Aalto University

About: Aalto University is a education organization based out in Espoo, Finland. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 9969 authors who have published 32648 publications receiving 829626 citations. The organization is also known as: TKK & Aalto-korkeakoulu.

A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness.

Abstract: The uncanny valley hypothesis, proposed already in the 1970s, suggests that almost but not fully humanlike artificial characters will trigger a profound sense of unease. This hypothesis has become widely acknowledged both in the popular media and scientific research. Surprisingly, empirical evidence for the hypothesis has remained inconsistent. In the present article, we reinterpret the original uncanny valley hypothesis and review empirical evidence for different theoretically motivated uncanny valley hypotheses. The uncanny valley could be understood as the naive claim that any kind of human-likeness manipulation will lead to experienced negative affinity at close-to-realistic levels. More recent hypotheses have suggested that the uncanny valley would be caused by artificial–human categorization difficulty or by a perceptual mismatch between artificial and human features. Original formulation also suggested that movement would modulate the uncanny valley. The reviewed empirical literature failed to provide consistent support for the naive uncanny valley hypothesis or the modulatory effects of movement. Results on the categorization difficulty hypothesis were still too scarce to allow drawing firm conclusions. In contrast, good support was found for the perceptual mismatch hypothesis. Taken together, the present review findings suggest that the uncanny valley exists only under specific conditions. More research is still needed to pinpoint the exact conditions under which the uncanny valley phenomenon manifests itself.

Semi-Supervised Learning with Ladder Networks

Abstract: We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on the Ladder network proposed by Valpola (2015), which we extend by combining the model with supervision. We show that the resulting model reaches state-of-the-art performance in semi-supervised MNIST and CIFAR-10 classification, in addition to permutation-invariant MNIST classification with all labels.

Integrated internal communications: a multidisciplinary perspective

Abstract: Purpose – This article's purpose is to explore the multidisciplinary nature of internal communications, and argue that an integrated approach to internal communications is beneficial when assessing knowledge sharing in organisations.Design/methodology/approach – This article presents a review of relevant current literature, and explores the issue through qualitative interview data from a world‐leading multinational companies (MNC).Findings – The empirical data provide support for the integrated view, and also enhance our understanding of knowledge sharing as an important and strategic function of integrated internal communications.Originality/value – The integrated perspective here contributes to the understanding of internal communications in two ways. First, it draws from the domains of business, management, corporate, and organisational communication, which together incorporate both practical and theoretical knowledge. Second, it includes all formal and informal communication that take place inside an ...