Knowledge Transfer Strategies Research Articles

Knowledge transfer for R&D‐sales cross‐functional cooperation: Unpacking the intersections between institutional expectations and human resource practices

AbstractThis article addresses the challenges of R&D–sales cross‐functional cooperation by exploring how HR practices encourage knowledge transfer, minimising peer‐to‐peer friction and maximising the effectiveness of the exchanges for performance innovation. We combined document analysis of professional HR management magazines and semi‐structured interviews at a Finnish digital media firm, in order to identify manifestations of institutional work expressing and legitimising knowledge transfer‐focused HR management practices, and how employees make sense of them. Our findings identify manifestations of institutional work expressing, sustaining and legitimising knowledge transfer and relate these manifestations to distinct dimensions of human resource management practices. We integrate prevailing institutional expectations and employees' attributions of knowledge transfer‐focused human resource practices, which highlights the connections between human resource management and the knowledge management field, as well as the dichotomy between the more humanist knowledge transfer strategies and those that are information technology‐oriented. Our original integration of institutional expectations and a case analysis of employees' attributions of knowledge transfer‐focused human resource practices contribute directly to management practice dealing with the effects of organising strategically for knowledge‐based innovation outcomes. This has implications in terms of the development of interpersonal skills, the engagement and development of cross‐team work; the design of participative mechanisms; and the implementation of knowledge transfer principles, practices and systems.

Multi-task convolutional neural network with coarse-to-fine knowledge transfer for long-tailed classification

Long-tailed classifications make it very challenging to deal with class-imbalanced problems using deep convolutional neural networks (CNNs). Existing solutions based on re-balancing methods perform well and use single-task CNNs to train each fine-grained class independently. However, classification tasks are multiplex and involve a coarse-to-fine hierarchical relation. In this paper, we propose a coarse-to-fine knowledge transfer based multi-task CNN, which utilizes the coarse-to-fine structure to promote long-tailed learning. First, we construct a tail hierarchical structure in a coarse-to-fine way to pay greater attention to tail classes than head classes. Second, a multi-task CNN is adopted to simultaneously train coarse- and fine-grained tasks to extract a more generalized knowledge representation than the single-task CNN. Third, we design a coarse-to-fine knowledge transfer strategy to adaptively adjust the task weights to improve fine-grained performance. Extensive experiments on benchmark datasets show that our model achieves better gains than the re-balancing methods. In particular, the proposed model is 3.25% more accurate than the second-best method on the long-tailed tieredImageNet dataset.

A Multifactorial Optimization Framework Based on Adaptive Intertask Coordinate System.

The searching ability of the population-based search algorithms strongly relies on the coordinate system on which they are implemented. However, the widely used coordinate systems in the existing multifactorial optimization (MFO) algorithms are still fixed and might not be suitable for various function landscapes with differential modalities, rotations, and dimensions; thus, the intertask knowledge transfer might not be efficient. Therefore, this article proposes a novel intertask knowledge transfer strategy for MFOs implemented upon an active coordinate system that is established on a common subspace of two search spaces. The proper coordinate system might identify some common modality in a proper subspace to some extent. In this article, to seek the intermediate subspace, we innovatively introduce the geodesic flow that starts from a subspace, reaching another subspace in unit time. A low-dimension intermediate subspace is drawn from a uniform distribution defined on the geodesic flow, and the corresponding coordinate system is given. The intertask trial generation method is applied to the individuals by first projecting them on the low-dimension subspace, which reveals the important invariant features of the multiple function landscapes. Since intermediate subspace is generated from the major eigenvectors of tasks' spaces, this model turns out to be intrinsically regularized by neglecting the minor and small eigenvalues. Therefore, the transfer strategy can alleviate the influence of noise led by redundant dimensions. The proposed method exhibits promising performance in the experiments.

RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins.

RNA binding proteins (RBPs) are critical for the post-transcriptional control of RNAs and play vital roles in a myriad of biological processes, such as RNA localization and gene regulation. Therefore, computational methods that are capable of accurately identifying RBPs are highly desirable and have important implications for biomedical and biotechnological applications. Here, we propose a two-stage deep transfer learning-based framework, termed RBP-TSTL, for accurate prediction of RBPs. In the first stage, the knowledge from the self-supervised pre-trained model was extracted as feature embeddings and used to represent the protein sequences, while in the second stage, a customized deep learning model was initialized based on an annotated pre-training RBPs dataset before being fine-tuned on each corresponding target species dataset. This two-stage transfer learning framework can enable the RBP-TSTL model to be effectively trained to learn and improve the prediction performance. Extensive performance benchmarking of the RBP-TSTL models trained using the features generated by the self-supervised pre-trained model and other models trained using hand-crafting encoding features demonstrated the effectiveness of the proposed two-stage knowledge transfer strategy based on the self-supervised pre-trained models. Using the best-performing RBP-TSTL models, we further conducted genome-scale RBP predictions for Homo sapiens, Arabidopsis thaliana, Escherichia coli, and Salmonella and established a computational compendium containing all the predicted putative RBPs candidates. We anticipate that the proposed RBP-TSTL approach will be explored as a useful tool for the characterization of RNA-binding proteins and exploration of their sequence–structure–function relationships.

TwinNet: Twin Structured Knowledge Transfer Network for Weakly Supervised Action Localization

Action recognition and localization in untrimmed videos is important for many applications and have attracted a lot of attention. Since full supervision with frame-level annotation places an overwhelming burden on manual labeling effort, learning with weak video-level supervision becomes a potential solution. In this paper, we propose a novel weakly supervised framework to recognize actions and locate the corresponding frames in untrimmed videos simultaneously. Considering that there are abundant trimmed videos publicly available and well-segmented with semantic descriptions, the instructive knowledge learned on trimmed videos can be fully leveraged to analyze untrimmed videos. We present an effective knowledge transfer strategy based on inter-class semantic relevance. We also take advantage of the self-attention mechanism to obtain a compact video representation, such that the influence of background frames can be effectively eliminated. A learning architecture is designed with twin networks for trimmed and untrimmed videos, to facilitate transferable self-attentive representation learning. Extensive experiments are conducted on three untrimmed benchmark datasets (i.e., THUMOS14, ActivityNet1.3, and MEXaction2), and the experimental results clearly corroborate the efficacy of our method. It is especially encouraging to see that the proposed weakly supervised method even achieves comparable results to some fully supervised methods.

Opportunity recognition and knowledge transfer in the champagne industry: A conceptual analysis

AbstractThe distinctive advantage in an entrepreneurial venture derives from the adoption of embedded knowledge to create and renew cultural capital. Champagne houses pursue a knowledge transfer strategy to achieve a competitive advantage related to the cultural and managerial orientation of the defined champagne house cluster. The distinctive advantage in an entrepreneurial setup derives from adopting embedded knowledge within the organization to create and renew cultural capital.

TransLearn: A clustering based knowledge transfer strategy for improved time series forecasting

The widespread usage of time series prediction has led to the formulation of several hybrid and statistical algorithms. These algorithms assume the availability of significant quantities of labeled training data and utilize historical data observations to estimate future patterns. However, the assumption may be invalid in developing countries like India, where data availability is minimal due to several reasons such as fewer sensor deployments, newly added sensor locations and many more. Hence, the data scarcity issue poses a significant challenge to the target model’s prediction accuracy and generalization capabilities. To address this issue, a transfer learning model integrating the clustering analysis with deep neural architectures (TransLearn) is proposed in the current research study. The clustering algorithm with Soft-Dynamic Time-warping measure creates time-series groups following similar characteristics, trends and seasonality patterns, thus, creating a larger dataset for deep neural models training. The performance estimation of the proposed TransLearn strategy is conducted on the novel energy consumption dataset of several northern states of India. It involves employing clustering to group states with similar load variation patterns. Subsequently, four transfer learning-based deep neural models are trained on the clustered datasets. Lastly, the fine-tuned results of these TransLearn models on each individual state are evaluated against the conventional deep neural model building strategy. The comparative evaluation in terms of widely used performance measures shows that the TransLearn approach outperforms the conventional neural models by generating the least prediction error.

Evolutionary Many-Task Optimization Based on Multisource Knowledge Transfer

Multitask optimization aims to solve two or more optimization tasks simultaneously by leveraging intertask knowledge transfer. However, as the number of tasks increases to the extent of many-task optimization, the knowledge transfer between tasks encounters more uncertainty and challenges, thereby resulting in degradation of optimization performance. To give full play to the many-task optimization framework and minimize the potential negative transfer, this article proposes an evolutionary many-task optimization algorithm based on a multisource knowledge transfer mechanism, namely, EMaTO-MKT. Particularly, in each iteration, EMaTO-MKT determines the probability of using knowledge transfer adaptively according to the evolution experience, and balances the self-evolution within each task and the knowledge transfer among tasks. To perform knowledge transfer, EMaTO-MKT selects multiple highly similar tasks in terms of maximum mean discrepancy as the learning sources for each task. Afterward, a knowledge transfer strategy based on local distribution estimation is applied to enable the learning from multiple sources. Compared with the other state-of-the-art evolutionary many-task algorithms on benchmark test suites, EMaTO-MKT shows competitiveness in solving many-task optimization problems.

Evolutionary Multitask Optimization With Adaptive Knowledge Transfer

Evolutionary multitask optimization (EMTO) studies how to simultaneously solve multiple optimization tasks via evolutionary algorithms (EAs) while making the useful knowledge acquired from solving one task to assist solving other tasks, aiming to improve the overall performance of solving each individual task. Recent years have seen a large body of EMTO works based on different kinds of EAs and studying one or more aspects in how to represent, extract, transfer, and reuse knowledge. A key challenge to EMTO is the occurrence of negative knowledge transfer between tasks, which becomes severer when the total number of tasks increases. To address this issue, we propose an adaptive EMTO (AEMTO) framework. This framework can adapt knowledge transfer frequency, knowledge source selection, and knowledge transfer intensity in a synergistic way to make the best use of knowledge transfer, especially when facing many tasks. We implement the proposed AEMTO framework and evaluate our implementation on three suites of MTO problems with 2, 10, and 50 tasks and one real-world MTO problem with 2000 tasks in comparison to several state-of-the-art EMTO methods with certain adaptation strategies regarding knowledge transfer and the single-task optimization counterpart of the proposed method. Experimental results have demonstrated the effectiveness of the adaptive knowledge transfer strategies used in AEMTO and the overall performance superiority of AEMTO.

Exploring self-determined urban Indigenous adult education in an Indigenous organization

ABSTRACT Urban Indigenous communities in Canada are sites of dynamic knowledge transfer among Indigenous people who build community together both from within similar cultural frameworks and across difference. These “inter-national” urban Indigenous communities face distinct challenges and opportunities for implementing Indigenous knowledge transfer processes. This article examines the mechanisms through which knowledge transfer occurs at the Ontario Federation of Indigenous Friendship Centres, a large urban Indigenous organization in Toronto, Ontario. The researchers used interviews and focus groups to explore strategies for knowledge transfer among Elders, Knowledge Keepers, leaders, and staff. We argue that urban Indigenous communities transfer knowledge through processes that are sensitized to the diverse inter-national nature of these environments while at the same time oriented toward achieving the continuity of a common knowledge base. Thinking about these processes and their underlying goals through an educational lens helps broaden understandings of where Indigenous education occurs to include professional workplaces.

The LED Lamp metaphor: Knowledge and the creative process in new media art

In the context of new media art, the intrinsic interplay of disciplines poses constant learning challenges to the artists, as it requires a continuous acquisition and reconfiguration of knowledge. This article seeks to understand to what extent knowledge processes of creation, transfer and adoption contribute to the creative collaborative outcomes of new media artists, by investigating: i) explicit and tacit knowledge flows of new media artists; ii) strategies for knowledge creation, transfer and adoption; iii) contexts in which these processes are facilitated. Based on qualitative methods, findings indicate that the way in which artists learn and interact is related to their personality; values such as openness, curiosity, respect or trust shape the context for a shared interest in knowledge creation, adoption, and exchange. A holistic approach to knowledge-creating place (‘Ba’) contributes to understanding the fluid contexts in which creative processes occur.

Decoding Knowledge Transfer for Neural Text-to-Speech Training

Neural end-to-end text-to-speech (TTS) is superior to conventional statistical methods in many ways. However, the exposure bias problem, that arises from the mismatch between the training and inference process in autoregressive models, remains an issue. It often leads to performance degradation in face of out-of-domain test data. To address this problem, we study a novel decoding knowledge transfer strategy, and propose a multi-teacher knowledge distillation (MT-KD) network for Tacotron2 TTS model. The idea is to pre-train two Tacotron2 TTS teacher models in teacher forcing and scheduled sampling modes, and transfer the pre-trained knowledge to a student model that performs free running decoding. We show that the MT-KD network provides an adequate platform for neural TTS training, where the student model learns to emulate the behaviors of the two teachers, at the same time, minimizing the mismatch between training and run-time inference. Experiments on both Chinese and English data show that MT-KD system consistently outperforms the competitive baselines in terms of naturalness, robustness and expressiveness for in-domain and out-of-domain test data. Furthermore, we show that knowledge distillation outperforms adversarial learning and data augmentation in addressing the exposure bias problem.

A Multi-Directional and Agile Academic Knowledge Transfer Strategy for Healthcare Technology.

Technology, especially cognitive agents and robots, has significant potential to improve the healthcare system and patient care. However, innovation within academia seldomly finds its way into practice. At least in Germany, there is still a digitalization gap between academia and healthcare practice and little understanding of how healthcare facilities can successfully purchase, implement, and adopt new knowledge and technology. Therefore, the aim of this study is to develop a successful academic knowledge transfer strategy for healthcare technology. We conducted a qualitative study with academic staff working in higher education in Germany and professionals in their practice partner organizations. In 15 semi-structured interviews, we aimed to assess interviewees experiences with knowledge transfer, to identify perceived influencing factors, and to understand the key aspects of a successful knowledge transfer strategy. The Dynamic Knowledge Transfer Model by Wehn and Montalvo, 2018 was used for data analysis. Based on our findings, we suggest that a successful transfer strategy between academia and practice needs to be multi-directional and agile. Moreover, partners within the transfer need to be on equal terms about expected knowledge transfer project outcomes. Our proposed measures focus particularly on regular consultations and communication during and after the project proposal phase.

The role of firm-level and country-level antecedents in explaining emerging versus advanced economy multinationals' R&D internationalization strategies

We examined firm-level and country-level antecedents of R&D internationalization strategies, focusing on differences between enterprises in emerging and advanced economies. Previous research often focuses on the relative importance of home-base-exploiting versus home-base-augmenting knowledge transfer strategies. We suggest that country-level and firm-level effects differ for the two strategies, and hence, we examined each strategy independently. Collecting data in China, India, the United States, and Germany, we demonstrated that firms' relative technological position as a firm-level characteristic can explain differences in home-base-exploiting strategies between emerging and advanced economies. In contrast, home-base-augmenting is more closely related to exploratory institutional environments, a country-level factor. Thus, either firm- or country-level antecedents can gain a dominant role, depending on the strategy implemented.

Black book blackboard black book: A drawing-based strategy for knowledge transfer

This paper reflects on a project run in a first-year class of art and design degree, in the Curricular Unit of Art and Design Theory. The objective of the project was to investigate the potential of a teaching protocol where a set of drawings were generated in class by the teacher to facilitate knowledge transfer in the classroom. The drawings generated by the teacher in class have not been treated or explored as a strategy as such, but they supported the delivery of theoretical content in the classroom. As part of the teaching theory protocol, a series of drawings were built as a sequential visual narrative, in the form of a story; these drawings, acting as visual narratives, sought to enable students to understand the theoretical content. At the end of the sessions, all students involved in the project were evaluated through surveys, to gather evidence of their understanding of theory. The results obtained suggest that the use of drawing as a tool in explaining theory facilitates a better understanding of theoretical concepts for students. It also allows the teacher to clarify and adjust unclear points in the lectures, and as such this protocol could function as a recursive strategy. In conclusion, the simplicity of this strategy could benefit students with cognitive difficulties, offering a complementary approach in the dialogue between teacher and student. This approach is particularly useful in contributing to the transfer of knowledge in the classroom in a digital age.

Self-Regulated Particle Swarm Multi-Task Optimization.

Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.

Cross-fertilization in Teaching and Learning between Institutions after COVID-19 Shutdown: A Collaborative Effort

Research reported in this article investigated the effectiveness of collaborative effort between schools to bridge the existing gaps that erupted during the outbreak of COVID-19. There are gaps in teaching and learning within the previously privileged institutions and the marginalised institutions in South Africa. Such gaps might have a negative impact on how teaching and learning are conducted. The purpose of this article was to investigate the creative and constructive ways of managing the anxiety related to socio-economic impacts in schools, amongst which were the introduction of online teaching and learning technologies, new methodologies and contents. This article is underpinned by the concepts of the establishment of advantageous strategies for collaboration, knowledge sharing and knowledge transfer. Qualitative document analysis has been adopted as a methodology to collect information for this study. The outbreak of COVID-19 has evoked inequalities amongst the communities. However, the re-opening of schools exacerbated multiple challenges. While teaching and learning is the centre of the education project in South Africa, which the country has to adapt to, most institutions were faced with a number of challenges. This study’s major finding revealed that collaboration is synergistic and can bring out the best contribution that might currently perceive partnership within the school system. This article recommends ways of bridging the gap between schools through developing partnerships between institutions through cross-fertilisation.

Evolutionary multi-task optimization with hybrid knowledge transfer strategy

As an emerging research paradigm in the field of evolutionary computation, evolutionary multi-task optimization (EMTO) has received an increasing amount of attention due to its capability in concurrently solving multiple related optimization tasks. In EMTO, without any prior knowledge about the complementarity between different tasks, the task relatedness is mainly captured dynamically through the evolving population. However, how to transfer the knowledge across tasks in accordance with different degrees of relatedness as the search proceeds has received little visibility. To address this issue and further enhance the performance of EMTOs, this paper proposes a hybrid knowledge transfer (HKT) strategy. In HKT, a population distribution-based measurement (PDM) technique is designed to evaluate the task relatedness based on the distribution characteristics of the evolving population, and then a multi-knowledge transfer (MKT) mechanism is employed to conduct multiple strategies of knowledge transfer according to the degree of relatedness between tasks. By incorporating the HKT strategy into EMTO, the resultant algorithmic framework, termed EMTO–HKT, is presented. The experimental results on the single-objective multi-task optimization test suite demonstrate the superiority of EMTO–HKT compared with other state-of-the-art EMTO algorithms.

Danza y universidad en tiempos de la Covid-19

The article presents the results of documentary research that reflects in a systematic way about virtual formative experiences, open to the public and led by dance programs at the university studies education in times of Covid-19. This global event that gained strength in 2020 impacted the training practices in all educational institutions and destabilized their knowledge transfer mechanisms. Therefore, the research is posed as questions: ?What are the knowledge transfer strategies that the Universities with Dance programs carried out during the year 2020? Likewise, what are the complementary contents and topics to the regular study plan that were privileged during said transfers? The conceptual basis on which the research is based involves the formative processes in university studies education and the dancing body, taking into consideration the statements of the World Health Organization, the Inter-American Development Bank, and the guidelines in university studies education raised by the Ministry of Education of Colombia.

A knowledge-guide hierarchical learning method for long-tailed image classification

Deep visual recognition methods have achieved excellent performance on artificially constructed image datasets where the data distribution is balanced. However, in real-world scenarios, data distribution is usually extremely imbalanced and exhibit a long-tailed distribution where data in each head class is more than the class in the tail. Many efficient deep learning methods fail to work normally, i.e., they perform well in the head class while poor in the tail class. In this paper, we propose a two-layer Hierarchical-Learning Long-Tailed Recognition (HL-LTR) algorithm which transforms the long-tailed problem into a hierarchical classification problem by constructing a hierarchical superclass tree in which each layer corresponds to a recognition task. In the first layer of the tree, the degree of data imbalance is largely decreased. The recognition task of the second layer is the original long-tailed recognition problem. The training of HL-LTR is top-down. The knowledge learned by the first layer transfers to classes of the second layer and guides the feature learning of the second layer by using attention mechanism module and knowledge distillation method. Compared with directly solving the most difficult long-tailed recognition task, HL-LTR achieves better performance due to its progressive learning method from easy to difficult and effective knowledge transfer strategy.