Automatic detection of change in address blocks for reply forms processing

In this paper, an automatic method to detect the presence of on-line erasures/scribbles/corrections/over-writing in the address block of various types of subscription and utility payment forms is presented. The proposed approach employs bottom-up segmentation of the address block. Heuristic rules based on structural features are used to automate the detection process. The algorithm is applied on a large dataset of 5,780 real world document forms of 200 dots per inch resolution. The proposed algorithm performs well with an average processing time of 108 milliseconds per document with a detection accuracy of 98.96%

Knowledge Transfer Needs and Methods

INE/AUTC 12.3

Approaches to knowledge‐transfer systems

The use of computers for knowledge transfer does not result automatically in success: the efficiency of the process depends on the approach to instruction underlying the knowledge‐transfer system in question. In this paper, the authors describe and compare computer technologies used for instruction in Computer‐Aided Instruction (CAI) courses, electronic books, and knowledge‐transfer systems based on heterogeneous information. They also deal with some psychological issues involved in dialogue, with the adaptability of knowledge‐transfer systems, and with psychologically‐based computerized instruction

Knowledge Transfer with Jacobian Matching

Classical distillation methods transfer representations from a "teacher" neural network to a "student" network by matching their output activations. Recent methods also match the Jacobians, or the gradient of output activations with the input. However, this involves making some ad hoc decisions, in particular, the choice of the loss function. In this paper, we first establish an equivalence between Jacobian matching and distillation with input noise, from which we derive appropriate loss functions for Jacobian matching. We then rely on this analysis to apply Jacobian matching to transfer learning by establishing equivalence of a recent transfer learning procedure to distillation. We then show experimentally on standard image datasets that Jacobian-based penalties improve distillation, robustness to noisy inputs, and transfer learning

Review of the Use of the Terms ‘Knowledge Transfer’ and ‘Knowledge Exchange’

Knowledge Transfer is a topic taught on MBA courses across the world and recognised as a professional discipline (e.g. the Institute of Knowledge Transfer), an academic discipline (e.g. the Journal of Knowledge Transfer), as an academic qualification (e.g. the Open University/AURIL Post Graduate Certificate in Knowledge Transfer) and as one of a select few key Government funded business support initiatives (e.g. Knowledge Transfer Partnerships – KTPs and Knowledge Transfer Networks - KTNs). Within the public sector there is currently a debate regarding the use of the term Knowledge Transfer as it is considered by some that Knowledge Exchange more correctly describes the multi-directional sharing of knowledge (both explicit knowledge in the form of rules, theories and models and tacit knowledge in the form of skills, experience and understanding). This research investigates the views of UK Universities in an attempt to gauge the feelings of KT Offices involved in the delivery of knowledge transfer based activities

'Getting out of the closet': Scientific authorship of literary fiction and knowledge transfer

Some scientists write literary fiction books in their spare time. If these books contain scientific knowledge, literary fiction becomes a mechanism of knowledge transfer. In this case, we could conceptualize literary fiction as non-formal knowledge transfer. We model knowledge transfer via literary fiction as a function of the type of scientist (academic or non-academic) and his/her scientific field. Academic scientists are those employed in academia and public research organizations whereas non-academic scientists are those with a scientific background employed in other sectors. We also distinguish between direct knowledge transfer (the book includes the scientist's research topics), indirect knowledge transfer (scientific authors talk about their research with cultural agents) and reverse knowledge transfer (cultural agents give scientists ideas for future research). Through mixed-methods research and a sample from Spain, we find that scientific authorship accounts for a considerable percentage of all literary fiction authorship. Academic scientists do not transfer knowledge directly so often as non-academic scientists, but the former engage into indirect and reverse transfer knowledge more often than the latter. Scientists from History stand out in direct knowledge transfer. We draw propositions about the role of the academic logic and scientific field on knowledge transfer via literary fiction. We advance some tentative conclusions regarding the consideration of scientific authorship of literary fiction as a valuable knowledge transfer mechanism.Comment: Paper published in Journal of Technology Transfe