Готові списки джерел за темами / Emotional filtering

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Добірка наукової літератури з теми "Emotional filtering"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Emotional filtering"

Kim, Tae-Yeun, Hoon Ko, Sung-Hwan Kim, and Ho-Da Kim. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering." Sensors 21, no. 6 (March 12, 2021): 1997. http://dx.doi.org/10.3390/s21061997.

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Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

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Jenkins, Jeffrey. "Detecting emotional ambiguity in text." MOJ Applied Bionics and Biomechanics 4, no. 3 (May 25, 2020): 55–57. http://dx.doi.org/10.15406/mojabb.2020.04.00134.

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An approach for determining emotional ambiguity in text data is described in this paper. The prediction confidences output from a text classifier are used to measure amount of ambiguity found in target entries. This measure can be used as a filtering mechanism to identify entries that require human feedback. This feedback loop can be implemented in a workflow which retrains a classifier model including newly disambiguated entries and resulting in a boost to classifier accuracy. This emotion ambiguity measure can be utilized to discover concrete emotional content in text data as well as reveal topics which do not have a concrete emotional consensus.

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RULE, R. R., A. P. SHIMAMURA, and R. T. KNIGHT. "Orbitofrontal cortex and dynamic filtering of emotional stimuli." Cognitive, Affective, & Behavioral Neuroscience 2, no. 3 (September 1, 2002): 264–70. http://dx.doi.org/10.3758/cabn.2.3.264.

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Guerzoni, Michael A. "Vicarious trauma and emotional labour in researching child sexual abuse and child protection: A postdoctoral reflection." Methodological Innovations 13, no. 2 (May 2020): 205979912092634. http://dx.doi.org/10.1177/2059799120926342.

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Criminology almost inevitably involves the study of sensitive and sorrowful research topics. Consequently, criminologists fall victim to the inherent risks of exposure to vicarious trauma, requiring many to practice emotional labour in the field, in the lecture hall, and perhaps, even along the corridors of the university campus itself. This article offers a reflective account of the experiences of vicarious trauma and the self-imposed, protective practice of emotional labour within doctoral research on child protection initiatives within a religious institution. It explores my experience of self-regulating my emotions in response to the reading of disturbing content, and of the active filtering of points of conversation when asked about my research within professional, familial and social settings, to prevent disturbing the emotions of others. The article encourages potential doctoral students to consider how they might prepare for themselves emotionally, socially and physically, for their inevitable encounter with difficult content, prior to the commencement of candidature, thereby increasing their resilience in facing the difficult components of a doctoral degree tasked with exploring content of a bleak and emotionally unnerving nature.

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Nosshi, Anthony, Aziza Saad Asem, and Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model." International Journal of Information Retrieval Research 9, no. 3 (July 2019): 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.

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Анотація:

With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.

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Santamaria-Granados, Luz, Juan Francisco Mendoza-Moreno, Angela Chantre-Astaiza, Mario Munoz-Organero, and Gustavo Ramirez-Gonzalez. "Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data." Sensors 21, no. 23 (November 25, 2021): 7854. http://dx.doi.org/10.3390/s21237854.

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The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

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Prete, Giulia, Bruno Laeng, and Luca Tommasi. "Modulating adaptation to emotional faces by spatial frequency filtering." Psychological Research 82, no. 2 (November 26, 2016): 310–23. http://dx.doi.org/10.1007/s00426-016-0830-x.

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Wang, Shu, Chonghuan Xu, Austin Shijun Ding, and Zhongyun Tang. "A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network." Electronics 10, no. 15 (July 24, 2021): 1769. http://dx.doi.org/10.3390/electronics10151769.

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Анотація:

Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

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Mičieta, Branislav, Vladimíra Biňasová, Beáta Furmannová, Gabriela Gabajová, and Marta Kasajová. "Emotional intelligence as an aspect in the performance of the work of a global manager." SHS Web of Conferences 129 (2021): 12002. http://dx.doi.org/10.1051/shsconf/202112912002.

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Research background: Emotional intelligence is a set of emotional and social abilities and skills of a manager. Nowadays, the environment is global and very complex, and the association between emotional intelligence and performance in enterprises remains an important area of worry for managers and employees' globally. The article focuses on the aspect and abilities of managers dealing with increasing the performance of their subordinates, their relationships in the workplace, division of labour and the overall organization of the team regarding their emotions and individual feeling of importance in the work process. Purpose of the article: The aim of the survey was to find out how today's managers behave in common situations that occur in the daily work of managers. It was also investigated to what extent managers use emotional intelligence and whether they are emotionally stable enough to work as a manager. Methods: A questionnaire survey was attended by managers. The questionnaire contained two parts. Firstly, the filtering questions and secondly, the specific situations in managerial life were analyzed, from which the level of emotional intelligence of the given manager was evaluated. Findings & Value added: These results in the work served to suggest improving awareness and the importance of emotional intelligence in work environments. The knowledge gained from the questionnaire will help in possible further research to create similar activities and improvements to imply emotional intelligence in more efficient operation of the company. A manager with high emotional intelligence can communicate effectively with others, can tolerate, solve problems, and build relationships with and between his employees.

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Kadiri, Sudarsana Reddy, and B. Yegnanarayana. "Epoch extraction from emotional speech using single frequency filtering approach." Speech Communication 86 (February 2017): 52–63. http://dx.doi.org/10.1016/j.specom.2016.11.005.

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Більше джерел

Дисертації з теми "Emotional filtering"

Gobl, Christer. "The Voice Source in Speech Communication - Production and Perception Experiments Involving Inverse Filtering and Synthesis." Doctoral thesis, KTH, Speech Transmission and Music Acoustics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3665.

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This thesis explores, through a number of production andperception studies, the nature of the voice source signal andhow it varies in spoken communication. Research is alsopresented that deals with the techniques and methodologies foranalysing and synthesising the voice source. The main analytictechnique involves interactive inverse filtering for obtainingthe source signal, which is then parameterised to permit thequantification of source characteristics. The parameterisationis carried by means of model matching, using the four-parameterLF model of differentiated glottal flow.

The first three analytic studies focus on segmental andsuprasegmental determinants of source variation. As part of theprosodic variation of utterances, focal stress shows for theglottal excitation an enhancement between the stressed voweland the surrounding consonants. At a segmental level, the voicesource characteristics of a vowel show potentially majordifferences as a function of the voiced/voiceless nature of anadjacent stop. Cross-language differences in the extent anddirectionality of the observed effects suggest differentunderlying control strategies in terms of the timing of thelaryngeal and supralaryngeal gestures, as well as in thelaryngeal tensions settings. Different classes of voicedconsonants also show differences in source characteristics:here the differences are likely to be passive consequences ofthe aerodynamic conditions that are inherent to the consonants.Two further analytic studies present voice source correlatesfor six different voice qualities as defined by Laver'sclassification system. Data from stressed and unstressedcontexts clearly show that the transformation from one voicequality to another does not simply involve global changes ofthe source parameters. As well as providing insights into theseaspects of speech production, the analytic studies providequantitative measures useful in technology applications,particularly in speech synthesis.

The perceptual experiments use the LF source implementationin the KLSYN88 synthesiser to test some of the analytic resultsand to harness them to explore the paralinguistic dimension ofspeech communication. A study of the perceptual salience ofdifferent parameters associated with breathy voice indicatesthat the source spectral slope is critically important andthat, surprisingly, aspiration noise contributes relativelylittle. Further perceptual tests using stimuli with differentvoice qualities explore the mapping between voice quality andits paralinguistic function of expressing emotion, mood andattitude. The results of these studies highlight the crucialrole of voice quality in expressing affect as well as providingpointers to how it combines withf₀for this purpose.

The last section of the thesis focuses on the techniquesused for the analysis and synthesis of the source. Asemi-automatic method for inverse filtering is presented, whichis novel in that it optimises the inverse filter by exploitingthe knowledge that is typically used by the experimenter whencarrying out manual interactive inverse filtering. A furtherstudy looks at the properties of the modified LF model in theKLSYN88 synthesiser: it highlights how it differs from thestandard LF model and discusses the implications forsynthesising the glottal source signal from LF model data.Effective and robust source parameterisation for the analysisof voice quality is the topic of the final paper: theeffectiveness of global, amplitude-based, source parameters isexamined across speech tokens with large differences inf₀. Additional amplitude-based parameters areproposed to enable a more detailed characterisation of theglottal pulse.

Keywords:Voice source dynamics, glottal sourceparameters, source-filter interaction, voice quality,phonation, perception, affect, emotion, mood, attitude,paralinguistic, inverse filtering, knowledge-based, formantsynthesis, LF model, fundamental frequency,f₀.

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Nallamilli, Sai Chandra Sekhar Reddy, and Nihanth Kandi. "Detection of Human Emotion from Noise Speech." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19610.

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Анотація:

Detection of a human emotion from human speech is always a challenging task. Factors like intonation, pitch, and loudness of signal vary from different human voice. So, it's important to know the exact pitch, intonation and loudness of a speech for making it a challenging task for detection. Some voices exhibit high background noise which will affect the amplitude or pitch of the signal. So, knowing the detailed properties of a speech to detect emotion is mandatory. Detection of emotion in humans from speech signals is a recent research field. One of the scenarios where this field has been applied is in situations where the human integrity and security are at risk In this project we are proposing a set of features based on the decomposition signals from discrete wavelet transform to characterize different types of negative emotions such as anger, happy, sad, and desperation. The features are measured in three different conditions: (1) the original speech signals, (2) the signals that are contaminated with noise or are affected by the presence of a phone channel, and (3) the signals that are obtained after processing using an algorithm for Speech Enhancement Transform. According to the results, when the speech enhancement is applied, the detection of emotion in speech is increased and compared to results obtained when the speech signal is highly contaminated with noise. Our objective is to use Artificial neural network because the brain is the most efficient and best machine to recognize speech. The brain is built with some neural network. At the same time, Artificial neural networks are clearly advanced with respect to several features, such as their nonlinearity and high classification capability. If we use Artificial neural networks to evolve the machine or computer that it can detect the emotion. Here we are using feedforward neural network which is suitable for classification process and using sigmoid function as activation function. The detection of human emotion from speech is achieved by training the neural network with features extracted from the speech. To achieve this, we need proper features from the speech. So, we must remove background noise in the speech. We can remove background noise by using filters. wavelet transform is the filtering technique used to remove the background noise and enhance the required features in the speech.

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Houdek, Miroslav. "Rozpoznání emočního stavu člověka z řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218117.

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This master thesis concerns with emotional states and gender recognition on the basis of speech signal analysis. We used various prosodic and cepstral features for the description of the speech signal. In the text we describe non-invasive methods for glottal pulses estimation. The described features of speech were implemented in MATLAB. For their classification we used the GMM classifier, which uses the Gaussian probability distribution for modeling a feature space. Furthermore, we constructed a system for recognition of emotional states of the speaker and a system for gender recognition from speech. We tested the success of created systems with several features on speech signal segments of various lengths and compared the results. In the last part we tested the influence of speaker and gender on the success of emotional states recognition.

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Patacca, Alessia. "The impact of emotional stressors on distractor filtering." Doctoral thesis, 2019. http://hdl.handle.net/11562/995343.

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Human beings constantly deal with an enormous amount of information that cannot be processed at once. Given the limited cognitive resources available for the processing of incoming information, visual selective attention has the role to differentiate between competing stimuli in order to facilitate the processing of stimuli that are relevant for adaptive behaviours. From an evolutionary perspective, stimuli with emotional content, in particular those signalling danger or threat, are very powerful in attracting and holding attention even if they are task-irrelevant. Moreover, emotional stimuli get higher processing priority compared with other competing stimuli and their access to further processing and conscious perception is thought to be automatic, at least when sufficient cognitive resources are available. Therefore, avoiding emotional stimuli, especially those with negative content, requires a conspicuous amount of resources that, if engaged for a prolonged period of time in a highly demanding cognitive task, they can undergo depletion, and eventually lead to the mental fatigue phenomenon. We propose that the amount of resources specifically dedicated to selective attention are also limited, and that they can be depleted specifically, and possibly independently, from the resources available for other cognitive mechanisms. This work was planned in order to directly explore this possibility, assuming that the crucial resources necessary to overcome the impact of irrelevant emotional distractors are also involved in attentional processing, and – more specifically – in the filtering of distracting visual information. We expected that by heavily engaging these inhibitory mechanisms, providing conditions of heavy and persistent distraction, we would observe phenomena suggesting that they were being depleted during the course of the experimental session (i.e. one-hour session). In a series of visual search experiments, young adult participants had to discriminate a target stimulus, while ignoring a task-irrelevant distractor that could be present in a portion of trials. According to the aim of our research, in order to increase, on the one hand, the attentional load and, on the other, the need to filter out distracting information, task-irrelevant stimuli with emotional content were introduced prior to each visual search trial. I then measured performance to evaluate the overall impact of emotional stimuli, revealing that while the onset of all emotional stimuli affected attentional deployment in the subsequent trial, such impact was different according to the valence of the stimuli involved. Analysing the efficiency of distractor filtering processes over the experimental session, I observed changes in performance suggesting that the attentional resources specifically involved during the inhibition of distractors in the visual search task could indeed be depleted. By this new approach, in this series of studies I offered new evidence relative to the depletion of cognitive resources specific associated with selective attention. I demonstrated that these domain-specific resources can be depleted in a relatively short period of time (i.e., one-hour session). Moreover, I highlighted how emotional activation can either enhance or impair cognitive performance depending on the emotional valence of the stimuli involved, with negative emotions leading to detrimental effects and positive emotions leading to restorative effects on cognitive resources. I also provided evidence on the fact that under condition of high load on attentional processing, the active engagement of top-down behavioural control may limit, or even abolish, the detrimental effects of negative emotional stimuli.

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Dahmane, Mohamed. "Analyse de mouvements faciaux à partir d'images vidéo." Thèse, 2011. http://hdl.handle.net/1866/7120.

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Lors d'une intervention conversationnelle, le langage est supporté par une communication non-verbale qui joue un rôle central dans le comportement social humain en permettant de la rétroaction et en gérant la synchronisation, appuyant ainsi le contenu et la signification du discours. En effet, 55% du message est véhiculé par les expressions faciales, alors que seulement 7% est dû au message linguistique et 38% au paralangage. L'information concernant l'état émotionnel d'une personne est généralement inférée par les attributs faciaux. Cependant, on ne dispose pas vraiment d'instruments de mesure spécifiquement dédiés à ce type de comportements. En vision par ordinateur, on s'intéresse davantage au développement de systèmes d'analyse automatique des expressions faciales prototypiques pour les applications d'interaction homme-machine, d'analyse de vidéos de réunions, de sécurité, et même pour des applications cliniques. Dans la présente recherche, pour appréhender de tels indicateurs observables, nous essayons d'implanter un système capable de construire une source consistante et relativement exhaustive d'informations visuelles, lequel sera capable de distinguer sur un visage les traits et leurs déformations, permettant ainsi de reconnaître la présence ou absence d'une action faciale particulière. Une réflexion sur les techniques recensées nous a amené à explorer deux différentes approches. La première concerne l'aspect apparence dans lequel on se sert de l'orientation des gradients pour dégager une représentation dense des attributs faciaux. Hormis la représentation faciale, la principale difficulté d'un système, qui se veut être général, est la mise en œuvre d'un modèle générique indépendamment de l'identité de la personne, de la géométrie et de la taille des visages. La démarche qu'on propose repose sur l'élaboration d'un référentiel prototypique à partir d'un recalage par SIFT-flow dont on démontre, dans cette thèse, la supériorité par rapport à un alignement conventionnel utilisant la position des yeux. Dans une deuxième approche, on fait appel à un modèle géométrique à travers lequel les primitives faciales sont représentées par un filtrage de Gabor. Motivé par le fait que les expressions faciales sont non seulement ambigües et incohérentes d'une personne à une autre mais aussi dépendantes du contexte lui-même, à travers cette approche, on présente un système personnalisé de reconnaissance d'expressions faciales, dont la performance globale dépend directement de la performance du suivi d'un ensemble de points caractéristiques du visage. Ce suivi est effectué par une forme modifiée d'une technique d'estimation de disparité faisant intervenir la phase de Gabor. Dans cette thèse, on propose une redéfinition de la mesure de confiance et introduisons une procédure itérative et conditionnelle d'estimation du déplacement qui offrent un suivi plus robuste que les méthodes originales.
In a face-to-face talk, language is supported by nonverbal communication, which plays a central role in human social behavior by adding cues to the meaning of speech, providing feedback, and managing synchronization. Information about the emotional state of a person is usually carried out by facial attributes. In fact, 55% of a message is communicated by facial expressions whereas only 7% is due to linguistic language and 38% to paralanguage. However, there are currently no established instruments to measure such behavior. The computer vision community is therefore interested in the development of automated techniques for prototypic facial expression analysis, for human computer interaction applications, meeting video analysis, security and clinical applications. For gathering observable cues, we try to design, in this research, a framework that can build a relatively comprehensive source of visual information, which will be able to distinguish the facial deformations, thus allowing to point out the presence or absence of a particular facial action. A detailed review of identified techniques led us to explore two different approaches. The first approach involves appearance modeling, in which we use the gradient orientations to generate a dense representation of facial attributes. Besides the facial representation problem, the main difficulty of a system, which is intended to be general, is the implementation of a generic model independent of individual identity, face geometry and size. We therefore introduce a concept of prototypic referential mapping through a SIFT-flow registration that demonstrates, in this thesis, its superiority to the conventional eyes-based alignment. In a second approach, we use a geometric model through which the facial primitives are represented by Gabor filtering. Motivated by the fact that facial expressions are not only ambiguous and inconsistent across human but also dependent on the behavioral context; in this approach, we present a personalized facial expression recognition system whose overall performance is directly related to the localization performance of a set of facial fiducial points. These points are tracked through a sequence of video frames by a modification of a fast Gabor phase-based disparity estimation technique. In this thesis, we revisit the confidence measure, and introduce an iterative conditional procedure for displacement estimation that improves the robustness of the original methods.

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Книги з теми "Emotional filtering"

Pessoa, Luiz. Attention, Motivation, and Emotion. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.001.

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The first part of the chapter describes effects of motivation on attention at the behavioural and physiological levels. For example, reward increases detection sensitivity (dprime) in both endogenous attention and exogenous attention tasks, enhances stimulus coding, and influences the filtering of task-irrelevant stimuli. These recent findings are surprising insofar as traditional psychological models have described motivation as a fairly unspecific ‘force’. The results reviewed are far from global. Instead they reflect specific mechanisms that are manifested selectively both at behavioural and neural levels. The second part of the chapter describes the role of attention when emotion-laden visual stimuli are processed. When one considers the bulk of the evidence, emotional processing is revealed to be capacity-limited. Yet, emotional processing is prioritized relative to that of neutral items.

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Cox, Fiona. Mary Zimmerman. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779889.003.0006.

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Mary Zimmerman’s dramatic adaptation of the Metamorphoses (1998) follows the staging of the Odyssey and represents a further instance of her ongoing engagement with the classical world. By filtering the Ovidian myths through a network of allusions (to Rilke, Jung, and Freud, among others) she reminds her audiences of the consolidation of classical myth within the Western tradition. At the same time she uses the play to meditate upon issues such as unbridled greed and capitalism, anorexia, and emotionally damaged ‘rich kids’. Her play is also underpinned by a profound sense of loss and grief. The innate sorrow of the play was heightened still further when audiences came to watch it after the 9/11 attacks, and their response to the play was informed by the horrors and deaths that they had experienced within their own communities.

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Частини книг з теми "Emotional filtering"

Kwon, Hyeong-Joon, Hyeong-Oh Kwon, and Kwang-Seok Hong. "Personalized Emotional Prediction Method for Real-Life Objects Based on Collaborative Filtering." In Engineering Psychology and Cognitive Ergonomics, 45–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21741-8_6.

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Salmeron-Majadas, Sergio, Miguel Arevalillo-Herráez, Olga C. Santos, Mar Saneiro, Raúl Cabestrero, Pilar Quirós, David Arnau, and Jesus G. Boticario. "Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts." In Lecture Notes in Computer Science, 429–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19773-9_43.

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Matsumoto, Kazuyuki, Fuji Ren, Minoru Yoshida, and Kenji Kita. "Refinement by Filtering Translation Candidates and Similarity Based Approach to Expand Emotion Tagged Corpus." In Communications in Computer and Information Science, 260–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-52758-1_15.

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Nosshi, Anthony, Aziza Saad Asem, and Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, 1076–100. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch056.

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Mavelli, Luca. "The emotional value of refugees." In Neoliberal Citizenship, 82–112. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192857583.003.0004.

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This chapter further explores the notion of ‘emotional value’ introduced in the previous chapter. To this end, it builds on the existing literature on ‘humanitarian government’ by showing how the growing intermingling of humanitarianism and security is part of a process of filtering aimed at granting protection only to refugees regarded as valuable. The chapter elaborates on how the notion of ‘ideal refugee’ signals an ultimate commodification of solidarity which subordinates the demand of refugees to their perceived market value. It shows how these neoliberal rationalities of value are mobilized through and overlap with a biopolitical rationality of care of the host population, and thus how neoliberal citizenship crucially rests on the mobilization of biopolitical racism. The exclusion of those lacking in economic and emotional capital becomes mandatory under neoliberal rationalities of value and biopolitical rationalities of care, which therefore become indistinguishable. To illustrate these arguments, the death of Alan Kurdi and several other cases from the UK, Germany, France, Italy, and Spain are discussed.

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Bisio, Igor, Alessandro Delfino, Fabio Lavagetto, and Mario Marchese. "Opportunistic Detection Methods for Emotion-Aware Smartphone Applications." In Creating Personal, Social, and Urban Awareness through Pervasive Computing, 53–85. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4695-7.ch003.

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Human-machine interaction is performed by devices such as the keyboard, the touch-screen, or speech-to-text applications. For example, a speech-to-text application is software that allows the device to translate the spoken words into text. These tools translate explicit messages but ignore implicit messages, such as the emotional status of the speaker, filtering out a portion of information available in the interaction process. This chapter focuses on emotion detection. An emotion-aware device can also interact more personally with its owner and react appropriately according to the user’s mood, making the user-machine interaction less stressful. The chapter gives the guidelines for building emotion-aware smartphone applications in an opportunistic way (i.e., without the user’s collaboration). In general, smartphone applications might be employed in different contexts; therefore, the to-be-detected emotions might be different.

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Petridis, Sergios, Theodoros Giannakopoulos, and Constantine D. Spyropoulos. "A Low Cost Pupillometry Approach." In Virtual and Mobile Healthcare, 765–77. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9863-3.ch037.

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The need for low-cost health monitoring is increasing with the continuous increase of the elderly population. In this context, unobtrusive audiovisual monitoring methods can be of great importance. More particularly, the diameter of the pupil is a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using common low-cost hardware. This paper describes a method for monitoring pupil sizes using a common, low-cost web camera in real time. The proposed approach detects the face and the eyes area at first stage. Subsequently, optimal iris and sclera location and radius, modeled as ellipses, are found using efficient spatial filtering. As a final step, the pupil center and radius is estimated by optimal filtering within the area of the iris. Experimental results show both the efficiency and the effectiveness of our approach.

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Akkarapatty, Neethu, Anjaly Muralidharan, Nisha S. Raj, and Vinod P. "Dimensionality Reduction Techniques for Text Mining." In Collaborative Filtering Using Data Mining and Analysis, 49–72. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0489-4.ch003.

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Sentiment analysis is an emerging field, concerned with the analysis and understanding of human emotions from sentences. Sentiment analysis is the process used to determine the attitude/opinion/emotions expressed by a person about a specific topic based on natural language processing. Proliferation of social media such as blogs, Twitter, Facebook and Linkedin has fuelled interest in sentiment analysis. As the real time data is dynamic, the main focus of the chapter is to extract different categories of features and to analyze which category of attribute performs better. Moreover, classifying the document into positive and negative category with fewer misclassification rate is the primary investigation performed. The various approaches employed for feature selection involves TF-IDF, WET, Chi-Square and mRMR on benchmark dataset pertaining diverse domains.

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Anitha R, Surya Koti Kiran A, Anurag K, and Nikhil Y. "An Efficient Algorithm for Movie Recommendation System." In Advances in Parallel Computing Technologies and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/apc210124.

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Now a day’s recommendation system has changed the fashion of looking the items of our interest. OTT Movie Application Recommendation for mobile users is crucial. It performs a complete aggregation of user preferences, reviews and emotions to help you make suitable movies. It needs every precision and timeliness, however,this can be info filtering approach that’s accustomed predict the preference of that user. Recommender System may be a system that seeks to predict or filter preferences in keeping with the user’s selections. The very common purpose where recommender system is applied are OTT platforms, search engines, articles, music, videos etc During this work we tend to propose a Collaborative approach-based Movie Recommendation system. it is supported collaborative filtering approach that creates use of the knowledge provided by users, analyzes them so recommends the flicks that’s best suited to the user at that point. The suggested motion picture list is sorted in keeping with the ratings given to those movies by previous users. It conjointly helps users to search out of their selections supported the movie expertise of alternative users in economical and effective manner while not wasting a lot of time in useless browsing [1]. Therefore, we tend to offer the item-oriented methodology of the analysis of social network as the steering force of this method to further improve accuracy within the recommendation system. We tend to propose economic healthcare associates during this paper The algorithmic rule of the Film Recommendation supported improved KNN strategy that measures simpler advisory system accuracy. However, to evaluate performance, the k closest victimized neighbors, the maximum inner circles, as well as the basic inner strategies are used [2]. The exception to this is the projected results, which use algorithms to check for (supposedly) involvement.The performance results show that the projected strategies improve additional accuracy of the Movie recommendation system than the other strategies employed in this experiment.

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Тези доповідей конференцій з теми "Emotional filtering"

Golbeck, Jennifer. "Improving Emotional Well-Being on Social Media with Collaborative Filtering." In WebSci '20: 12th ACM Conference on Web Science. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394332.3402833.

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Jomaa, Inès, Emilie Poirson, Catherine Da Cunha, and Jean-François Petiot. "Design of a Recommender System Based on Customer Preferences: A Comparison Between Two Approaches." In ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/esda2012-82771.

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This work addresses the design of a preference based system that suggests relevant products to customers. It aims at helping them with their purchase decision (on electronic commerce websites). A use case that consists in making spontaneous recommendations to the customers, on the basis of their previous ratings is described. The product considered to illustrate the approach is a comic. This paper is focused on two recommender approaches. The first approach, “the traditional” approach, is based on the collaborative filtering while the second approach, is based on a new proposed algorithm. Collaborative filtering is a technique to making recommendations by matching people with the same preferences (preferential similarity). The second approach which is proposed is a combination of the traditional collaborative filtering and the perceptual similarities approach between customers (perceptual similarity). Perceptive data include emotional, sensory and semantic ratings of the products. The purpose of this paper is to evaluate the performance of the proposed approach and to compare it with the traditional approach. A test procedure is thus implemented. It consists in simulating customers’ behavior according to a set of products, and to compute a performance criterion of the recommender system, measuring the relevance of the proposed products. The performance of the proposed algorithm is compared with that of the traditional one. The results show that the consideration of perceptual assessments of products by customers generally helps in the relevance of the propositions of the system.

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Aloufi, Ranya, Hamed Haddadi, and David Boyle. "Emotion Filtering at the Edge." In the 1st Workshop. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3362743.3362960.

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Pathak, Bageshree Sathe, Manali Sayankar, and Ashish Panat. "Emotion transformation from neutral to 3 emotions of speech signal using DWT and adaptive filtering techniques." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030389.

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Kim, Tae-Yeun, and Sung-Hwan Kim. "Emotion and Collaborative Filtering-Based Recommendation System." In SMA 2020: The 9th International Conference on Smart Media and Applications. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3426020.3426119.

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Huang, Zhaocheng, and Julien Epps. "An Investigation of Emotion Dynamics and Kalman Filtering for Speech-Based Emotion Prediction." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1707.

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Huang, Dong, Haihong Zhang, Kaikeng Ang, Cuntai Guan, Yaozhang Pan, Chuanchu Wang, and Juanhong Yu. "Fast emotion detection from EEG using asymmetric spatial filtering." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6287952.

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Aloufi, Ranya, Hamed Haddadi, and David Boyle. "Privacy preserving speech analysis using emotion filtering at the edge." In SenSys '19: The 17th ACM Conference on Embedded Networked Sensor Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3356250.3361947.

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Petrantonakis, Panagiotis C., and Leontios J. Hadjileontiadis. "EEG-based emotion recognition using hybrid filtering and higher order crossings." In 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009). IEEE, 2009. http://dx.doi.org/10.1109/acii.2009.5349513.

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Shiqing Zhang and Zhijin Zhao. "Feature selection filtering methods for emotion recognition in Chinese speech signal." In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697464.

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