We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.

Bibliographic data such as collections of scientific articles and citation networks have been studied extensively in information visualization and visual analytics research. Powerful systems have been built to support various types of bibliographic analysis, but they require some training and cannot be used to disseminate the insights gained. In contrast, we focused on developing a more accessible visual analytics system, called SurVis, that is ready to disseminate a carefully surveyed literature collection. The authors of a survey may use our Web-based system to structure and analyze their literature database. Later, readers of the survey can obtain an overview, quickly retrieve specific publications, and reproduce or extend the original bibliographic analysis. Our system employs a set of selectors that enable users to filter and browse the literature collection as well as to control interactive visualizations. The versatile selector concept includes selectors for textual search, filtering by keywords and meta-information, selection and clustering of similar publications, and following citation links. Agreement to the selector is represented by word-sized sparkline visualizations seamlessly integrated into the user interface. Based on an analysis of the analytical reasoning process, we derived requirements for the system. We developed the system in a formative way involving other researchers writing literature surveys. A questionnaire study with 14 visual analytics experts confirms that SurVis meets the initially formulated requirements.

In this paper we explore the potential and limitations of vibrotactile displays in practical wearable applications, by comparing users' detection rate and response time to stimuli applied across the body in varied conditions. We examined which body locations are more sensitive to vibrations and more affected by movement; whether visual workload, expectation of location, or gender impact performance; and if users have subjective preferences to any of these conditions. In two experiments we compared these factors using five vibration intensities on up to 13 body locations. Our contributions are comparisons of tactile detection performance under conditions typifying mobile use, an experiment design that supports further investigation in vibrotactile communication, and guidelines for optimal display location given intended use.

Abstract:Graduates of university programs addressing sustainable resource management are likely to shape strategies for natural resource use in the future. Their academic training needs to foster student knowledge of the multiple dimensions of natural resource management. This paper investigates university student understanding of such challenges. We differentiated situational, conceptual, and procedural types of knowledge, and three domains of knowledge (ecological, socio-economic and institutional knowledge), and sampled beginners (third semester) and seniors (seventh semester) of seven natural resource related programs at the leading Indonesian institution of higher education in the field of natural resource management (IPB Bogor; n = 882). The questionnaire consisted of multiple choice and rating scale items covering ‗locally' relevant open-access resource use issues. With a confirmatory tau-equivalent LISREL model, construct validity was assessed. The ability to extract relevant information from problem descriptions provided (situational knowledge) did not differ between third and seventh semester students. While it was high for ecological and socio-economic items, it was markedly lower for institutional knowledge. Knowledge of relevant scientific concepts (conceptual knowledge) increased in OPEN ACCESSSustainability 2013, 5 1444 the ecological and socio-economic domains but the effect was small. Conceptual knowledge in the socio-economical and institutional domains tended to be lower than ecological knowledge. Although there was certain improvement, student judgments on the efficacy of resource management options (procedural knowledge) differed strongly from expert judgments for beginners as well as for senior students. We conclude that many of the university students in the sampled programs displayed substantial gaps in their capacity to solve complex, real-world natural resource management problems. Specifically, the socio-economic and institutional knowledge domains-and their integration with ecological knowledge-may require attention by educational planners.

No abstract