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In our present society, the cinema has become one of the major forms of entertainment providing unlimited contexts of emotion elicitation for the emotional needs of human beings. Since emotions are universal and shape all aspects of our interpersonal and intellectual experience, they have proved to be a highly multidisciplinary research field, ranging from psychology, sociology, neuroscience, etc., to computer science. However, affective multimedia content analysis work from the computer science community benefits but little from the progress achieved in other research fields. In this paper, a multidisciplinary state-of-the-art for affective movie content analysis is given, in order to promote and encourage exchanges between researchers from a very wide range of fields. In contrast to other state-of-the-art papers on affective video content analysis, this work confronts the ideas and models of psychology, sociology, neuroscience, and computer science. The concepts of aesthetic emotions and emotion induction, as well as the different representations of emotions are introduced, based on psychological and sociological theories. Previous global and continuous affective video content analysis work, including video emotion recognition and violence detection, are also presented in order to point out the limitations of affective video content analysis work. [ABSTRACT FROM AUTHOR]

Exponential growth of information generated by online social networks demands effective and scalable recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social contextual information has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users’ motivation of social behaviors into social recommendation. In this paper, we investigate the social recommendation problem on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online behavior prediction. Then we propose a novel probabilistic matrix factorization method to fuse them in latent space. We further provide a scalable algorithm which can incrementally process the large scale data. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network data sets. The empirical results and analysis on these two large data sets demonstrate that our method significantly outperforms the existing approaches. [ABSTRACT FROM AUTHOR]

Trust is a fundamental concept that underpins the coherence and resilience of social systems and shapes human behavior. Despite the importance of trust as a social and psychological concept, the concept has not gained much attention from evolutionary game theorists. In this letter, an N -player trust-based social dilemma game is introduced. While the theory shows that a society with no untrustworthy individuals would yield maximum wealth to both the society as a whole and the individuals in the long run, evolutionary dynamics show this ideal situation is reached only in a special case when the initial population contains no untrustworthy individuals. When the initial population consists of even the slightest number of untrustworthy individuals, the society converges to zero trusters, with many untrustworthy individuals. The promotion of trust is an uneasy task, despite the fact that a combination of trusters and trustworthy trustees is the most rational and optimal social state. This letter presents the game and results of replicator dynamics in a hope that researchers in evolutionary games see opportunities in filling this critical gap in the literature. [ABSTRACT FROM PUBLISHER]

Understanding co-occurrence in urban human mobility (i.e. people from two regions visit an urban place during the same time span) is of great value in a variety of applications, such as urban planning, business intelligence, social behavior analysis, as well as containing contagious diseases. In recent years, the widespread use of mobile phones brings an unprecedented opportunity to capture large-scale and fine-grained data to study co-occurrence in human mobility. However, due to the lack of systematic and efficient methods, it is challenging for analysts to carry out in-depth analyses and extract valuable information. In this paper, we present TelCoVis, an interactive visual analytics system, which helps analysts leverage their domain knowledge to gain insight into the co-occurrence in urban human mobility based on telco data. Our system integrates visualization techniques with new designs and combines them in a novel way to enhance analysts' perception for a comprehensive exploration. In addition, we propose to study the correlations in co-occurrence (i.e. people from multiple regions visit different places during the same time span) by means of biclustering techniques that allow analysts to better explore coordinated relationships among different regions and identify interesting patterns. The case studies based on a real-world dataset and interviews with domain experts have demonstrated the effectiveness of our system in gaining insights into co-occurrence and facilitating various analytical tasks. [ABSTRACT FROM AUTHOR]

Common reporting styles for statistical results in scientific articles, such as $p$ p -values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the $p$ p -value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments on researchers with expertise in statistical analysis to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in researchers’ subjective confidence in the results. We also asked the respondents’ opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations – measured as the ‘cliff effect’: the sudden drop in confidence around $p$ p -value 0.05 – compared with classic CI visualization and textual representation of the CI with $p$ p -values. All data and analyses are publicly available at https://github.com/helske/statvis. [ABSTRACT FROM AUTHOR]

The computer science (CS) education field is engaging in unprecedented efforts to expand learning opportunities in K-12 CS education, but one group of students is often overlooked: those with specific learning disabilities and related attention deficit disorders. As CS education initiatives grow, K-12 teachers need research-informed guidance to make computing more accessible for students who learn differently. This article reports on the first phase of a National Science Foundation-supported exploratory research study to address this problem. The authors present their education research-practice partnership, initial findings, and highlights of a collaborative process that has furthered their work to support more equitable learning in CS. [ABSTRACT FROM PUBLISHER]