В данной дипломной работе была поставлена цель разработки эффективной рекомендательной системы для туристических достопримечательностей на основе графов и алгоритмов машинного обучения. Основная задача состояла в создании системы, которая может анализировать обширный набор данных о туристических достопримечательностях, извлекаемых из Википедии. Используя дампы Википедии, содержащие информацию о миллионах статей, был выполнен обзор существующих рекомендательных систем и методов машинного обучения, применяемых для предоставления рекомендаций в области туризма. Затем были выбраны определенные категории туристических достопримечательностей, которые были использованы для построения моделей рекомендаций. Для обработки и анализа данных из Википедии был использован современный технический стек инструментов, включающий Python, библиотеки networkx и pandas для работы с графами и данными, а также библиотеку scikit-learn для применения алгоритмов машинного обучения. Кроме того, для разработки интерактивного веб-интерфейса был использован фреймворк Streamlit. Процесс работы включал сбор и предварительную обработку данных из Википедии, включая информацию о достопримечательностях, связях между ними и характеристиках. Для создания графа данных на основе загруженных и обработанных данных были применены выбранные алгоритмы машинного обучения. Алгоритм PageRank был использован для определения важности каждой достопримечательности в графе и формирования персонализированных рекомендаций. Демонстрационный пользовательский интерфейс, разработанный на основе фреймворка Streamlit, позволяет пользователям взаимодействовать с системой, вводить запросы о местах и получать персонализированные рекомендации. С помощью выпадающего списка можно выбрать конкретную достопримечательность, к которой требуется получить рекомендации, а с помощью ползунка можно настроить количество рекомендаций. / This thesis aimed to develop an effective recommendation system for tourist attractions based on graphs and machine learning algorithms. The main challenge was to create a system that can analyze a large set of tourist attraction data extracted from Wikipedia. Using Wikipedia dumps containing information on millions of articles, a review of existing recommender systems and machine learning methods used to provide recommendations in the field of tourism was performed. Specific categories of tourist attractions were then selected and used to build recommendation models. To process and analyze data from Wikipedia, a modern technical stack of tools was used, including Python, the networkx and pandas libraries for working with graphs and data, as well as the scikit-learn library for applying machine learning algorithms. In addition, the Streamlit framework was used to develop an interactive web interface. The work process included the collection and preliminary processing of data from Wikipedia, including information about attractions, connections between them and characteristics. Selected machine learning algorithms were applied to create a data graph based on the downloaded and processed data. The PageRank algorithm was used to determine the importance of each point of interest in the graph and generate personalized recommendations. The demo user interface, developed using the Streamlit framework, allows users to interact with the system, enter queries about places and receive personalized recommendations. Using the drop-down list, you can select a specific attraction for which you want to receive recommendations, and using the slider, you can adjust the number of recommendations.

В данной магистерской работе мы исследуем возможности построения словаря волн биомедицинских сигналов электрокардиограммы, который в дальнейшем позволит применять методы NLP для обработки временных рядов биомедицинских сигналов. В частности, мы сосредоточимся на анализе структуры пиков и интервалов электрокардиограммы здоровых и больных аритмией и другими заболеваниями людей, средствами языка python и автоматизации этого процесса для извлечения значимой информации из биомедицинских временных рядов ЭКГ. Наша конечная цель – улучшение точности и эффективности обработки и анализа биомедицинских сигналов, что имеет важное значение как для клинической диагностики, так и для научных исследований. Решение этой задачи имеет большое практическое значение для различных областей, таких как медицина, биология и фармакология, где обработка и анализ временных рядов играют важную роль. / In this master's thesis, we are exploring the possibility of constructing a dictionary of waves of biomedical electrocardiogram signals, which in the future will allow the use of NLP methods for processing time series of biomedical signals. In particular, we will focus on analyzing the structure of peaks and intervals of the electrocardiogram of healthy people and patients with arrhythmia and other diseases, using the Python language and automating this process to extract meaningful information from biomedical ECG time series. Our ultimate goal is to improve the accuracy and efficiency of biomedical signal processing and analysis, which is important for both clinical diagnostics and scientific research. The solution to this problem is of great practical importance for various fields, such as medicine, biology and pharmacology, where processing and analysis of time series play an important role.

No description available.

Angesichts der zentralen Rolle der Root-Zertifizierungsstellen als Vertrauensanker der Web PKI und der in der Vergangenheit aufgetretenen Vorfälle mit unberechtigt oder inkorrekt ausgestellten Zertifikaten, ist die Transparenz und Verantwortlichkeit dieser Root CAs von großer Bedeutung. Seit der Einführung von Certificate Transparency Logs werden alle von Certificate Authorities ausgestellten Zertifikate in diese öffentlichen Logs eingetragen. Die Arbeit stellt die Problematik der eingeschränkten Zugänglichkeit dieser Daten für die Wissenschaft dar und entwickelt ein Werkzeug, dass eine unabhängige Aufzeichnung und Auswertung von Certificate Transparency Logs ermöglicht. Das entwickelte System nutzt eine containerbasierte Architektur und Elasticsearch zur effizienten Speicherung und Analyse der Daten. Es bewältigt ein hohes Datenaufkommen von durchschnittlich 25 Millionen Log-Einträgen pro Tag und ermöglicht eine anpassbare Datenverarbeitung und -auswertung. Die Vorverarbeitung und Indexierung sowie die Auswertung der Daten erfolgte mit Python, was eine flexible Anpassung des Systems an unterschiedliche Forschungsfragen erlaubt. Über einen Zeitraum von 42 Tagen wurden insgesamt 645 Millionen CT Log-Einträge aufgezeichnet und analysiert. Aus den Auswertungen geht hervor, wie verschiedene CAs und deren Root-Zertifikate genutzt werden und wie stark die unterschiedlichen CT Logs von CAs verwendet werden. Die Arbeit identifiziert jedoch auch Herausforderungen, wie den hohen Speicherbedarf und notwendige Optimierungen in der Datenindexierung.:1 Einleitung 1.1 Problemstellung 1.2 Zielstellung 2 Grundlagen 2.1 X509-Zertifikate 2.1.1 Felder 2.1.2 Erweiterungen 2.2 Certificate Transparency 2.2.1 Certificate Transparency Log 2.2.2 Überprüfung durch User Agents 2.2.3 Überprüfung durch Monitors 2.2.4 Eintragung durch Certificate Authorities 3 Konzeptionierung 3.1 Abfrage der CT Logs 3.2 Verarbeitung der Zertifikate 3.3 Speicherung & Auswertung der Daten 3.4 Überwachung 3.5 Docker 4 Implementierung 4.1 Plattform 4.2 Überwachung 4.3 certstream-server 4.4 Verarbeitung 4.4.1 Pufferung (stream-to-queue-publisher) 4.4.2 Vorverarbeitung (cert-indexer) 4.5 Elasticsearch 4.5.1 Speicherverbrauch 4.5.2 Field Mappings 5 Auswertung 5.1 Logs & Log-Betreiber 5.2 Certificate Authorites 5.3 Zertifikats-Größe 5.4 Gültigkeitsdauer 6 Schluss 6.1 Fazit 6.2 Ausblick A Beispiel X509 Leaf-Zertifikat B Beispiel X509 Root-Zertifikat C Beispiele Elasticsearch Abfragen Literatur Abbildungsverzeichnis Tabellenverzeichnis / In view of the central role of the root certification authorities as trust anchors of the Web PKI and the incidents that have occurred in the past with unauthorised or incorrectly issued certificates, the transparency and accountability of these root CAs is of great importance. With the introduction of Certificate Transparency Logs, all certificates issued by Certificate Authorities are now entered in public logs. The work presents the problem of the limited accessibility of this data for science and develops a tool that enables an independent recording and evaluation of Certificate Transparency Logs. The developed system uses a container-based architecture and Elasticsearch to efficiently store and analyse the data. It can handle a high volume of data, averaging 25 million log entries per day, and enables customisable data processing and analysis. Python was used to pre-process, index and analyse the data, allowing the system to be flexibly adapted to different research questions. A total of 645 million CT log entries were recorded and analysed over a period of 42 days. The analyses show how different CAs and their root certificates are used and how much the different CT logs are used by CAs. However, the work also identifies challenges, such as the high memory requirements and necessary optimisations in data indexing.:1 Einleitung 1.1 Problemstellung 1.2 Zielstellung 2 Grundlagen 2.1 X509-Zertifikate 2.1.1 Felder 2.1.2 Erweiterungen 2.2 Certificate Transparency 2.2.1 Certificate Transparency Log 2.2.2 Überprüfung durch User Agents 2.2.3 Überprüfung durch Monitors 2.2.4 Eintragung durch Certificate Authorities 3 Konzeptionierung 3.1 Abfrage der CT Logs 3.2 Verarbeitung der Zertifikate 3.3 Speicherung & Auswertung der Daten 3.4 Überwachung 3.5 Docker 4 Implementierung 4.1 Plattform 4.2 Überwachung 4.3 certstream-server 4.4 Verarbeitung 4.4.1 Pufferung (stream-to-queue-publisher) 4.4.2 Vorverarbeitung (cert-indexer) 4.5 Elasticsearch 4.5.1 Speicherverbrauch 4.5.2 Field Mappings 5 Auswertung 5.1 Logs & Log-Betreiber 5.2 Certificate Authorites 5.3 Zertifikats-Größe 5.4 Gültigkeitsdauer 6 Schluss 6.1 Fazit 6.2 Ausblick A Beispiel X509 Leaf-Zertifikat B Beispiel X509 Root-Zertifikat C Beispiele Elasticsearch Abfragen Literatur Abbildungsverzeichnis Tabellenverzeichnis

This thesis investigates the automation of forensic analysis in identifying and categorising forensically interestingchanges across different versions of chat applications on Android platforms. The focus is primarily on thedifferentiation of Android Package Kit (APK) using reverse-engineering techniques to reconstruct the originalsource code and comparing the source code from two different versions of the APK. Given the rapid evolutionof chat applications and their frequent updates, it is crucial for forensic investigators to understand thesechanges to maintain the integrity of legal investigations.The research introduces a comprehensive framework leveraging the open-source tools, Ghidra and BinDiff, toautomate the decompilation and differential analysis of APK files. This approach not only makes forensicanalysis less complicated but also ensures that investigators can keep pace with the continuous updates in chatapplications.Tests on the system are conducted on various versions of the Signal chat application. These tests aim todemonstrate the proposed tool in capturing significant changes between APK versions, such as alterations inlogging mechanisms, database interactions, and the use of encryption and cypher libraries.The results confirm that the integration of Ghidra and BinDiff provides a solution for automated forensicanalysis, facilitating the identification of changes and categorisation of methods based on their forensicrelevance. The study shows that the tool can pinpoint modifications and structural changes, which are essentialfor forensic investigations.

The Salish Sea is a ecologically important coastal region located on the southwest part of British Columbia. Optical measurements were taken using a set of hyperspectral radiometers, the SAS Solar Tracker developed by Satlantic. This sensor is installed on the Queen of Oak Bay ferry, that runs between Nanaimo and Vancouver, as part of the Ferry Ocean Colour Observation Systems (FOCOS) project. We developed a computer program to process the raw sensor data and generate remote sensing reflectance (Rrs) values. This performs similar functions to Prosoft, Satlantic’s own software to process the data. However, we added new features such as an additional preprocessing step to filter the data based on longitude, and new meteorological flag testing and wind speed calculations. The system was tested using Pearson correlation to compare our output with the output from Satlantic Prosoft. Testing helped us identify a few issues, such as adding longitude flags to remove data at the start and end of the trip where the sensor could produce inaccurate results if aiming at land instead of water. Another issue was where the SAS Solar Tracker does not update its pointing angle fast enough when the ferry makes sharp turns and could result in inaccurate data. / Graduate

Research projects in Optimality Theory tend to take a synchronic view of a particular generalisation, and set their standards for rigour in typological terms (see for example Suzuki 1998 on dissimilation, Crosswhite 2001 on vowel reduction). The goal of this thesis is to use Stratal OT to take a diachronic view of multiple generalisations within the morpho-phonology of one language, namely Latin, with the principal empirical aim of producing an analysis that is demonstrably true to all the attested facts of the generalisations in question. To that end, I have written PyOT, a computer program implementing the OT calculus and a theory of phonological representations, which I use in this work to model the histories of Lachmann’s Law, rhotacism and the phonologically conditioned allomorphy of the -alis/aris- suffix as active generalisations within the phonological component of the grammar. Appendix A gives the results of the computer model applied to a dataset consisting of 185 attested Latin forms, which suffice to illustrate the exact conditions of the generalisations in question. I show that producing a complete analysis of the three generalisations I have chosen to model entails analysis of other generalisations that interact with them, including the treatment of the Indo-European voiced aspirates in Latin (which interacts with rhotacism), and reduplication in forming perfect stems (which interacts with Lachmann’s Law). Constraint rankings sufficient to model these interactions, and consistent with the general conditions of the interacting generalisations have been included in the model. The intention is for this work to illustrate both the utility of formal phonological theory in advancing hypotheses within historical-comparative linguistics, and the potential of PyOT as a tool for producing Optimality-Theoretic models of (eventually) a language’s entire phonology.

Le code source de la libraire développée accompagne ce dépôt dans l'état où il était à ce moment. Il est possible de trouver une version plus à jour sur github (http://github.com/abergeron). / Le calcul scientifique sur processeurs graphiques (GPU) est en plein essor depuis un certain temps, en particulier dans le domaine de l'apprentissage machine. Cette thèse présente les efforts pour établir une structure de données de table au multidimensionnel de manière efficace sur GPU. Nous commençons par faire une revue de ce qui est actuellement similaire dans le domaine et des désavantages d'avoir une multitude d'approches. Nous nous intéresserons particulièrement aux calculs fait à partir du langage Python. Nous décrirons des techniques intéressantes telles que la réduction d'ordre et le calcul asynchrone automatique. Pour terminer nous présenterons l'utilisation du module développé dans le cadre de cette thèse. / Scientific computing on GPU (graphical processing units) is on the rise, specifically in machine learning. This thesis presents the implementation of an efficient multidimensional array on the GPU. We will begin by a review of what currently implements similar functionality and the disadvantage of a fragmented approach. We will focus on packages that have a Python interface. We will explain techniques to optimize execution such as order reduction and automatic asynchronous computations. Finally, we will present the functionality of the module developed for this thesis.

This paper focuses on the translation of audiovisual humour. I have analysed Petr Palouš's translation of Monty Python's Flying Circus. The translator of audiovisual humour has to deal with the same issues and problems as the translator of literature, that is he has to find equivalents to word plays, idioms, register and so on, but at the same time he also has to comply with the restrictions of audiovisual medium. I have identified the most common translation problems when translating humour and audiovisual texts and possible strategies of their translation into the target language, and then analysed how Palouš dealt with these cruces translatorum when translating the Flying Circus.

In this work we apply data assimilation onto meteorological model WRF for local domain. We use bayesian statistics, namely Sequential Monte Carlo method combined with particle filtering. Only surface wind data are considered. An application written in Python programming language is also part of this work. This application forms interface with WRF, performs data assimilation and provides set of charts as output of data assimilation. In case of stable wind conditions, wind predictions of assimilated WRF are significantly closer to measured data than predictions of non-assimilated WRF. In this kind of conditions, this assimilated model can be used for more accurate short-term local weather predictions. Powered by TCPDF (www.tcpdf.org)