Knowledge Transfer Strategies Research Articles

KNOWLEDGE TRANSFER PRACTICES AND STRATEGIES IN HIGHER EDUCATION

This study aims to investigate knowledge transfer practices and strategies measured by knowledge vision, leadership, sharing culture, strategies, and IT infrastructures. It also examines how the variables affect organizational performance. By involving 118 respondents collected through an online survey and analyzed using descriptive statistics and multiple linear regression, the study found that knowledge-sharing and IT infrastructure significantly influence organizational performance, while knowledge vision, knowledge leadership, and knowledge-sharing culture do not significantly affect organizational performance. Practices such as public lectures, seminars, workshops, collegial interchange, internships, and research collaborations are considered effective in supporting knowledge transfer activities in higher education. In addition, the knowledge creation process through socialization, externalization, combination, and internalization, and factors such as vision, leadership, culture, and IT infrastructure must be managed properly to support the knowledge transfer process and formulate appropriate strategies.

A multi-population evolutionary algorithm based on knowledge transfer for constrained many-objective optimization

ABSTRACT Constrained Many-objective Optimization Problems (CMaOPs) are challenging in handling objectives and constraints simultaneously. Here, a novel Constrained Many-objective Optimization Evolutionary Algorithm (CMaOEA) based on Multi-population, Knowledge transfer and Improved environmental selection called CMaMKI is proposed to handle CMaOPs. The proposed framework evolves a task population to solve the original CMaOP and evolves another population to solve a helper problem derived from the original one. To assist solving the original CMaOP, a knowledge expression and transfer strategy is designed to share useful information in the helper population with the task population. Meanwhile, to balance population convergence, diversity and feasibility, an enhanced environmental selection strategy is devised by combining the ε-constrained technique, θ-dominance and subregional density evaluation. The proposed algorithm is evaluated and contrasted with six state-of-the-art algorithms on a set of benchmark CMaOPs. The experimental results demonstrate the superiority and competitiveness of the proposed method.

Knowledge Transfer of Site Based Construction Management Team of ABC Company: Basis for the Development of Knowledge Transfer Plan

The study determines the readiness of ABC Company and the status of the project site based construction management team (CMT) on knowledge transfer (KT) as basis for the development of the proposed knowledge transfer plan (KTP). Descriptive method was used to obtain information on the levels of readiness on organizational characteristics and resources requirement of ABC Company and on the gaps in terms of idea creation, sharing, dissemination, evaluation and adoption in the project site based CMT. Descriptive statistics was used to present the data on readiness; anything below 100% in terms of idea creation, sharing, dissemination, evaluation and adoption was considered a gap. The study revealed that ABC Company has a moderately ready status on its readiness. Improvement was needed on KT strategy, rewards systems, knowledge hoarding, knowledge required, budget, and even on the core and support team that was essential in the development of the proposed KTP. The project site based CMT had gaps to be attended to on idea creation, sharing and dissemination. Those gaps were considered valuable input in the development of the proposed KTP. The development of the proposed KTP was grounded on quality management system that considered the readiness of ABC Company and the gaps found in the project site based CMT as inputs. The proposed KTP composed of four stages, namely, Management Responsibility, Resource Management, Knowledge Transfer Realization and Measurement, Analysis and Improvement. Intervention strategies presented in the KTP would address the particular area determined to be either absent or not consistently implemented with feed forward and feedback for continual improvement.

Knowledge transfer and protection in international joint ventures: An integrative review

Cross-border transfer and the protection of knowledge are important for multinational enterprises (MNEs) to develop their network partners’ capabilities while simultaneously safeguarding competitive advantages. However, they can be challenging for MNEs due to cultural and institutional differences between home and host markets. This poses a dilemma for MNEs, which is how to strike a balance between their knowledge transfer (KT) and knowledge protection (KP) strategies. It is notable that, so far, research has primarily investigated these two areas independently, lacking an integrative view. Therefore, in this article, we reviewed 98 academic articles exploring knowledge transfer/protection in MNEs operating under international joint venture (IJV) arrangements and assessed publications from the last two decades (2000–2022). Drawing from institutional theory and the bargaining power perspective, we developed a conceptual framework highlighting the external and internal factors influencing KT and KP. Subsequently, we contextualized these factors within the specific domain of IJVs, drawing on insights gleaned from the studies in our sample. The interplay of these factors, along with their contextual nuances, provides a holistic and in-depth understanding of how knowledge is managed within the complex dynamics of IJVs. In addition, our review contributes to our understanding of knowledge management in MNEs by identifying novel gaps in the literature and suggesting a number of avenues for future research.

An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain

Spiking neural networks (SNNs) are rich in spatio-temporal dynamics and are suitable for processing event-based neuromorphic data. However, event-based datasets are usually less annotated than static datasets. This small data scale makes SNNs prone to overfitting and limits their performance. In order to improve the generalization ability of SNNs on event-based datasets, we use static images to assist SNN training on event data. In this paper, we first discuss the domain mismatch problem encountered when directly transferring networks trained on static datasets to event data. We argue that the inconsistency of feature distributions becomes a major factor hindering the effective transfer of knowledge from static images to event data. To address this problem, we propose solutions in terms of two aspects: feature distribution and training strategy. Firstly, we propose a knowledge transfer loss, which consists of domain alignment loss and spatio-temporal regularization. The domain alignment loss learns domain-invariant spatial features by reducing the marginal distribution distance between the static image and the event data. Spatio-temporal regularization provides dynamically learnable coefficients for domain alignment loss by using the output features of the event data at each time step as a regularization term. In addition, we propose a sliding training strategy, which gradually replaces static image inputs probabilistically with event data, resulting in a smoother and more stable training for the network. We validate our method on neuromorphic datasets, including N-Caltech101, CEP-DVS, and N-Omniglot. The experimental results show that our proposed method achieves better performance on all datasets compared to the current state-of-the-art methods. Code is available at https://github.com/Brain-Cog-Lab/Transfer-for-DVS.

Transnational networks of human resources and knowledge in East Asia: a case study of Taiwan merchant Wang Xuenong (1870–1915) and his trading company

Abstract Taiwan is an island and trade has always been the locomotive of its economic development. From the 1620s to the 1960s, cane sugar was Taiwan's most representative export commodity. Yet little attention has been paid to the business strategies of sugar traders and the changes in their thinking. How did the Takow (Kaohsiung) merchants who first went to Japan and Hong Kong to conduct cross-border trade in person learn about international trade and build a network of human resources that crossed borders, especially after the 1870s? And how did they face the great changes of an era in which tradition and modernity were intertwined, so that, following the regime transfer in the 1890s from the Qing Dynasty to the Japanese empire, they were able to expand their business territory and become major sugar merchants in southern Taiwan? This article examines the career of Wang Xuenong—a well-known sugar merchant in Taiwan during the Meiji period (1868–1912). It attempts to explain, from the perspective of cross-cultural knowledge transfer and human resource strategies, why and how sugar merchants such as Wang, who had gone to Japan in the early years of the Meiji Restoration for purposes of cross-border trade, introduced a trading company system that incorporated a mixture of East Asian and Western elements. It further investigates how they expanded their business from the sugar trade into a wider commercial domain that included mechanical rice milling and steamship transport. Finally, it looks at how their actions affected a transformation of Taiwan's commercial culture from the late Qing Dynasty to the early days of Japanese rule, and the historical significance of these changes in Taiwan's transitions towards industrialisation and modernisation.

Knowledge investment and search for innovation: evidence from the UK firms

AbstractRecent research on innovation management and knowledge transfer has demonstrated that industry knowledge collaboration and knowledge spillovers matter for innovation, but so does a firm's Research and Development (R&D). Conditional to a firm's R&D investment, this study makes a theoretical investigation into the role of two knowledge transfer strategies—industry coopetition and industry knowledge spillovers for a firm's innovation. Based on an analysis of a sample of 17,859 UK firms from 2002 to 2014, we demonstrated why and under what conditions firms will (a) invest in internal R&D, (b) engage in coopetition, and (c) access knowledge spillovers to introduce new to firm (incremental innovation) and new to market products (radical innovation). The results of this study demonstrate that firm managers who choose knowledge spillovers versus coopetition are likely to achieve radical vis-à-vis incremental innovation. Benefits from the coopetition can be achieved with low investment in R&D, while R&D is essential in recognizing the knowledge spillover for radical innovation. By deciding whether to deploy its costly R&D and access external knowledge via industry coopetition or spillovers, the firm is also making a concomitant decision about the type of innovative activity it will generate. Thus, a firm strategy for knowledge transfer and investing in knowledge internally is inextricably linked to a firm strategy involving the type of innovative output.

Diversified Knowledge Transfer Strategy for Multitasking Particle Swarm Optimization.

Evolutionary multitasking optimization (EMTO) has capability of performing a population of individuals together by sharing their intrinsic knowledge. However, the existed methods of EMTO mainly focus on improving its convergence using parallelism knowledge belonging to different tasks. This fact may lead to the problem of local optimization in EMTO due to unexploited knowledge on behalf of the diversity. To address this problem, in this article, a diversified knowledge transfer strategy is proposed for multitasking particle swarm optimization algorithm (DKT-MTPSO). First, according to the state of population evolution, an adaptive task selection mechanism is introduced to manage the source tasks that contribute to the target tasks. Second, a diversified knowledge reasoning strategy is designed to capture the knowledge of convergence, as well as the knowledge associated with diversity. Third, a diversified knowledge transfer method is developed to expand the region of generated solutions guided by acquired knowledge with different transfer patterns so that the search space of tasks can be explored comprehensively, which is favor of EMTO alleviating local optimization. Finally, the performance of the proposed algorithm is evaluated in comparison with some other state-of-the-art EMTO algorithms on multiobjective multitasking benchmark test suits, and the practicality of the algorithm is verified in a real-world application study. The results of experiments demonstrate the superiority of DKT-MTPSO compared to other algorithms.

Evolutionary Multiform Optimization With Two-Stage Bidirectional Knowledge Transfer Strategy for Point Cloud Registration

Point cloud registration is an important task in computer vision, where the goal is to estimate a transformation to align a pair of point clouds. Most of the existing registration methods face the problems of poor robustness and getting stuck in local optima. Evolutionary multitasking is an effective paradigm to enhance global search capability and improve convergence characteristics through knowledge transfer across multiple related tasks. Inspired by evolutionary multitasking, this paper proposes a multiform optimization approach through evolutionary multitasking for solving the point cloud registration problems. we first construct two related registration tasks with different functional landscapes to form a multiform optimization problem. Compared with methods that only focus on a single registration attribute, the two proposed tasks focus on robustness and precision of registration, respectively. Then, a new two-stage bidirectional knowledge transfer strategy is presented, which can implement efficient knowledge transfer among two related tasks. Finally, both simulations and real experiments show the power of our method. The proposed method is robust to noise, outliers, and partial overlaps, and is effective in multiple real registration scenarios, such as object registration, scene reconstruction, simultaneous localization and mapping.

Higher Order Knowledge Transfer for Dynamic Community Detection With Great Changes

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.

Challenges and Strategies for Knowledge Transfer in Multinational Corporations: The Case of Hotel “Maria the Great”

This case study examines the difficulties multinational corporations (MNCs) encounter when sharing knowledge among their various business units. The study pinpoints four main challenges to knowledge transfer: the hoarding-of-expertise challenge (HEC), the foreigner challenge (FC), the not-invented-here challenge (NIH), and the absorptive capacity challenge (ACC) and presents approaches to tackle these issues. The case study centers around the A&A hotel chain, specifically its subsidiary, “Maria the Great Hotel,” located on Kish Island in Iran. The study identified challenges within the context of this multinational hotel chain. It introduced strategies that can be used to address these challenges, emphasizing the significance of knowledge transfer in attaining organizational success and gaining a competitive edge.

PKDGAN: Private Knowledge Distillation With Generative Adversarial Networks

The deployment of deep learning applications has to address the increasing privacy concerns when using private and sensitive data for training. A conventional deep learning model is prone to privacy attacks that can recover the sensitive information from either model parameters or accesses to the inference model. Recently, differential privacy (DP) has been proposed to offer provable privacy guarantees by randomizing the training process of neural networks. However, many approaches tend to provide the worst case privacy protection for model publishing, inevitably impairing the accuracy of the trained models. Thus, we present a novel private knowledge transfer strategy, where the private teacher trained on sensitive data is not publicly accessible but the student models can be released with privacy guarantees. In this paper, a three-player (teacher-student-discriminator) learning framework, Private Knowledge Distillation with Generative Adversarial Networks (PKDGAN), is proposed, where the student acquires the distilled knowledge from the teacher and is trained with the discriminator to generate similar outputs as the teacher. Moreover, a cooperative learning strategy is also suggested to support the collective training of multiple students against the discriminator when each student is with insufficient unlabelled training data. To enforce rigorous privacy guarantees, PKDGAN applies a Rényi differential privacy mechanism throughout the training process, and use it with a moment accountant technique to track the privacy cost. PKDGAN allows students to be trained with unlabelled public data and very few epochs, which avoids the exposure of training data while ensuring model performance. In the experiments, PKDGAN is found to have consistently good performance on various datasets (MNIST, SVHN, CIFAR-10, and Market-1501). When compared to prior works [1], [2], PKDGAN exhibits 5-82% accuracy loss improvement without compromising any privacy guarantee.

A Multiform Optimization Framework for Multi-objective Feature Selection in Classification

Feature selection in machine learning as a key data processing technique has two conflicting goals: minimizing the classification error rate and minimizing the number of features selected. However, most of the existing multi-objective feature selection methods face the problems of easily falling into local optima and slow convergence by virtue of their problem characteristics, such as partially conflicting objectives and highly discontinuous Pareto fronts. To solve these problems, this article proposes a multiform optimization framework to solve a multi-objective feature selection task together with several auxiliary single-objective feature selection tasks in a multitask environment. The proposed framework uses the problem-solving experience of single-objective tasks to assist the multi-objective feature selection task in exploring more promising regions and accelerating the convergence speed. Specifically, a knowledge transfer strategy based on the search experience of different tasks is developed to accomplish multiform optimization. In addition, a diversity enhancement mechanism is presented to improve search ability in promising decision space areas by considering historical information about the population. In most cases, the experiment results on 27 datasets demonstrate that the proposed technique can uncover more diversified feature subsets on the Pareto front in less time than existing state-of-the-art methods.

Towards entrepreneurial universities in Spain: Evidence from vice‐rectors and KTO directors

AbstractTo its traditional missions of education and research, the entrepreneurial university adds a ‘third mission’: knowledge transfer (KT), which aims to contribute to the economic and social development of the university's environment. To this end, it is the role of government to implement regulations and design policies that push universities to be more entrepreneurial. In addition, an entrepreneurial university should interact with industry and society as a whole and implement a clear strategy—endorsed at the highest level—to incorporate the function of KT. However, there has been a lack of critical reflection in the literature about the ways in which universities implement the strategy, and administrators and coordinators, who play a pivotal role in implementing it, are usually underrepresented in university research. The exploratory research presented here addresses this gap by focussing on the current vice‐rectors and KTO directors in charge of implementing KT strategy at Spanish universities. Our findings suggest that the respondents think KT is an objective shared by the government; however, they do not perceive it to be an objective for top and middle managers in universities.

An improved YOLOv5s model for assessing apple graspability in automated harvesting scene.

With continuously increasing labor costs, an urgent need for automated apple- Qpicking equipment has emerged in the agricultural sector. Prior to apple harvesting, it is imperative that the equipment not only accurately locates the apples, but also discerns the graspability of the fruit. While numerous studies on apple detection have been conducted, the challenges related to determining apple graspability remain unresolved. This study introduces a method for detecting multi-occluded apples based on an enhanced YOLOv5s model, with the aim of identifying the type of apple occlusion in complex orchard environments and determining apple graspability. Using bootstrap your own atent(BYOL) and knowledge transfer(KT) strategies, we effectively enhance the classification accuracy for multi-occluded apples while reducing data production costs. A selective kernel (SK) module is also incorporated, enabling the network model to more precisely identify various apple occlusion types. To evaluate the performance of our network model, we define three key metrics: APGA, APTUGA, and APUGA, representing the average detection accuracy for graspable, temporarily ungraspable, and ungraspable apples, respectively. Experimental results indicate that the improved YOLOv5s model performs exceptionally well, achieving detection accuracies of 94.78%, 93.86%, and 94.98% for APGA, APTUGA, and APUGA, respectively. Compared to current lightweight network models such as YOLOX-s and YOLOv7s, our proposed method demonstrates significant advantages across multiple evaluation metrics. In future research, we intend to integrate fruit posture and occlusion detection to f]urther enhance the visual perception capabilities of apple-picking equipment.

One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer.

The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from existing annotated WM tracts are of interest, additional annotations are required for their segmentation. Due to the cost of manual annotations, methods have been developed for few-shot segmentation of novel WM tracts, where the segmentation knowledge is transferred from existing WM tracts to novel WM tracts and the amount of annotated data for novel WM tracts is reduced. Despite these developments, it is desirable to further reduce the amount of annotated data to the one-shot setting with a single annotated image. To address this problem, we develop an approach to one-shot segmentation of novel WM tracts. Our method follows the existing pretraining/fine-tuning framework that transfers segmentation knowledge from existing to novel WM tracts. First, as there is extremely scarce annotated data in the one-shot setting, we design several different data augmentation strategies so that extensive data augmentation can be performed to obtain extra synthetic training data. The data augmentation strategies are based on image masking and thus applicable to the one-shot setting. Second, to address overfitting and knowledge forgetting in the fine-tuning stage that can be more severe given limited training data, we propose an adaptive knowledge transfer strategy that selects the network weights to be updated. The data augmentation and adaptive knowledge transfer strategies are combined to train the segmentation model. Considering that the different data augmentation strategies can generate synthetic data that contain potentially conflicting information, we apply the data augmentation strategies separately, each leading to a different segmentation model. The results predicted by the different models are fused to produce the final segmentation. We validated our method on two brain dMRI datasets, including a public dataset and an in-house dataset. Different settings were considered for the validation, and the results show that the proposed method improves the one-shot segmentation of novel WM tracts.

Medication use of Canadians with chronic obstructive pulmonary disease: a cohort study

ObjectivesThe objectives of this study were to describe medication use, treatment patterns and adherence, as well as acute exacerbations of chronic obstructive pulmonary disease (AECOPD) among adults living with COPD.MethodsA...

Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach

With the rapid advancements in smart meters, energy internet, and high-performance computing technologies, deep learning methods are increasingly used for detecting anomalies in building electricity consumption. However, these models grapple with challenges like outliers, missing data, and imbalanced categories. Moreover, these models are often considered as black boxes due to their lack of interpretability. This limitation impedes the practical deployment of deep learning methods. Focusing on these issues, this study presents an anomaly detection method aimed at improving anomaly detection accuracy and interpretability in low-quality and class imbalanced data. Initially, our method established data preprocessing models to handle with outliers and missing data. Subsequently, an unbalance elimination model, named GRU-MACGANS, was designed to augment the minority category data. The SA-CNN-BiGRU-FCN model, incorporating a knowledge transfer strategy, was implemented to identify anomalous behavior. Finally, the results of SA-CNN-BiGRU-FCN model were interpreted based on the contrastive explanations method. Our case study of a low-voltage distribution system demonstrates the effectiveness of our approach. Data preprocessing models and GRU-MACGANS enhance data quality and alleviate category imbalance issues in anomaly detection. The interpretable analysis provides insights into the ability of detection models to identify representative features of different electricity consumption behaviors for effective anomaly detection.

The podcast as strategy to professional development and knowledge transfer in Health Care Planning

Abstract Background Is a notorious concern to seek new ways to use technologies in strategies for the development of health professionals with a focus on changes in the work process of health teams and in management practices. The PlanificaSUS Project implements the Health Care Planning (HCP) methodology in Brazil, with a goal to organizing and integrating Primary Health Care (PHC) and Specialized Outpatient Care (SOC) services. Some materials present podcasts as one of the pedagogical resources for reflection and discussion of heath themes. In this sense, each episode of the podcast has a specific theme. Objective: Report the experience of using podcast as a strategy to professional development and knowledge transfer in Health Care Planning. Results So far, nine podcast episodes, lasting ten minutes each, have been made available in the HCP materials and total more than 15,000 views. This strategy reached 4,000 health professionals, who work in 1,635 health units, distributed in 19 federative units in Brazil. All podcasts episodes are prepared using language that dialogues with the health professional and stimulates discussions of changes through cycles of continuous improvement. In specific HCP evaluation moments, participants demonstrated that, in their practice scenarios, the use of the podcast together with repetitive listening, helped to significantly improve their understanding. Conclusions The podcast presents flexibility in its aspects of production and distribution, supporting pedagogical possibilities. As one of the great challenges the training of teacher specialists to produce podcasts, this requires not only technological fluency, but also pedagogical fluency. The use and study of this tool in the educational context of health in Brazil is still incipient, but possible in the different realities of the country. Main messages: The podcast strategy has promising attributes for developing knowledge transfer strategies among healthcare professionals. Key messages • The podcast support possibility of making educational material available in the form of lectures or audio interviews, being accessible at any time without limitation of location. • It is important to provide information access strategies for professionals who have difficulty obtaining protected time to read and study.

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Many-task optimization problem is a kind of challenging multi-task optimization problem with more than three tasks. Two significant issues in solving many-task optimization problems are measuring inter-task similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the inter-task similarity because the inter-task similarity is a concept with multiple different aspects. To address this limitation, this paper proposes a bi-objective knowledge transfer framework, which aims firstly to accurately measure different types of inter-task similarity using two different measurements and secondly to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure inter-task similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of inter-task similarity. We compare the bi-objective knowledge transfer framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world many-task optimization problems with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.