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.

Designing motivation using persuasive ambient mirrors

Abstract: In this article, we describe four case studies of ubiquitous persuasive technologies that support behavior change through personalized feedback reflecting a user's current behavior or attitude. The first case study is Persuasive Art, reflecting the current status of a user's physical exercise in artistic images. The second system, Virtual Aquarium, reflects a user's toothbrushing behavior in a Virtual Aquarium. The third system, Mona Lisa Bookshelf, reflects the situation of a shared bookshelf on a Mona Lisa painting. The last case study is EcoIsland, reflecting cooperative efforts toward reducing CO2 emissions as a set of virtual islands shared by a neighborhood. Drawing from the experience of designing and evaluating these systems, we present guidelines for the design of persuasive ambient mirrors: systems that use visual feedback to effect changes in users' everyday living patterns. In particular, we feature findings in choosing incentive systems, designing emotionally engaging feedback, timing feedback, and persuasive interaction design. Implications for current design efforts as well as for future research directions are discussed.

Causal Inference by Independent Component Analysis: Theory and Applications*

Abstract: Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this study, we present a recently developed method for estimating such models, which uses non-normality to recover the causal structure underlying the observations. We show how the method can be applied to both microeconomic data (to study the processes of firm growth and firm performance) and macroeconomic data (to analyse the effects of monetary policy).

On the relation between accuracy and fairness in binary classification.

Abstract: Our study revisits the problem of accuracy-fairness tradeoff in binary classification. We argue that comparison of non-discriminatory classifiers needs to account for different rates of positive predictions, otherwise conclusions about performance may be misleading, because accuracy and discrimination of naive baselines on the same dataset vary with different rates of positive predictions. We provide methodological recommendations for sound comparison of non-discriminatory classifiers, and present a brief theoretical and empirical analysis of tradeoffs between accuracy and non-discrimination.

Mobile kits and laptop trays: managing multiple devices in mobile information work

Abstract: A study at a large IT company shows that mobile information workers frequently migrate work across devices (here: smartphones, desktop PCs, laptops). While having multiple devices provides new opportunities to work in the face of changing resource deprivations, the management of devices is often problematic. The most salient problems are posed by 1) the physical effort demanded by various management tasks, 2) anticipating what data or functionality will be needed, and 3) aligning these efforts with work, mobility, and social situations. Workers' strategies of coping with these problems center on two interwoven activities: the physical handling of devices and cross-device synchronization. These aim at balancing risk and effort in immediate and subsequent use. Workers also exhibit subtle ways to handle devices in situ, appropriating their physical and operational properties. The design implications are discussed.

Critical Assessment of Small Molecule Identification 2016: automated methods

Abstract: The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in “Category 2: Best Automatic Structural Identification—In Silico Fragmentation Only”, won by Team Brouard with 41% challenge wins. The winner of “Category 3: Best Automatic Structural Identification—Full Information” was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for “known unknowns”. As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for “real life” annotations. The true “unknown unknowns” remain to be evaluated in future CASMI contests.