Effective Knowledge Transfer Research Articles

ESG Performance and Corporate Specialization

Specialized division of labor can help hasten the creation of a new development pattern while also increasing the endogenous power and reliability of the domestic cycle. Based on research data from A-share listed company in Shanghai and Shenzhen from 2012 to 2022, this paper develops a two-way fixed-effects model to evaluate the impact of ESG performance on the division of labor specialization. The findings indicate that (1) ESG performance has a positive and significant effect on the division of specialization, and the result remains significant after the robustness and endogeneity tests. (2) ESG performance influences the division of specialization via two channels: governance and knowledge transfer effects. (3) Heterogeneity analysis reveals that the aforementioned effects are more pronounced for state-owned enterprises and those in the central and eastern areas. This report contributes to the research on the economic effects of ESG by providing empirical evidence for enterprise specialization.

Multi-task Information Enhancement Recommendation model for educational Self-Directed Learning System

In Self-Directed Learning Systems (SDLS), suggesting specific exercises to students after viewing educational videos is crucial, which promotes the integration of learning resources and improves learning efficiency. Given that exercises and educational videos are distinct learning resources, suggesting exercises centered on educational videos containing knowledge concepts is referred to as cross-type recommendations. Traditional cross-type recommendation methods usually separate data representation and recommendation tasks, which hampers effective knowledge transfer and information fusion. Furthermore, educational resources frequently feature an irregular distribution of information, posing a significant challenge to existing models in extracting crucial information. Besides, due to the inadequate data, the cross-type recommendation system needs help in developing a robust model. To address these challenges, we propose a Multi-task Information Enhancement Recommendation (MIER) Model in this paper. This model provides a comprehensive recommendation capability by integrating resource representation and merging recommendations. Multi-task learning improves the capacity to integrate information and transfer knowledge. Then, we propose an Information Enhancement Encoder (IEE) module that utilizes the attention mechanism to enhance the model’s ability to extract essential information from educational videos and exercises. Finally, to address the problem of inadequate data, we design a fusion structure that incorporates the concept knowledge graph as expert information into the model. To evaluate the effectiveness of the MIER, we compare it with other models on both predicting concepts and recommending exercises task. The corresponding results demonstrate that the MIER outperforms all the other models. We comprehensively evaluated the MIER model’s performance across a range of datasets varying in labeled data quantity. The findings demonstrate that the model consistently achieves outstanding performance and exhibits robustness, even under conditions of limited label availability.

Vocational teachers’ perceptions on workplace learning in facilitating students’ professional engagement in the context of industry-university collaboration in China

PurposeThis study aims to explore vocational teachers' perceptions regarding workplace learning that align with students' training models and collaborative teaching involving specialised professionals within the context of industry-university collaboration.Design/methodology/approachUsing a qualitative approach, the study conducted nine semi-structured interviews from three subject areas to better understand how vocational teachers’ work-based learning enhances their pedagogical practice in guiding students’ professional training. Thematic analysis was adopted to identify patterns that emerged from concepts and theories related to coding categories.FindingsThe authors identified three key components: vocational teachers’ workplace learning in connection with students’ training models, collaborative teaching with specialised professionals and teachers’ regular interactions with enterprises. The findings demonstrate that vocational teachers’ engagement in workplace learning pertaining to specific subjects provides a valuable avenue for enhancing curriculum design with collaboration with industry experts. This is key for supporting vocational students’ transitions into the workplace and ensuring their knowledge and skills are tailored to the industry-standard practice.Research limitations/implicationsThe data are limited to the review of interviews from three vocational subject areas as the representative sector in the study. However, this research implies effective knowledge transfer between workplace settings and vocational institutions, and vocational teachers need to integrate work-based vocational knowledge and skills in a relevant and applicable way across diverse classroom settings.Practical implicationsFostering collaborative partnerships with local industries and professionals can be a primary way to facilitate authentic learning experiences that are linked to a specific vocational field and bridge the gap between diverse classroom learning and real-world work scenarios.Originality/valueThis study combines contemporary workplace learning theories with the conceptual understanding of vocational teachers’ involvement with industry-specific practice. Connecting teachers’ knowledge to the industry extends the input and collaboration from professionals and field experts to the diverse vocational classrooms.

A relationship-aware mutual learning method for lightweight skin lesion classification

A relationship-aware mutual learning method for lightweight skin lesion classification

An Empirical Study on the Relationship between Internalization and Externalization of Chinese Wushu Tacit Knowledge Transfer

In view of the unclear relationship between internalization and externalization of current martial arts knowledge, and the unclear interaction mechanism, this study firstly analyzes the master-apprentice model of martial arts dissemination, and then uses the questionnaire survey method, factor analysis method and structural equation model method to discuss The relationship between master-apprentice relationship, master’s ability to transmit knowledge, apprentice’s ability to receive knowledge, organizational culture and its characteristics, and the relationship between the tacit degree of martial arts knowledge and the internalization and externalization effects of tacit knowledge transfer, the internalization effect of martial arts tacit knowledge is obtained with influence on externalizing effects. The conclusion is that in the process of Wushu tacit knowledge transfer, the degree of internalization of tacit knowledge has a positive impact on the degree of externalization, and it is also affected by the master’s ability to send knowledge, the apprentice’s ability to accept knowledge, and the transfer environment.

Boundary spanners as a key to value creation in infrastructure procurement

Purpose Infrastructure forms a basis for the operations and sustainability of the modern society. This paper aims to recognize value creation from the infrastructure procurement ecosystem perspective to achieve those goals. The pursuit of enhancing value creation involves an examination of infrastructure procurement challenges, boundaries as well as boundary spanners that facilitate effective knowledge transfer and interaction. Design/methodology/approach The qualitative study is based on content analysis of 25 thematic interviews. Data was transcribed and coded via Atlas.ti software. Findings Infrastructure procurement value creation challenges appear complex and related to boundaries that hamper collaboration, coordination and knowledge sharing. Our results show that these boundaries locate within and between different levels of procurement ecosystem. Therefore, value creation in infrastructure procurement requires boundary spanners for leveraging knowledge sharing and interaction. Artifacts, discussion, processes and brokers as identified boundary spanners are strongly nested and interrelated in the industry. Special attention should be given to supporting individuals to act as brokers, since they play the key roles in trust building, culture steering and usage of other boundary spanners. Social implications Promoting value creation in infrastructure procurement helps to achieve socio-economic development goals. Originality/value This study offers a unique perspective on value creation in the context of infrastructure by adopting an ecosystem lens and examining boundary crossing mechanisms. The results support future development of collaboration and knowledge sharing practices fostering procurement productivity.

Learning from feature and label spaces’ bias for uncertainty-adaptive facial emotion recognition

Developing an accurate deep model for facial emotion recognition is a long-term challenge. It is because the uncertainty of emotions, stemming from the ambiguity of different emotional categories and the difference of subjective annotations, can ruin the ability of model to achieve the desired optimization. This paper constructs two distinct datasets, namely original sample set and ambiguous sample set, to explore an effective ambiguous knowledge transfer method to realize the adaptive awareness of uncertainty in facial emotion recognition. The original sample set is the weakly-augmented data with relatively low uncertainty, as most emotions are clean in reality. Meanwhile, the ambiguous sample set is strongly-augmented data that introduces feature and label bias with regard to emotion, which are with relatively high uncertainty. The proposed framework consists of two sub-nets, which are trained using the original set and the ambiguous set respectively. To achieve uncertainty-adaptive learning for two sub-nets, we introduce two modules. One is the cross-space attention consistency learning module that performs attention coupling across original and ambiguous feature spaces, achieving uncertainty-aware representation learning in feature granularity. The other is the soft-label learning module that models and utilizes uncertainty in label granularity, through aligning the posterior distributions between original label space and ambiguous label space. Experimental studies on public datasets indicate that our method is competitive with the state-of-the-art.

Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning-based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility.Scientific contributionConsidering the potential correlation between activity prediction tasks for different kinases, we propose a novel meta learning algorithm MetaILMC, which learns a prior of strong generalization capacity during meta-training from the tasks with sufficient training samples, such that it can be easily and quickly adapted to the new tasks of the kinase with scarce data during meta-testing. Thus, MetaILMC can effectively alleviate the data scarcity problem in the prediction of kinase inhibitors.

Knowledge transfer for adaptive maintenance policy optimization in engineering fleets based on meta-reinforcement learning

Maintenance policy optimization is crucial for ensuring the efficient functioning of structures and systems and mitigating the risk of deterioration. Reinforcement learning methods, especially when combined with deep neural networks, have seen significant progress in supporting maintenance decisions. However, deep reinforcement learning (DRL) typically necessitates an extensive number of interactions with the system to acquire the optimal policy, resulting in data inefficiency issues that limit the application of DRL in practical engineering fleet problems. Deriving optimal policies with DRL repeatedly for every individual in the engineering fleet can be computationally expensive or even prohibitive. To address the data inefficiency issues, this study proposes a novel maintenance optimization approach that can transfer knowledge from previously learned maintenance cases to the new cases to accelerate the DRL process. Meta-reinforcement learning (Meta-RL) method is proposed to realize the concept of knowledge transfer within a fleet by learning a meta-learned policy. In particular, the meta-learned policy can be quickly adapted to each individual case of the engineering fleet, thereby reducing the required computational burden for maintenance policy optimization. Two examples are used to demonstrate the effectiveness of knowledge transfer.

Transfer learning under the Cox model with interval‐censored data

AbstractTransfer learning, focusing on information borrowing to address limited sample size issues, has gained increasing attention in recent years. Our method aims to utilize data from other population groups as a complement to enhance risk factor discernment and failure time prediction among underrepresented subgroups. However, a literature gap exists in effective knowledge transfer from the source to the target for risk assessment with interval‐censored data while accommodating population incomparability and privacy constraints. Our objective is to bridge this gap by developing a transfer learning approach under the Cox proportional hazards model. We introduce the tuning‐free Trans‐Cox‐MIC algorithm, enabling adaptable information sharing in regression coefficients and baseline hazards, while ensuring computational efficiency. Our approach accommodates covariate distribution shifts, coefficient variations, and baseline hazard discrepancies. Extensive simulations showcase the method's accuracy, robustness, and efficiency. Application to the prostate cancer screening data demonstrates enhanced risk estimation precision and predictive performance in the African American population.

Evolutionary Multitasking for Costly Task Offloading in Mobile-Edge Computing Networks

The offloading of computation-intensive tasks to an edge server near resource-constrained mobile devices can provide improved application performance and user experience. However, with the rapid growth of mobile devices connected to the edge server, it is challenging to directly obtain an optimal task offloading scheme due to increasing computational cost and problem scale. In this study, we model the costly task offloading problem (CTOP) in mobile edge computing networks to achieve efficient joint optimization of energy consumption and processing latency for mobile devices. Inspired by the success of evolutionary multitasking in solving complex optimization problems by leveraging the experience of simple optimization problems, we develop a novel multitasking framework whose effectiveness is demonstrated in solving the CTOP. In this framework, auxiliary tasks are created to optimize the local processing overhead and the edge processing overhead of task offloading. On this basis, we propose an effective multitask evolutionary algorithm that includes segmented knowledge transfer and auxiliary task update. Specifically, source and extended decision variables are considered as different knowledge to be utilized, while the auxiliary tasks are allowed to be updated dynamically. Related knowledge that is learned from cheap and simple auxiliary tasks promotes the evolutionary search for CTOP. Experimental results verify the effectiveness of knowledge transfer. Compared to existing multitasking and single-tasking algorithms, the proposed algorithm shows competitive performance in CTOP instances and achieves better comprehensive performance in terms of energy consumption and processing latency.

Recommendation system using bio-inspired algorithms for urban orchards

According to the Food and Agriculture Organization of the United Nations (FAO), climate change is exponentially affecting agricultural production worldwide, with food prices expected to increase by up to 90 percent by 2030 and hunger and malnutrition rates to rise by 2050. This paper presents the development of a platform based on the Internet of Things (IoT) for monitoring urban gardens as a strategy to mitigate hunger, promote food sovereignty and circular economy in areas of food shortage. To this end, an Internet of Things (IoT) architecture is proposed and implemented that involves a social design layer that allows an effective transfer of knowledge to communities and a recommendation system based on evolutionary computation to optimize and maximize the productivity of urban orchards, and thus contribute to the 2030 agenda of the Sustainable Development Goals (SDGs). Finally, three experiments in urban gardens are shown to validate evolutionary computation and artificial intelligence models, such as multiple linear regression, genetic algorithms, ant colony algorithms and spatial estimation and inference algorithms such as the Kriging algorithm. The productivity of urban lettuce orchards is increased between 25 and 45%.

Short communication: Opportunities and challenges for Peruvian fine flavored cocoa

The notable increase in the price of cocoa globally has generated interest both in the scientific community and in the many actors involved in the production chain. The objective of this communication was to collect some opinions on the opportunities and challenges that must be addressed from the perspective of small producers, academia and government. The need to improve native varieties of fine-flavor cocoa to be highly productive and resilient to climate change is discussed, as well as the urgency of effective knowledge transfer from academia in optimizing the conditions of fermentation, drying, and roasting processes, according to cocoa variety and considering producers' conditions. Actions to promote cocoa consumption, such as its inclusion in gastronomy, promotion of its nutraceutical properties, redefinition of fair objectives, and formation of specialized taster schools, are also suggested. Finally, it is noted that it is necessary to clarify state interventions to avoid redundant efforts, as well as to allocate a budget to fulfil the "National Plan for the Development of the Cocoa and Chocolate Production Chain by 2030".

Data-Free Generalized Zero-Shot Learning

Deep learning models have the ability to extract rich knowledge from large-scale datasets. However, the sharing of data has become increasingly challenging due to concerns regarding data copyright and privacy. Consequently, this hampers the effective transfer of knowledge from existing data to novel downstream tasks and concepts. Zero-shot learning (ZSL) approaches aim to recognize new classes by transferring semantic knowledge learned from base classes. However, traditional generative ZSL methods often require access to real images from base classes and rely on manually annotated attributes, which presents challenges in terms of data restrictions and model scalability. To this end, this paper tackles a challenging and practical problem dubbed as data-free zero-shot learning (DFZSL), where only the CLIP-based base classes data pre-trained classifier is available for zero-shot classification. Specifically, we propose a generic framework for DFZSL, which consists of three main components. Firstly, to recover the virtual features of the base data, we model the CLIP features of base class images as samples from a von Mises-Fisher (vMF) distribution based on the pre-trained classifier. Secondly, we leverage the text features of CLIP as low-cost semantic information and propose a feature-language prompt tuning (FLPT) method to further align the virtual image features and textual features. Thirdly, we train a conditional generative model using the well-aligned virtual image features and corresponding semantic text features, enabling the generation of new classes features and achieve better zero-shot generalization. Our framework has been evaluated on five commonly used benchmarks for generalized ZSL, as well as 11 benchmarks for the base-to-new ZSL. The results demonstrate the superiority and effectiveness of our approach. Our code is available in https://github.com/ylong4/DFZSL.

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.

Modeling Adaptive Inter-Task Feature Interactions via Sentiment-Aware Contrastive Learning for Joint Aspect-Sentiment Prediction

Aspect prediction (AP) and sentiment prediction (SP) are representative applications in fine-grained sentiment anal- ysis. They can be considered as sequential tasks, where AP identifies mentioned aspects in a sentence, and SP infers fine-grained sentiments for these aspects. Recent models perform the aspect-sentiment prediction in a joint man-ner, but heavily rely on the feature interactions of aspect and sentiment. One drawback is that they ignore correlation strength varies between aspect features and sentiment fea- tures across different sentences, and employ a fixed feature interaction strategy may limit effective knowledge transfer across tasks. To tackle this issue, in this paper, we propose an Adaptive Inter-task Feature Interaction framework, AIFI, for joint aspect-sentiment prediction. Specifically, we introduce a novel contrast-based alignment method based on contrastive learning. Our approach considers the AP-specific and SP-specific representations of a given sentence as a positive pair, while representation of another random sentence serves as a negative example. Moreover, we propose an inter-task feature correlation network to predict the contrast strength, which is determined by the temperature coefficient in the InfoNCE loss. This dynamic correlation adjustment enhances model’s ability to capture proper feature interactions more efficiently. Experimental results on three datasets validate the effectiveness of our approach.

The impact of open innovation on innovation performance: the chain mediating effect of knowledge field activity and knowledge transfer

The impact of open innovation on innovation performance: the chain mediating effect of knowledge field activity and knowledge transfer

Increasing the Operational Safety of NPP Pumping Equipment by Using Interactive Automated Remote Educational and Training Systems

The main attention during the operation of the nuclear power plant is paid to the issue of nuclear and radiation safety. NPP pumping equipment is the most important element of the safety system. Specialists who operate NPP pumping equipment need an understanding of the role and purpose of each pump type in the overall nuclear power plant system. The reliable and uninterrupted operation of energy facilities depends on the training and maintenance of personnel qualification at the appropriate level. Due to the correct equipment operation, it is possible to increase the safety and reliability of power units, extend the service life, increase the energy efficiency of mechanisms and systems, which requires a high level of qualification of maintenance specialists.
 An effective solution for training of NPP technical specialists is offered. The structure of an interactive educational system with the introduction of learning scenarios is considered, which allows effective preservation and transfer of knowledge and skills. Ways of evolution for this class systems and areas of further research are considered.

Enhancing Few-Shot Learning in Lightweight Models via Dual-Faceted Knowledge Distillation.

In recent computer vision research, the pursuit of improved classification performance often leads to the adoption of complex, large-scale models. However, the actual deployment of such extensive models poses significant challenges in environments constrained by limited computing power and storage capacity. Consequently, this study is dedicated to addressing these challenges by focusing on innovative methods that enhance the classification performance of lightweight models. We propose a novel method to compress the knowledge learned by a large model into a lightweight one so that the latter can also achieve good performance in few-shot classification tasks. Specifically, we propose a dual-faceted knowledge distillation strategy that combines output-based and intermediate feature-based methods. The output-based method concentrates on distilling knowledge related to base class labels, while the intermediate feature-based approach, augmented by feature error distribution calibration, tackles the potential non-Gaussian nature of feature deviations, thereby boosting the effectiveness of knowledge transfer. Experiments conducted on MiniImageNet, CIFAR-FS, and CUB datasets demonstrate the superior performance of our method over state-of-the-art lightweight models, particularly in five-way one-shot and five-way five-shot tasks.

Target Detection and Segmentation Technology for Zero-shot Learning

Zero-shot learning (ZSL) in the field of computer vision refers to enabling the model to recognize and understand categories that have not been encountered during the training phase. It is particularly critical for object detection and segmentation tasks, because these tasks require the model to have good generalization capabilities to unknown categories. Object detection requires the model to determine the location of the object, while segmentation further requires the precise demarcation of the object's boundaries. In ZSL research, knowledge representation and transfer are core issues. Researchers have tried to use semantic attributes as a knowledge bridge to connect categories seen during the training phase and categories not seen during the testing phase. These attributes may be color, shape, etc., but this method requires accurate attribute annotation, which is often not easy to achieve in practice. Therefore, researchers have begun to explore the use of non-visual information such as knowledge maps and text descriptions to enrich the recognition capabilities of models, but this also introduces the challenge of information integration and alignment. At present, ZSL has made certain progress in target detection and segmentation tasks, but there is still a significant gap compared with traditional supervised learning. This is mainly due to the limited ability of ZSL models to generalize to new categories. To this end, researchers have begun to explore combining ZSL with other technologies, such as generative adversarial networks (GANs) and reinforcement learning, to enhance the model's detection and segmentation capabilities for new categories. Future research needs to focus on several aspects. The first is how to design a more effective knowledge representation and transfer mechanism so that the model can better utilize existing knowledge. The second step is to develop new algorithms to improve the performance of ZSL in complex environments. In addition, research should focus on how to reduce the dependence on computing resources so that the ZSL method can run effectively in resource-limited environments. In summary, the research on target detection and segmentation technology of zero-shot learning is a cutting-edge topic in the field of computer vision. Despite the challenges, with the deepening of research, we expect these technologies to contribute to improving the generalization ability and intelligence level of computer vision systems.