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.

An ecometric analysis of the fossil mammal record of the Turkana Basin.

Abstract: Although ecometric methods have been used to analyse fossil mammal faunas and environments of Eurasia and North America, such methods have not yet been applied to the rich fossil mammal record of e...

An augmented reality interface to contextual information

Abstract: In this paper, we report on a prototype augmented reality (AR) platform for accessing abstract information in real-world pervasive computing environments. Using this platform, objects, people, and the environment serve as contextual channels to more information. The user’s interest with respect to the environment is inferred from eye movement patterns, speech, and other implicit feedback signals, and these data are used for information filtering. The results of proactive context-sensitive information retrieval are augmented onto the view of a handheld or head-mounted display or uttered as synthetic speech. The augmented information becomes part of the user’s context, and if the user shows interest in the AR content, the system detects this and provides progressively more information. In this paper, we describe the first use of the platform to develop a pilot application, Virtual Laboratory Guide, and early evaluation results of this application.

Contory: a middleware for the provisioning of context information on smart phones

Abstract: Context-awareness can serve to make ubiquitous applications deployed for mobile devices adaptive, personalized, and accessible in dynamically changing environments. Unfortunately, existing approaches for the provisioning of context information in ubiquitous computing environments rarely take into consideration the resource constraints of mobile devices and the uncertain availability of sensors and service infrastructures. This paper presents the design, prototype implementation, and experimental evaluation of Contory, a middleware specifically designed to accomplish efficient context provisioning on mobile devices. To make context provisioning flexible and adaptive based on dynamic operating conditions, Contory integrates multiple context provisioning strategies, namely internal sensors-based, external infrastructure-based, and distributed provisioning in ad hoc networks. Applications can request context information provided by Contory using a declarative query language which features on-demand, periodic, and event-based context queries. Experimental results obtained in a testbed of smart phones demonstrate the feasibility of our approach and quantify the cost of supporting context provisioning in terms of energy consumption.

Bayesian Group Factor Analysis

Abstract: We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics tables to measure one property of interest. We show that by assuming group-wise sparse factors, active in a subset of the sets, the variation can be decomposed into factors explaining relationships between the sets and factors explaining away set-specific variation. We formulate the assumptions in a Bayesian model which provides the factors, and apply the model to two data analysis tasks, in neuroimaging and chemical systems biology.

Identifying meaningful locations

Abstract: Existing context-aware mobile applications often rely on location information. However, raw location data such as GPS coordinates or GSM cell identifiers are usually meaningless to the user and, as a consequence, researchers have proposed different methods for inferring so-called places from raw data. The places are locations that carry some meaning to user and to which the user can potentially attach some (meaningful) semantics. Examples of places include home, work and airport. A lack in existing work is that the labeling has been done in an ad hoc fashion and no motivation has been given for why places would be interesting to the user. As our first contribution we use social identity theory to motivate why some locations really are significant to the user. We also discuss what potential uses for location information social identity theory implies. Another flaw in the existing work is that most of the proposed methods are not suited to realistic mobile settings as they rely on the availability of GPS information. As our second contribution we consider a more realistic setting where the information consists of GSM cell transitions that are enriched with GPS information whenever a GPS device is available. We present four different algorithms for this problem and compare them using real data gathered throughout Europe. In addition, we analyze the suitability of our algorithms for mobile devices.