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.

A fast normalized maximum likelihood algorithm for multinomial data

Abstract: Stochastic complexity of a data set is defined as the shortest possible code length for the data obtainable by using some fixed set of models. This measure is of great theoretical and practical importance as a tool for tasks such as model selection or data clustering. In the case of multinomial data, computing the modern version of stochastic complexity, defined as the Normalized Maximum Likelihood (NML) criterion, requires computing a sum with an exponential number of terms. Furthermore, in order to apply NML in practice, one often needs to compute a whole table of these exponential sums. In our previous work, we were able to compute this table by a recursive algorithm. The purpose of this paper is to significantly improve the time complexity of this algorithm. The techniques used here are based on the discrete Fourier transformand the convolution theorem.

HCI and innovation

Abstract: The user-centered design (UCD) process in HCI has recently been criticized for not delivering breakthrough innovations in technology. In this paper we consider this critique through a literature review and two case studies of innovation. Our conclusions suggest that there is nothing wrong with the attitude of user-centered design which has probably been present in all major innovations down the centuries. Rather, the practice of UCD in HCI lacks attention to business factors and long term uptake of technology in society. This compromises its impact on products and should be incorporated into the study of HCI itself.

Density-Friendly Graph Decomposition

Abstract: Decomposing a graph into a hierarchical structure via k-core analysis is a standard operation in any modern graph-mining toolkit. k-core decomposition is a simple and efficient method that allows to analyze a graph beyond its mere degree distribution. More specifically, it is used to identify areas in the graph of increasing centrality and connectedness, and it allows to reveal the structural organization of the graph. Despite the fact that k-core analysis relies on vertex degrees, k-cores do not satisfy a certain, rather natural, density property. Simply put, the most central k-core is not necessarily the densest subgraph. This inconsistency between k-cores and graph density provides the basis of our study. We start by defining what it means for a subgraph to be locally dense, and we show that our definition entails a nested chain decomposition of the graph, similar to the one given by k-cores, but in this case the components are arranged in order of increasing density. We show that such a locally dense decomposition for a graph Ge(V,E) can be computed in polynomial time. The running time of the exact decomposition algorithm is O(vVv2vEv) but is significantly faster in practice. In addition, we develop a linear-time algorithm that provides a factor-2 approximation to the optimal locally dense decomposition. Furthermore, we show that the k-core decomposition is also a factor-2 approximation, however, as demonstrated by our experimental evaluation, in practice k-cores have different structure than locally dense subgraphs, and as predicted by the theory, k-cores are not always well-aligned with graph density.

Group key establishment for secure multicasting in IoT-enabled Wireless Sensor Networks

Abstract: Wireless Sensor Network (WSN) is a fundamental technology of the Internet of Things (IoT). Group communications in the form of broadcasting and multicasting incur efficient message deliveries among resource-constrained sensors in IoT-enabled WSNs. Secure and efficient key management is significant to protect the authenticity, integrity, and confidentiality of multicast messages. This paper develops two group key establishment protocols for secure multicast communications among resource-constrained devices in IoT. The applicability of the two protocols are analyzed and justified by performance and security analysis.

Efficient Implementation of XML Security for Mobile Devices

Abstract: The population of mobile devices capable of participating in the Internet has increased dramatically in the last few years. To include this population into the Web service world requires support for the most important features, in particular security at the message level. This paper covers our approach to implement XML security specifications on mobile devices that allows efficient single-pass processing of XML encryption and signatures. Furthermore, we propose extensions to security specifications to better take into account the needs of mobile devices. We demonstrate the performance of our implementation, as well as our proposed extensions, through experiments, carried out in a real mobile environment.