• Digital Humanities and Cultural Heritage
• TW close
• NARCIS close
• Digital Humanities and Cultural Her... close

No Abstract.

The Museum of White Terror is a former political prison turned National Human Rights Museum in Taipei. We visit here as a group of international law students viewing the museum as a transnational legal actor of sorts. The literary genre this chapter engages with is that of the museum, specifically the museum guided tour. We view the guided tour as a moment of encounter in which the museum presents its carefully designed story. We contend that where the international legal status of Taiwan remains uncertain, the museum takes ownership of Taiwan’s past in pursuit of an alternative diplomacy. We thus view the story of this museum as a plea for Taiwanese sovereignty and ultimately independence.

AbstractObsessive and Compulsive Symptoms (OCS) or Obsessive Compulsive Disorder (OCD) in the context of schizophrenia or related disorders are of clinical importance as these are associated with a range of adverse outcomes. Natural Language Processing (NLP) applied to Electronic Health Records (EHRs) presents an opportunity to create large datasets to facilitate research in this area. This is a challenging endeavour however, because of the wide range of ways in which these symptoms are recorded, and the overlap of terms used to describe OCS with those used to describe other conditions. We developed an NLP algorithm to extract OCS information from a large mental healthcare EHR data resource at the South London and Maudsley NHS Foundation Trust using its Clinical Record Interactive Search (CRIS) facility. We extracted documents from individuals who had received a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder. These text documents, annotated by human coders, were used for developing and refining the NLP algorithm (600 documents) with an additional set reserved for final validation (300 documents). The developed NLP algorithm utilized a rules-based approach to identify each of symptoms associated with OCS, and then combined them to determine the overall number of instances of OCS. After its implementation, the algorithm was shown to identify OCS with a precision and recall (with 95% confidence intervals) of 0.77 (0.65–0.86) and 0.67 (0.55–0.77) respectively. The development of this application demonstrated the potential to extract complex symptomatic data from mental healthcare EHRs using NLP to facilitate further analyses of these clinical symptoms and their relevance for prognosis and intervention response.

Music as a form of art is intentionally composed to be emotionally expressive. The emotional features of music are invaluable for music indexing and recommendation. In this paper we present a cross-comparison of automatic emotional analysis of music. We created a public dataset of Creative Commons licensed songs. Using valence and arousal model, the songs were annotated both in terms of the emotions that were expressed by the whole excerpt and dynamically with 1 Hz temporal resolution. Each song received 10 annotations on Amazon Mechanical Turk and the annotations were averaged to form a ground truth. Four different systems from three teams and the organizers were employed to tackle this problem in an open challenge. We compare their performances and discuss the best practices. While the effect of a larger feature set was not very apparent in the static emotion estimation, the combination of a comprehensive feature set and a recurrent neural network that models temporal dependencies has largely outperformed the other proposed methods for dynamic music emotion estimation.

This chapter introduces the computational infrastructure that is used to bridge the gap between results from sensorimotor processing and language. It consists of a system called Incremental Recruitment Language (IRL) that is able to configure a network of cognitive operations to achieve a particular communicative goal. IRL contains mechanisms for finding such networks, chunking subnetworks for more efficient later reuse, and completing partial networks (as possibly derived from incomplete or only partially understood sentences). The research reported here was carried out at the Sony Computer Science Laboratory in Paris and partially funded by the FP7 EU Project ALEAR. Peer Reviewed

Grounding language in sensorimotor spaces is an important and difficult task. In order, for robots to be able to interpret and produce utterances about the real world, they have to link symbolic information to continuous perceptual spaces. This requires dealing with inherent vagueness, noise and differences in perspective in the perception of the real world. This paper presents two case studies for spatial language and quantification that show how cognitive operations – the building blocks of grounded procedural semantics – can be efficiently grounded in sensorimotor spaces.

The electrocardiogram (ECG) signal is adopted extensively as a low-cost diagnostic procedure to provide information concerning the healthy status of the heart. Heart rate variability (HRV) is a physiological phenomenon where the time interval between heart beats varies. It is measured by the variation in the beat-to-beat interval. Based on the HRV analysis, the medical experts can make a health assessment for our body, especially for the heart. This chapter presents a fuzzy markup language (FML)-based HRV ontology to apply to ECG domain knowledge. The ontology technologies are used to construct the personalized HRV ontology, and the FML is used to describe the knowledge base and rule base of the HRV. The ECG signals were collected from the involved students before they took exams and after they finished the exams. Based on the constructed FML-based HRV ontology, the variation in the nervous level between before exams and after exams could be inferred when implementing the proposed approach. An experimental platform has been constructed to test the performance of the ontology. The results indicate that the proposed method can work feasibly. © 2012 Springer-Verlag London Limited.