The diploma thesis deals with design and implementation of cooperative work on documents in the application TEXonWeb. The diploma theses includes the analysis of competitive solutions and key technologies for implementation. The cooperative work on documents is a functionality which allows users to cooperate simply on one document in real-time. Text of the thesis describes the process of implementation using the technology which is described in the initial analysis. The diploma theses also includes some ideas for next possible development.

Diplomová práce mapuje vliv kurzového rizika na finanční řízení mezinárodních firem. Predstavuje metody a nástroje mezinárodního finančního řízení a řízení treasury. Dále popisuje instrumenty, které společnosti používají k zabezpečení proti kurzovému riziku. Na praktickém příkladu velké mezinárodní korporace práce ukázuje procedury a prostředky řízení tohoto rizika.

The 2018 Swedish general election saw the largest collective polling error so far in the twenty-first century. As in most other advanced democracies Swedish pollsters have faced extensive challenges in the form of declining response rates. To deal with this problem a new method based on search query data is proposed. This thesis predicts the Swedish general election using Google Trends data by introducing three models based on the assumption, that during the pre-election period actual voters of one party are searching for that party on Google. The results indicate that a model that exploits information about searches close to the election is in general a good predictor. However, I argue that this has more to do with the underlying weight this model is based on and little to do with Google Trends data. However, more analysis needs to be done before any direct conclusion, about the use of search query data in election prediction, can be drawn.

In this dissertation project, I make three separate contributions on how we can improve aggregate election forecasting models with respect to modeling choices, interpretability, and performance. Two of the three papers are applications to particular cases, the U.S. and France specifically, while the third points to a cross-national pattern in polling errors. The first paper addresses how we can make more reasonable prior choices for key parameters – such as the variability of non-sampling error – by using past pre-election polls. I showcase this approach on U.S. presidential elections. The second paper shows how to create and aggregate predictions in a multi-party contest while keeping the individual forecasts intact. This is useful to see convergences or divergences in the forecasts which might affect our confidence in the aggregate prediction. I develop a new aggregate forecasting model for French presidential elections to demonstrate this idea. The last paper shows and investigates a pattern in polling errors. We see that across multiple countries and electoral systems, polling errors favor the lesser party in two-party contests, i.e. polling errors favor Democratic candidates in Republican states and vice versa. We demonstrate a simple adjustment procedure based on this pattern to reduce the mean absolute polling error. We achieve a 16% reduction in the 2016 U.S. presidential election.

This paper analyses the players’ perception of the feedback and polling systems of OldSchool RuneScape to determine what level of participatory design is achieved by the feedback and polling systems used in the game. The aim of the research is to increase the availability of knowledge regarding participatory design in live-service games, as only limited coverage of the topic exists. The study uses an adapted survey created by Segalowitz and Chamorro-Koc that uses three different metrics to measure genuine participation. The survey was presented to in-game players and users of the game’s different forums. It was determined that a high level of genuine participation and participatory design is achieved.

Pre-election polls are used to test the political landscape and predict election results. The relative weights for the state-level data from the 2006 U.S. senatorial races are considered based on the date on which the polls were conducted. Long- and short-memory weight functions are developed to specify the relative value of historical polling data. An optimal weight function is estimated by minimizing the discrepancy function between estimates from weighted polls and the election outcomes.

Industry bias is the bias associated with polling estimates, and that is caused by systematic errors. We aim to test for its presence in Swedish political polling from 2010 to 2022 and simulate industry bias as a consequence of nonresponse bias. We estimate industry bias per party as the mean error of the last polls per polling house before an election and calculate the probability of observing the number of polling underestimates and overestimates per party and election year. Our results indicate that industry bias is present to varying degrees in each Swedish parliamentary election year from 2010 to 2022. Moreover, our results indicate that polls with and without industry bias as a consequence of nonrespondent bias can be replicated.

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Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance. / Master of Science