Ariadne's Thread - Interactive Navigation in a World of Networked Information

This work-in-progress paper introduces an interface for the interactive visual exploration of the context of queries using the ArticleFirst database, a product of OCLC. We describe a workflow which allows the user to browse live entities associated with 65 million articles. In the on-line interface, each query leads to a specific network representation of the most prevailing entities: topics (words), authors, journals and Dewey decimal classes linked to the set of terms in the query. This network represents the context of a query. Each of the network nodes is clickable: by clicking through, a user traverses a large space of articles along dimensions of authors, journals, Dewey classes and words simultaneously. We present different use cases of such an interface. This paper provides a link between the quest for maps of science and on-going debates in HCI about the use of interactive information visualisation to empower users in their search.Comment: CHI'15 Extended Abstracts, April 18-23, 2015, Seoul, Republic of Korea. ACM 978-1-4503-3146-3/15/0

Learning spectro-temporal features with 3D CNNs for speech emotion recognition

In this paper, we propose to use deep 3-dimensional convolutional networks (3D CNNs) in order to address the challenge of modelling spectro-temporal dynamics for speech emotion recognition (SER). Compared to a hybrid of Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our proposed 3D CNNs simultaneously extract short-term and long-term spectral features with a moderate number of parameters. We evaluated our proposed and other state-of-the-art methods in a speaker-independent manner using aggregated corpora that give a large and diverse set of speakers. We found that 1) shallow temporal and moderately deep spectral kernels of a homogeneous architecture are optimal for the task; and 2) our 3D CNNs are more effective for spectro-temporal feature learning compared to other methods. Finally, we visualised the feature space obtained with our proposed method using t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct clusters of emotions.Comment: ACII, 2017, San Antoni

Towards Speech Emotion Recognition "in the wild" using Aggregated Corpora and Deep Multi-Task Learning

One of the challenges in Speech Emotion Recognition (SER) "in the wild" is the large mismatch between training and test data (e.g. speakers and tasks). In order to improve the generalisation capabilities of the emotion models, we propose to use Multi-Task Learning (MTL) and use gender and naturalness as auxiliary tasks in deep neural networks. This method was evaluated in within-corpus and various cross-corpus classification experiments that simulate conditions "in the wild". In comparison to Single-Task Learning (STL) based state of the art methods, we found that our MTL method proposed improved performance significantly. Particularly, models using both gender and naturalness achieved more gains than those using either gender or naturalness separately. This benefit was also found in the high-level representations of the feature space, obtained from our method proposed, where discriminative emotional clusters could be observed.Comment: Published in the proceedings of INTERSPEECH, Stockholm, September, 201

Learning spectral-temporal features with 3D CNNs for speech emotion recognition

In this paper, we propose to use deep 3-dimensional convolutional networks (3D CNNs) in order to address the challenge of modelling spectro-temporal dynamics for speech emotion recognition (SER). Compared to a hybrid of Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our proposed 3D CNNs simultaneously extract short-term and long-term spectral features with a moderate number of parameters. We evaluated our proposed and other state-of-the-art methods in a speaker-independent manner using aggregated corpora that give a large and diverse set of speakers. We found that 1) shallow temporal and moderately deep spectral kernels of a homogeneous architecture are optimal for the task; and 2) our 3D CNNs are more effective for spectro-temporal feature learning compared to other methods. Finally, we visualised the feature space obtained with our proposed method using t-distributed stochastic neighbour embedding (T-SNE) and could observe distinct clusters of emotions

Effectiveness of different farrowing induction protocols in sows using alphaprostol on day 114 of gestation

Farrowing induction in sows is frequently applied and different protocols were described. Most studies induce farrowing between day 111 and 113 of gestation, but hardly any later during gestation. The aim of this study was to investigate the effects of different induction protocols when applied on d114 of gestation. We randomly assigned 118 sows to four treatment groups: single injection of prostaglandin (1 Chi PG), prostaglandin combined with oxytocin 24 hours later (PG+OT), half the dosage of prostaglandin twice with six hours interval (2 Chi 1/2PG), and a control group of which farrowing wasn't induced. All injections were administered intramuscularly in the neck on day 114 of gestation. A significantly higher percentage of sows started farrowing between 22 and 32 hours after induction in the PG+OT-group (68 per cent) and the 2 Chi 1/2PG-group (52 per cent) compared to the control group (23 per cent). The 1 Chi PG-group (46 per cent) tended to differ from the control group (P=0.087). More sows from the PG+OT-group were constipated at farrowing (P=0.042). Farrowing duration, birth interval, per cent stillborn piglets, dystocia and other variables were similar between the groups. In the present herd and with continuous farrowing supervision, applying PG+OT or 2 Chi 1/2PG on d114 of gestation was efficient to induce onset of farrowing in most of the sows 22-32 hours after induction without considerable negative effects