<p>Abstract</p><p>Title: Watching eyes – for better or worse (Vakande ögon – på gott eller ont?)</p><p>Number of pages: 34 (41 including enclosures)</p><p>Author: Josefine Nyberg</p><p>Tutor: Göran Svensson</p><p>Course: Media and Communication Studies C</p><p>Period: Autumn term 2007</p><p>University: Division of Media and Communication, Department of Information Science, Uppsala University.</p><p>Purpose/Aim: The aim of this essay is partly to make a study about how people understand camera supervise, and partly discuss what their opinions are concering the subject.</p><p>Key Questions: Which or what is the mainpurpose of camerasurveillance? What does the public think about the subject? When can there be an insult against the integrity while using the cameras? What is private and what is public? What is the posibilities and threats in the future?</p><p>Material/Method: A qualitative method has been used. Four interviewes, one personale interview with a policeofficer from Uppsala and three focusgroupinterviwes with people from Stockholm, have been done. Then the results from the interviewes have been compared with earlier theories and reaserches. By reading publicdocuments, press releases and reports from the government, I have gather information for the essay.</p><p>Main results: Even if the public are aware of the cameras and knows that they allmoust allways are whatched by the electronic eye, they don´t seem to care that much about the constant surveillance. Even if they observe the cameras at first they quickly forget about them. The cameras real purpose is working fairly good, even if the public thinks that thecameras should obstruct all the crimes. Allthough the cameras are preventing manycrimes and discourage people from committing minor crime, just by its appearance.</p><p>Keywords: Attitudes, opinions, interviews, Panopticon, public, camera supervise, privacy and public.</p>

Abstract Title: Watching eyes – for better or worse (Vakande ögon – på gott eller ont?) Number of pages: 34 (41 including enclosures) Author: Josefine Nyberg Tutor: Göran Svensson Course: Media and Communication Studies C Period: Autumn term 2007 University: Division of Media and Communication, Department of Information Science, Uppsala University. Purpose/Aim: The aim of this essay is partly to make a study about how people understand camera supervise, and partly discuss what their opinions are concering the subject. Key Questions: Which or what is the mainpurpose of camerasurveillance? What does the public think about the subject? When can there be an insult against the integrity while using the cameras? What is private and what is public? What is the posibilities and threats in the future? Material/Method: A qualitative method has been used. Four interviewes, one personale interview with a policeofficer from Uppsala and three focusgroupinterviwes with people from Stockholm, have been done. Then the results from the interviewes have been compared with earlier theories and reaserches. By reading publicdocuments, press releases and reports from the government, I have gather information for the essay. Main results: Even if the public are aware of the cameras and knows that they allmoust allways are whatched by the electronic eye, they don´t seem to care that much about the constant surveillance. Even if they observe the cameras at first they quickly forget about them. The cameras real purpose is working fairly good, even if the public thinks that thecameras should obstruct all the crimes. Allthough the cameras are preventing manycrimes and discourage people from committing minor crime, just by its appearance. Keywords: Attitudes, opinions, interviews, Panopticon, public, camera supervise, privacy and public.

Self-reflection is an integral part of student?s training, including practical training. Self-reflection can be seen as a professional development and also as a development of personality. The aim was to characterize the reflection of social work students within the linking theory and professional practice. In the theoretical part of this diploma, author focuses on the concept of self-reflection, reflection and self-knowledge. Author also briefly describes social work, skills required for the performance of social work and the importance of appropriate professional experience. Author deals with how theoretical knowledge and practical experience can be connected and how personal experience can be reflected. In subsequent chapters is described supervision as one of its goals is self-reflection. The focus is mainly on auto-supervision and development supervision. In the last chapter author tries to outline the impact of ethics, rules and their dilemmas that affect social work. In the research part is set the main objective: "To characterize the reflection of social work students within the linking theory and professional practice." Author also sets two partial objectives: "To determine the nature of reflection of social work students in relation to the length of study." And: "To identify the extent of students' use of reflection during their professional practice." To fulfill the objectives method of qualitative research technique with half standardized interview was chosen. The interview had structure of broadly worded questions that pointed to the knowledge of the problem. The research group consists of students of the Faculty of Health and Social Studies who completed Bachelor and subsequent Master study in the discipline of Rehabilitation ? psychosocial care for handicapped children, adults and seniors. The character of reflection in relation to length of study and level of use of reflection is defined in the proposed hypotheses. The gained finding can be used to improve students' self-reflection and the development of social work professional practice.

The name of my dissertation is ``The efficiency of primary prevention of drug addictions from the view of drug addicts as well as the drug experts in the Region of South Bohemia.{\crqq} The dissertation aims to provide an insight into the issue of perceiving the efficiency of drug addictions{\crq} primary prevention by individual target groups of the research. A method of qualitative research was chosen to reach the objective. Two research questions were specified as follows: 1.How is the efficiency of drug addictions{\crq} primary prevention perceived by individual target groups? 2. Is the primary prevention of drug addictions applied systematically, timely and to a sufficient extent? To obtain the data, a non-standardised questionnaire with rather open questions was used. The research sample contained at the end 40 addicts, possibly abstaining and 31 experts working in the field of drug problems. Using graphic charts as well as commentary description, the findings part shows the elicited situation regarding the issue. Most of the experts consider primary prevention rather effective. If expressed in percentage, 30% efficiency of drug addictions{\crq} primary prevention was the most frequent choice of answers. On the other hand, the addicts evaluated the prevention as rather inefficient; however, in the percentage expression they shared the number with the experts - 30% efficiency was the most frequent choice. What the experts considered the weakest point of the current situation was the lack of funds into the primary prevention and a strong anti-drug lobby as well as the absence or inappropriate evaluation of the efficiency of primary prevention. The addicts saw the weakest points of the primary prevention in the lack of information on the consequences, as the information on effects prevailed over the after-effects. The dissertation confirmed some of the presumptive facts (insufficient/inappropriate evaluation of the efficiency of primary prevention) and indicated the propriety of similar research with a more extended view. Such research should be systematic and long-term with a broad platform of cooperating experts and media.

1 Abstract Thesis Burnout syndrome amowg social workers in residental service deals with the issue of burnout as such. It focuses on the definition of the term as well as its development. So are related phenomena - stress, depression, fatigue, but also existential neurosis. It also mentions internal and external influences, which can often be the cause of burnout, as well as high-risk persons - ie the most risky types of personality as well as professions who often fall victim to burnout. In the second large chapter, the common symptoms of burnout are described and what changes occur in a person who is affected by burnout. In the third chapter are introduced individual options of prevention of burnout but also supervision, which is also helpful against the burnout. This chapter also describes the diagnostic methodology - various methods that help to identify or detect certain changes in the worker's behavior and thus to re-evaluate their behavior in a timely manner. In the final chapter - fourth is carried out quantitative research through a questionnaire. The collected data are then evaluated and presented in a graphical representation.

L'apprentissage profond est un domaine de recherche en forte croissance en apprentissage automatique qui est parvenu à des résultats impressionnants dans différentes tâches allant de la classification d'images à la parole, en passant par la modélisation du langage. Les réseaux de neurones récurrents, une sous-classe d'architecture profonde, s'avèrent particulièrement prometteurs. Les réseaux récurrents peuvent capter la structure temporelle dans les données. Ils ont potentiellement la capacité d'apprendre des corrélations entre des événements éloignés dans le temps et d'emmagasiner indéfiniment des informations dans leur mémoire interne. Dans ce travail, nous tentons d'abord de comprendre pourquoi la profondeur est utile. Similairement à d'autres travaux de la littérature, nos résultats démontrent que les modèles profonds peuvent être plus efficaces pour représenter certaines familles de fonctions comparativement aux modèles peu profonds. Contrairement à ces travaux, nous effectuons notre analyse théorique sur des réseaux profonds acycliques munis de fonctions d'activation linéaires par parties, puisque ce type de modèle est actuellement l'état de l'art dans différentes tâches de classification. La deuxième partie de cette thèse porte sur le processus d'apprentissage. Nous analysons quelques techniques d'optimisation proposées récemment, telles l'optimisation Hessian free, la descente de gradient naturel et la descente des sous-espaces de Krylov. Nous proposons le cadre théorique des méthodes à région de confiance généralisées et nous montrons que plusieurs de ces algorithmes développés récemment peuvent être vus dans cette perspective. Nous argumentons que certains membres de cette famille d'approches peuvent être mieux adaptés que d'autres à l'optimisation non convexe. La dernière partie de ce document se concentre sur les réseaux de neurones récurrents. Nous étudions d'abord le concept de mémoire et tentons de répondre aux questions suivantes: Les réseaux récurrents peuvent-ils démontrer une mémoire sans limite? Ce comportement peut-il être appris? Nous montrons que cela est possible si des indices sont fournis durant l'apprentissage. Ensuite, nous explorons deux problèmes spécifiques à l'entraînement des réseaux récurrents, à savoir la dissipation et l'explosion du gradient. Notre analyse se termine par une solution au problème d'explosion du gradient qui implique de borner la norme du gradient. Nous proposons également un terme de régularisation conçu spécifiquement pour réduire le problème de dissipation du gradient. Sur un ensemble de données synthétique, nous montrons empiriquement que ces mécanismes peuvent permettre aux réseaux récurrents d'apprendre de façon autonome à mémoriser des informations pour une période de temps indéfinie. Finalement, nous explorons la notion de profondeur dans les réseaux de neurones récurrents. Comparativement aux réseaux acycliques, la définition de profondeur dans les réseaux récurrents est souvent ambiguë. Nous proposons différentes façons d'ajouter de la profondeur dans les réseaux récurrents et nous évaluons empiriquement ces propositions. / Deep Learning is a quickly growing area of research in machine learning, providing impressive results on different tasks ranging from image classification to speech and language modelling. In particular, a subclass of deep models, recurrent neural networks, promise even more. Recurrent models can capture the temporal structure in the data. They can learn correlations between events that might be far apart in time and, potentially, store information for unbounded amounts of time in their innate memory. In this work we first focus on understanding why depth is useful. Similar to other published work, our results prove that deep models can be more efficient at expressing certain families of functions compared to shallow models. Different from other work, we carry out our theoretical analysis on deep feedforward networks with piecewise linear activation functions, the kind of models that have obtained state of the art results on different classification tasks. The second part of the thesis looks at the learning process. We analyse a few recently proposed optimization techniques, including Hessian Free Optimization, natural gradient descent and Krylov Subspace Descent. We propose the framework of generalized trust region methods and show that many of these recently proposed algorithms can be viewed from this perspective. We argue that certain members of this family of approaches might be better suited for non-convex optimization than others. The last part of the document focuses on recurrent neural networks. We start by looking at the concept of memory. The questions we attempt to answer are: Can recurrent models exhibit unbounded memory? Can this behaviour be learnt? We show this to be true if hints are provided during learning. We explore, afterwards, two specific difficulties of training recurrent models, namely the vanishing gradients and exploding gradients problem. Our analysis concludes with a heuristic solution for the exploding gradients that involves clipping the norm of the gradients. We also propose a specific regularization term meant to address the vanishing gradients problem. On a toy dataset, employing these mechanisms, we provide anecdotal evidence that the recurrent model might be able to learn, with out hints, to exhibit some sort of unbounded memory. Finally we explore the concept of depth for recurrent neural networks. Compared to feedforward models, for recurrent models the meaning of depth can be ambiguous. We provide several ways in which a recurrent model can be made deep and empirically evaluate these proposals.

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