The New Aesthetic and Art: Constellations of the Postdigital is an interdisciplinary analysis focusing on new digital phenomena at the intersections of theory and contemporary art. Asserting the unique character of New Aesthetic objects, Contreras-Koterbay and Mirocha trace the origins of the New Aesthetic in visual arts, design, and software, find its presence resonating in various kinds of digital imagery, and track its agency in everyday effects of the intertwined physical world and the digital realm. Contreras-Koterbay and Mirocha bring to light an original perspective that identifies an autonomous quality in common digital objects and examples of art that are increasingly an important influence for today’s culture and society.Influenced by a diverse range of figures, ranging from Vilém Flusser, Arthur Schopenhauer, Immanuel Kant, David Berry, Lev Manovich, Olga Goriunova, Ernst Mayr, Bruce Sterling and, of course, James Bridle, The New Aesthetic and Art: Constellations of the Postdigital doesn’t just propose a description of a new set of objects but radically asserts that New Aesthetic objects analogously function as organisms within a broader digital-physical ecosystems of things and agents.
Background: Art therapy (AT) is frequently offered to children and adolescents with psychosocial problems. AT is an experiential form of treatment in which the use of art materials, the process of creation in the presence and guidance of an art therapist, and the resulting artwork are assumed to contribute to the reduction of psychosocial problems. Although previous research reports positive effects, there is a lack of knowledge on which (combination of) art therapeutic components contribute to the reduction of psychosocial problems in children and adolescents. Method: A systematic narrative review was conducted to give an overview of AT interventions for children and adolescents with psychosocial problems. Fourteen databases and four electronic journals up to January 2020 were systematically searched. The applied means and forms of expression, therapist behavior, supposed mechanisms of change, and effects were extracted and coded. Results: Thirty-seven studies out of 1,299 studies met the inclusion criteria. This concerned 16 randomized controlled trials, eight controlled trials, and 13 single-group pre–post design studies. AT interventions for children and adolescents are characterized by a variety of materials/techniques, forms of structure such as giving topics or assignments, and the use of language. Three forms of therapist behavior were seen: non-directive, directive, and eclectic. All three forms of therapist behavior, in combination with a variety of means and forms of expression, showed significant effects on psychosocial problems. Conclusions: The results showed that the use of means and forms of expression and therapist behavior is applied flexibly. This suggests the responsiveness of AT, in which means and forms of expression and therapist behavior are applied to respond to the client's needs and circumstances, thereby giving positive results for psychosocial outcomes. For future studies, presenting detailed information on the potential beneficial effects of used therapeutic perspectives, means, art techniques, and therapist behavior is recommended to get a better insight into (un)successful art therapeutic elements.
Concerns have been raised over the increased prominence ofgenerative AI in art. Some fear that generative models could replace theviability for humans to create art and oppose developers training generative models on media without the artist's permission. Proponents of AI art point to the potential increase in accessibility. Is there an approach to address the concerns artists raise while still utilizing the potential these models bring? Current models often aim for autonomous music generation. This, however, makes the model a black box that users can't interact with. By utilizing an AI pipeline combining symbolic music generation and a proposed sample creation system trained on Creative Commons data, a musical looping application has been created to provide non-expert music users with a way to start making their own music. The first results show that it assists users in creating musical loops and shows promise for future research into human-AI interaction in art.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
Het doel van dit interdisciplinaire SIA KIEM project Fluïde Eigenschap in de Creatieve Industrie is te onderzoeken of en hoe gedeelde vormen van eigenaarschap in de creatieve industrie kunnen bijdragen aan het creëren van een democratischer en duurzamer economie, waarin ook het MKB kan participeren in digitale innovatie. Het project geeft een overzicht van beschikbare vormen van (gedeeld) eigenaarschap, hun werking en hoe deze creatieve professionals kunnen ondersteunen bij de transitie naar de platformeconomie. Dit wordt toegepast op een concrete case, dat van een digitale breimachine. Naast het leveren van een goede praktijk, moet het project leiden tot een groter internationaal onderzoeksvoorstel over Fluid Ownership in the Creative Industry, dat dieper ingaat op de beschikbare eigendomsoplossingen en hoe deze waarde zullen creëren voor de creatieve professional.
Het Lectorenplatform en Expertisenetwerk Duurzaam Stedelijk Toerisme brengt minimaal 16 lectoren enonderzoekers samen (o.a. Hogeschool Inholland, Breda University of Applied Sciences, StendenNHL,Hotelschool The Hague, Hogeschool Rotterdam, Hogeschool van Amsterdam, Saxion Hogeschool en deHaagse Hogeschool) samen. Zij willen zich binnen het platform bezighouden met de vraag hoe toerisme teontwikkelen die bijdraagt aan een duurzame stedelijke leefomgeving. Momenteel blijkt dit problematisch. Indrukke steden zoals Amsterdam is het zogenaamde "overtoerisme"een belangrijk punt van debat, terwijlandere steden (Rotterdam, Delft, Zwolle) willen leren hoe toerisme plaatsen en voorzieningen (cultuur, OV) inde stad kan helpen verbeteren.Het doel van het platform is om met lokale partners (gemeenten en Destination Management Organisations(DMOs), musea, hotels en (inter)nationale partners (NBTC, DMOs, European Tour Operator Association,Regenerative Tourism movement, Fairbnb) vanuit een gezamenlijke onderzoeks- en innovatieagenda te lerenhoe toerisme als middel te gebruiken voor verbetering van plaats en leven. Transdisciplinaire samenwerkingtussen verschillende lectoraten maakt het hierbij mogelijk om toerisme als maatschappelijk fenomeen teanalyseren, in plaats van als economische sector. Een dergelijk perspectief is innovatief en essentieel voor dehet beantwoorden van de vraag hoe ambitieuze visies voor de toekomst van toerisme (EC, 2021; NBTC, 2019;RLI, 2019) vertaald kunnen worden naar transities in de praktijk en welke sleutelmethodologieën (KEMs)kunnen helpen bij het versnellen van innovatie- en transitieprocessen voor stedelijke toerisme.Het streven is om, door samenwerking en het gericht afstemmen van expertise, binnen 5 à 10 jaar eenwereldwijd erkend kennisplatform op het gebied van duurzaam stedelijk toerisme te worden. Ommaatschappelijke impact te bereiken bundelen we hiervoor onze krachten met die van het Expertise NetwerkDuurzaam Stedelijk Toerisme (ENSUT) - gericht op het verbinden van belanghebbenden op dit thema inbinnen- en buitenland - en het Centre of Expertise Leisure, Tourism & Hospitality (CELTH).Partners:In Holland, Hotelschool Den Haag, NHL Stenden.
