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Ambitionen med denna uppsats är att kunna ge ett bidrag i kampen för ökad delaktighet i samhället för personer med funktionsnedsättningar. Forskning visar att det är viktigt att personer med psykiska funktionshinder syns ute i arbetslivet för att motverka stigmatiserande effekter. Det visar sig dock att personer med psykiska funktionshinder idag är den grupp som har det svårast att ta sig in på arbetsmarknaden. Syfte är således att öka förståelsen för funktionshindrades situation på arbetsmarknaden. Studien är av kvalitativ art och har genomförts med stöd av intervjuer vilka senare har tolkats utifrån ett intersektionellt perspektiv samt med stöd av teori kring rationalisering (Weber) och teori om social kategorisering (Tideman). Resultatet ger en bild av hur arbetsmarknaden idag genomsyras av rationalisering, effektivisering, flexibilitet och vinststrävan. Med detta följer behov av att finna arbetskraft som kan leva upp till dessa krav. Vidare leder detta till att arbetssökande tenderar att kategoriseras alltmer i termer av funktionsförmåga och effektivitet. Det visar sig dock att situationen kan vara mer komplex än så, då även faktorer som etnicitet, kön, ålder, trosuppfattning och klass har betydelse. Potentiell arbetskraft tenderar att väljas ut inom ramen för vad vi idag betraktar som normalitet men också utifrån vem arbetsgivaren och dennes organisation kan identifiera sig med mest. Stereotypa föreställningar kring människor som indelats i olika sociala kategorier, tycks många gånger vara avgörande när det kommer till vem som får arbete och inte. / This paper aims to provide a contribution in the fight for greater participation in society for persons with disabilities. Research shows that it is important for people with mental disabilities to be seen in the labor market to counter stigmatizing effects. It turns out, however, that people with mental disabilities today is the group that has the most difficulties in entering the labor market. The purpose is to increase an understanding of the situation of disabled people in the labor market. The study is qualitative in nature and is based on interviews that are interpreted by an intersectional perspective, theory of rationalization (Weber) and social categorization (Tideman). The result shows a picture of how the labor market today is permeated by rationalization, efficiency, flexibility and profit endeavor. This brings with it a need to find employees who can meet these requirements. Furthermore, this leads to that job seekers tend to be categorized more in terms of functional capacity and efficiency. It turns out, however, that the situation may be more complex than this, since factors such as ethnicity, gender, age, religion and class matters. Potential labor tends to be selected within the framework of what we now regard as normality but also on who the employer and his organization can identify with most. Stereotypical notions about people, who are divided into different social categories, frequently appear to be crucial when it comes to who gets the job or not.

I characterized dusky dolphin (Lagenorhynchus obscurus) underwater bait-balling behaviors and acoustic signals, and compared data between Argentina and New Zealand (NZ) to investigate the roles of ecology versus social learning. I quantified prey herding and capturing behaviors from video footage, and I analyzed acoustic signals from narrowband recordings. In both locations, I related bait-balling behaviors and acoustic signals to group and prey ball sizes. In NZ, I also related dolphin behaviors to prey ball escape behaviors and acoustic signal parameters to examine proximate functions. Observed herding behaviors typically involved dolphins swimming around or under a prey ball using a side body orientation, while dolphins typically captured fish from the side of a prey ball using a ventral orientation. Coordinated prey-capture behaviors may have made it easier for dolphins to capture fish by trapping fish between dolphins. Signals were categorized as click trains, burst pulses, and combinations due to a bimodal inter-click interval distribution. I observed 3 whistle-like chirp-screams, but no whistles. Sequences of burst pulses also occurred that contained 2-14 burst pulses that aurally and visually appeared closely matched. Similarities between locations suggest that ecological context related to broad behavioral and acoustic parameters, while social learning differences may occur on a finer scale. In NZ, prey balls exhibited horizontal and vertical movements, but the only behavior that preceded escape was “funneling”, the brief formation of a ball shape where the height was at least twice the width. Dolphin behaviors that related to prey balls ascending were type of herding pass, location of prey-capture attempts, and body orientation during attempts. These behavioral parameters may also be used to counter vertical prey escape behaviors. In NZ, all signal categories had a direct or indirect role in capturing prey. Click train-burst pulses were likely used for echolocating on prey, burst pulses and sequences appeared to have communication roles, and the role of click trains was ambiguous. No signal categories appeared to have a herding function, but the sheer number of signals emitted may have caused fish to cluster together more tightly and therefore facilitated capture.

In the information age of today, vast amounts of sensitive and confidential data is exchanged over an array of different mediums. Accompanied with this phenomenon is a comparable increase in the number and types of attacks to acquire this information. Information security and data consistency have hence, become quintessentially important. Log file analysis has proven to be a good defense mechanism as logs provide an accessible record of network activities in the form of server generated messages. However, manual analysis is tedious and prohibitively time consuming. Traditional log analysis techniques, based on pattern matching and data mining approaches, are ad hoc and cannot readily adapt to different kinds of log files. The goal of this research is to explore the use of grammar inference for log file analysis in order to build a more adaptive, flexible and generic method for message categorization, anomaly detection and reporting. The grammar inference process employs robust parsing, islands grammars and source transformation techniques. We test the system by using three different kinds of log file training sets as input and infer a grammar and generate message categories for each set. We detect anomalous messages in new log files using the inferred grammar as a catalog of valid traces and present a reporting program to extract the instances of specified message categories from the log files. / Thesis (Master, Computing) -- Queen's University, 2008-05-22 14:12:30.199

In this research, Communicative Accommodation Theory (CAT) is investigated while native speakers address nonnative peers. For the intentions of this research, three native speakers of Canadian English were asked to have conversations with native and nonnative peers. The conversations were in the form of giving directions on the map. Later on, the participants’ formants and vowel durations were measured and used for comparing native-nonnative peer effect(s) on the speakers’ vowel formants and duration. Based on the analyses, it is suggested that accommodation may take place based on providing stereotypical vowel durations and formants, as well as reducing inter-token variations in the nonnative peer context.

The aim of the thesis is to show how gender, ethnicity and generation membership categories are constituted in talk-in-interaction. The main material comprises seven video recordings of multi-participant conversations among school pupils, aged 16 to 19. An important theoretical term is intersectionality, i.e. the interplay between different social categories. The tools of analysis are mainly derived from conversation analysis and discursive psychology. Identity is seen as a dynamic phenomenon and I analyse the identities the participants themselves make relevant during the course of the conversations. The investigation, aided by membership categorisation analysis, is carried out into how social categories are negotiated and used in establishing identity. In the analyses, social categories in particular are used in order to constitute identities by the participants’ creating contrasts between in-group, we, and out-group, them. Category-bound activities are used to constitute social categories. The participants also use more specific resources for talk-in-interaction – for example, active voicing and extreme case formulations – to establish or negotiate social categories. Interactional strategies and tools are used in resistance to avoid being attributed membership in a certain category, and partly consist of various ways of renegotiating the implication of belonging to a certain category. Thus, generalising notions about social groups become more nuanced and the adolescents avoid being categorised as passive victims of cultural notions. Gender, ethnicity and generation membership are furthermore constituted through storytelling. To sum up, the above linguistic resources are used first and foremost for three different types of discursive work during the group conversations. First, the adolescents argue that they are unique and independent and therefore not dependent on cultural expectations. Secondly, they place themselves in relation to the categories by their enacting themselves as normal in various ways. Thirdly, the adolescents establish a positive self image by modifying or renegotiating the non-desirable activities associated with the categories. The results show how the categories have situational relevance and are dealt with locally, and invoke normative expectations as to how members of social groups ought to behave.

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This thesis consists of four independent papers. The first two papers use experimental methods to study pro-social behaviors. The other two use theoretical methods to investigate questions about belief formation. The first paper “Communication: Content or Relationship?” investigates the effect on communication on generosity in a dictator game. In the basic experiment (the control), subjects in one room are dictators and subjects in another room are recipients. The subjects are anonymous to each other throughout the whole experiment. Each dictator gets to allocate a sum of 100 SEK between herself and an unknown recipient in the other room. In the first treatment we allow each recipient to send a free-form message to his dictator counterpart, before the dictator makes her allocation decision. In order to separate the effect of the content of the communication, from the relationship-building effect of communication, we carry out a third treatment, where we take the messages from the previous treatment and give each of them to a dictator in this new treatment. The dictators are informed that the recipients who wrote the messages are not the recipients they will have the opportunity to send money to. We find that this still increases donation compared to the baseline but not as much as in the other treatment. This suggests that both the impersonal content of the communication and the relationship effect matters for donations. The second paper, “Limbic justice – Amygdala Drives Rejection in the Ultimatum Game”, is about the neurological basis for the tendency to punish norm violators in the Ultimatum Game. In the Ultimatum Game, a proposer proposes a way to divide a fixed sum of money. The responder accepts or rejects the proposal. If the proposal is accepted the proposed split is realized and if the proposal is rejected both subjects gets zero. Subjects were randomly allocated to receive either the benzodiazepine oxazepam or a placebo substance, and then played the Ultimatum Game in the responder role, while lying in and fMRI camera. Rejection rate is significantly lower in the treatment group than in the control group. Moreover a mygdala was relatively more activated in the placebo group than in the oxazepam group for unfair offers. This is mirrored by differences in activation in the medial prefrontal cortex (mPFC) and right ACC. Our findings suggest that the automatic and emotional response to unfairness, or norm violations, are driven by amygdala and that balancing of such automatic behavioral responses is associated with parts of the prefrontal cortex. The conflict of motives is monitored by the ACC. In order to decide what strategy to choose, a player needs to form beliefs about what other players will do. This requires the player to have a model of how other people form beliefs – what psychologists call a theory of mind. In the third paper “Evolution of Theories of Mind” I study the evolution of players’ models of how other players think. When people play a game for the first time, their behavior is often well predicted by the level-k, and related models. According to this model, people think in a limited number of steps, when they form beliefs about other peoples' behavior. Moreover, people differ with respect to how they form beliefs. The heterogeneity is represented by a set of cognitive types {0,1,2,...}, such that type 0 randomizes uniformly and type k&gt;0 plays a k times iterated best response to this. Empirically one finds that most experimental subjects behave as if they are of type 1 or 2, and individuals of type 3 and above are very rare. When people play the same game more than once, they may use their experience to predict how others will behave. Fictitious play is a prominent model of learning, according to which all individuals believe that the future will be like the past, and best respond to the average of past play. I define a model of heterogeneous fictitious play, according to which there is a hierarchy of types {1,2,...}, such that type k plays a k time iterated best response to the average of past play. The level-k and fictitious play models, implicitly assume that players lack specific information about the cognitive types of their opponents. I extend these models to allow for the possibility that types are partially observed. I study evolution of types in a number of games separately. In contrast to most of the literature on evolution and learning, I also study the evolution of types across different games. I show that an evolutionary process, based on payoffs earned in different games, both with and without partial observability, can lead to a polymorphic population where relatively unsophisticated types survive, often resulting in initial behavior that does not correspond to a Nash equilibrium. Two important mechanisms behind these results are the following: (i) There are games, such as the Hawk-Dove game, where there is an advantage of not thinking and behaving like others, since choosing the same action as the opponent yields an inefficient outcome. This mechanism is at work even if types are not observed. (ii) If types are partially observed then there are Social dilemmas where lower types may have a commitment advantage; lower types may be able to commit to strategies that result in more efficient payoffs. The importance of categorical reasoning in human cognition is well-established in psychology and cognitive science, and one of the most important functions of categorization is to facilitate prediction. Prediction on the basis of categorical reasoning is relevant when one has to predict the value of a variable on the basis of one's previous experience with similar situations, but where the past experience does not include any situation that was identical to the present situation in all relevant aspects. In such situations one can classify the situation as belonging to some category, and use the past experiences in that category to make a prediction about the current situation. In the fourth paper, “Optimal Categorization”, I provide a model of categorizations that are optimal in the sense that they minimize prediction error. From an evolutionary perspective we would expect humans to have developed categories that generate predictions which induce behavior that maximize fitness, and it seems reasonable to assume that fitness is generally increasing in how accurate the predictions are. In the model a subject starts out with a categorization that she has learnt or inherited early in life. The categorization divides the space of objects into categories. In the beginning of each period, the subject observes a two-dimensional object in one dimension, and wants to predict the object’s value in the other dimension. She has a data base of objects that were observed in both dimensions in the past. The subject determines what category the new object belongs to on the basis of observation of its first dimension. She predicts that its value in the second dimension will be equal to the average value among the past observations in the corresponding category. At the end of each period the second dimension is observed, and the observation is stored in the data base. The main result is that the optimal number of categories is determined by a trade-off between (a) decreasing the size of categories in order to enhance category homogeneity, and (b) increasing the size of categories in order to enhance category sample size. In other words, the advantage of fine grained categorizations is that objects in a category are similar to each other. The advantage of coarse categorizations is that a prediction about a category is based on a large number of observations, thereby reducing the risk of over-fitting. Comparative statics reveal how the optimal categorization depends on the number of observations as well as on the frequency of objects with different properties. The set-up does not presume the existence of an objectively true categorization “out there”. The optimal categorization is a framework we impose on our environment in order to predict it. / <p>Diss. Stockholm : Handelshögskolan, 2010. Sammanfattning jämte 4 uppsatser.</p>

In this research, Communicative Accommodation Theory (CAT) is investigated while native speakers address nonnative peers. For the intentions of this research, three native speakers of Canadian English were asked to have conversations with native and nonnative peers. The conversations were in the form of giving directions on the map. Later on, the participants’ formants and vowel durations were measured and used for comparing native-nonnative peer effect(s) on the speakers’ vowel formants and duration. Based on the analyses, it is suggested that accommodation may take place based on providing stereotypical vowel durations and formants, as well as reducing inter-token variations in the nonnative peer context.

El consum de la música ha canviat dràsticament en els últims anys. Amb l’arribada de la música digital, el cost de producció s’ha reduït considerablement. L’expansió de la Web ha ajudat a promoure l’exploració de molt més contingut musical. Algunes botigues musicals on-line, com iTunes o Amazon, posseeixen milions de cançons a les seves col.leccions. No obstant, accedir a aquestes col.leccions d’una manera eficient és encara un gran repte. En aquesta tesis ens centrem en el problema d’anotar col.leccions musicals amb paraules semàntiques, també conegudes com tags. Els mètodes utilitzats en aquesta tesi estan fonamentats sobre els camps de recuperació de la informació, l’inteligència artificial, i el procesament del senyal. Proposem un algorisme per anotar música automàticament, utilitzant similitud d’audio a nivell de contingut per propagar tags entre cançons. L’algorisme s’avalua extensament utilitzant múltiples col.leccions musicals de diferent mida i qualitat de les dades, incloent una col.lecció de més de mig milió de cançons, anotades amb tags socials derivats d’una comunitat musical. Avaluem la qualitat del nostre algorisme mitjançant una comparació amb algorismes de l’estat de l’art. Addicionalment, discutim la importància d’utilitzar mesures de avaluació que cobreixen diferents dimensions, és a dir, avaluacions a nivell de cançó i a nivell de tag. El nostre algorisme ha estat avaluat i s’ha classificat en altes posicions en el concurs d’avaluació internacional MIREX 2011. Els resultats obtinguts també demostren algunes limitacions de l’anotació automàtica, relacionades amb les inconsistències en les dades, la correlació de conceptes i la dificultat de capturar alguns tags personals amb informació del contingut. Això és més evident en les comunitats musicals, on els usuaris poden anotar cançons amb qualsevol paraula, sigui aquesta contextual o no. Per tal d’abordar aquestes limitacions, presentem un ampli estudi sobre la naturalesa de les folksonomies musicals. Concretament, estudiem si les anotacions fetes per una gran comunitat d’usuaris coincideixen amb un vocabulari més controlat i estructurat per part d’experts en el camp. Els resultats revelen que alguns tags estan clarament definits i compresos tant des del punt de vista dels experts com el de la saviesa popular, mentre que n’hi ha d’altres sobre els quals és difícil trobar un consens. Finalment, estenem el nostre previ treball a un ampli ventall de conceptes semàntics. Presentem un nou métode per a descobrir conceptes semàntics implícits en els tags socials, i classificar aquests tags pel que fa als conceptes semàntics. Les darreres troballes poden ajudar a entendre la naturalesa dels tags socials, i per tant ser beneficials per a una addicional millora de la anotació automàtica de la música. / Music consumption has changed drastically in the last few years. With the arrival of digital music, the cost of production has substantially dropped. The expansion of the World Wide Web has helped to promote the exploration of many more music content. Online stores, such as iTunes or Amazon, own music collections in the order of millions of songs. Accessing these large collections in an effective manner is still a big challenge. In this dissertation we focus on the problem of annotating music collections with semantic words, also called tags. The foundations of all the methods used in this dissertation are based on techniques from the fields of information retrieval, machine learning, and signal processing. We propose an automatic music annotation algorithm that uses content-based audio similarity to propagate tags among songs. The algorithm is evaluated extensively using multiple music collections of varying size and quality of the data, including a large music collection of more than a half million songs, annotated with social tags derived from a music community. We assess the quality of our proposed algorithm by comparing it with several state of the art approaches. We also discuss the importance of using evaluation measures that cover different dimensions; per– song and per–tag evaluation. Our proposal achieves state of the art results, and has ranked high in the MIREX 2011 evaluation campaign. The obtained results also show some limitations of automatic tagging, related to data inconsistencies, correlation of concepts and the difficulty to capture some personal tags with content information. This is more evident in music communites, where users can annotate songs with any free text word. In order to tackle these issues, we present an in-depth study of the nature of music folksonomies. We concretely study whether tag annotations made by a large community (i.e. a folksonomy) correspond with a more controlled, structured vocabulary by experts in the music and the psychology fields. Results reveal that some tags are clearly defined and understood both by the experts and the wisdom of crowds, while it is difficult to achieve a common consensus on the meaning of other tags. Finally, we extend our previous work to a wide range of semantic concepts. We present a novel way to uncover facets implicit in social tagging, and classify the tags with respect to these semantic facets. The latter findings can help to understand the nature of social tags, and thus be beneficial for further improvement of semantic tagging of music. Our findings have significant implications for music information retrieval systems that assist users to explore large music collections, digging for content they might like. / El consumo de la música ha cambiado drásticamente en los últimos años. Con la llegada de la música digital, el coste de producción se ha reducido considerablemente. La expansión de la Web ha ayudado a promover la exploración de mucho más contenido musical. Algunas tiendas musicales on-line, como iTunes o Amazon, poseen millones de canciones en sus colecciones. Sin embargo, acceder a estas colecciones de una manera eficiente es todavía un gran reto. En esta tesis nos centramos en el problema de anotar colecciones musicales con palabras semánticas, también conocidas como tags. Los métodos utilizados en esta tesis están cimentados sobre los campos de recuperación de la información, la inteligencia artifical, y el procesamiento del señal. Proponemos un algoritmo para anotar música automáticamente, usando similitud de audio a nivel de contenido para propagar tags entre canciones. El algoritmo se evalúa extensamente usando múltiples colecciones musicales de distinto tamaño y calidad de los datos, incluyendo una colección de más de medio millón de canciones, anotadas con tags sociales derivados de una comunidad musical. Evaluamos la calidad de nuestro algoritmo mediante una comparación con algoritmos del estado del arte. Adicionalmente, discutimos la importancia de usar medidas de evaluación que cubren diferentes dimensiones; es decir, evaluaciones a nivel de canción y a nivel de tag. Nuestro algoritmo ha sido evaluado y se clasificado en altas posiciones en el concurso de evaluación internacional MIREX 2011. Los resultados obtenidos también demuestran algunas limitaciones de la anotación automática, relacionadas con las inconsistencias en los datos, la correlación de conceptos y la dificultad de capturar algunos tags personales con información del contenido. Esto es más evidente en las comunidades musicales, donde los usuarios pueden anotar canciones con cualquier palabra, sea esta contextual o no. Con el fin de abordar estas limitaciones, presentamos un amplio estudio sobre la naturaleza de las folksonomías musicales. Concretamente, estudiamos si las anotaciones hechas por una gran comunidad de usuarios concuerdan con un vocabulario más controlado y estructurado por parte de expertos en el campo. Los resultados revelan que algunos tags están claramente definidos y comprendidos tanto desde el punto de vista de los expertos como el de la sabiduría popular, mientras que hay otros tags sobre los cuales es difícil encontrar un consenso. Por último, extendemos nuestro previo trabajo a un amplio abanico de conceptos semánticos. Presentamos un método novedoso para descubrir conceptos semánticos implícitos en los tags sociales, y clasificar dichos tags con respecto a los conceptos semánticos. Los últimos hallazgos pueden ayudar a entender la naturaleza de los tags sociales, y por consiguiente ser beneficiales para una adicional mejora para la anotación automática de la música.