22,144 research outputs found
Casino Atmospherics from a Customer\u27s Perspective: A Re-Examination
Considerable research in recent years has examined the influence of physical evidence, or atmosphere, in a variety of service settings, including leisure services. Not fully covered has been the area of atmosphere in a casino gaming setting. This extension of a previous study further investigates atmospherics in casinos. The findings showed that customers defined casino atmosphere in five key elements: theme, floor layout, ceiling height, employee uniforms, and noise level. Three of the five contributed positively to a player\u27s satisfaction with the gaming experience as shown by the regression analysis. This reinforces previous indications of the need for casino management to create an inviting atmosphere that will maximize customer satisfaction, with specific attention to those aspects that players appear to value most highly
A picture is worth a thousand words: The perplexing problem of indexing images
Indexing images has always been problematic due to their richness of content and innate subjectivity. Three traditional approaches to indexing images are described and analyzed. An introduction of the contemporary use of social tagging is presented along with its limitations. Traditional practices can continue to be used as a stand-alone solution, however deficiencies limit retrieval. A collaborative technique is supported by current research and a model created by the authors for its inception is explored. CONTENTdm® is used as an example to illustrate tools that can help facilitate this process. Another potential solution discussed is the expansion of algorithms used in computer extraction to include the input and influence of human indexer intelligence. Further research is recommended in each area to discern the most effective method
Towards automatic extraction of expressive elements from motion pictures : tempo
This paper proposes a unique computational approach to extraction of expressive elements of motion pictures for deriving high level semantics of stories portrayed, thus enabling better video annotation and interpretation systems. This approach, motivated and directed by the existing cinematic conventions known as film grammar, as a first step towards demonstrating its effectiveness, uses the attributes of motion and shot length to define and compute a novel measure of tempo of a movie. Tempo flow plots are defined and derived for four full-length movies and edge analysis is performed leading to the extraction of dramatic story sections and events signaled by their unique tempo. The results confirm tempo as a useful attribute in its own right and a promising component of semantic constructs such as tone or mood of a film
6 Seconds of Sound and Vision: Creativity in Micro-Videos
The notion of creativity, as opposed to related concepts such as beauty or
interestingness, has not been studied from the perspective of automatic
analysis of multimedia content. Meanwhile, short online videos shared on social
media platforms, or micro-videos, have arisen as a new medium for creative
expression. In this paper we study creative micro-videos in an effort to
understand the features that make a video creative, and to address the problem
of automatic detection of creative content. Defining creative videos as those
that are novel and have aesthetic value, we conduct a crowdsourcing experiment
to create a dataset of over 3,800 micro-videos labelled as creative and
non-creative. We propose a set of computational features that we map to the
components of our definition of creativity, and conduct an analysis to
determine which of these features correlate most with creative video. Finally,
we evaluate a supervised approach to automatically detect creative video, with
promising results, showing that it is necessary to model both aesthetic value
and novelty to achieve optimal classification accuracy.Comment: 8 pages, 1 figures, conference IEEE CVPR 201
High-Level Concepts for Affective Understanding of Images
This paper aims to bridge the affective gap between image content and the
emotional response of the viewer it elicits by using High-Level Concepts
(HLCs). In contrast to previous work that relied solely on low-level features
or used convolutional neural network (CNN) as a black-box, we use HLCs
generated by pretrained CNNs in an explicit way to investigate the
relations/associations between these HLCs and a (small) set of Ekman's
emotional classes. As a proof-of-concept, we first propose a linear admixture
model for modeling these relations, and the resulting computational framework
allows us to determine the associations between each emotion class and certain
HLCs (objects and places). This linear model is further extended to a nonlinear
model using support vector regression (SVR) that aims to predict the viewer's
emotional response using both low-level image features and HLCs extracted from
images. These class-specific regressors are then assembled into a regressor
ensemble that provide a flexible and effective predictor for predicting
viewer's emotional responses from images. Experimental results have
demonstrated that our results are comparable to existing methods, with a clear
view of the association between HLCs and emotional classes that is ostensibly
missing in most existing work
Ishiguro's Inhuman Aesthetics
The question of what it means to be human pervades Kazuo Ishiguro's novel Never Let Me Go, which gradually reveals a counterfactual twentieth-century England where clone colonies provide ready supplies of organs for donation. In the tradition of Aldous Huxley's Brave New World (1932) and George Orwell's 1984 (1949), the novel envisions a dystopian civil society where clones struggle to comprehend the significance of their own circumscribed personhood. Perhaps unsurprisingly, this interrogation of what it means to be human emerges through a critique of Romantic-inspired assumptions about aesthetics and empathy. While the novel attracts attention for its theme of genetic engineering, its deepest anxieties arguably concern the ethics of artistic production and consumption in an age of multiculturalism and globalization. Through its veneer of science fiction, Never Let Me Go offers an allegory both for national concerns about the state of England and for transnational fears about rising global inequality. In its portrait of the systematic exploitation of the clones and its implicit exploration of vulnerable actors in our modern economic order, the novel indicts humanist conceptions of art as a form of extraction that resembles forced organ donation. If Romantic-inspired views of empathy rely on the claim that art reveals the human soul, Ishiguro's novel implies that the concept of the soul invokes a fundamentally exploitative discourse of use value. In this respect, Never Let Me Go shares in a pervasive late-twentieth-century cultural skepticism about the viability of empathetic art. [End Page 785]
Yet Ishiguro's critique does not—as might be expected—abandon the ethical potential of works of art. Instead, it makes a case for an ethics offering a very different approach to art and empathy that relies on the recognition of the inhuman. As an alternative to humanist modes of representation, Ishiguro's inhuman style suggests that only by recognizing what in ourselves is mechanical, manufactured, and replicated—in a traditional sense, not fully human—will we escape the barbarities committed in the name of preserving purely human life. Never Let Me Go implies that if there is to be any empathetic connection with Ishiguro's protagonists, it will not occur through the consoling liberal realization that clones are humans, just like us. It will evolve through the darker realization that art, along with the empathy it provokes, needs to escape the traditional concept of the human. The novel thus calls for what seems like a contradiction in terms: an empathetic inhuman aesthetics that embraces the mechanical, commodified, and replicated elements of personhood. While inhuman is often used as a synonym for cruel or unethical, Ishiguro's novel suggests exactly the reverse. As its aesthetics of replication allows us to sympathize with others without recourse to such constraining ideals, Never Let Me Go reinvents empathy for a posthumanist age
Seeing sound “How to generate visual artworks by analysing a music track and representing it in terms of emotion analysis and musical features?”
Music and visual artwork are a valuable part of our daily life. Since both media induce
human emotion, this thesis demonstrates how to convert music into visual artwork such
as generative art. Especially, the project shows the method of connecting music emotion
to the theme of colour. This thesis describes the human emotional model based on
arousal and valence. Also, this thesis explains how colour affects our emotion. In order to
connect music emotion into the colour theme, this thesis shows the method to retrieve
music information which includes arousal and valence of the music. In order to generate
visual artwork from the music, this thesis demonstrates the implementation of working
software that integrates music emotion and musical characteristics such as frequency
analysis. Besides, this thesis presents how to apply generative artwork into our daily life
products. This thesis discusses learning outcomes from the project based on
practice-based research methodology. Also, this thesis introduces a further plan related
to AI
Musemo: Express Musical Emotion Based on Neural Network
Department of Urban and Environmental Engineering (Convergence of Science and Arts)Music elicits emotional responses, which enable people to empathize with the emotional states induced by music, experience changes in their current feelings, receive comfort, and relieve stress (Juslin & Laukka, 2004). Music emotion recognition (MER) is a field of research that extracts emotions from music through various systems and methods. Interest in this field is increasing as researchers try to use it for psychiatric purposes. In order to extract emotions from music, MER requires music and emotion labels for each music. Many MER studies use emotion labels created by non-music-specific psychologists such as Russell???s circumplex model of affects (Russell, 1980) and Ekman???s six basic emotions (Ekman, 1999). However, Zentner, Grandjean, and Scherer suggest that emotions commonly used in music are subdivided into specific areas, rather than spread across the entire spectrum of emotions (Zentner, Grandjean, & Scherer, 2008). Thus, existing MER studies have difficulties with the emotion labels that are not widely agreed through musicians and listeners. This study proposes a musical emotion recognition model ???Musemo??? that follows the Geneva emotion music scale proposed by music psychologists based on a convolution neural network. We evaluate the accuracy of the model by varying the length of music samples used as input of Musemo and achieved RMSE (root mean squared error) performance of up to 14.91%. Also, we examine the correlation among emotion labels by reducing the Musemo???s emotion output vector to two dimensions through principal component analysis. Consequently, we can get results that are similar to the study that Vuoskoski and Eerola analyzed for the Geneva emotion music scale (Vuoskoski & Eerola, 2011). We hope that this study could be expanded to inform treatments to comfort those in need of psychological empathy in modern society.clos
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