In the pursuit of a dramaturgy of telematics as a compositional practice, the author builds a position on technology as externalized technique, develops a dramaturgic strategy with notions of an extended theatre practice and reports on a realized telematized performance where a practical implementation was explored. The theatrical site is viewed as a construct of attention, generated by the performers through the performance composition. The composition determines the audience experience, orchestrated by how the construction of the site manages their perspectives, (...) and how the performers are situated in the consciousness site of the audience. In the performed event the performers articulate as a collective extended entity, incorporating the transformation of the performance site via the articulation strategies implemented in the staging. Telematics is argued to be a technology facilitating specific modes of thinking, generating specific modes of actions and presence, which enable new performance sites and new modes of situating performance. (shrink)

It is suggested how one can imaginatively use a design strategy utilizing performers as tools in the creation of augmented, performative artwork. In order to establish meaningful constructions of performative and augmentative technology, one can use the formal methodologies of advanced formal body language as a tool in the creation process, and thereby have ‘the actual experience’ present in the process as a monitor and a constructive tool. To use a human being as a design tool is a method that (...) uses the skilled performer as a ‘super-’ or ‘extra-’ human tool to investigate through action and analysis. It is a way to test how realities occur under fluid and dynamic conditions within a controlled environment of time and space. This ‘technical’ use of the body as an extra-human behavioural entity, involves the total complex spectrum of intellect, emotions, desires, memory, actions, etc. as a formalized ‘machine’. (shrink)

With the increasing prevalence of Internet usage, Internet-Delivered Psychological Treatment (IDPT) has become a valuable tool to develop improved treatments of mental disorders. IDPT becomes complicated and labor intensive because of overlapping emotion in mental health. To create a usable learning application for IDPT requires diverse labeled datasets containing an adequate set of linguistic properties to extract word representations and segmentations of emotions. In medical applications, it is challenging to successfully refine such datasets since emotion-aware labeling is time consuming. Other (...) known issues include vocabulary sizes per class, data source, method of creation, and baseline for the human performance level. This paper focuses on the application of personalized mental health interventions using Natural Language Processing (NLP) and attention-based in-depth entropy active learning. The objective of this research is to increase the trainable instances using a semantic clustering mechanism. For this purpose, we propose a method based on synonym expansion by semantic vectors. Semantic vectors based on semantic information derived from the context in which it appears are clustered. The resulting similarity metrics help to select the subset of unlabeled text by using semantic information. The proposed method separates unlabeled text and includes it in the next active learning mechanism cycle. Our method updates model training by using the new training points. The cycle continues until it reaches an optimal solution, and it converts all the unlabeled text into the training set. Our in-depth experimental results show that the synonym expansion semantic vectors help enhance training accuracy while not harming the results. The bidirectional Long Short-Term Memory (LSTM) architecture with an attention mechanism achieved 0.85 Receiver Operating Characteristic (ROC curve) on the blind test set. The learned embedding is then used to visualize the activated word's contribution to each symptom and find the psychiatrist's qualitative agreement. Our method improves the detection rate of depression symptoms from online forum text using the unlabeled forum texts. (shrink)