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Institution

Helsinki Institute for Information Technology

FacilityEspoo, Finland
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.


Papers
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Journal ArticleDOI
TL;DR: The following improvements to the IOKR framework are brought forward: a model that can be understood as mapping molecular structures into the MS/MS feature space and solving a pre-image problem to find the molecule whose predicted spectrum is the closest to the input MS/ MS spectrum.
Abstract: In small molecule identification from tandem mass (MS/MS) spectra, input-output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum of the unknown molecule, and solving a pre-image problem to find the molecule with the most similar fingerprint. In this paper, we bring forward the following improvements to the IOKR framework: firstly, we formulate the IOKRreverse model that can be understood as mapping molecular structures into the MS/MS feature space and solving a pre-image problem to find the molecule whose predicted spectrum is the closest to the input MS/MS spectrum. Secondly, we introduce an approach to combine several IOKR and IOKRreverse models computed from different input and output kernels, called IOKRfusion. The method is based on minimizing structured Hinge loss of the combined model using a mini-batch stochastic subgradient optimization. Our experiments show a consistent improvement of top-k accuracy both in positive and negative ionization mode data.

13 citations

Proceedings ArticleDOI
03 Jul 2013
TL;DR: This work introduces a cooperative mode of operation conditioned on social relations between Cognitive Radios, and proposes a social-aware cooperative sensing scheme that exploiting social metrics assists cooperative sensing in CRNs and a model with social relations embedded will fit better to the next decade's networking paradigm.
Abstract: Previous works in cognitive radio networks (CRNs) have shown that cooperation in sensing improves sensing reliability and in turn enhances the network throughput. However, the cooperative behavior is accepted as the default mode of operation, which may not always hold. In this work, we loose this assumption and introduce a cooperative mode of operation conditioned on social relations between Cognitive Radios (CRs). Rather than taking CRs as wireless devices with no context, we associate each CR with its user that has some social relations, e.g. friendship, community, selfishness. Using these relations among CRs, we propose a social-aware cooperative sensing scheme and analyze its effects on sensing performance. We believe that exploiting social metrics assists cooperative sensing in CRNs and a model with social relations embedded will fit better to the next decade's networking paradigm.

13 citations

29 Jun 2018
TL;DR: This paper presents a method for automatically creating slogans, aimed to facilitate a human slogan designer in her creative process, and introduces a novel method for generating nominal metaphors based on a metaphor interpretation model.
Abstract: Slogans are an effective way to convey a marketing message. In this paper, we present a method for automatically creating slogans, aimed to facilitate a human slogan designer in her creative process. By taking a target concept (e.g. a computer) and an adjectival property (e.g. creative) as input, the proposed method produces a list of diverse expressions optimizing multiple objectives such as semantic relatedness, language correctness, and usage of rhetorical devices. A key component in the process is a novel method for generating nominal metaphors based on a metaphor interpretation model. Using the generated metaphors, the method builds semantic spaces related to the objectives. It extracts skeletons from existing slogans, and finally fills them in, traversing the semantic spaces, using the genetic algorithm to reach interesting solutions (e.g. “Talent, Skill and Support.”). We evaluate both the metaphor generation method and the overall slogan creation method by running two crowdsourced questionnaires.

13 citations

Journal ArticleDOI
TL;DR: The results of this evaluation indicate that PromotionRank is also capable of targeting advertisements, improving sales compared to a baseline that selects random advertisements.
Abstract: We present PromotionRank, a technique for generating a personalized ranking of grocery product promotions based on the contents of the customer’s personal shopping list. PromotionRank consists of four phases. First, information retrieval techniques are used to map shopping list items onto potentially relevant product categories. Second, since customers typically buy more items than what appear on their shopping lists, the set of potentially relevant categories is expanded using collaborative filtering. Third, we calculate a rank score for each category using a statistical interest criterion. Finally, the available promotions are ranked using the newly computed rank scores. To validate the different phases, we consider 12 months of anonymized shopping basket data from a large national supermarket. To demonstrate the effectiveness of PromotionRank, we also present results from two user studies. The first user study was conducted in a controlled setting using shopping lists of different lengths, whereas the second study was conducted within a large national supermarket using real customers and their personal shopping lists. The results of the two studies demonstrate that PromotionRank is able to identify promotions that are considered both relevant and interesting. As part of the second study, we used PromotionRank to identify relevant promotions to advertise and measure the influence of the advertisements on purchases. The results of this evaluation indicate that PromotionRank is also capable of targeting advertisements, improving sales compared to a baseline that selects random advertisements.

13 citations

Proceedings ArticleDOI
08 Jun 2015
TL;DR: An overview of existing secure VPLS architectures with a performance evaluation is provided and the performance penalty of security on throughput, latency and jitter in a real world testbed is evaluated.
Abstract: Virtual Private LAN Services (VPLS) is a widely utilized Layer 2 (L2) Virtual Private Network (VPN) architecture in industrial networks. In the last few years, VPLS networks gained an immense popularity as an ideal network architecture to interconnect industrial legacy SCADA (Supervisory Control and Data Acquisition) and process control devices over a shared network. However, legacy VPLS architectures are highly vulnerable to security threats which are initiated at the insecure shared network segment. Thus, secure VPLS architectures are becoming popular among industrial enterprises. In this article, we provide an overview of existing secure VPLS architectures with a performance evaluation. We evaluate the performance penalty of security on throughput, latency and jitter in a real world testbed. From these experiments, we seek to highlight the drawbacks of existing secure VPLS architectures after implementing them in a real networking environment. Moreover, we try to underscore future research questions that will help to improve the performance of secure VPLS networks.

13 citations


Authors

Showing all 632 results

NameH-indexPapersCitations
Dimitri P. Bertsekas9433285939
Olli Kallioniemi9035342021
Heikki Mannila7229526500
Jukka Corander6641117220
Jaakko Kangasjärvi6214617096
Aapo Hyvärinen6130144146
Samuel Kaski5852214180
Nadarajah Asokan5832711947
Aristides Gionis5829219300
Hannu Toivonen5619219316
Nicola Zamboni5312811397
Jorma Rissanen5215122720
Tero Aittokallio522718689
Juha Veijola5226119588
Juho Hamari5117616631
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20224
202185
202097
2019140
2018127