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.

Interactive faceted query suggestion for exploratory search: Whole-session effectiveness and interaction engagement

Abstract: The outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole‐session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed‐query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole‐session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole‐session performance.

Augmented reality painting and collage: evaluating tangible interaction in a field study

Abstract: Tangible computing applications are rarely evaluated with field studies in real settings, which can contribute as formative studies to understand the challenges and benefits of tangible interfaces in real world practices. We present an AR environment for painting, with a physical brush, digital textures on physical models and creating dynamic stages for the model with spatial collages providing different backgrounds. We report on an evaluation of this AR environment in an architecture school, where 8 groups of students used it as a practical assignment. The evaluation demonstrated the benefits of specific features of the environment and of its tangible interfaces: immersiveness, public availability, supporting collaboration, flexibility, dynamicism and resulting rapidity in creating mixed media representations. Several challenges surfaced from the evaluation especially in connection to the distribution of the interface. The physical, spatial, and computational separation of interface components raised issues on accountability and ergonomics. We link our observations to design guidelines.

LAF: Logic Alignment Free and its application to bacterial genomes classification.

Abstract: Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (k-mers) that occur in the analyzed sequences. In this paper, we present Logic Alignment Free (LAF), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples to their taxa. This method searches for a minimal subset of k-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (if-then rules). We apply LAF successfully to the classification of bacterial genomes to their corresponding taxonomy. In particular, we succeed in obtaining reliable classification at different taxonomic levels by extracting a handful of rules, each one based on the frequency of just few k-mers. State of the art methods to adjust the frequency of k-mers to the character distribution of the underlying genomes have negligible impact on classification performance, suggesting that the signal of each class is strong and that LAF is effective in identifying it.

Graph Operations on Parity Games and Polynomial-Time Algorithms

Abstract: Parity games are games that are played on directed graphs whose vertices are labeled by natural numbers, called priorities. The players push a token along the edges of the digraph. The winner is determined by the parity of the greatest priority occurring infinitely often in this infinite play. A motivation for studying parity games comes from the area of formal verification of systems by model checking. Deciding the winner in a parity game is polynomial time equivalent to the model checking problem of the modal mu-calculus. Another strong motivation lies in the fact that the exact complexity of solving parity games is a long-standing open problem, the currently best known algorithm being subexponential. It is known that the problem is in the complexity classes UP and coUP. In this paper we identify restricted classes of digraphs where the problem is solvable in polynomial time, following an approach from structural graph theory. We consider three standard graph operations: the join of two graphs, repeated pasting along vertices, and the addition of a vertex. Given a class C of digraphs on which we can solve parity games in polynomial time, we show that the same holds for the class obtained from C by applying once any of these three operations to its elements. These results provide, in particular, polynomial time algorithms for parity games whose underlying graph is an orientation of a complete graph, a complete bipartite graph, a block graph, or a block-cactus graph. These are classes where the problem was not known to be efficiently solvable. Previous results concerning restricted classes of parity games which are solvable in polynomial time include classes of bounded tree-width, bounded DAG-width, and bounded clique-width. We also prove that recognising the winning regions of a parity game is not easier than computing them from scratch.

Genome-wide off-targets of drugs: risks and opportunities.

Abstract: Most approved drugs and small-molecule compounds modulate their therapeutic and toxic effects through multiple protein targets and biological pathways. Proteome-wide compound-target interaction networks (among both onand off-targets) are therefore essential resources to better understand and make use of such polypharmacological effects of promiscuous compounds (Knight et al. 2010). Interestingly, systematic bioactivity profiling has revealed that some compounds may have higher potency against their unintended off-targets, compared to the primary on-targets (Tang et al. 2018). Unexplored off-target potencies of approved drugs may therefore lead to novel drug repurposing opportunities, i.e., finding new uses or targets for existing drugs (Pemovska et al. 2015; Kuenzi et al. 2019). A prime example of the utilization of compound-target interaction networks comes from finding synergistic compound combinations that lead to increased efficacy. For instance, the recent AstraZeneca-Sanger Drug Combination DREAM Challenge benchmarked a number of machine learning algorithms in terms of their accuracy to predict synergistic drug pairs and associated biomarkers for 910 drug combinations across 85 cancer cell lines (http://dreamchallenges.org/initialr e su l t s f r omthe a s t r a zeneca s ange r -d rug combination-prediction-challenge-presented/). Among 160 participants, all the winning methods incorporated prior knowledge of putative drugtarget interactions (Menden et al. 2019). However, proteome-wide target profiles are not important merely when exploring therapeutic potential, e.g., synergistic drug pairs, but also when profiling excess toxicity prior to clinical studies.