Syftet med denna studie är att analysera situationer av kunnande i matematik utifrån en specifik undervisningskontext med stora drag av aktiv inlärning. Den är upplagd som en fallstudie och genom observation av klassrumsmiljön samt genom intervjuer av matematikläraren och sju elever i skolår sex analyseras i vilken mån den undervisning som bedrivs kan utnyttjas vid en bedömning som stärker elevernas lärande. Huvudresultatet av studien pekar på att den undervisningskontext som utmärker detta fall, där bedömning till stora delar görs utifrån intuition, inte ger eleverna möjligheten att visa läraren vad de kan, utan snarare vad de inte kan utifrån elevens synvinkel. När prov infördes blev bedömningen mer strukturerad och eleverna fick större möjligheter att visa vad de kan i matematik. Studien visar på nödvändigheten av fortsatt forskning om vad läraren lokalt behöver för att kunna föra en undervisning baserad på aktiva elever som själva får sätta sina mål.

Autonomous driver assistance systems are standard in vehicles. These systems help the driver to prevent an accident by automatically applying brakes on the vehicle. They assist the driver and help to prevent injuries and casualties caused by traffic accidents yearly.  This report shows data about how a vehicles Autonomous Emergency Braking (AEB) system reacts to a road surface with lower friction, for example in winter conditions. To help with the test was a steering robot (SR60 Orbit) and a pedal robot (CBAR 500) used to make the accuracy higher. The target that was used during tests was a Global Vehicle target (GVT). The tests were performed at ArcticFalls proving ground outside Älvsbyn on both asphalt and snow.  The tests show a noticeable difference between the distance it takes the car to stop on asphalt and snow. It emerges from the tests that systems like AEB can’t handle low friction, which is a huge risk for an accident. To prevent the risk of a collision is systems that can measure friction a priority.

Autonomous braking systems are becoming more common in modern cars. Autonomous EmergencyBraking (AEB) can help a driver avoid collision by automatically applying the brakes and stop thevehicle before an accident occurs. This can help save lives and reduce the risk of injuries in traffic.Previous work shows that AEB only works well on asphalt. On more slippery surfaces like snow theAEB has a hard time preventing a collision. This report will process the possibility to make an AEB thatwill reduce the risk of collision and injuries by adapting the braking distance for different surfaces. Aroad condition sensor was used to determine the different surfaces and the estimate of the tire toroad friction. This is an optical sensor that is used to categorize surfaces such as dry/wet asphalt,snow, and ice. In order to achieve good repeatability an SR60 Orbit steering robot combined with aCBAR 500 pedal robot was used. For comparison to the car’s AEB a GVT (Global Vehicle Target) wasused as a target.The results from the test show that a surface adapted AEB can make a difference. The adapted AEBstarted braking earlier than the car’s AEB and prevented collisions on snow, whilst the regular AEB had collisions with the GVT on snow.

With the implementation of ‘the Swedish model’ and the new Rehn-Meidner-system, active labor marketpolicies became an important part of Swedish social democracy in the 1950-and 60s. With the term ‘active’ onerefers to direct methods of improving labor market conditions funded by the state, such as education and supportof the unemployed. In contrast, passive labor market policies only refer to state funded allowances. With acontinued rise of interest regarding these policies, this study aims to investigate and map out the politicalattitude of three Swedish parties regarding their own labor market policies. These are then compared to theviews of the traditional labor market policies, which presents us with interesting similarities and differences.The parties which are treated in this research are the Social Democrats, the Centre Party and the Moderate Party.In order to analyze how these parties actively frame their preferred policies, this study develops an appliancebased on the principles of framing as a rhetorical method, which then is used to analyze three kinds of officialtexts published by the parties themselves. One then finds that while the Social Democrats share a lot ofimportant similarities to their traditional values, some interesting differences appear regarding the importance ofnational businesses and entrepreneurship. Moreover, the Centre Party proves to have some important values incommon with the reference point, although it pushes more policies in line with economic liberalism whichwould prefer less involvement from the state. Lastly, the Moderate Party proves its liberal-conservative core as itgoes against most active labor market propositions. This study therefore concludes that, while there are sometraces of tradition left, Swedish labor market policy seems to be affected by a more liberal view of the marketand its actors.

The diploma thesis deals with financial stability and its indications. In the theoretical part is firstly described the evolution of theory of money in order to clarify the role of money in financial stability. After that the financial instability is defined according to the authors Borio and Lowe (2002) as a rapid credit expansion combined with strong growth in asset prices. The mutual relationship of credit and asset prices is examined, especially how the credit creation of money can contribute to the creation of asset price bubbles. Empirical part analyses the possibility to use volumes of credit and asset prices to indicate financial instability. The empirical analysis is performed on the time series of the volume of credit, residential property prices and stock indices for the USA and the Eurozone. At first the tightness of relationship of credit and asset prices is examined by the rolling correlations and it is followed by testing Granger exogeneity to reveal causal links. Based on the results of the empirical analysis the recommendation for monetary authorities is made regarding the indication of financial instability.

The diploma thesis focuses on comparing the accounting treatment and reporting of intangible assets in accordance with rules in the CR and IFRS. This thesis is concentrated on finding significant differences. A practical example shows the form of intangible assets in practice a few selected companies from different sectors.

Thesis deals with issues of recognition and capture of permanent and temporary changes in the value of assets and liabilities in financial accounting from the perspective of International Financial Reporting Standards (IFRS), US generally accepted accounting principles (US GAAP) and the Czech accounting law. Top of the thesis discusses the bases for accounting for changes in the value of assets in the accounts - attention is paid to the fundamental accounting assumptions and principles, cost basis, latent reserves and deferred tax. A separate section compares the issue of depreciation of fixed assets in terms of IFRS, US GAAP and the Czech accounting law. The main part discusses the rules for identifying and capturing the transitional changes in the value of assets and liabilities in their subsequent valuation rules under IFRS, US GAAP and Czech accounting regulations. There is also the issue of exchange rate differences.

Autonomous driving is one of the megatrends in today’s automotive industry. Passengers are expected to do more non-driving tasks in an autonomous driving vehicle. Therefore, the comfort of the vehicle has become a more important factor for the passengers. This thesis investigates the possibility of increasing comfort through an integrated active chassis control strategy. First, this thesis has defined comfort in objective ways. Then, the objective comfort evaluation variables are used for comfort evaluation of the vehicle in different scenarios. The improvement in comfort is evaluated for four active chassis systems, including active suspension, active anti-roll bar, active rear-wheel steering and torque vectoring systems. Since more than one active chassis system can affect vehicle body motion in one direction, those four active chassis systems should be controlled in an integrated way. The model predictive control (MPC) is used because it can control a multi-input multi-output system in an optimized way. Two MPC controllers have been developed in this thesis to control multiple active chassis systems for comfort improvement. The original MPC controller is a linear MPC controller that uses a time-invariant state-space vehicle model. The adaptive MPC controller is a linear MPC controller that uses a time-variant state-space vehicle model. These two controllers are tested in the simulation software CarMaker with various scenarios, such as slalom, double lane-change, and bumps that are both symmetrical and shifted unsymmetrical. Finally, the simulation results are evaluated with objective comfort evaluation methods to assess the controller performances in comfort improvement. In conclusion, the model predictive control can be a feasible way to improve comfort with multiple active chassis systems. The simulation results show that the two MPC controllers can reduce the objective comfort evaluation variables. The discussions of the design process and simulation results point out future works that need to be done before this project becomes a product of real vehicles. / Autonom körning är en av megatrenderna i dagens bilindustri. Passagerare förväntas utföra fler icke-körrelaterade uppgifter i ett autonomt fordon. Därför har fordonets komfort blivit en allt viktigare faktor för passagerarna. Denna avhandling undersöker möjligheten att öka komforten genom en integrerad aktiv chassikontrollstrategi. Som utgångspunkt har denna avhandling definierat komfort på objektiva sätt. Sedan används de objektiva komfortvärderingsvariablerna för komfortutvärdering av fordonet i olika scenarier. Förbättringen av komfort utvärderas för fyra aktiva chassisystem, inkluderande aktiv fjädring, aktiv krängningshämmare, aktiv bakhjulsstyrning och drivmomentvektorisering. Eftersom mer än ett aktivt chassisystem kan påverka fordonets rörelse i en riktning, bör dessa fyra aktiva chassisystem styras på ett integrerat sätt. Modellprediktiv reglering (MPC) används eftersom den kan styra ett multi-input multi-output system på ett optimerat sätt. Två MPC-reglersystem har utvecklats för att styra flera aktiva chassisystem för komfortförbättring. Den ursprungliga MPC-reglerenheten är en linjär MPC-regulator som använder en tidsinvariant fordonsmodell. Den adaptiva MPC-reglerenheten är en linjär MPC-regulator som använder en tidsvariant fordonsmodell. Dessa två reglersystem testas i simuleringsprogramvaran CarMaker i olika scenarier, till exempel slalom, dubbelt körfältsbyte och väg-gupp som är både symmetriska och osymmetriska. Slutligen utvärderas simuleringsresultaten med objektiva komfortutvärderingsmetoder för att bedöma reglersystemens komfortförbättring. Sammanfattningsvis kan modellprediktiv reglering vara ett genomförbart sätt att förbättra komforten med flera aktiva chassisystem. Simuleringsresultaten visar att de två MPC-regulatorerna kan reducera de objektiva komfortutvärderingsvariablerna. Diskussionerna om designprocessen och simuleringsresultaten tar upp framtida arbeten som behöver göras innan detta projekt kan förverkligas i riktiga fordon.

Cognitive Limitations and Behavioral Biases In The Asset Pricing Context MAER Thesis Asbtract Giorgi Chavchanidze I incorporate behavioral and bounded rationality elements into a single asset-pricing frame- work by setting up a two-period consumption-based portfolio selection problem in which a representative agent has biased priors, does not observe the current state and thus has in- complete information about future state probabilities. He forms posterior beliefs using signals that he selects according to the rational inattention discrete choice framework of Matějka and McKay (2015), where the precision of the beliefs depend intuitively on the priors and the cost of information λ. In the case of log-utility, the optimal portfolio is a convex combination of the N portfolios the investor would have selected in each of the N states if they were fully observable, where the weights reflect the subjective posterior likelihood of time-zero states. The posterior beliefs are induced by parsimonious reweighing of priors, where the weights depend on λ, discount factor β and the relative entropies of the future state distributions induced by different time-zero states. Using a two-state example, I demonstrate how the cost of information and biases can be jointly analyzed in this framework and discuss implied...

In the domain of cyber security, machine learning promises advanced threat detection. However, the volume of available unlabeled data poses challenges for efficient data management. This study investigates the potential for active learning, a subset of interactive machine learning, to reduce the effort required for manual data labelling. Through different query strategies, the most informative unlabeled data points were selected for manual labelling. The performance of different query strategies was assessed by testing a transformer model’s ability to accurately distinguish tweets mentioning names of advanced persistent threats. The findings suggest that the K-means diversity-based query strategy outperformed both the uncertainty-based approach and the random data point selection, when the amount of labelled training data was limited. This study also evaluated the cost-effective active learning approach, which incorporates high-confidence data points into the training dataset. However, this was shown to be the least effective strategy. Lastly, the study acknowledges that the computational time taken for each query strategy varies significantly between strategies. Hence, an optimal query strategy selection requires a balanced consideration of F-score performance taken together with time efficiency. / Maskininlärning skulle kunna användas för avancerad hotdetektion i cyberdomänen. Dock utgör behovet av träningsdata tillsammans med den stora tillgången till oannoterad data en utmaning. Detta arbete undersöker huruvida aktiv inlärning, en delmängd av interaktiv maskininlärning, kan minska behovet av annoterad data. Genom olika frågestrategier valdes de mest informativa datapunkterna ut för mänsklig annotering. Resultaten för de olika frågestrategierna utvärderades sedan genom att testa en maskininlärningsmodells förmåga att korrekt urskilja tweets som innehåller namn på cyberhotsaktörer. Resultaten tyder på att när mängden annoterad data var begränsad, presterade den diversifieringsbaserade strategin K-means bättre än både den osäkerhetsbaserade frågestrategin och strategin som väljer ut datapunkter slumpmässigt. Denna studie utvärderade också kostnadseffektiv aktiv inlärning som lägger till datapunkter som modellen redan är relativt säker på till träningsdatamängden. Denna metod visade sig dock vara den minst effektiva strategin. Slutligen visar arbetet att beräkningstiden som krävs för varje frågestrategi varierar avsevärt. För att utse den mest optimala frågestrategin krävs därför ett övervägande av både prestanda och tidsåtgång.