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.

Measuring networked social privacy

Abstract: Much privacy research focuses on concerns about data protection and has established metrics, such as privacy scales, for evaluating those concerns. Recent work recognizes the importance of understanding interpersonal and interactional privacy concerns in social media, but ways to measure privacy within these contexts remain unsettled. This workshop aims to cultivate an understanding of the current landscape for interpersonal privacy framework and ways to measure social privacy for networked settings. For full details, visit http://networkedprivacy2013.wordpress.com/

Directed Hamiltonicity and Out-Branchings via Generalized Laplacians

Abstract: We are motivated by a tantalizing open question in exact algorithms: can we detect whether an $n$-vertex directed graph $G$ has a Hamiltonian cycle in time significantly less than $2^n$? We present new randomized algorithms that improve upon several previous works: 1. We show that for any constant $0<\lambda<1$ and prime $p$ we can count the Hamiltonian cycles modulo $p^{\lfloor (1-\lambda)\frac{n}{3p}\rfloor}$ in expected time less than $c^n$ for a constant $c<2$ that depends only on $p$ and $\lambda$. Such an algorithm was previously known only for the case of counting modulo two [Bjorklund and Husfeldt, FOCS 2013]. 2. We show that we can detect a Hamiltonian cycle in $O^*(3^{n-\alpha(G)})$ time and polynomial space, where $\alpha(G)$ is the size of the maximum independent set in $G$. In particular, this yields an $O^*(3^{n/2})$ time algorithm for bipartite directed graphs, which is faster than the exponential-space algorithm in [Cygan et al., STOC 2013]. Our algorithms are based on the algebraic combinatorics of "incidence assignments" that we can capture through evaluation of determinants of Laplacian-like matrices, inspired by the Matrix--Tree Theorem for directed graphs. In addition to the novel algorithms for directed Hamiltonicity, we use the Matrix--Tree Theorem to derive simple algebraic algorithms for detecting out-branchings. Specifically, we give an $O^*(2^k)$-time randomized algorithm for detecting out-branchings with at least $k$ internal vertices, improving upon the algorithms of [Zehavi, ESA 2015] and [Bjorklund et al., ICALP 2015]. We also present an algebraic algorithm for the directed $k$-Leaf problem, based on a non-standard monomial detection problem.

GFA: exploratory analysis of multiple data sources with group factor analysis

Abstract: The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies between subsets of the data sources, decomposed into latent factors. The package also implements sparse priors for the factorization, providing interpretable biclusters of the multi-source data.

Flexible hardware configurations for studying mobile usability

Abstract: The main challenges for mobile usability labs, as measurement instruments, lay not so much on being able to record what happens on the user interface, but capturing the interactional relationship between the user and the environment. An ideal mobile usability lab would enable recording, with sufficient accuracy and reliability, the user's deployment of gaze, the hands, the near bodyspace, proximate and distant objects of interest, as well as abrupt environmental events. An inherent complication is that the equipment will affect these events and is affected by them. We argue that a balance between coverage and obtrusiveness must be found on a per case basis. We present a modular solution to mobile usability labs, allowing both belt- and backpack-worn configurations and flexible division of equipment between the user, the moderator, and the environment. These benefits were achieved without sacrificing data quality, operational duration, or light weight. We describe system design rationale and report first experiences from a field experiment. Current work concentrates on simplifying the system to improve cost-efficiency.

Synchronous Counting and Computational Algorithm Design

Abstract: Consider a complete communication network on n nodes, each of which is a state machine with s states. In synchronous 2-counting, the nodes receive a common clock pulse and they have to agree on which pulses are "odd" and which are "even". We require that the solution is self-stabilising (reaching the correct operation from any initial state) and it tolerates f Byzantine failures (nodes that send arbitrary misinformation). Prior algorithms are expensive to implement in hardware: they require a source of random bits or a large number of statesas. We use computational techniques to construct very compact deterministic algorithms for the first non-trivial case of f = 1. While no algorithm exists for n < 4, we show that as few as 3 states are sufficient for all values n ≤ 4. We prove that the problem cannot be solved with only 2 states for n = 4, but there is a 2-state solution for all values n ≤ 6.