Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Audio-based context recognition

Abstract: The aim of this paper is to investigate the feasibility of an audio-based context recognition system. Here, context recognition refers to the automatic classification of the context or an environment around a device. A system is developed and compared to the accuracy of human listeners in the same task. Particular emphasis is placed on the computational complexity of the methods, since the application is of particular interest in resource-constrained portable devices. Simplistic low-dimensional feature vectors are evaluated against more standard spectral features. Using discriminative training, competitive recognition accuracies are achieved with very low-order hidden Markov models (1-3 Gaussian components). Slight improvement in recognition accuracy is observed when linear data-driven feature transformations are applied to mel-cepstral features. The recognition rate of the system as a function of the test sequence length appears to converge only after about 30 to 60 s. Some degree of accuracy can be achieved even with less than 1-s test sequence lengths. The average reaction time of the human listeners was 14 s, i.e., somewhat smaller, but of the same order as that of the system. The average recognition accuracy of the system was 58% against 69%, obtained in the listening tests in recognizing between 24 everyday contexts. The accuracies in recognizing six high-level classes were 82% for the system and 88% for the subjects.

Emergence of innovations in services

Abstract: Along with the ‘servicisation’ of society, innovation in services has become a topical issue. However, analytical and detailed discussion about the nature of service innovations and their emergence is only beginning. This article aims to contribute to this discussion through a theoretical analysis supplemented with findings from two empirical case studies. The theories examined are multi-disciplinary including general service theories, general innovation theories and theories linked to new service development and innovation management. The empirical studies have been carried out in Finland in the fields of real estate and construction services and of knowledge-intensive business services.

Dimensionality reduction by random mapping: fast similarity computation for clustering

Abstract: When the data vectors are high-dimensional it is computationally infeasible to use data analysis or pattern recognition algorithms which repeatedly compute similarities or distances in the original data space It is therefore necessary to reduce the dimensionality before, for example, clustering the data If the dimensionality is very high, like in the WEBSOM method which organizes textual document collections on a self-organizing map, then even the commonly used dimensionality reduction methods like the principal component analysis may be too costly It is demonstrated that the document classification accuracy obtained after the dimensionality has been reduced using a random mapping method will be almost as good as the original accuracy if the final dimensionality is sufficiently large (about 100 out of 6000) In fact, it can be shown that the inner product (similarity) between the mapped vectors follows closely the inner product of the original vectors

Analysis of a simple self-organizing process

Abstract: This paper contains mathematical results relating to a recently discovered self-organizing process. In particular, an analysis of two partial processes is presented. In the first one, a set of numerical representations assumes the correct order, and in the second one the final map of the representations converges to its asymptotic form.

The Compassionate Brain: Humans Detect Intensity of Pain from Another's Face

Abstract: Understanding another person's experience draws on "mirroring systems," brain circuitries shared by the subject's own actions/feelings and by similar states observed in others. Lately, also the experience of pain has been shown to activate partly the same brain areas in the subjects' own and in the observer's brain. Recent studies show remarkable overlap between brain areas activated when a subject undergoes painful sensory stimulation and when he/she observes others suffering from pain. Using functional magnetic resonance imaging, we show that not only the presence of pain but also the intensity of the observed pain is encoded in the observer's brain-as occurs during the observer's own pain experience. When subjects observed pain from the faces of chronic pain patients, activations in bilateral anterior insula (AI), left anterior cingulate cortex, and left inferior parietal lobe in the observer's brain correlated with their estimates of the intensity of observed pain. Furthermore, the strengths of activation in the left AI and left inferior frontal gyrus during observation of intensified pain correlated with subjects' self-rated empathy. These findings imply that the intersubjective representation of pain in the human brain is more detailed than has been previously thought.