In this work potentials of carbon nanomaterials calculated with Density Functional Theory (DFT) are approximated using an Artificial Neural Network (ANN). Previous work in this field has focused on estimating potential energies of bulk structures. We investigate the possibility to approximate both the potential energies and the forces of periodic carbon nanotubes (CNTs) and fullerenes. The results indicate that for test structures similar to those in the training set the ANN approximates the energies to within 270 meV/atom (&lt; 3.7% error, RMSE 40 meV/atom) and the forces to within 7.5 eV/Å (&lt; 73% error, RMSE 1.34 eV/Å) per atom compared with DFT calculations. Furthermore, we investigate how well the ANN approximates the potentials and forces in structures that are combinations of CNTs and fullerenes (capped CNTs) and find that the ANN generalizes the potential energies to within 100 meV/atom (&lt; 1.1% error, RMSE 78 meV/atom) and the forces to within 6 eV/Å (&lt; 60% error, RMSE 0.55 eV/Å) per atom. The ANN approximated potentials and forces are used to geometry optimize CNTs and we observe that the optimized periodic CNTs match DFT calculated structures and energies while the capped CNTs result in comparable energies but incorrect structures compared to DFT calculations. Considering geometry optimization performed with ANN on CNTs the errors lie within 170 meV/atom (&lt; 1.8% error) with an RMSE of 20 meV/atom. For the geometry optimizations of the capped CNTs the errors are within 430 meV/atom (&lt; 5.5% error) with an RMSE of 14 meV/atom. All results are compared with empirical potentials (ReaxFF) and we find that the ANN approximated potentials are more accurate than the best tested empirical potential. This work shows that machine learning may be used to approximate DFT calculations. However, for further applications our conclusion is that the error of the estimated forces must be reduced further. Finally, we investigate the computing time (number of core hours) required and find that the ANN is about two orders of magnitude faster than DFT and three to four orders of magnitude slower than ReaxFF. For the unseen data the ANN is still around 2 orders of magnitude quicker than the DFT but here it is around 4 order of magnitude slower than ReaxFF. / <p>Supervisors: Daniel Hedman and Fredrik Sandin</p> / F7042T - Project in Engineering Physics

As more robots and sensors are deployed in work and home environments, there is a growing need for these devices to act with some degree of autonomy to fulfill their purpose. Automated planning can be used to synthesize plans of action that achieve this. The main challenge addressed in this thesis is to consider how the automated planning problem changes when considered in the context of environments that are populated by humans. Humans have their own plans, and automatically generated plans should not interfere with these. We refer to this as social acceptability. Opportunities for proactive behavior often arise during execution. The planner should be able to identify these opportunities and proactively plan accordingly. Both social acceptability and proactivity require the planner to identify relevant situations from available information. We refer to this capability as context-awareness, and it may require complex inferences based on observed human activities. Finally, planning may have to consider cooperation with humans to reach common goals or to enable robots and humans to support one another. This thesis analyzes the requirements that emerge from human-aware planning — what it takes to make automated planning socially acceptable, proactive, context aware, and to make it support cooperation with humans. We formally state the human-aware planning problem, and propose a planning and execution framework for human-aware planning that is based on constraint reasoning and flaw-resolution techniques, and which fulfills the identified requirements. This approach is modular and extendable: new types of constraints can be added and solvers can be exchanged and re-arranged. This allows us to address the identified requirements for humanaware planning. In particular, we introduce Interaction Constraints (ICs) for this purpose, and propose patterns of Ics for social acceptability, proactivity, and contextawareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. introduce Interaction Constraints (ICs) for this purpose, and propose patterns of ICs for social acceptability, proactivity, and context-awareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system.

Ett digitalt spel har historiskt sätt varit färdigprogrammerat och narrativet nedskrivet när det släpps för konsumtion. Detta medför att det narrativ som upplevs av spelare inte går att förändra genom sitt spelande. Det här arbetet undersöker flera former av interaktiva narrativ i spel och andra medieformer. Frågeställningen som arbetet vill önska besvara är hur spelare upplever ett spelnarrativ där deras agerande har en direkt inverkan på narrativet. För att undersöka detta skapades en spelartefakt där spelarnas val och muntliga svar på en öppen fråga direkt påverkade hur en epilog skrevs för spelets narrativ. Epilogen följdes upp av en semi-strukturerad gruppintervju där spelarnas upplevelser av artefakten diskuterades. Efter analys av spelarnas svar kunde slutsatsen dras att deltagarna ansåg möjligheten att påverka spelnarrativet som positivt. För framtida forskning finns det utrymme att expandera antalet deltagare och på så sett få en mer nyanserad och säkrare bild av resultatet.

The continued cost reduction for sequencing genomics data is causing an exponentialgrowth in the amount of data available. Moving both storage and calculation of thisdata to the cloud has been a common trend, but the way to do it is not alwaysobvious. This report compares three different alternatives for doing ad-hoc queries ina cloud based setting: two solutions using data lakes and one solution using arelational database hosted in the cloud. The data lake solutions proved to be easy toset up and fully functional for querying genomics data. The relational database wasmore complicated to set up, but the queries were more time efficient and more costefficient when performing more than 1200 queries per month on at least 100GB ofdata. To make the cloud computing possible for genomics data it had to betransformed into a file format supported by the cloud providers. For this purpose theParquet file format was chosen, tested, and proven to work well

Gränssnitt finns inte bara i själva spelet utan också som inställningsmeny i First Person Shooter spel och massa andra spelgenrer. Det är viktigt att inställningsmenyn fungerar bra då inställningsmeny styr många element för hur ett spel kan spelas. Det är väldigt vanligt att heuristiker används för att skapa ett gränssnitt, heuristikregler har då applicerat på en inställningsmeny i ett FPS-spel för att se hur spelare påverkas av det. Detta arbete jämför två olika gränssnitt med varandra där ena gränssnittet är konstruerad med hjälp av heuristikregler och den andra gränssnittet är konstruerad med försök att inte använda sig av heuristikregler. Genom att jämföra dem två gränssnitten med varandra i en undersökning konstateras det att gränssnittet konstruerad med heuristikregler upplevdes som bättre än gränssnittet utan heuristikregler. Framtida arbeten behövs för att se om detta stämmer för spelare i alla åldrar och kön då arbetet bara har behandlat manliga spelare i åldern 21-26. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

With the introduction of Spectre and Meltdown, two new attacks thattarget the speculative instructions due to Out-of-Order execution intoday's processors, a new way to handle speculative loads has beenproposed. Instead of performing the speculative load, the approach isto predict them. This is a new way to use value predictors. In thiswork, the Last Value Predictor, which predicts based on the previouslyseen value, Value TAgged GEometric history length Predictor (VTAGE),which predicts based on the global branch history, and a stridepredictor, which predicts with help of strides, has been compared tosee which one has the best fit for this new use. They have been runwith the SPEC CPU 2017 benchmark suite in three different tests,different sizes, different threshold confidence and for VTAGE,different associativity. The VTAGE predictor performed best in terms ofvalues predicted and values correctly predicted. The thresholdconfidence level plays an important role in how many incorrectpredictions were made. The associativity in the VTAGE did not do muchdifference to the results.

The traffic on Swedish railways is increasing and punctuality is of important matter for both passenger and freight trains. The problem of modeling train delay evolution is complex since conflicts between trains can occur and since a delay can have a wide variety of causes. Swedish railways faces in addition harsh winter climate. Studies of railways in Scandinavia have shown that harsh winter climate decreases the punctuality of trains. This thesis work investigates the possibilities of modeling train delay evolution as continuous time Markov processes and which specific modeling choices are preferable. It also further assesses the impact of a harsh winter climate on the delay evolution. The studied segments are Stockholm - Umeå and Luleå - Kiruna. Both over one winter season. It was found that a change in the time schedule, which in a way redefines the delay, allows for a better fit and better prediction capabilities. It reduced the MSE of the prediction by 50 %. As for the weather variables, four variables were included together with their week long moving averages. Low temperatures were found to increase the risk of a delay (Hazard ratio of 1.10) as well as to decrease the chance of recovering from a delay (Hazard ratio of 0.91). No other significant weather impacts were found.

Information security awareness (ISA) focuses on the user’s responsibility and understanding of risk, to ensure that acceptable working practices are adopted under these broad principles, thereby reducing the likelihood of legal, financial and reputational risk related to the organization and individual. However, the third sector organization is behind in the security awareness maturity level. This research aims to understand and evaluate the level of information security awareness (ISA) knowledge in third sector organizations. The study was conducted with mixed-method design, combining the qualitative and quantitative approaches. A semi-structured interview method was used to gather data, transcribe it, and analyse it with a thematic framework analysis. The quantitative approach uses a questionnaire survey method was used to investigate the knowledge of information security awareness. The main findings present a lack of security awareness in the third sector and gaps in good security behaviour.

Purpose This thesis analyses what are the current information security risks and opportunities of social media in a business context based on publications from 2015 to 2020. Design/methodology/approach This papers follows a qualitative method, particularly a Systematic Literature Review guided by Okoli and and Schabram (2010), the concept-centric approach described by Webster and Watson (2002) and thematic analysis described by Braun and Clarke (2006). Findings Data leaks, non-compliance and reputational risks seem to be the most significant corporate social media risks. Adopting social media policies and providing employees social media security education, training and awareness are the most mentioned controls by the reviewed literature. Social media are more and more used as a threat intelligence source and for cyber security prediction and detection. Furthermore, social media may be used for InfoSec discussion, as a tool for Information Security Training and Awareness, for internal cyber threat sharing and for incident response handling. Originality/value This thesis provides an overall view of the risks, controls and opportunities that social media use implies for private organizations. Further research is needed that focuses primarily on the opportunities that social media offer to strengthen business Information Security.

In this thesis, we show how a text-based Recommendation Systems can greatly benefit from neural statistical language models, more particularly BERT. We evaluate the framework on a digital and collaborative platform for radiologists, by automatically suggesting scientific papers from the medical database PubMed, to provide evidence in diagnostic radiology. The models use contextualized vectors to represent text, accounting for writing style, misspelling and jargon. By using pre-computed representations of text passages, we are able to use compute-heavy statistical language models in production environments, where supercomputers are not available during inference. The results suggest pre-computed embeddings are very effective when the texts came from the same domain, and less effective (but still useful) in capturing the interaction between clinical and scientific text. Nonetheless, the suggested solutions hold promises in this and other areas in medicine. Possibly, the results are transferable to other domains, such as processing of legal documents and patent search.