Abstract

The ability to recognize emotions is necessary for the normal childs adjustment to the social and educational environment, and to identify disorders including autism, anxiety and depression in the early stages of development. An experiment was performed to understanding this process. The participants were 47 children distributed in three groups (6-7, 8-9. 10-11 years). Children had to see pictures of facial and corporal expressions of happiness, sadness, anger and fear. Participants were than asked to say if they recognized the facial and corporal expressed emotion, from a labels related to these basic emotions and represented by emoticons. The results showed enhanced recognition of facial and corporal expressions of fear in children of 8- 11 years olds compared to those of 6-7 years olds (p < .01, r > .50). On the other hand, the result showed significant differences (p < .01, r > .50), between children of 6-7 and 8-11 years olds, in the recognition of facial expressions of anger than the corporal expressions of anger. Children of 6-7 years olds showed enhanced recognition of facial expression of anger than corporal expression of anger. These differences were reversed in children of 8-11 years olds. One may conclude that anger and fear facial expressions recognition is an important indicator of the proper development of children. Emotional learning from 7 years olds, will ensure better emotional regulation, and a beneficial effect on the childs performance in school .

Abstract

This paper aims to explore the perceptual recognition of topographical building patterns from real-world OpenStreetMap data on virtual globes. An implementation was developed in which all geographical and contextual information was layered and, for the purpose of this study, what solely remained were building patterns as viewed from above. This was developed as a module for the planetarium visualization software Uniview. The aim was to determine how cities with different building patterns were perceived by participants in terms of size, scale, and building density. This was measured as the comparative difference between city pairs, that is, how much they differed in the percentage of the area that they covered. Two quantitative studies were conducted, one smaller controlled study with 19 participants and one larger online crowd-sourced study with 72 participants. The results show that participants are generally able to discern building patterns when the comparative difference is greater than a certain critical threshold. This critical threshold was determined to be at approximately 0.5% for both studies and for accuracy levels above 60%. Thus it was concluded that below this critical threshold users should be provided with visual feedback or other means of identifiers in order to allow for definite recognition, depending on what kind of information a certain type of visualization is trying to convey. Den här rapporten avser att utforska den perpetuella igenkänningen av topografiska byggnadsmönster genom att använda geografisk data från OpenStreetMap som avbildas på virtuella sfärer. En implementation utvecklades där geografisk data samt kontextuell information ordnades i överlappande lager som filtrerades, och där endast byggnadsmönster sett från ovan kvarstod. Denna modul utvecklades för Uniview som är en mjukvara för visualisering i planetarier. Målet var att avgöra hur deltagare uppfattade städer med olika byggnadsmönster med hänsyn till storlek, skala, samt byggnadsdensitet. Detta mättes genom den procentuella skillnaden mellan städer, dvs. skillnaden i procent för varje stads geografiska utsträckning. Två kvantitativa studier utfördes, en mindre kontrollerad studie med 19 deltagare samt en större nätbaserad studie med 72 deltagare. Resultatet visar att deltagare generellt kunde bedöma den procentuella skillnaden i byggnadsmönster upp till en viss kritisk gräns. Denna kritiska gräns fastställdes till runt 0.5% för båda studier och för noggrannhetsnivåer över 60%. Slutsatsen från detta är att användare bör ges visuella indikatorer för nivåer under denna kritiska gräns för att säkerställa definitiv igenkänning beroende på vilken information som skall förmedlas i en viss typ av visualisering.

Abstract

The development of adequate simulation models from geometric CAD assemblies is one of the most important tasks in early design phases. With this step requiring a lot of manual effort, the desire for a process efficiency improvement via an automated solution rises. In order to derive information about the assembly to build Finite-Element (FE) models, various different steps have to be taken which require visual assessment and engineering evaluation, knowledge and judgement. The approach described in this research mimics the engineer's logic and way of thinking to automate these steps. Thereof, the recognition of entities plays a fundamental role for further processing. To achieve the desired recognition, methods have been developed to retrieve criteria like form, function, context and positioning from the available geometry data. The developed recognition framework supports and provides a component categorization so that specifically optimized process chains for each category can be implemented, depicting a more robust and reasonable overall process.