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Happiness is an important mental emotion and yet becoming a major health concern nowadays. For this reason, better recognizing the objective understanding of how humans respond to event-related observations in their daily lives is especially important.

This paper uses non-intrusive technology (hyperspectral imaging [HSI]) for happiness recognition. Experimental setup is conducted for data collection in real-life environments where observers are showing spontaneous expressions of emotions (calm, happy, unhappy: angry) during the experimental process. Based on facial imaging captured from HSI, this work collects our emotional database defined as SWU Happiness DB and studies whether the physiological signal (i.e. tissue oxygen saturation [StO2], obtained by an optical absorption model) can be used to recognize observer happiness automatically. It proposes a novel method to capture local dynamic patterns (LDP) in facial regions, introducing local variations in facial StO₂ to fully use physiological characteristics with regard to hyperspectral patterns. Further, it applies a linear discriminant analysis-based support vector machine to recognize happiness patterns.

The results show that the best classification accuracy is 97.89 per cent, objectively demonstrating a feasible application of LDP features on happiness recognition.

This paper proposes a novel feature (i.e. LDP) to represent the local variations in facial StO₂ for modeling the active happiness. It provides a possible extension to the promising practical application.

Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner.

Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words.

The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach.

By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.

Visually impaired people usually struggle with doing daily tasks due to a lack of visual cues. For image captioning assistive applications, most applications require an Internet connection for the image captioning generation function to work properly. In this study, we developed MyUEVision, an application that assists visually impaired people by generating image captions that can work with and without the Internet. This work also involves reviewing some image captioning models for this application.

The author has selected and experimented with three image captioning models for online models and two image captioning models for offline models. The user experience (UX) design was designed based on the problems faced by visually impaired users when using mobile applications. The application is developed for the Android platform, and the offline model is integrated into the application for the image captioning generation function to work offline.

After conducting experiments for selecting online and offline models, ExpansionNet V2 is chosen for the online model and VGG16 + long short-term memory (LSTM) is chosen for the offline model. The application is then developed and assessed, and the results show that the application can generate image captions with or without the Internet, providing the best result when having an Internet connection, and the image is captured in good lighting with a few objects.

MyUEVision stands out for its both online and offline functionality. This approach ensures the image captioning generator works with or without the Internet, setting it apart as a unique solution to address the needs of visually impaired individuals.

The purpose of this paper is to improve the classification of families having children with affective-behavioral maladies, and thus giving the families a suitable orientation.

The proposed methodology includes three steps. Step 1 addresses initial data preprocessing, by noise filtering or data condensation. Step 2 performs a multiple feature sets selection, by using genetic algorithms and rough sets. Finally, Step 3 merges the candidate solutions and obtains the selected features and instances.

The new proposal show very good results on the family data (with 100 percent of correct classifications). It also obtained accurate results over a variety of repository data sets. The proposed approach is suitable for dealing with non-symmetric similarity functions, as well as with high-dimensionality mixed and incomplete data.

Previous work in the state of the art only considers instance selection to preprocess the schools for children with affective-behavioral maladies data. This paper explores using a new combined instance and feature selection technique to select relevant instances and features, leading to better classification, and to a simplification of the data.

The aims of this study are to examine affective responses of university students when viewing their own predictive learning analytics (PLA) dashboards, and to analyse how those responses are perceived to affect their self-regulated learning behaviour.

A total of 42 Northern Irish students were shown their own predicted status of academic achievement on a dashboard. A list of emotions along with definitions was provided and the respondents were instructed to verbalise them during the experience. Post-hoc walk-through conversations with participants further clarified their responses. Content analysis methods were used to categorise response patterns.

There is a significant variation in ways students respond to the predictions: they were curious and motivated, comforted and sceptical, confused and fearful and not interested and doubting the accuracy of predictions. The authors show that not all PLA-triggered affective states motivate students to act in desirable and productive ways.

This small-scale exploratory study was conducted in one higher education institution with a relatively small sample of students in one discipline. In addition to the many different categories of students included in the study, specific efforts were made to include “at-risk” students. However, none responded. A larger sample from a multi-disciplinary background that includes those who are categorised as “at-risk” could further enhance the understanding.

The authors provide mixed evidence for students' openness to learn from predictive learning analytics scores. The implications of our study are not straightforward, except to proceed with caution, valuing benefits while ensuring that students' emotional well-being is protected through a mindful implementation of PLA systems.

Understanding students' affect responses contributes to the quality of student support in higher education institutions. In the current era on online learning and increasing adaptation to living and learning online, the findings allow for the development of appropriate strategies for implementing affect-aware predictive learning analytics (PLA) systems.

The current study is unique in its research context, and in its examination of immediate affective states experienced by students who viewed their predicted scores, based on their own dynamic learning data, in their home institution. It brings out the complexities involved in implementing student-facing PLA dashboards in higher education institutions.

Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions.

A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation).

Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN.

The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities.

The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

Due to an immense rise of social media in recent years, the purpose of this paper is to investigate who, how and why participates in creating content at football websites. Specifically, it provides a sentiment analysis of user comments from gender perspective, i.e. how differently men and women write about football. The analysis is based on user comments published on Facebook pages of the top five 2015-2016 Premier League football clubs during the 1st and the 19th week of the season.

This analysis uses a data collection via social media website and a sentiment analysis of the collected data.

Results show certain unexpected similarities in social media activities between male and female football fans. A comparison of the user comments from Facebook pages of the top five 2015-2016 Premier League football clubs revealed that men and women similarly express hard emotions such as anger or fear, while there is a significant difference in expressing soft emotions such as joy or sadness.

This paper provides an original insight into qualitative content analysis of male and female comments published at social media websites of the top five Premier League football clubs during the 1st and the 19th week of the 2015-2016 season.