The Investigation of Meta-Affective Differences Between Gender in Vocational High Schools During Learning Science

The presence of feelings has an impact on science learning, which involves not only studying theory but also practising. Making groups in learning sometimes do not pay attention to the ability to recognize and regulate the feelings of male and female students. This survey research is to investigate meta-affective based on students' experiences in learning science. The research instrument is a questionnaire compiled based on the Awareness Dimension (10 statements) and Regulation Dimension (7 statements) with the level of response, namely frequency (scale 1-6). The research subjects involved were 160 students in Vocational High Schools, with 80 males and 80 females. The results show a comparison between male and female students based on average responses. Students who were female had higher average response scores than students who were male in both the tests of affective awareness and affective control. With the exception of one item, both versions have the same score. The items are MATS-12 "If I get angry with myself when I do not understand a topic, I notice that feeling" on affective awareness and MATS-10 "If I feel angry when I am not successful, I try to control it". These results conclude that noticing angry feelings and controlling them is the same ability between male and female students. This suggests that, except for rage, which can be recognized and managed by both sexes of students, female students are better able to recognize and manage their emotions

A survey on deep learning in image polarity detection: Balancing generalization performances and computational costs

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost

Affective Image Content Analysis: Two Decades Review and New Perspectives

Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM