This paper presents a method for generating player-driven narratives from visual inputs by exploring the visual analysis capabilities of multimodal large language models. By employing Bartle’s taxonomy of player types—Achievers, Explorers, Socializers, and Killers—our method creates stories that are tailored to different player characteristics. We conducted a fourfold experiment using a set of images extracted from a well-known game, generating distinct narratives for each player type that are aligned with the visual elements of the input images and specific player motivations. By adjusting narrative elements to emphasize achievement for Achievers, exploration for Explorers, social connections for Socializers, and competition for Killers, our system produced stories that adhere to established narratology principles while resonating with the characteristics of each player type. This approach can serve as a helping tool for game designers, offering new insights into how players might engage with game worlds through personalized image-driven narratives.
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Some Native Peoples didn 't want to be photographed because they believed that with every copy of their image, a part of their souls would disappear.By making a copy of an aspect of my existence - a photo, a film, a sound recording, or even a text - my existence goes beyond the immediate here and now. The copy will lead a life of its own. In addition there are young people who process a photo of themselves by smart algorithms in an image-processing app and take this to the plastic surgeon with a request to be operated on this image. Thus we are either lived by producing soulless images, or we strive to become an image of an image. All those soulless images ruin your here and now. Only now I understand that those Native Peoples were right!
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The huge number of images shared on the Web makes effective cataloguing methods for efficient storage and retrieval procedures specifically tailored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for image tagging with a focus on the needs of database expansion for a news broadcasting company. First, we determine the feasibility of using AIA in such a context with the aim of minimizing an extensive retraining whenever a new tag needs to be incorporated in the tag set population. Then, an image annotation tool integrating a Convolutional Neural Network model (AlexNet) for feature extraction and a K-Nearest-Neighbours classifier for tag assignment to images is introduced and tested. The obtained performances are very promising addressing the proposed approach as valuable to tackle the problem of image tagging in the framework of a broadcasting company, whilst not yet optimal for integration in the business process.
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The days where memes were plain image macros (an image that consists out of an imagecombined with text) are long behind us. New meme formats, some more obscure than others,are popping up like weeds. Memetic logics are also moving beyond the image into ‘the realworld’ (whatever that may be), but that’s a story for another time.In January 2022, when I was a participant in the Digital Methods Winter School of theUniversity of Amsterdam, my teammates and I came to an interesting conclusion. Inspired bythe forthcoming paper by Richard Rogers and Giulia Giorgi, called ‘What is a Meme TechnicallySpeaking’, we analyzed various meme collections taken from different software environments.1After a close reading of the dominant types of images, their ontology and epistemology, one ofthe most interesting findings, at least for me, was that our meme datasets were full of tweetscreenshots – which felt rather counterintuitive at first. However, once I started thinking aboutit, I realized that I have been noticing meme admins posting tweet screenshots on their pagesfor months now. One of my meme muses even told me that she did not know what her memepage has become, seeing all the tweets and other content she has been sharing lately. It hadme wondering. When leaving the technical and data driven context aside for a minute, has thetweet in its screenshot form become a viral image? Has the tweet become an actualmeme?
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In 1960 Kevin Lynch analysed the ‘city-image’ in The Image of the City; seven years later American artist Robert Smithson surveyed the suburb of Passaic in ‘A Tour of the Monuments of Passaic, New Jersey’. Both approaches use narrativity as an instrument to connect urban space with the lived experience of its users. Where Kevin Lynch analyzes the visual perception and mental representation (‘imageability’) of the postwar American metropolis, Robert Smithson explores the temporality of its peripheral terrain vague. Where Kevin Lynch frames his inquiry within then-current conventions of perception and cognition, Robert Smithson rejects these conventions precisely because they do no justice to his experience of the suburb and offer him no method to analyze or describe it. In his analysis, there is no coherent map of the territory, no mental representation to consult. How does Smithson’s practice relate to the paradigm of ‘imageability’? What is being narrated, and how does narrativity operate? By juxtaposing the two approaches this text reflects on some ideas and issues that surround a narrative analysis of urban landscape.
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We Are Not Sick is a hybrid lecture/music performance by Geert Lovink and John Longwalker. Combining a diversity of text-, image-, and music-genres, the project reflects on the encroaching sadness provoked by social media architectures.Through this project they push for new modalities in both music and critical theory, to shake up both the dance floor and the lecture circuit. Utilizing a range of electronic musical genres for maximum reach, the Sad By Design album is not a soundtrack to a book of theory but rather a new attempt at expressing those same themes, using the same words but achieving different vectors of critique. Sad By Design is a ‘carrier wave for critical theory,’ crafted over two years of refining our answer to the question of what this new ‘critical music’ hybrid feels like to experience.
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Ulus Baker (1960 – 2007) was a Turkish-Cypriot sociologist, philosopher, and public intellectual. He was born in Ankara, Turkey in 1960. He studied Sociology at Middle East Technical University in Ankara, where he taught as a lecturer until 2004. Baker wrote prolifically in influential Turkish journals and made some of the first Turkish translations of various works of Gilles Deleuze, Antonio Negri, and other contemporary political philosophers. His profuse and accessible work and the novelty of the issues he enthusiastically introduced to Turkish-speaking intellectual circles, earned him a widely spread positive reputation in early age. He died in 2007 in Istanbul.The text in this edition is edited from essays and notes Ulus Baker wrote between 1995 and 2002. In these essays, Baker criticizes the sociological research turning into an analysis of people’s opinions. He explores with an exciting clarity the notion of ‘opinion’ as a specific form of apprehension between knowledge and point of view, then looks into ‘social types’ as an analytical device deployed by early sociologists. He associates the form of ‘comprehension’ the ‘social types’ postulate with Spinoza’s notion of ‘affections’ (as a dynamic, non-linguistic form of the relation between entities). He finally discusses the possibilities of reintroducing this device for understanding our contemporary world through cinema and documentary filmmaking, by reinstating images in general as ‘affective thought processes’.Baker’s first extensive translation to English provides us with a much-needed intervention for re-imagining social thought and visual media, at a time when sociology tends to be reduced to an analysis of ‘big data’, and the pedagogical powers of the image are reduced to data visualization and infographics.
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BACKGROUND: In many genomics projects, numerous lists containing biological identifiers are produced. Often it is useful to see the overlap between different lists, enabling researchers to quickly observe similarities and differences between the data sets they are analyzing. One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram: a diagram consisting of two or more circles in which each circle corresponds to a data set, and the overlap between the circles corresponds to the overlap between the data sets. Venn diagrams are especially useful when they are 'area-proportional' i.e. the sizes of the circles and the overlaps correspond to the sizes of the data sets. Currently there are no programs available that can create area-proportional Venn diagrams connected to a wide range of biological databases.RESULTS: We designed a web application named BioVenn to summarize the overlap between two or three lists of identifiers, using area-proportional Venn diagrams. The user only needs to input these lists of identifiers in the textboxes and push the submit button. Parameters like colors and text size can be adjusted easily through the web interface. The position of the text can be adjusted by 'drag-and-drop' principle. The output Venn diagram can be shown as an SVG or PNG image embedded in the web application, or as a standalone SVG or PNG image. The latter option is useful for batch queries. Besides the Venn diagram, BioVenn outputs lists of identifiers for each of the resulting subsets. If an identifier is recognized as belonging to one of the supported biological databases, the output is linked to that database. Finally, BioVenn can map Affymetrix and EntrezGene identifiers to Ensembl genes.CONCLUSION: BioVenn is an easy-to-use web application to generate area-proportional Venn diagrams from lists of biological identifiers. It supports a wide range of identifiers from the most used biological databases currently available. Its implementation on the World Wide Web makes it available for use on any computer with internet connection, independent of operating system and without the need to install programs locally. BioVenn is freely accessible at http://www.cmbi.ru.nl/cdd/biovenn/.
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Virtual reality offers a both realistic and controlled research environment. That is why VR is the future for carrying out valid and reliable research in the social sciences.
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