or marketing. Therefore it creates a local-friendly platform to further engage local partners- viewer, advertiser, media & government to take part in and achieve a multi-purposes success – the 5R synergy- Local Relevant, Rating, Relationship, Recognition & Revenue. Key words: internationalization, Discovery Channel, real-world entertainment, standardization & localization