Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Embodied interaction with a 3D versus 2D mobile map

Abstract: In comparison to 2D maps, 3D mobile maps involve volumetric instead of flat representation of space, realistic instead of symbolic representation of objects, more variable views that are directional and bound to a first-person perspective, more degrees of freedom in movement, and dynamically changing object details. We conducted a field experiment to understand the influence of these qualities on a mobile spatial task where buildings shown on the map were to be localized in the real world. The representational differences were reflected in how often users interact with the physical environment and in when they are more likely to physically turn and move the device, instead of using virtual commands. 2D maps direct users into using reliable and ubiquitous environmental cues like street names and crossings, and 2D better affords the use of pre-knowledge and bodily action to reduce cognitive workload. Both acclaimed virtues of 3D mobile maps--rapid identification of objects and ego-centric alignment--worked poorly due reasons we discuss. However, with practice, some 3D users learned to shift to 2D-like strategies and could thereby improve performance. We conclude with a discussion of how representational differences in mobile maps affect strategies of embodied interaction.

Finding reliable subgraphs from large probabilistic graphs

Abstract: Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.

A look at aerosol formation using data mining techniques

Abstract: . Atmospheric aerosol particle formation is frequently observed throughout the atmosphere, but despite various attempts of explanation, the processes behind it remain unclear. In this study data mining techniques were used to find the key parameters needed for atmospheric aerosol particle formation to occur. A dataset of 8 years of 80 variables collected at the boreal forest station (SMEAR II) in Southern Finland was used, incorporating variables such as radiation, humidity, SO2, ozone and present aerosol surface area. This data was analyzed using clustering and classification methods. The aim of this approach was to gain new parameters independent of any subjective interpretation. This resulted in two key parameters, relative humidity and preexisting aerosol particle surface (condensation sink), capable in explaining 88% of the nucleation events. The inclusion of any further parameters did not improve the results notably. Using these two variables it was possible to derive a nucleation probability function. Interestingly, the two most important variables are related to mechanisms that prevent the nucleation from starting and particles from growing, while parameters related to initiation of particle formation seemed to be less important. Nucleation occurs only with low relative humidity and condensation sink values. One possible explanation for the effect of high water content is that it prevents biogenic hydrocarbon ozonolysis reactions from producing sufficient amounts of low volatility compounds, which might be able to nucleate. Unfortunately the most important biogenic hydrocarbon compound emissions were not available for this study. Another effect of water vapour may be due to its linkage to cloudiness which may prevent the formation of nucleating and/or condensing vapours. A high number of preexisting particles will act as a sink for condensable vapours that otherwise would have been able to form sufficient supersaturation and initiate the nucleation process.

UCPCD: user-centered product concept design

Abstract: Traditional user-centered methodologies have been developed for system-level and detailed-level design and not for concept-level design. This paper discusses thinking models and user-centered practices useful in the earliest phase of product developmentoproduct concept design.