Using auto ethnography as a learning tool within the social work class-room: the experience of delivering an ‘immersive’ module

This paper explores the first delivery of an introductory module, ‘What is Social Work’ to a Year 1 cohort of students on a B.A Social Work programme. Unusually, this module is delivered in an immersive format. Delivery of teaching via the vehicle of an ‘immersive module’ lies under an umbrella term for shortened, intensive courses. The immersive module is constructed with an aim of achieving double/triple loop learning via auto ethnographic practice. Specifically, with relation to Social Work education, auto ethnography is utilized within this accelerated teaching space to assist students to assimilate a rigorous form of critical reflection. Auto ethnography also provides the educator with a form of data collection and method of analysis. My findings reveal how this method of teaching provides an opportunity to model practice that is contextualised and relationship-based. This is in contrast to a current U.K practice background of largely statutory based de-politicized, individualistic Social Work

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Accented Body and Beyond: a Model for Practice-Led Research with Multiple Theory/Practice Outcomes

Dance has always been a collaborative or interdisciplinary practice normally associated with music or sound and visual arts/design. Recent developments with technology have introduced additional layers of interdisciplinary work to include live and virtual forms in the expansion of what Fraleigh (1999:11) terms ‘the dancer oriented in time/space, somatically alive to the experience of moving’. This already multi-sensory experience and knowledge of the dancer is now layered with other kinds of space/time and kinetic awarenesses, both present and distant, through telematic presence, generative systems and/or sensors. In this world of altered perceptions and ways of being, the field of dance research is further opened up to alternative processes of inquiry, both theoretically and in practice, and importantly in the spaces between the two

Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

Topic Similarity Networks: Visual Analytics for Large Document Sets

We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to "tease out" non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData 2014

Advanced photonic and electronic systems - WILGA 2017

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers more than 350 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET by PAN and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2017 was the XL edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2017 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE