This diploma thesis is focused on the description and implementation of the API wrapper application, which works on top of computational core ibp bioinformatics. The first half of the thesis is focused on the summary of basic knowledge in the field of DNA research, as well as the specification of the problem and description of selected technologies. The other half deals with the actual implementation, distribution, and evaluation of application applicability on specific DNA sequences.

Reusing code can expedite software creation, analysis and exploration of data. Expediency can be particularly valuable for users of computational notebooks, where duplication allows them to quickly test hypotheses and iterate over data, without creating code from scratch. In this thesis, I’ll explore the topic of code duplication and the behaviour of code reuse for Jupyter notebooks; quantifying and describing snippets of code and explore potential barriers for reuse. As part of this thesis I conducted two studies into Jupyter notebooks use. In my first study, I mined GitHub repositories, quantifying and describing code duplicates contained within repositories that contained at least one Jupyter notebook. For my second study, I conducted an observational user study using a contextual inquiry, where my participants solved specific tasks using notebooks, while I observed and took notes. The work in this thesis can be categorized as exploratory, since both my studies were aimed at generating hypotheses for which further studies can build upon. My contributions with this thesis is two-fold: a thorough description of code duplicates contained within GitHub repositories and an exploration of the behaviour behind code reuse in Jupyter notebooks. It is my desire that others can build upon this work to provide new tools, addressing some of the issues outlined in this thesis. / Graduate

Until recently, most educational tools have worked in silos. If a teacher wanted her students to complete small programming exercises, record videos, and collaborate through discussion boards, three disconnected tools were probably needed. Learning Tools Interoperability (LTI) is a communication protocol that enables different learning tools to talk to each other and share scores with a Learning Management System (LMS). While most commercial LMS now support LTI, most educational software developed by small research efforts do not. This is often because of the lack of resources needed to understand the working of LTI and the process of using LTI in their applications. Our aim is to encourage the use of LTI within the CS Education community. We have developed tutorials that include example applications. We also provide a use case of how LTI is implemented in the OpenDSA eTextbook system. As another use case, we have enabled auto-grading of Jupyter Notebook assignments by providing immediate feedback to students and updating scores to the Canvas gradebook. We provide a Jupyter plugin to upload notebook files to the Web-CAT auto-grading system. We integrate Aalto University's ACOS content into OpenDSA as a third use case. / Master of Science / Until recently, most educational tools have worked in silos. If a teacher wanted her students to complete small programming exercises, record videos, and collaborate through discussion boards, three disconnected tools were probably needed. These disconnected tools did not integrate with the Learning Management Systems (LMS), such as Canvas and Moodle. Instructors had to manually manage these separate tools and enter scores into the LMS. There are standards such as Learning Tools Interoperability (LTI) that these learning tools can implement to enable them to talk to each other and to share scores with an LMS. However, most educational software developed by small research efforts do not support LTI. This is often because of the lack of resources needed to understand the working of LTI and the process of using LTI in their applications. We aim to encourage the use of LTI within the CS Education community. We have developed tutorials that include example applications. We also provide a use case of how LTI is implemented in OpenDSA, an eTextbook system developed at Virginia Tech. As another use case, we have enabled auto-grading of Jupyter Notebook (documents that run in a browser and can contain equations, visualizations, live code, and text) assignments by providing immediate feedback to students and updating scores to the Canvas gradebook. We provide a plugin to upload notebook files to the WebCAT auto-grading system directly from the browser. We integrate Aalto University’s ACOS content (Python and Java exercises) into OpenDSA as a third use case.

Cellular network deployment scenarios refer to how cellular networks are implementedand deployed by network operators to provide wireless connectivity to end users.These scenarios can vary based on capacity requirements, type of geographical area, populationdensity, and specific use cases. Radio Access Networks of different generations,such as 4G and 5G, may also have different deployments. Network deployment scenarioscover many aspects, but two major components are Configuration settings and PerformanceMeasures which refer to the network nodes, hardware build-up and softwaresettings, and the end user behavior and connectivity experience in the area covered by thewireless network.In this master thesis, the aim is to understand how different area types - such as Rural,Suburban, and Urban – affect the cellular network deployment in such areas. A novelframework was developed to label each node (base station) with the area type it is associatedwith. The framework utilizes spatial analytics on the dataset provided by Ericsson forthe LTE nodes working with 4G technology in combination with open-source libraries anddatasets such as GeoPy and H3 Kontur population dataset respectively, to create area typelabels. The area types are labeled based on the calculated population density served byeach node and are considered true labels based on manual sanity checks performed. A supervisedmachine learning model was used to predict the nodes based on the CM and PMdata to understand the strength of the relationship between the features and true labels.This thesis also includes analysis and insights about characteristic deployment scenariosunder different area types. The main goal of this master thesis is to utilize machinelearning to uncover the characteristic features of a variety of node groups inherent in atelecom network, which, in the long run, contributes to better service operation and optimizationof existing cellular infrastructure. Nodes (base station) are labeled in the datato be able to distinguish their associated area-type. In addition to this clustering is performedto uncover the inherent characteristic behavior groups in the data and comparethem against the output from the classification model. Lastly, the investigation was doneon the potential impact of node placements such as indoor or outdoor, on the correspondingfeatures.In conclusion, the study’s results showed us that a correlation exists between deploymentscenarios and the different areas. There are a few prevalent common denominatorsbetween the node groups such as Pathloss and NR Cell Relations that drive the classificationmodel to a better classification metric, F1 score. Clustering of CM and PM data uncoversinherent patterns in different node groups under different area types and providesinformation about characteristic features of the groups such as CM data displaying twoconfiguration setting clusters, and PM data showing three different user behavior patterns.

This master's thesis deals with Big data processing in distributed system Apache Spark using tools, which allow remotely entry and execution of Spark tasks through web inter- face. Author describes the environment of Spark in the first part, in the next he focuses on the Apache Livy project, which offers REST API to run Spark tasks. Contemporary solutions that allow interactive data analysis are presented. Author further describes his own application design for interactive entry and launch of Spark tasks using graph repre- sentation of them. Author further describes the web part of the application as well as the server part of the application. In next section author presents the implementation of both parts and, last but not least, the demonstration of the result achieved on a typical task. The created application provides an intuitive interface for comfortable working with the Apache Spark environment, creating custom components, and also a number of other options that are standard in today's web applications.

We show that in connection with the digitalization of musicology a special kind of mathematical and logical thinking, i. e. computational thinking/literacy, is in need. Computational thinking is characterized by effective procedures whereas computational literacy includes the implementation of these procedures on machines, i.e. programming. Both are the core of formalization, model building and computer simulation. Furthermore, we point out that “computation” as a central concept for the sciences in the 21st century and its use in cognitive science and the computational sciences make it necessary to reassess the basic assumptions underlying musicological research as science of mind (Geisteswissenschaft). We propose a digital habitat to integrate computational thinking/literacy in musicology and to become acquainted with model building and computer simulation. Jupyter Notebook provides a basis for such a digital habitat. We describe our use of Jupyter Notebook as a teaching environment for computational thinking/literacy.

In 2019, 770 million people worldwide lived without electricity. As many as 490 million people could be electrified with 210,000 mini-grids by 2030. Obtaining information for decision-making is crucial to determine the viability of such a project. Currently, it is a major challenge for mini-grid developers to gather this information at the speed and scale necessary to make effective investment choices. Village Data Analytics (VIDA) is a decision-making tool used for mini-grid project planning and site selection. This paper presents a method to estimate the cost of a mini-grid distribution network on a site-by-site basis. This method can estimate the total demand, potential connections, distribution infrastructure components and corresponding costs for each site. The model can make predictions for 50 sites within two hours so the tool is especially useful for preliminary estimates in the planning phase. A more detailed study of the individual sites is recommended. Comparison with a benchmark has shown that on-site conditions often reveal activities that can only be captured by a survey. However, collecting on-site data is time-consuming and costly. Therefore, GIS and modelling tools can serve as a good approximation of the on-ground reality and are relevant to accelerate planning and support timely decision-making. / 2019 levde 770 miljoner människor världen över utan elektricitet. Så många som 490 miljoner människor skulle kunna elektrifieras med 210 000 mininät till 2030. Att få information för beslutsfattande är avgörande för att avgöra om ett sådant projekt är lönsamt. För närvarande är det en stor utmaning för utvecklare av mininät att samla in denna information i den hastighet och skala som krävs för att göra effektiva investeringsval. Village Data Analytics (VIDA) är ett beslutsfattande verktyg som används för projektering av mininät och platsval. Det här dokumentet presenterar en metod för att uppskatta kostnaden för ett distributionsnät för mininät på plats för plats. Denna metod kan uppskatta den totala efterfrågan, potentiella anslutningar, komponenter för distribution sinfrastruktur och motsvarande kostnader för varje plats. Modellen kan göra förutsägelser för 50 platser inom två timmar, så verktyget är särskilt användbart för preliminära uppskattningar i planeringsfasen. En mer detaljerad studie av de enskilda platserna rekommenderas. Jämförelse med ett riktmärke har visat att förhållanden på plats ofta avslöjar aktiviteter som bara kan fångas genom en undersökning. Men att samla in data på plats är tidskrävande och kostsamt. Därför kan GIS- och modelleringsverktyg fungera som en bra approximation av verkligheten på marken och är relevanta för att påskynda planering och stödja beslutsfattande i rätt tid.