8,787 research outputs found

    Sense - Recommendation System based on Affective Interpretations of Social Media Posts: A Proposed User Interface Design

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    It is now easier and cheaper for people to get involved in technology, and most people are obtaining or receiving tons of information nowadays, such as recommendations. Different recommendations are filled with our devices like smartphones, tablets or even wearable devices. However, most searches and recommendation systems fail to take into consideration the emotional context related to a user’s input and action, and as a consequence, people can hardly receive personalized, emotion-related, user-based recommendations. Though people are given various choices about lives based on searches and saved data, those recommendations can hardly improve the stress and difficulties of accomplishing daily life goals caused by the developing of technology. Therefore, People need more user-centered recommendations that incorporate emotion and action related evaluations to reach more precise results. This thesis project presents a possible design solution to improve this situation. An affective recommendation system designed to determine a person’s emotional state or condition based on the affective interpretations of their social media content. Combining bio information, exercise or activity records, it provides personalized recommendations like food, entertainment, activities or exercise suggestions related to the users. This project demonstrates how a new user-centered, emotion and activity based recommendation system can leverage elements of emerging technologies such as conversational User Interfaces (CUI), context recognition, and expression recognition to create a more user-friendly and more meaningful experience

    Leveraging contextual-cognitive relationships into mobile commerce systems

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    A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyMobile smart devices are becoming increasingly important within the on-line purchasing cycle. Thus the requirement for mobile commerce systems to become truly context-aware remains paramount if they are to be effective within the varied situations that mobile users encounter. Where traditionally a recommender system will focus upon the user – item relationship, i.e. what to recommend, in this thesis it is proposed that due to the complexity of mobile user situational profiles the how and when must also be considered for recommendations to be effective. Though non-trivial, it should be, through the understanding of a user’s ability to complete certain cognitive processes, possible to determine the likelihood of engagement and therefore the success of the recommendation. This research undertakes an investigation into physical and modal contexts and presents findings as to their relationships with cognitive processes. Through the introduction of the novel concept, disruptive contexts, situational contexts, including noise, distractions and user activity, are identified as having significant effects upon the relationship between user affective state and cognitive capability. Experimental results demonstrate that by understanding specific cognitive capabilities, e.g. a user’s perception of advert content and user levels of purchase-decision involvement, a system can determine potential user engagement and therefore improve the effectiveness of recommender systems’ performance. A quantitative approach is followed with a reliance upon statistical measures to inform the development, and subsequent validation, of a contextual-cognitive model that was implemented as part of a context-aware system. The development of SiDISense (Situational Decision Involvement Sensing system) demonstrated, through the use of smart-phone sensors and machine learning, that is was viable to classify subjectively rated contexts to then infer levels of cognitive capability and therefore likelihood of positive user engagement. Through this success in furthering the understanding of contextual-cognitive relationships there are novel and significant advances that are now viable within the area of m-commerce

    Modeling media as latent semantics based on cognitive components

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    Music as a tool for foreign language learning in Early Childhood Education and Primary Education : Proposing innovative CLIL Music teaching approaches

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    The present paper aims at promoting pedagogical reflection by providing the theoretical foundations on the connections between foreign language learning (FLL) and music, which shapes a CLIL Music program named MOVIC (Movement & Music in English). It also encourages the implementation CLIL Music approaches in EFL classrooms. The paper focuses on the benefits that music brings to the English as a Foreign Language (EFL) classroom, as well as it contextualizes the current situation in the Spanish education system and the pedagogical possibilities that the use of music encompasses. Finally, it presents the EFL approach to MOVIC, together with a sample activity, and it providesAquest article té com a objectiu promoure la reflexió educativa proporcionant els fonaments teòrics sobre les connexions entre l'aprenentatge de llengües estrangeres i la música, el qual configura els pilars d'un programa CLIL de Música en anglès anomenat MOVIC (Movement& Music in English). També pretén fomentar els enfocaments CLIL de música a les aules d'anglès com a llengua estrangera. L'article presenta els avantatges que la música aporta a l'aula d'anglès, a més de contextualitzar la situació actual del sistema educatiu espanyol i les possibilitats pedagògiques que comporta l'ús de la música. Per últim, presenta l'enfocament pedagògic de MOVIC, juntament amb un exemple d'activitat, i seguidament exposa un seguit d'implicacions pedagògiques per a educadors de llengües estrangeres i polítiques educatives.Este artículo tiene como objetivo promover la reflexión educativa proporcionando los fundamentos teóricos sobre las conexiones entre el aprendizaje de lenguas extranjeras y la música, el cual configura los pilares de un programa CLIL de Música en inglés llamado MOVIC (Movement & Music in English). También pretende fomentar los enfoques CLIL de música en las aulas de inglés como lengua extranjera. El artículo presenta las ventajas que la música aporta en el aula de inglés, además de contextualizar la situación actual del sistema educativo español y las posibilidades pedagógicas que conlleva el uso de la música. Por último, presenta el enfoque pedagógico de MOVIC, junto con un ejemplo de actividad, y seguidamente expone una serie de implicaciones pedagógicas para educadores de lenguas extranjeras y políticas educativas

    Retrieval and Annotation of Music Using Latent Semantic Models

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    PhDThis thesis investigates the use of latent semantic models for annotation and retrieval from collections of musical audio tracks. In particular latent semantic analysis (LSA) and aspect models (or probabilistic latent semantic analysis, pLSA) are used to index words in descriptions of music drawn from hundreds of thousands of social tags. A new discrete audio feature representation is introduced to encode musical characteristics of automatically-identified regions of interest within each track, using a vocabulary of audio muswords. Finally a joint aspect model is developed that can learn from both tagged and untagged tracks by indexing both conventional words and muswords. This model is used as the basis of a music search system that supports query by example and by keyword, and of a simple probabilistic machine annotation system. The models are evaluated by their performance in a variety of realistic retrieval and annotation tasks, motivated by applications including playlist generation, internet radio streaming, music recommendation and catalogue searchEngineering and Physical Sciences Research Counci

    A Fuzzy-Based Multimedia Content Retrieval Method Using Mood Tags and Their Synonyms in Social Networks

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    The preferences of Web information purchasers are rapidly evolving. Cost-effectiveness is now becoming less regarded than cost-satisfaction, which emphasizes the purchaser’s psychological satisfaction. One method to improve a user’s cost-satisfaction in multimedia content retrieval is to utilize the mood inherent in multimedia items. An example of applications using this method is SNS (Social Network Services), which is based on folksonomy, but its applications encounter problems due to synonyms. In order to solve the problem of synonyms in our previous study, the mood of multimedia content is represented with arousal and valence (AV) in Thayer’s two-dimensional model as its internal tag. Although some problems of synonyms could now be solved, the retrieval performance of the previous study was less than that of a keyword-based method. In this paper, a new method that can solve the synonym problem is proposed, while simultaneously maintaining the same performance as the keyword-based approach. In the proposed method, a mood of multimedia content is represented with a fuzzy set of 12 moods of the Thayer model. For the analysis, the proposed method is compared with two methods, one based on AV value and the other based on keyword. The analysis results demonstrate that the proposed method is superior to the two methods

    Fusion of musical contents, brain activity and short term physiological signals for music-emotion recognition

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    In this study we propose a multi-modal machine learning approach, combining EEG and Audio features for music emotion recognition using a categorical model of emotions. The dataset used consists of film music that was carefully created to induce strong emotions. Five emotion categories were adopted: Fear, Anger, Happy, Tender and Sad. EEG data was obtained from three male participants listening to the labeled music excerpts. Feature level fusion was adopted to combine EEG and Audio features. The results show that the multimodal system outperformed the EEG mono modal system. Additionally, we evaluated the contribution of each audio feature in the classification performance of the multimodal system. Preliminary results indicate a significant contribution of individual audio features in the classification accuracy, we also found that various audio features that noticeably contributed in the classification accuracy were also reported in previous research studying the correlation between audio features and emotion ratings using the same dataset.
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