This study analyzes snow maintenance data of Borlänge municipality of Sweden based on the data of 2017 to 2018. The goal of this study is to reduce operational overhead of snow maintenance, for example, fuel and time consumption of the snow maintenance vehicles, work hour of dedicated personnel, etc. Borlänge Energy equipped the snow maintenance vehicles with GPS devices which stored the record of the snow maintenance activities. The initial part of this study obtained insights out of the GPS data by using spatiotemporal data analysis. Derivation of the different snow maintenance treatments (plowing, sanding and salting) as well as the efficiency of the sub-contractors (companies which are responsible for snow maintenance) and inspectors (personnel who are liable to call the subcontractors if they think it is time for snow maintenance) are performed in the beginning of this study. The efficiency of the subcontractors and inspectors are measured to compare their performance with each other. The latter part of this study discusses a simulated annealing-based heuristics technique to find out optimal location for dispatching snow maintenance vehicles. In the existing system of snow maintenance, drivers of the maintenance vehicles decide to start location of maintenance work based on their experience and intuition, which might vary from one driver to another driver. The vehicle dispatch locations are calculated based on the availability of the vehicles. For example, if a subcontractor has three vehicles to perform snow maintenance on a specific road map, the proposed solution would suggest three locations to dispatch those vehicles. The purpose of finding the optimal dispatch location is to reduce the total travel distance of the maintenance vehicles, which yield less fuel and time consumption. The study result shows the average travel distance for 1, 3, and 5 vehicles on 15 road networks. The proposed solution would yield 18% less travel than the existing system of snow maintenance.

This case study provides new insights on the designer role in co-design processes. Co-design has shifted the designer role into a facilitator, which requires a new skillset. In this thesis, this new role is investigated through the three psychological constructs; empowerment, ownership and power dynamics. The process consists of two main parts: pre-study and case study. In the pre-study, designers with different roles are interviewed about co-design. In the case study, three co-design sessions are observed and the facilitators interviewed in retrospect. Several strategies and actions are identified for empowering participants, some for the participants to feel ownership and almost none for balancing out power dynamics. The identified strategies include using straightforward methods and having a selfless mindset. However, there are indications that long term relationships are prioritized over actual outputs. This notion might be explained by the fact that the case companies are consultants. The need to always satisfy the clients makes the facilitators disregard some of the power differences in the room.

The state of queer representation in games is poor, and queer consumers are growing increasingly vocal in their demands for nuanced portrayals. This thesis investigates how queer players perceive the tropes and stereotypes commonly used to portray them in games. By sorting through existing representation and using the most common tropes found, this study created two example characters which were represented both narratively and visually. These characters were then the subject of a study of 29 participants. The comments and opinions of these 29 participants were then analysed to find a largely negative consensus which is chiefly concerned with making portrayals less tragic. From this, this study proceeds to analyse the desires of queer consumers and contextualize them in relation to a world which still actively oppresses them.

Cloud services and solutions have served as a shift in the computer industry and create new opportunities for users. Clouds have been described as easily usable and fluid in terms of expansion and contraction depending on the real-time needs. Although the cloud is promoted with several benefits, it is not always apparent for the users that this is the case. Understanding both the benefits and challenges that exist is substantial for a successful adoption to cloud. This master’s thesis is conducted in collaboration with Exsitec ABand aims to investigate how the adoption of the cloud service Microsoft Azure will affect the development process. Also, it aims to provide a best practice for potentially needed updated working procedures, in terms of satisfaction and learnability. The investigation was performed through interviews and the System Usability Scale, to assess how the end users experienced development in a cloud environment. The thesis revealed that the Azure portal has low overall usability, but that there also exists an inconsistency of that perception. Two major factors that contributed to the satisfaction and learnability was the lack of documentation and that the Azure portal was considered hard to master. The SUS score revealed that the mean value was below an acceptable level, and thus changes in the company’s working procedures need to be implemented. Internal documentation regarding how the company should use both cloud in general, as well as the portal in particular, are required in order to increase the learnability and subjective satisfaction.

Under recent years the gambling industry has been moving towards providing their customer the possibility to gamble online instead of visiting a physical location. Aggressive marketing, fast growth and a multitude of actors within the market have resulted in a spike of customers who have developed a gambling problem. Decision makers are trying to fight back by regulating markets in order to make the companies take responsibility and work towards preventing these problems. One method of working proactively in this regards is to identify vulnerable customers before they develop a destructive habit. In this work a novel method of predicting customers that have a higher risk in regards to gambling-related problems is explored. More concretely, a recurrent neural network with long short-term memory cells is created to process raw behaviour data that are aggregated on a daily basis to classify them as high-risk or not. Supervised training is used in order to learn from historical data, where the usage of permanent self-exclusions due to gambling related problems defines problem gamblers. The work consists of: obtain a local optimal configuration of the network which enhances the performance for identifying problem gam- blers who favour the casino section over sports section, and analyze the model to provide insights in the field. This project was carried out together with LeoVegas Mobile Gaming Group. The group offers both online casino games and sports booking in a number of countries in Europe. This collaboration made both data and expertise within the industry accessible to perform this work. The company currently have a model in production to perform these predictions, but want to explore other approaches. The model that has been developed showed a significant increase in performance compared to the one that is currently used at the company. Specifically, the precision and recall which are two metrics important for a two class classification model, increased by 37% and 21% respectively. Using raw time series data, instead of aggregated data increased the responsiveness regarding customers change in behaviour over time. The model also scaled better with more history compared to the current model, which could be a result of the nature of a recurrent network compared to the current model used.

Fantasy creatures are an essential part of many games, but while there are several studies focusing on body language and how expressive gaming companions may enhance the player’s experience, creatures and animals are rarely the focal points. Personality is closely related to believability, which is what most game developers work towards hence believability may improve the gaming experience. The purpose of this paper was to explore how the personality of a fantasy creature would be perceived by the observer when using different animal motion references. A 3D-model was created and animated in three different styles using motion references from a cat and lizard. A survey with Likert-scales was then formed with the intention to evaluate the animations. The participants in the survey were assigned one of the three animations to rate statements regarding personality and believability. Rather than a certain type of animal being associated with a certain type of personality, the result suggests that it was mainly certain motion cues and postures that affected the participants’ ratings. The study was deemed to be insufficient for a reliable result. In the discussion part, there are ideas on how the study could be improved with the aim of continuing the research to gain a clearer insight into the subject of personality and creatures.

Studying radar pulses and looking for certain patterns is critical in order to assess the threat level of the environment around an antenna. In order to study the electromagnetic pulses emitted from a certain radar, one must first register and identify these pulses. Usually there are several active transmitters in anenvironment and an antenna will register pulses from various sources. In order to study the different pulse trains, the registered pulses first have to be sorted sothat all pulses that are transmitted from one source are grouped together. This project aims to solve this problem, using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and compare the results with those obtained by Fuzzy Adaptive Resonance Theory (FART). We aim to further dig into these methods and map out how factors such as feature selection and training time affects the results. A solution based on the DBSCAN method is proposed which allows online clustering of new points introduced to the system. The methods are implemented and tested on simulated data. The data consists of pulse trains from simulated transmitters with unique behaviors. The deployed methods are then tested varying the parameters of the models as well as the number of pulse trains they are asked to deinterleave. The results when applying the models are then evaluated using the adjusted Rand index (ARI). The results indicate that in most cases using all possible data (in this case the angle of arrival, radio frequency, pulse width and amplitudes of the pulses) generate the best results. Rescaling the data further improves the result and tuning the parameters shows that the models work well when increasing the number of emitters. The results also indicate that the DBSCAN method can be used to get accurate estimates of the number of emitters transmitting. The online DBSCAN generates a higher ARI than FART on the simulated data set but has a higher worst case computational cost.

Efficient quantum computers will break most of today’s public-key cryptosystems. Therefore, the National Institute of Standards and Technology (NIST) calls for proposals to standardise one or more quantum-secure cryptographic schemes. Eventually, banks must adopt the standardised schemes, but little is known about how efficient such an implementation would be in Java, one of the standard programming languages for banks. In this thesis, we test and evaluate a post-quantum secure encryption scheme known as FrodoKEM, which is based on a hard lattice problem known as Learning With Errors (LWE). We found that a post-quantum secure encryption version of FrodoKEM provides strong theoretical security regarding the criteria given by NIST, and is also sufficiently fast for key generation, encryption and decryption. These results imply that it could be possible to implement these types of post-quantum secure algorithms in high-level programming languages such as Java, demonstrating that we no longer are limited to use low-level languages such as C. Consequently, we can easier and cheaper implement post-quantum secure cryptography.

Project Meme Evolution Programme (Project MEP) is a research program directed by Raazesh Sainudiin, Uppsala University, Sweden, that collects and analyzes datasets from Twitter. Twitter can be used to understand how ideas spread in social media. This project aims to produce interactive visualizations for datasets collected in Project MEP. Such interactive visualizations will facilitate exploratory data analysis in Project MEP. Several technologies had to be learned to produce the visualizations, most notably JavaScript, D3, and Scala. Three interactive visualizations were produced; one that allows for exploration of a Twitter user timeline and two that allows for exploration and understanding of a Twitter retweet network. The interactive visualizations are accessible as Scala functions and in a website developed in this project and uploaded to GitHub. The interactive visulizations contain some known bugs but they still allow for useful exploratory data analysis of Project MEP datasets and the project goal is therefore considered met. / Project Meme Evolution Programme

This thesis proposes an approach for automatic code generation from a Colored Petri net specification. Two tools were developed for the aforementioned purpose, a Colored Petri net editor to create and modify Colored Petri nets, and an automatic code generator to generate code from a Colored Petri net specification. Through the use of the editor four models were created, these models were used as input to the automatic code generator. The automatic code generator successfully generated code from the Colored Petri net specification, code in the form of component scripts for the Unity3D game engine. However, the approach used by the code generator had flaws such as introducing overhead in the generated code, failing to deal with concurrency, and restricting the types of Colored Petri nets which could be used as input. The aforementioned tools could be used in the future to research the benefits and disadvantages of modeling game systems with Colored Petri nets, and automatically generating code from Colored Petri nets.