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Needs analysis of the outreach program's clients in the Beroun region The needs of outreach programme clients depends if the the drug scene is open or closed. The thesis is focused on outreach programme in a town, where the drug scene is ussually closed. The theoretical part is focused on the location advantages and disadvantages, service provided and outreach work based on the harm reduction principles. In the Czech Republic, there are 53 outreach programmes, not only in large cities, but also in smaller ones, where outreach work attitude is adapted to clients needs. Needs analysis in one of the evalution tools that helps to view the current needs of clients and also suggest their further improvement and development. The aim of the thesis is to describe the scope, characteristics and needs of problem users and to evaluate the other services accessibility in the Beroun region. The partial goals of the work are the closed drug scene monitoring and the need for establishing a contact center in the region. The method of data collection was a semi-structured interview recorded on a record sheet and IN- COME questionnaires realized with clients of the outreach programme in the Beroun region. There was also a structuring of interventions provided by other organizations in the locality. The selection of...

This study attempts to improve forecasts of solar energy production (SEP), so that energy trading companies can propose more accurate bids to Nord Pool. The aim ismake solar energy a more lucrative business, and therefore lead to more investments in this green energy form. The model that is introduced is a hidden Markov model (HMM) that we call a Time-dynamic Markov-chain (TDMC). The TDMC is presented in general, but applied to the energy sector SE4 in south of Sweden. A simple linear regression model is used to compare with the performance of the TDMC model. Regarding the mean absolute error (MAE) and the root-mean-square error (RMSE), the TDMC model outperforms a simple linear regression; both when the training data is relatively fresh and also when the training data has not been updated in over 300 days. A paired t-test also shows a non-significant deviation from the true SEP per day, at the 0.05 significance level, when simulating the first two months of 2023 with the TDMC model. The simple linear regression model, however, shows a significant difference from reality, in comparison.

Den här studien är en etnografisk studie som eftersöker svenskämnets muntlighet och den dolda läroplanen i muntlighet. Studien har observerat en undervisningsgrupp på 19 elever som läser svenska 1 på en gymnasieskola i norra Sverige. Klassrumsobservationer och etnografiska intervjuer har använts som datainsamlingsmetoder och datan har analyserats enligt grundad teori. I arbetet med dataanalysen har teorierna delaktighet, self-efficacy och den dolda läroplanen framträtt som centrala och en generalisering av resultatet har knutits an till tidigare forskning. Resultatet visar på hur klassrumsmiljön möjliggör och begränsar elevers muntliga utveckling, främst för att elevernas olika förutsättningar ger dem olika möjlighet till social delaktighet. Slutsatsen som dras av arbetet identifierar den dolda läroplanen för muntlighet och presenterar hur dess existens påverkar elevers muntlighet i klassrummet. / This ethnographic study investigates students’ orality in the classroom, with the purpose of identifying the hidden curriculum in orality. Factors such as classroom discussions, teachers’ questions, and answers to students, as well as groups in the classroom has been identified as factors that effects the student’s oral progress. The study followed a class of 19 students who takes the course Swedish 1 at an upper secondary school in the north of Sweden. The data collection methods that have been used are classroom observations and ethnographic interviews. The data has been analyzed according to grounded theory. In the process of data analyses, the theories participation, self-efficacy, and the hidden curriculum has been prominent, and the results has been compared to previous research, to strengthen the findings to theory. The results have shown how the classroom environment enables and limits students’ orality progress. Mainly due to, students’ different conditions and background knowledge, which gives them different opportunities for social participation. The findings of the study, identifies the hidden curriculum för orality and presents how its existence effects students orality in the classroom.

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Young Adult Dystopian Film exercises an influence over young people of which they are not aware. As part of a structure of domination these films teach students to participate in their own oppression by the capitalist system. The film industry maintains a hidden curriculum like that utilized in school classrooms to conceal the oppression from the masses. One particularly effective means is the portrayal of resistance against oppression in the narratives of the YA Dystopian Film. Young people are drawn to that narrative and end up supporting the structure of domination financially and ideologically. Modes of resistance to this oppression can be found in Media Literacy Education and Public Pedagogy (e.g. culture-jamming). Teachers can incorporate Media Literacy and culture–jamming into a form of radical pedagogy to emancipate students from that oppressive relationship.This thesis investigates the usage of this radical pedagogy though an action research project in a high school drama class in the intermountain west. The students learned the theories, critically reflected on the situation, and created a live culture-jamming performance. The results of the action research show the affordances and limitations of this approach and offer suggestions for instigating its usage by media literacy educators.

This thesis focuses on comparing an online parameter estimator to an offline estimator, both based on the PaRIS-algorithm, when estimating parameter values for a stochastic volatility model. By modeling the stochastic volatility model as a hidden Markov model, estimators based on particle filters can be implemented in order to estimate the unknown parameters of the model. The results from this thesis implies that the proposed online estimator could be considered as a superior method to the offline counterpart. The results are however somewhat inconclusive, and further research regarding the subject is recommended. / Detta examensarbetefokuserar på att jämföra en online och offline parameter-skattare i stokastiskavolatilitets modeller. De två parameter-skattarna som jämförs är båda baseradepå PaRIS-algoritmen. Genom att modellera en stokastisk volatilitets-model somen dold Markov kedja, kunde partikelbaserade parameter-skattare användas föratt uppskatta de okända parametrarna i modellen. Resultaten presenterade idetta examensarbete tyder på att online-implementationen av PaRIS-algorimen kanses som det bästa alternativet, jämfört med offline-implementationen.Resultaten är dock inte helt övertygande, och ytterligare forskning inomområdet

The usage of signatures for authentication is widely accepted, and remains one of the most familiar biometric in our society. Efforts to digitalise and automate the verification of these signatures are hot topics in the field of Machine Learning, and a plethora of different tools and methods have been developed and adapted for this purpose. The intention of this report is to study the authentication of handwritten signatures on digital whiteboards, and how to most effectively set up a dual verification system based on Hidden Markov models (HMMs) and global aggregate features such as average speed. The aim is to gauge which features are Suitable for determining that a signature is in fact genuine Suitable for rejecting forgeries Unsuitable for gauging the authenticity of a signature all together In addition, we take a look at the configuration of the HMMs themselves, in order to find good configurations for The number of components used in the model What type of covariance to use The best threshold to draw the line between a genuine signature and a forgery For the research, we collected a total of 200 signatures and 400 forgeries, gathered from 10 different people on digital whiteboards. We concluded that the best configurations of our HMMs had 11 components, used a full covariance model, and observed about five features, where pressure, angle and speed were the most important. Among the global features, we discarded 11 out of 35 due to either strong correlation with other features, or contained too little discriminatory information. The strongest global features were the ones pertaining to speed, acceleration, direction, and curvature. Using the combined verification we obtained an EER of 7 %, which is in the typical range of contemporary studies. We conclude that the best way to combine global feature verification with local HMM verification is to perform both separately, and only accept signatures that are admissible by both, with a tolerance level for the global and local verifications of 1.2 and 2.5 standard deviations, respectively. / Användandet av signaturer för autentisering är allmänt accepterat, och är fortfarande den mest använda biometriken i vårt samhälle. Arbetet med att digitalisera och automatisera verifieringen av dessa signaturer är ett populärt ämne inom maskininlärning, och en uppsjö av olika verktyg och metoder har utvecklats och anpassats för detta ändamål. Avsikten med denna studie är att bestämma hur man mest framgångsrikt kan inrätta ett verifikationssystem för handskrivna signatures på digitala whiteboards baserat på dolda Markovmodeller (HMMs) och globalt aggregerade attribut. Syftet är att bedöma vilka features som är Lämpliga för att bestämma huruvida en signatur är äkta Lämpliga för att avvisa förfalskningar Olämpliga för att mäta äktheten hos en signatur över huvud taget Utöver detta studerar vi HMM-konfigurationen själv, i syfte att hitta bra konfigurationer för Antalet komponenter som används i modellen Vilken typ av kovarians som ger bäst resultat Det bästa tröskelvärdet vid vilken att dra gränsen för huruvida en signatur är äkta eller förfalskad För forskningen samlade vi totalt in 200 signaturer och 400 förfalskningar från 10 olika personer med hjälp av digitala whiteboards. Vi drog slutsatsen att de bästa konfigurationerna hade 11 komponenter, använde komplett kovarians, och använde cirka fem features, där tryck, vinkel och hastighet var det viktigaste. Bland våra globala features kastade vi 11 av 35 på grund av att de antingen korrelerade för starkt med andra features, eller på grund av att de innehöll för lite information för att utröna huruvida en signatur var äkta eller ej. Våra bästa globala features var de som hänförde sig till hastighet, acceleration, riktning och krökning. Genom att använda den kombinerade verifieraren fick vi en EER på 7 %, vilket är i linje med liknande studier. Vi drog även slutsatsen att det bästa sättet att kombinera global verifiering med lokal HMM-verifiering är att utföra dem separat och endast acceptera signaturer som godkänns av bägge två. Den bästa toleransnivån för den globala och lokala verifieraren var 1,2 och 2,5 standardavvikelser, respektive.

yes / This paper presents a methodology for fault detection, fault prediction and fault isolation based on the integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid framework that captures causality in the complex engineered system. The proposed methodology is based on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of detected/predicted faults, using the information propagated from the HMM model as empirical evidence. The feasibility and effectiveness of the presented approach are discussed in conjunction with the application to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the implementation of the methodology to this case study, with data available from real-world usage of the system. The results show that the proposed methodology identifies the fault faster and attributes the fault to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its applicability is much wider to the fault detection and prediction problem of any similar complex engineered system.