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 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

Knowledge-based Transfer Learning Explanation

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.Comment: Accepted by International Conference on Principles of Knowledge Representation and Reasoning, 201

An Interpretable Knowledge Transfer Model for Knowledge Base Completion

Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness. We propose a novel embedding model, \emph{ITransF}, to perform knowledge base completion. Equipped with a sparse attention mechanism, ITransF discovers hidden concepts of relations and transfer statistical strength through the sharing of concepts. Moreover, the learned associations between relations and concepts, which are represented by sparse attention vectors, can be interpreted easily. We evaluate ITransF on two benchmark datasets---WN18 and FB15k for knowledge base completion and obtains improvements on both the mean rank and Hits@10 metrics, over all baselines that do not use additional information.Comment: Accepted by ACL 2017. Minor updat

Knowledge Transfer Needs and Methods

INE/AUTC 12.3

Knowledge transfer to organic fruit industry

Although consumer demand for organic fruit is strong, it is currently the least developed sector of the UK organic industry, represented by only a small number of commercial growers. One of the main constraints preventing growers becoming more involved in this sector has been the lack of technical information and guidance, resulting in very few growers having the knowhow and confidence to convert. The aim of this project was to produce technical guides for growers on two of the more important commercial fruit crops in the UK, one entitled ‘Organic Apple Production – pest and disease management’ and the other, ‘Organic Strawberry Production – a grower’s guide.’ These have now been published and the information contained within the two booklets should go a long way to meet the current lack of information on organic fruit growing for apple and strawberry crops. The guides will provide valuable information and advice for current and potential growers, researchers, advisors and colleges. The two guides are based on previous DEFRA funded studies, ‘Organic Fruit Production; a review of current practice and knowledge’ (OF0150) and ‘Economics of Organic Fruit Production in the UK’ (OF0151). During these studies, advisory material published by the Swiss Organic Agriculture Research Institute (FiBL) was identified as a useful source of information and extensively revised and updated with relevance to UK conditions. Much additional information for this project was obtained through discussion with growers, advisors and researchers. These included fruit researchers at HRI East Malling, ADAS Fruit Team, Farm Advisory Services Team (FAST Ltd), The Soil Association, The Organic Advisory Service at Elm Farm and The Organic Soft Fruit Working Group. Upon completion, the guides were extensively peer reviewed by organic growers, researchers and advisors both in the UK and abroad. The booklets have now been published as full colour, user-friendly guides (36 pages per guide) priced £8 each. The information contained in the booklets will also become accessible over the internet on the HDRA website. The booklets themselves will be publicised through relevant trade press and horticultural magazines together with relevant forthcoming horticultural / fruit shows. Through the production of these fruit booklets, HDRA has established good contacts and expertise within the organic fruit industry and is well placed to continue to provide growers with further information on organic fruit crops through future collaborative projects. Following successful collaboration with the Organic Soft Fruit Working Group on the strawberry technical guide, the opportunity exists to do similar information provision for other soft fruit crops and a proposal for a further booklet on organic cane and bush fruit production has now been accepted by DEFRA (OF0311)

Building knowledge-based economies: research projects in knowledge management and knowledge transfer

Small and medium-sized enterprises (SMEs) are viewed as the growth engines of the new knowledgebased economy. This new economic growth model differs from the old in significant ways, many of which are related to the knowledge base that will be required by the SMEs. Based upon prior research a set of factors important to the success of SMEs in a knowledge-based economy is described. Focusing on those factors related to the knowledge base, the paper concludes with a set of research questions and brief descriptions of three research projects on knowledge management and knowledge transfer