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.

EDUCOSM - personalized writable Web for learning communities

Abstract: Many of the possibilities of Web-based education are still unexplored. It seems that novel ways of thinking about both learning and technology are needed to get beyond the limitations of the traditional classroom setting. In this paper we introduce EDUCOSM, which focusses on the possibilities of collaboration and the open-ended nature of the Web. Its main features include sharing and annotation of arbitrary Web-pages, and an adaptive desktop for accessing the evolving contents of the system. EDUCOSM has been used in a real Web-based course, and the experiences are discussed along with a description of the tool. Although the approach requires both the teacher and the students to rethink their roles, the feedback received so far has been encouraging.

Sparse Dynamic Programming on DAGs with Small Width

Abstract: The minimum path cover problem asks us to find a minimum-cardinality set of paths that cover all the nodes of a directed acyclic graph (DAG). We study the case when the size k of a minimum path cover is small, that is, when the DAG has a small width. This case is motivated by applications in pan-genomics, where the genomic variation of a population is expressed as a DAG. We observe that classical alignment algorithms exploiting sparse dynamic programming can be extended to the sequence-against-DAG case by mimicking the algorithm for sequences on each path of a minimum path cover and handling an evaluation order anomaly with reachability queries.Namely, we introduce a general framework for DAG-extensions of sparse dynamic programming. This framework produces algorithms that are slower than their counterparts on sequences only by a factor k. We illustrate this on two classical problems extended to DAGs: longest increasing subsequence and longest common subsequence. For the former, we obtain an algorithm with running time O(kvEvlog vVv). This matches the optimal solution to the classical problem variant when the input sequence is modeled as a path. We obtain an analogous result for the longest common subsequence problem. We then apply this technique to the co-linear chaining problem, which is a generalization of the above two problems. The algorithm for this problem turns out to be more involved, needing further ingredients, such as an FM-index tailored for large alphabets and a two-dimensional range search tree modified to support range maximum queries. We also study a general sequence-to-DAG alignment formulation that allows affine gap costs in the sequence.The main ingredient of the proposed framework is a new algorithm for finding a minimum path cover of a DAG (V,E) in O(kvEvlog vVv) time, improving all known time-bounds when k is small and the DAG is not too dense. In addition to boosting the sparse dynamic programming framework, an immediate consequence of this new minimum path cover algorithm is an improved space/time tradeoff for reachability queries in arbitrary directed graphs.

Implementation of OpenFlow based cognitive radio network architecture: SDN&R

Abstract: The static conventional network architecture is ill-suited to the growing management complexity and highly dynamic wireless network topologies. Software Defined Radio systems and their extension to cognitive and smart radio are characterized by distinct control loops for management which constantly increase network complexity and management inefficiencies, due to clear-cut between radio and core network management. Adding numerous devices and networks together will constantly increase the management cost, thus hinders scalability. Therefore, a holistic solution to synchronize radio and networks status has an elevated demand. To interconnect these systems and devices together, there is a need for a common management interface. OpenFlow is the first standard interface that enables Software Defined Networking (SDN). It can be rolled out in a variety of networking devices to enable improved automation and management by using common Application Program Interfaces to abstract the underlying networking details. The Software Defined Networking & Radio (SDN&R) framework proposed here has a potential combination between SDN and Radio networks to discover the underlying dynamism in cognitive access networks with integrated radio management. By isolating the control plane from the data plane, SDN&R enables a flexible management framework empowered by end-to-end goals through OpenFlow. In this article, we propose, validate, and evaluate the SDN&R architecture. In doing so, first we implement the OpenFlow enabled cognitive basestations (BSs) on Wireless Open-Access Research Platform. Furthermore, we develop software agents on BSs to provide radio status information to the cognitive control application implemented on the SDN controller. The results verify that the proposed framework in-lines with layer-2 or layer-3 forwarding performance. We claim that this work represents the first successful implementation results which synergizes SDN with Cognitive networks that motivates researchers towards SDN based radio resource management.

Bayesian identification of bacterial strains from sequencing data

Abstract: Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an environmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, resistance and spread. Using advanced Bayesian statistical modelling and computation techniques we introduce a novel pipeline for bacterial identification that is shown to outperform the currently leading pipeline for this purpose. Our approach enables fast and accurate sequence-based identification of bacterial strains while using only modest computational resources. Hence it provides a useful tool for a wide spectrum of applications, including rapid clinical diagnostics to distinguish among closely related strains causing nosocomial infections. The software implementation is available at https://github.com/PROBIC/BIB.