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.

EnergyLife: pervasive energy awareness for households

Abstract: We present Energy Life a system utilizing wireless sensors, mobile and ambient interfaces that turn energy consumers into active players. Energy Life participants play through different levels collecting scores in savings and through advice tip reading and quizzes. We describe principles, logic of the game, implementation and user interfaces providing rationale for design choices. Key principles embodied in Energy Life are: situated and combined feedback including knowledge and consumption information, intuitiveness and non-intrusiveness by utilizing an always at hand solution on a touch enabled smart phone and lighting as an ambient interface, sustained interaction and engagement by using a applied game that connects players within and between households.

ParceLiNGAM: A causal ordering method robust against latent confounders

Abstract: We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually are violated. In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. The key idea is to detect latent confounders by testing independence between estimated external influences and find subsets (parcels) that include variables that are not affected by latent confounders. We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data.

Projection predictive model selection for Gaussian processes

Abstract: We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of variables based on their predictive relevance. Our results on synthetic and real world datasets show that the proposed method improves the assessment of variable relevance compared to the automatic relevance determination (ARD) via the length-scale parameters. We expect the method to be useful for improving explainability of the models, reducing the future measurement costs and reducing the computation time for making new predictions.

Probabilistic drug connectivity mapping

Abstract: The aim of connectivity mapping is to match drugs using drug-treatment gene expression profiles from multiple cell lines. This can be viewed as an information retrieval task, with the goal of finding the most relevant profiles for a given query drug. We infer the relevance for retrieval by data-driven probabilistic modeling of the drug responses, resulting in probabilistic connectivity mapping, and further consider the available cell lines as different data sources. We use a special type of probabilistic model to separate what is shared and specific between the sources, in contrast to earlier connectivity mapping methods that have intentionally aggregated all available data, neglecting information about the differences between the cell lines. We show that the probabilistic multi-source connectivity mapping method is superior to alternatives in finding functionally and chemically similar drugs from the Connectivity Map data set. We also demonstrate that an extension of the method is capable of retrieving combinations of drugs that match different relevant parts of the query drug response profile. The probabilistic modeling-based connectivity mapping method provides a promising alternative to earlier methods. Principled integration of data from different cell lines helps to identify relevant responses for specific drug repositioning applications.

Applying pervasive technologies to create economic incentives that alter consumer behavior

Abstract: Economic incentives are a powerful way of shaping consumer behavior towards more commercially efficient and environmentally sustainable patterns. In this paper, we explore the idea of combining pervasive computing techniques with electronic payment systems to create activity-based micro-incentives. Users who consume additional resources by e.g., occupying an air-conditioned space instead of a normal space are levied additional micro-payments. In an alternative approach, consumers who choose to save resources are rewarded with micro-rebates off the price of a service. As a result, the cost of using a service corresponds more closely with the resources used, leading market mechanisms to allocate resources efficiently. A key challenge is designing incentive mechanisms that alter consumer behavior in the desired fashion. We introduce four incentive models, and present evaluation results suggesting that consumers make different decisions depending on which model is used.