225,359 research outputs found

    Image Semantics in the Description and Categorization of Journalistic Photographs

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    This paper reports a study on the description and categorization of images. The aim of the study was to evaluate existing indexing frameworks in the context of reportage photographs and to find out how the use of this particular image genre influences the results. The effect of different tasks on image description and categorization was also studied. Subjects performed keywording and free description tasks and the elicited terms were classified using the most extensive one of the reviewed frameworks. Differences were found in the terms used in constrained and unconstrained descriptions. Summarizing terms such as abstract concepts, themes, settings and emotions were used more frequently in keywording than in free description. Free descriptions included more terms referring to locations within the images, people and descriptive terms due to the narrative form the subjects used without prompting. The evaluated framework was found to lack some syntactic and semantic classes present in the data and modifications were suggested. According to the results of this study image categorization is based on high-level interpretive concepts, including affective and abstract themes. The results indicate that image genre influences categorization and keywording modifies and truncates natural image description

    Prerequisites for Affective Signal Processing (ASP)

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    Although emotions are embraced by science, their recognition has not reached a satisfying level. Through a concise overview of affect, its signals, features, and classification methods, we provide understanding for the problems encountered. Next, we identify the prerequisites for successful Affective Signal Processing: validation (e.g., mapping of constructs on signals), triangulation, a physiology-driven approach, and contributions of the signal processing community. Using these directives, a critical analysis of a real-world case is provided. This illustrates that the prerequisites can become a valuable guide for Affective Signal Processing (ASP)

    Are CLIL students more motivated?: an analysis of affective factors and their relation to language attainment

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    This paper addresses the purported belief that Content and Language Integrat- ed Learning positively infuences students’ affective stance. It compares the motivation of CLIL and non-CLIL learners in seven state schools and one charter school in the province of Seville, both at Primary (n=194) and Compulsory Secondary Education (n=158) level. Affective aspects pertaining to motivation and anxiety are grouped around four clusters of factors: (i) desire to work and self-esteem (containing 10 items); (ii) anxiety in the face of exams (with a negative-inhibitory content and made up of 9 elements); (iii) lack of interest in studying (comprising 9 items); and (iv) realistic personal self-demand (consisting of 7 el- ements). The interaction of motivation and language attainment (considering use of English, vocabulary, listening, speaking, and reading) is also measured in order to confrm or refute prior fndings which tend to assign higher levels of motivation to CLIL strands. Keywords: CLIL, motivation and language attainment.Este artículo aborda la creencia de que el Aprendizaje Integrado de Conteni- dos y Lengua tiene una infuencia positiva sobre la situación afectiva del alumnado. En este trabajo se compara la motivación de estudiantes que cursan programas bilingües “AICLE” frente a los que no. La muestra pertenece a siete colegios públicos de la provincia de Sevilla en niveles de Educación Primaria (n=194) y Secundaria Obligatoria (n=158). Los factores afectivos relacionados con la motivación y la ansiedad se han agrupado en cuatro apartados: (i) el deseo de trabajar y la autoestima (con 10 ítems); (ii) la ansiedad frente a los exámenes (con un efecto negativo sobre el aprendizaje de contenido y que cuenta con 9 ítems); (iii) falta de interés por el estudio (con 9 ítems); y (iv) auto-exigencia personal (con 7 ítems). También se ha analizado la interacción entre la motivación y los resultados de aprendizaje lingüístico (considerando uso del inglés, vocabulario, destrezas de comprensión y expresión oral y lectura) con el objetivo de confrmar o refutar resultados de investigaciones previas que parecen asignar niveles altos de motivación asociados al AICLE

    Destination image analytics through traveller-generated content

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    The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users' opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability

    Fusion of Learned Multi-Modal Representations and Dense Trajectories for Emotional Analysis in Videos

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    When designing a video affective content analysis algorithm, one of the most important steps is the selection of discriminative features for the effective representation of video segments. The majority of existing affective content analysis methods either use low-level audio-visual features or generate handcrafted higher level representations based on these low-level features. We propose in this work to use deep learning methods, in particular convolutional neural networks (CNNs), in order to automatically learn and extract mid-level representations from raw data. To this end, we exploit the audio and visual modality of videos by employing Mel-Frequency Cepstral Coefficients (MFCC) and color values in the HSV color space. We also incorporate dense trajectory based motion features in order to further enhance the performance of the analysis. By means of multi-class support vector machines (SVMs) and fusion mechanisms, music video clips are classified into one of four affective categories representing the four quadrants of the Valence-Arousal (VA) space. Results obtained on a subset of the DEAP dataset show (1) that higher level representations perform better than low-level features, and (2) that incorporating motion information leads to a notable performance gain, independently from the chosen representation

    Affect Recognition in Ads with Application to Computational Advertising

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    Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

    Reasons and Theories of Sensory Affect

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    Some sensory experiences are pleasant, some unpleasant. This is a truism. But understanding what makes these experiences pleasant and unpleasant is not an easy job. Various difficulties and puzzles arise as soon as we start theorizing. There are various philosophical theories on offer that seem to give different accounts for the positive or negative affective valences of sensory experiences. In this paper, we will look at the current state of art in the philosophy of mind, present the main contenders, critically compare and contrast them. In particular, we want to examine how they handle the reason-giving power of affective states. We will look into two representationalist proposals (Evaluativism and Imperativism) and a functionalist proposal, and argue that, contrary to their own advertisements, the representationalist proposals don’t have good accounts of why and how sensory affect can motivate, rationalize, and justify subsequent behavior and intentional mental activity. We will show that our own functionalist proposal does a much better job in this regard, and that when the representationalist proposals are modified to do a better job, they fare better not because of their representationalist credentials but due to their functionalist ones
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