A virtual workspace for hybrid multidimensional scaling algorithms

In visualising multidimensional data, it is well known that different types of algorithms to process them. Data sets might be distinguished according to volume, variable types and distribution, and each of these characteristics imposes constraints upon the choice of applicable algorithms for their visualization. Previous work has shown that a hybrid algorithmic approach can be successful in addressing the impact of data volume on the feasibility of multidimensional scaling (MDS). This suggests that hybrid combinations of appropriate algorithms might also successfully address other characteristics of data. This paper presents a system and framework in which a user can easily explore hybrid algorithms and the data flowing through them. Visual programming and a novel algorithmic architecture let the user semi-automatically define data flows and the co-ordination of multiple views

Developmental Robots - A New Paradigm

It has been proved to be extremely challenging for humans to program a robot to such a sufficient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and effectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally “raise” the robot through “robot sitting” and “robot schools” instead of task-specific robot programming

Children's emotion understanding: A meta-analysis of training studies.

BACKGROUND: In the course of development, children show increased insight and understanding of emotions-both of their own emotions and those of others. However, little is known about the efficacy of training programs aimed at improving children's understanding of emotion. OBJECTIVES: To conduct an effect size analysis of trainings aimed at three aspects of emotion understanding: external aspects (i.e., the recognition of emotional expressions, understanding external causes of emotion, understanding the influence of reminders on present emotions); mental aspects (i.e., understanding desire-based emotions, understanding belief-based emotions, understanding hidden emotions); and reflective aspects (i.e., understanding the regulation of an emotion, understanding mixed emotions, understanding moral emotions). DATA SOURCES: A literature search was conducted using PubMed, PsycInfo, the Cochrane Library, and manual searches. REVIEW METHODS: The search identified 19 studies or experiments including a total of 749 children with an average age of 86 months (S.D.=30.71) from seven different countries. RESULTS: Emotion understanding training procedures are effective for improving external (Hedge's g = 0.62), mental (Hedge's g = 0.31), and reflective (Hedge's g = 0.64) aspects of emotion understanding. These effect sizes were robust and generally unrelated to the number and lengths of training sessions, length of the training period, year of publication, and sample type. However, training setting and social setting moderated the effect of emotion understanding training on the understanding of external aspects of emotion. For the length of training session and social setting, we observed significant moderator effects of training on reflective aspects of emotion. CONCLUSION: Emotion understanding training may be a promising tool for both preventive intervention and the psychotherapeutic process. However, more well-controlled studies are needed.R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH