A knowledge server including tools for professional know-how transfer

This paper presents a research in progress on the use of knowledge engineering and knowledge management techniques for the development of a strategic approach for the transfer of professional know-how. This transfer is based on the design of devices for sharing and learning clearly identified knowledge in the oil industry domains. This work is based on a pilot study which was carried out in the PED department (Petroleum Engineering & Development) and it deals with upstream activity of the oil group Sonatrach. After the different phases of knowledge mapping, critical knowledge assessment, and strategic alignment, the KM process focus on knowledge elicitation, sharing, transfer and learning, based on design and implementation of specific tools called Knowledge Server, including Knowledge Books and e-Learning.E-learning, Knowledge management, Knowledge transfer, Knowledge engineering, Knowledge servers, Computer assisted human learning, Case study

Questioning Knowledge Transfer And Learning Processes Across R&D Project Teams

This paper addresses popular notions of the generation and sharing of knowledge in organisations commonly described as knowledge transfer. We question the appropriateness of the notion of transfer of knowledge for increasing our understanding of knowledge creation and learning processes in R&D organisations. We suggest that this notion of "transfer", limits our understanding of the important interactive processes used to generate knowledge and to enhance the spread of knowledge. Findings from interviews with senior research scientists challenge the notion of knowledge transfer and instead provide support for the notion of knowledge as constructed meaning in an arena with multiple players and social interactions

Recurrent Neural Network Training with Dark Knowledge Transfer

Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision. This knowledge transfer learning has been employed to train simple neural nets with a complex one, so that the final performance can reach a level that is infeasible to obtain by regular training. In this paper, we employ the knowledge transfer learning approach to train RNNs (precisely LSTM) using a deep neural network (DNN) model as the teacher. This is different from most of the existing research on knowledge transfer learning, since the teacher (DNN) is assumed to be weaker than the child (RNN); however, our experiments on an ASR task showed that it works fairly well: without applying any tricks on the learning scheme, this approach can train RNNs successfully even with limited training data.Comment: ICASSP 201

Intergenerational knowledge transfer in the academic environment of knowledge-based economy

In the immediate future, intergenerational knowledge transfer is one of the knowledge-based economy’s main challenges since an inner motivational force powers knowledge transfer. Knowledge transfer from individuals to groups and organization must follow knowledge creation in order to transform individual into organizational knowledge, along the epistemological dimension of the Nonaka’s knowledge dynamics model. Moreover, the knowledge intensive organizations increase their fluxes of knowledge across different age layers and different departments, reducing in the same time the company knowledge loss. The academic environment is, by nature, an age layered field or a nested functional structure. Intergenerational knowledge transfer becomes any university main driving force, while understanding its dynamics is important for academic life improvement. The purpose of the paper is to present some of our research results in the field of intergenerational knowledge transfer in the academic environment of the knowledge-based economy. We performed a qualitative and quantitative research of the knowledge transfer process in the academic environment, using the Analytic Hierarchy Processes (AHP). We analyzed the faculty staff attitudes toward cooperation, competition, and innovation as main priorities in performing research, writing books and publishing scientific papers. The above-mentioned activities are based on intergenerational knowledge transfer and lead to learning processes at individual and organizational levels. Respondents are members of the academic staff of economics and business faculties from the main Romanian universities.knowledge, knowledge-based economy, knowledge transfer, university

Extending, broadening and rethinking existing research on transfer of training

Research on transfer of training has a long history, with thousands of empirical studies since the 1950s investigating whether, and under which conditions, knowledge and skills acquired during training are subsequently used in the work environment (see reviews by Baldwin and Ford, 1988, Blume et al., 2010 and Burke and Hutchins, 2007). The generation of such an abundance of research can be linked to organisations’ fundamental and ongoing concern to ensure that their employees possess the necessary knowledge and skills from their employer to maintain a competitive advantage and thrive economically. Training and development is, however, extremely costly to organisations, which has created the need to determine the effectiveness of training, and the conditions under which transfer of training is optimal. A recent overview of “what really matters” for successful transfer of training (Grossman & Salas, 2011), aimed at a training and development readership, summarized the most influential variables emerging from this vast body of research. Based on the expectation that the list of factors which may contribute to influence transfer could always be extended and that it would be impractical to incorporate every single factor in research designs, the authors recommended a shift in future research towards deeper investigations of the conditions under which selected variables are more or less influential in their relationship with training. This Special Issue contributes to this important research agenda and extends it further through the inclusion of a diverse collection of conceptual contributions and reviews, from several scientific disciplines, a plurality of theoretical perspectives and a range of methodological approaches. Expanding the theoretical grounding underpinning empirical work on transfer of training and scrutinizing existing conceptualizations of the notion of transfer is timely in light of widespread concerns from organisations about minimal return on investment in training, and repeated evidence in the transfer of training literature of an enduring “transfer problem”. The aim of this article is to explore the value of extending, broadening and rethinking existing research on transfer of training. The benefits of extending research on transfer of training is considered first, through examining how the contributions of this Special Issue add to the existing literature on transfer of training, and the implications of the new insights for addressing the “transfer problem”. How transfer of training research could be broadened, thus enriched, through incorporating ideas from recent literature on transfer of learning is considered next. Finally, proposals to rethink transfer as boundary crossing from an activity theory perspective are scrutinized for their potential to better understand the learning that takes place at the boundaries of training and work environments. The article concludes by elaborating on the conceptual value of a refocus on ‘transfer of learning from training’ within a perspective of adaptive learning, and a call for cross-fertilisation with the extensive theory grounded literatures on transfer of learning and boundary crossing

GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance trade-offs, and fast technological advancements. Although there has been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black-box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search, and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6 pages, 8 figure

Knowledge Transfer in Strategic Networks: A Systematic Review and Theoretical Propositions

The research is financed by Soft Science Research Project in Shanxi Province of China, No. 2018041041-2 Abstract Knowledge has been recognized the one of the most important resources to obtain competitive advantages for firms. The inter-firm knowledge transfer is considered as the effective way to obtain knowledge. This study firstly provides a comprehensive review of relevant literatures in terms of knowledge transfer and strategic networks. Moreover, this study develops the theoretical propositions regarding the relationship between learning motivation, learning capability and knowledge transfer in strategic networks. Keywords: Knowledge transfer, strategic network, learning motivation, learning capability DOI: 10.7176/EJBM/11-21-15 Publication date:July 31st 201

Knowledge management techniques for know-how transfer systems design : the case of an oil company

This paper presents a research in progress on the use of knowledge engineering and knowledge management techniques for the development of a strategic approach for the transfer of professional know-how. This transfer is based on the design of devices for sharing and learning clearly identified knowledge in the oil industry domains. This work is based on a pilot study which was carried out in the PED department (Petroleum Engineering & Development) and it deals with upstream activity of the oil group Sonatrach. A mapping of this know-how was established using a method based on mapping strategy. The main steps of the current approach are the strategic analysis for required competences, the processes analysis for professional know-how and a cross analysis for strategic competences and critical know-how. Emphasis is put on the strategic and critical aspects in the areas of knowledge where efforts are to be made in terms of capitalising, sharing, learning and transfer.Learning, E-learning, Knowledge management, Knowledge map, Strategy map, Knowledge engineering, Knowledge servers, Computer assisted human learning