Japanese Video Game Localization : A Case Study of Sony's Sairen Series

This thesis is a comparative case study in Japanese video game localization for the video games Sairen, Sairen 2 and Sairen Nyûtoransurêshon, and English-language localized versions of the same games as published in Scandinavia and Australia/New Zealand. All games are developed by Sony Computer Entertainment Inc. and published exclusively for Playstation2 and Playstation3 consoles. The fictional world of the Sairen games draws much influence from Japanese history, as well as from popular and contemporary culture, and in doing so caters mainly to a Japanese audience. For localization, i.e. the adaptation of a product to make it accessible to users outside the original market it was intended for in the first place, this is a challenging issue. Video games are media of entertainment, and therefore localization practice must preserve the games’ effects on the players’ emotions. Further, video games are digital products that are comprised of a multitude of distinct elements, some of which are part of the game world, while others regulate the connection between the player as part of the real world and the game as digital medium. As a result, video game localization is also a practice that has to cope with the technical restrictions that are inherent to the medium. The main theory used throughout the thesis is Anthony Pym’s framework for localization studies that considers the user of the localized product as a defining part of the localization process. This concept presupposes that localization is an adaptation that is performed to make a product better suited for use during a specific reception situation. Pym also addresses the factor that certain products may resist distribution into certain reception situations because of their content, and that certain aspects of localization aim to reduce this resistance through significant alterations of the original product. While Pym developed his ideas with mainly regular software in mind, they can also be adapted well to study video games from a localization angle. Since modern video games are highly complex entities that often switch between interactive and non-interactive modes, Pym’s ideas are adapted throughout the thesis to suit the particular elements being studied. Instances analyzed in this thesis include menu screens, video clips, in-game action and websites. The main research questions focus on how the games’ rules influence localization, and how the games’ fictional domain influences localization. Because there are so many peculiarities inherent to the medium of the video game, other theories are introduced as well to complement the research at hand. These include Lawrence Venuti’s discussions of foreiginizing and domesticating translation methods for literary translation, and Jesper Juul’s definition of games. Additionally, knowledge gathered from interviews with video game localization professionals in Japan during September and October 2009 is also utilized for this study. Apart from answering the aforementioned research questions, one of this thesis’ aims is to enrich the still rather small field of game localization studies, and the study of Japanese video games in particular, one of Japan’s most successful cultural exports.

Eigennamen in Pressetexten : Ein Vergleich zweier Online-Zeitungen

Tämä pro gradu -tutkielma vertailee korpuksen avulla erisnimien kvantitatiivista jakautumista luokkiin kahdessa saksalaisessa verkkolehdessä. Työn tavoitteena on selvittää, kuinka erisnimiä voidaan luokitella ja mitä eroja niiden avulla on havaittavissa lehtien raportoinnissa. Laajempana kehyksenä toimii kysymys siitä, voidaanko erisnimiä hyödyntäen hahmottaa lehtien sisältöjä. Korpus on kerätty Frankfurter Allgemeine Zeitungin ja Süddeutsche Zeitungin verkkolehtien http: //www.faz.net (FAZ) ja http://www.sueddeutsche.de (SZ) artikkeleista ajalta 2.11.2004-8.11.2004. Valitut sivustot edustavat Saksan arvostetuimpien päivittäisten, koko maan kattavien sanomaleh- tien verkkojulkaisuja. Näistä FAZ:ia pidetään konservatiivisena ja SZ:ia liberaalina lehtenä. Kumpikin korpus käsittelee USA:n presidentinvaaleja syksyllä 2004 ja sisältää hieman alle 30 000 sanaa noin 40 lehtiartikkelista. Aihesidonnaisen korpuksen valinta perustuu erityisesti siihen, että tutkimuksen päämääränä on saada erisnimien avulla selville, miltä osin FAZ ja SZ eroavat toisistaan käsitellessään samaa aihetta. Teoriaosassa käydään läpi saksalaisten verkkolehtien taustaa, työhön liittyviä tekstilingvistisiä teo- rioita sekä erisnimien erikoispiirteitä. Siinä käsitellään myös kolmea aiempaa, saksankielisen eris- nimitutkimuksen luokittelua ja yhtä englanninkielistä, kieliteknologian luokittelua. Näissä havaitut puutteet motivoivat yhdistelemään ja muuttamaan olemassa olevia luokitteluja tätä työtä varten. Uusi luokittelu sisältää neljä yläluokkaa (olentojen, maantieteelliset, instituutioden ja asioiden ni- met), jotka kaikki kattavat kahdesta yhdeksään alaluokkaa. Kummankin korpuksen erisnimet luo- kitellaan tämän perusteella. Kvantitatiivinen analyysi keskittyy ylä- ja alaluokkien vertailuun lehtien välillä. Lisäksi se kattaa sekä kummankin aineiston että pääluokkien frekventimpien sanojen tarkastelun. Vaikka FAZ ja SZ käyttivätkin pääosin samoja erisnimiä raportoinnissaan, voidaan lehtien välillä osoittaa selkeitä eroja alaluokkien kohdalla ja vähäisiä eroja erisnimien jakautumisessa yläluokkiin. chi2 -testin näytti kuitenkin, että erisnimien jakautuminen yläluokkiin on lehtisidonnaista. Siksi voidaan väittää, että muun muassa valittu media vaikuttaa erisnimivalintoihin. Erisnimien frekvenssit antavat ymmärtää, että SZ raportoisi monipuolisemmin kuin FAZ, joka käyttää erisnimiä keskitetymmin. SZ:in aineiston erisnimiä yhdistää eurooppalainen näkökulma vaaleihin, kun taas FAZ pyrkii tuomaan esille tapahtumia USA:n eri osavaltioissa. Niin lehdissä mainitut henkilöiden kuin instituutioden nimet tukevat tätä väitetettä. SZ korostaa maantieteellisesti kaupunkien merkitystä, FAZ osavaltioiden. Saadut tulokset osoittavat, että tämänkaltaisen erisnimitutkimuksen soveltaminen lehtiteksteihin on mahdollista. Luokitellut erisnimet heijastavat osittain käsiteltyjen aineistojen sisältöä ja paljastavat raportoinnin painopisteistä.

Methodological and Empirical Advances in the Quantitative Analysis of Spontaneous Responses in Psychophysiological Time Series

The aim of the present study was to advance the methodology and use of time series analysis to quantify dynamic structures in psychophysiological processes and thereby to produce information on spontaneously coupled physiological responses and their behavioral and experiential correlates. Series of analyses using both simulated and empirical cardiac (IBI), electrodermal (EDA), and facial electromyographic (EMG) data indicated that, despite potential autocorrelated structures, smoothing increased the reliability of detecting response coupling from an interindividual distribution of intraindividual measures and that especially the measures of covariance produced accurate information on the extent of coupled responses. This methodology was applied to analyze spontaneously coupled IBI, EDA, and facial EMG responses and vagal activity in their relation to emotional experience and personality characteristics in a group of middle-aged men (n = 37) during the administration of the Rorschach testing protocol. The results revealed new characteristics in the relationship between phasic end-organ synchronization and vagal activity, on the one hand, and individual differences in emotional adjustment to novel situations on the other. Specifically, it appeared that the vagal system is intimately related to emotional and social responsivity. It was also found that the lack of spontaneously synchronized responses is related to decreased energetic arousal (e.g., depression, mood). These findings indicate that the present process analysis approach has many advantages for use in both experimental and applied research, and that it is a useful new paradigm in psychophysiological research. Keywords: Autonomic Nervous System; Emotion; Facial Electromyography; Individual Differences; Spontaneous Responses; Time Series Analysis; Vagal System

Semantic Classes in Topic Detection and Tracking

Topic detection and tracking (TDT) is an area of information retrieval research the focus of which revolves around news events. The problems TDT deals with relate to segmenting news text into cohesive stories, detecting something new, previously unreported, tracking the development of a previously reported event, and grouping together news that discuss the same event. The performance of the traditional information retrieval techniques based on full-text similarity has remained inadequate for online production systems. It has been difficult to make the distinction between same and similar events. In this work, we explore ways of representing and comparing news documents in order to detect new events and track their development. First, however, we put forward a conceptual analysis of the notions of topic and event. The purpose is to clarify the terminology and align it with the process of news-making and the tradition of story-telling. Second, we present a framework for document similarity that is based on semantic classes, i.e., groups of words with similar meaning. We adopt people, organizations, and locations as semantic classes in addition to general terms. As each semantic class can be assigned its own similarity measure, document similarity can make use of ontologies, e.g., geographical taxonomies. The documents are compared class-wise, and the outcome is a weighted combination of class-wise similarities. Third, we incorporate temporal information into document similarity. We formalize the natural language temporal expressions occurring in the text, and use them to anchor the rest of the terms onto the time-line. Upon comparing documents for event-based similarity, we look not only at matching terms, but also how near their anchors are on the time-line. Fourth, we experiment with an adaptive variant of the semantic class similarity system. The news reflect changes in the real world, and in order to keep up, the system has to change its behavior based on the contents of the news stream. We put forward two strategies for rebuilding the topic representations and report experiment results. We run experiments with three annotated TDT corpora. The use of semantic classes increased the effectiveness of topic tracking by 10-30\% depending on the experimental setup. The gain in spotting new events remained lower, around 3-4\%. The anchoring the text to a time-line based on the temporal expressions gave a further 10\% increase the effectiveness of topic tracking. The gains in detecting new events, again, remained smaller. The adaptive systems did not improve the tracking results.

Mining Cell Transition Data

Cell transition data is obtained from a cellular phone that switches its current serving cell tower. The data consists of a sequence of transition events, which are pairs of cell identifiers and transition times. The focus of this thesis is applying data mining methods to such data, developing new algorithms, and extracting knowledge that will be a solid foundation on which to build location-aware applications. In addition to a thorough exploration of the features of the data, the tools and methods developed in this thesis provide solutions to three distinct research problems. First, we develop clustering algorithms that produce a reliable mapping between cell transitions and physical locations observed by users of mobile devices. The main clustering algorithm operates in online fashion, and we consider also a number of offline clustering methods for comparison. Second, we define the concept of significant locations, known as bases, and give an online algorithm for determining them. Finally, we consider the task of predicting the movement of the user, based on historical data. We develop a prediction algorithm that considers paths of movement in their entirety, instead of just the most recent movement history. All of the presented methods are evaluated with a significant body of real cell transition data, collected from about one hundred different individuals. The algorithms developed in this thesis are designed to be implemented on a mobile device, and require no extra hardware sensors or network infrastructure. By not relying on external services and keeping the user information as much as possible on the user s own personal device, we avoid privacy issues and let the users control the disclosure of their location information.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

Clustering Users of Online Content Service

Online content services can greatly benefit from personalisation features that enable delivery of content that is suited to each user's specific interests. This thesis presents a system that applies text analysis and user modeling techniques in an online news service for the purpose of personalisation and user interest analysis. The system creates a detailed thematic profile for each content item and observes user's actions towards content items to learn user's preferences. A handcrafted taxonomy of concepts, or ontology, is used in profile formation to extract relevant concepts from the text. User preference learning is automatic and there is no need for explicit preference settings or ratings from the user. Learned user profiles are segmented into interest groups using clustering techniques with the objective of providing a source of information for the service provider. Some theoretical background for chosen techniques is presented while the main focus is in finding practical solutions to some of the current information needs, which are not optimally served with traditional techniques.

Improving GPS time series for geodynamic studies

Accurate and stable time series of geodetic parameters can be used to help in understanding the dynamic Earth and its response to global change. The Global Positioning System, GPS, has proven to be invaluable in modern geodynamic studies. In Fennoscandia the first GPS networks were set up in 1993. These networks form the basis of the national reference frames in the area, but they also provide long and important time series for crustal deformation studies. These time series can be used, for example, to better constrain the ice history of the last ice age and the Earth s structure, via existing glacial isostatic adjustment models. To improve the accuracy and stability of the GPS time series, the possible nuisance parameters and error sources need to be minimized. We have analysed GPS time series to study two phenomena. First, we study the refraction in the neutral atmosphere of the GPS signal, and, second, we study the surface loading of the crust by environmental factors, namely the non-tidal Baltic Sea, atmospheric load and varying continental water reservoirs. We studied the atmospheric effects on the GPS time series by comparing the standard method to slant delays derived from a regional numerical weather model. We have presented a method for correcting the atmospheric delays at the observational level. The results show that both standard atmosphere modelling and the atmospheric delays derived from a numerical weather model by ray-tracing provide a stable solution. The advantage of the latter is that the number of unknowns used in the computation decreases and thus, the computation may become faster and more robust. The computation can also be done with any processing software that allows the atmospheric correction to be turned off. The crustal deformation due to loading was computed by convolving Green s functions with surface load data, that is to say, global hydrology models, global numerical weather models and a local model for the Baltic Sea. The result was that the loading factors can be seen in the GPS coordinate time series. Reducing the computed deformation from the vertical time series of GPS coordinates reduces the scatter of the time series; however, the long term trends are not influenced. We show that global hydrology models and the local sea surface can explain up to 30% of the GPS time series variation. On the other hand atmospheric loading admittance in the GPS time series is low, and different hydrological surface load models could not be validated in the present study. In order to be used for GPS corrections in the future, both atmospheric loading and hydrological models need further analysis and improvements.

Measurements of Volatile Organic Compounds - from Biogenic Emissions to Concentrations in Ambient Air

Volatile organic compounds (VOCs) affect atmospheric chemistry and thereafter also participate in the climate change in many ways. The long-lived greenhouse gases and tropospheric ozone are the most important radiative forcing components warming the climate, while aerosols are the most important cooling component. VOCs can have warming effects on the climate: they participate in tropospheric ozone formation and compete for oxidants with the greenhouse gases thus, for example, lengthening the atmospheric lifetime of methane. Some VOCs, on the other hand, cool the atmosphere by taking part in the formation of aerosol particles. Some VOCs, in addition, have direct health effects, such as carcinogenic benzene. VOCs are emitted into the atmosphere in various processes. Primary emissions of VOC include biogenic emissions from vegetation, biomass burning and human activities. VOCs are also produced in secondary emissions from the reactions of other organic compounds. Globally, forests are the largest source of VOC entering the atmosphere. This thesis focuses on the measurement results of emissions and concentrations of VOCs in one of the largest vegetation zones in the world, the boreal zone. An automated sampling system was designed and built for continuous VOC concentration and emission measurements with a proton transfer reaction - mass spectrometer (PTR-MS). The system measured one hour at a time in three-hourly cycles: 1) ambient volume mixing-ratios of VOCs in the Scots-pine-dominated boreal forest, 2) VOC fluxes above the canopy, and 3) VOC emissions from Scots pine shoots. In addition to the online PTR-MS measurements, we determined the composition and seasonality of the VOC emissions from a Siberian larch with adsorbent samples and GC-MS analysis. The VOC emissions from Siberian larch were reported for the fist time in the literature. The VOC emissions were 90% monoterpenes (mainly sabinene) and the rest sesquiterpenes (mainly a-farnesene). The normalized monoterpene emission potentials were highest in late summer, rising again in late autumn. The normalized sesquiterpene emission potentials were also highest in late summer, but decreased towards the autumn. The emissions of mono- and sesquiterpenes from the deciduous Siberian larch, as well as the emissions of monoterpenes measured from the evergreen Scots pine, were well described by the temperature-dependent algorithm. In the Scots-pine-dominated forest, canopy-scale emissions of monoterpenes and oxygenated VOCs (OVOCs) were of the same magnitude. Methanol and acetone were the most abundant OVOCs emitted from the forest and also in the ambient air. Annually, methanol and mixing ratios were of the order of 1 ppbv. The monoterpene and sum of isoprene 2-methyl-3-buten-2-ol (MBO) volume mixing-ratios were an order of magnitude lower. The majority of the monoterpene and methanol emissions from the Scots-pinedominated forest were explained by emissions from Scots pine shoots. The VOCs were divided into three classes based on the dynamics of the summer-time concentrations: 1) reactive compounds with local biological, anthropogenic or chemical sources (methanol, acetone, butanol and hexanal), 2) compounds whose emissions are only temperaturedependent (monoterpenes), 3) long-lived compounds (benzene, acetaldehyde). Biogenic VOC (methanol, acetone, isoprene MBO and monoterpene) volume mixing-ratios had clear diurnal patterns during summer. The ambient mixing ratios of other VOCs did not show this behaviour. During winter we did not observe systematical diurnal cycles for any of the VOCs. Different sources, removal processes and turbulent mixing explained the dynamics of the measured mixing-ratios qualitatively. However, quantitative understanding will require longterm emission measurements of the OVOCs and the use of comprehensive chemistry models. Keywords: Hydrocarbons, VOC, fluxes, volume mixing-ratio, boreal forest

Diagnostic Tests Based on Quantile Residuals for Nonlinear Time Series Models

This thesis studies quantile residuals and uses different methodologies to develop test statistics that are applicable in evaluating linear and nonlinear time series models based on continuous distributions. Models based on mixtures of distributions are of special interest because it turns out that for those models traditional residuals, often referred to as Pearson's residuals, are not appropriate. As such models have become more and more popular in practice, especially with financial time series data there is a need for reliable diagnostic tools that can be used to evaluate them. The aim of the thesis is to show how such diagnostic tools can be obtained and used in model evaluation. The quantile residuals considered here are defined in such a way that, when the model is correctly specified and its parameters are consistently estimated, they are approximately independent with standard normal distribution. All the tests derived in the thesis are pure significance type tests and are theoretically sound in that they properly take the uncertainty caused by parameter estimation into account. -- In Chapter 2 a general framework based on the likelihood function and smooth functions of univariate quantile residuals is derived that can be used to obtain misspecification tests for various purposes. Three easy-to-use tests aimed at detecting non-normality, autocorrelation, and conditional heteroscedasticity in quantile residuals are formulated. It also turns out that these tests can be interpreted as Lagrange Multiplier or score tests so that they are asymptotically optimal against local alternatives. Chapter 3 extends the concept of quantile residuals to multivariate models. The framework of Chapter 2 is generalized and tests aimed at detecting non-normality, serial correlation, and conditional heteroscedasticity in multivariate quantile residuals are derived based on it. Score test interpretations are obtained for the serial correlation and conditional heteroscedasticity tests and in a rather restricted special case for the normality test. In Chapter 4 the tests are constructed using the empirical distribution function of quantile residuals. So-called Khmaladze s martingale transformation is applied in order to eliminate the uncertainty caused by parameter estimation. Various test statistics are considered so that critical bounds for histogram type plots as well as Quantile-Quantile and Probability-Probability type plots of quantile residuals are obtained. Chapters 2, 3, and 4 contain simulations and empirical examples which illustrate the finite sample size and power properties of the derived tests and also how the tests and related graphical tools based on residuals are applied in practice.

Verkkomedian käyttö ja tutkiminen : Iltalehti Online 1995-2001

Marja Heinonen s dissertation Verkkomedian käyttö ja tutkiminen. Iltalehti Online 1995-2001 describes the usage of new internet based news service Iltalehti Online during its first years of existence, 1995-2001. The study focuses on the content of the service and users attitudes towards the new media and its contents. Heinonen has also analyzed and described the research methods that can be used in the research of any new media phenomenon when there is no historical perspective to do the research. Heinonen has created a process model for the research of net medium, which is based on a multidimensional approach. She has chosen an iterative research method inspired by Sudweeks and Simoff s CEDA-methodology in which qualitative and quantitative methods take turns both creating results and new research questions. The dissertation discusses and describes the possibilities of combining several research methods in the study of online news media. On general level it discusses the methodological possibilities of researching a completely new media form when there is no historical perspective. The result of these discussions is in favour for the multidimensional methods. The empiric research was built around three cases of Iltalehti Online among its users: log analysis 1996-1999, interviews 1999 and clustering 2000-2001. Even though the results of different cases were somewhat conflicting here are the central results from the analysis of Iltalehti Online 1995-2001: - Reading was strongly determined by the gender. - The structure of Iltalehti Online guided the reading strongly. - People did not make a clear distinction in content between news and entertainment. - Users created new habits in their everyday life during the first years of using Iltalehti Online. These habits were categorized as follows: - break between everyday routines - established habit - new practice within the rhythm of the day - In the clustering of the users sports, culture and celebrities were the most distinguishing contents. Users did not move across these borders as much as within them. The dissertation gives contribution to the development of multidimensional research methods in the field of emerging phenomena in media field. It is also a unique description of a phase of development in media history through an unique research material. There is no such information (logs + demographics) available of any other Finnish online news media. Either from the first years or today.

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.