Knowledge Transfer Process Research Articles

Crowdmeta: Crowdsourcing truth inference with meta-Knowledge transfer

Crowdsourcing provides a fast and low-cost solution to collect annotations for training data in computer vision. However, there are two challenges in crowdsourced image annotation: First, when crowdsourced workers perform annotation tasks in an unfamiliar domain, their accuracy will dramatically decline due to the lack of expertise; Second, the difficulties of tasks may be different due to the noises in images, which is only related to the features of images themselves and will affect the judgment of workers. It is well known that transferring knowledge from relevant domains can form a better representation for training samples, which benefits the estimation of workers’ expertise in truth inference models. However, the existing knowledge transfer processes for crowdsourcing require a considerable number of well-collected samples in source domains. Comprehensively considering the above issues, this paper proposes a novel probabilistic model for crowdsourcing truth inference, which fuses few-shot meta-learning and transfer learning. The proposed model transfers meta-knowledge from the source domain to form better high-level representations of the instances in the target domain. Simultaneously utilizing both high-level representations and instance features, the quality of workers and the difficulty of instances can be better modeled and inferred. Experimental results on a number of datasets show that the proposed model not only outperforms the state-of-the-art models but also significantly reduces the number of instances required in the source domain.

How to Shift Knowledge in Energy Transition Era (A Perspective to Extend the Business in Energy Sector Company)

This study proposes a strategy for managing knowledge shifting processes when a company's vision and mission evolve in tandem with the expansion of its strategy, business processes, and organizational architecture in response to environmental challenges and legislation. This paper is based on a case study of Pertamina, an Indonesian energy company. This paper used survey-based quantitative research. The findings revealed that companies must transfer their knowledge in order to successfully expand their business reach. The process of knowledge transfer will contribute to the success of the new business. Information shifting procedures can be accomplished by developing new knowledge that differs from existing business knowledge. Managing existing employees with new knowledge and managing new employees with new information are the two components of the knowledge transfer process. The elements of shift mentality, willingness to learn, teamwork, and age have a role in guiding existing employees to acquire new skills. In managing the new employee, the organization is able to hire new employees with new knowledge and select new employees with diverse educational backgrounds and experiences that match the expanded business scope. This research examined the process of knowledge transfer via the lens of Knowledge-Based View (KBV) theory.
 

A Virtual Reality Lab for Automotive Service Specialists: A Knowledge Transfer System in the Digital Age

Under the influence of the COVID-19 pandemic, there is an accelerated transition from the traditional form of knowledge transfer to online learning. Our study of 344 automotive students showed that the success of this transition depends on the readiness to introduce special digital tools for organizing knowledge and conducting practical forms of classes. In this regard, a modern digital form of organizing and transferring knowledge to automotive service engineers in the form of virtual laboratories was developed and presented in the article. The work scenarios, functionality, and minimum technical requirements of virtual laboratories as software systems are described and reviewed in the paper. The rationale for the effectiveness of the application, based on the results of using 109 university students in training practice, is presented as a result of the research. An analysis of the distributions of the student survey results and their training progress revealed differences at the p = 0.05 significance level. This confirmed the hypothesis that the use of methods for teaching engineers special disciplines and language skills using VR technologies is much more effective than the traditional one. An increase in students’ interest in learning was revealed, and their performance improved markedly. This proves that the immersive nature of VR technology makes it possible to better assimilate the studied material, increase the level of motivation of future car service specialists, and also allow the organization of the transfer of knowledge online. The very process of knowledge transfer becomes the point of acquiring new digital competencies necessary for high-tech industries.

ПРИРОДА И ФОРМА ЗНАНИЙ В ЭКОНОМИКЕ

For a long period of time, knowledge has been regarded as an important element of economic development, both in practical and theoretical aspects. This article is aimed at studying and systematizing the main theoretical features of the inclusion of knowledge in economic models. In models of knowledge production, this resource is an input for the functions of technology production and knowledge generation, contributing to a greater extent either to reduce costs or to accumulate information. Knowledge diffusion models take into account the types of participants in the knowledge transfer process and their absorptive capacity. As a result, economic models reveal patterns of positive and negative externalities from knowledge creation and dissemination.

Home Planning and Gender in Mandatory Palestine

Abstract The national Jewish awakening and the rise of Zionism set off several waves of Jewish immigration into Mandatory Palestine (1921–48), making housing an urgent issue. This article focuses on housing planning, using the lens of gender to explore the transfer and development of new household ideas from Germany to Palestine and the impact of these ideas on local housing debates. By tracing this process of knowledge transfer and contextualizing the kitchen discourse in Palestine within the context of contemporary European discourse, this study deepens our understanding of the development of Israeli home planning. Drawing on the professional opinions of architect Lotte Cohn and other women who described their own experiences in the home, it explores everyday aspects of the Zionist enterprise and how these intersected with the concept of the “new woman” in the Jewish community in Palestine.

Joint Semantic Transfer Network for IoT Intrusion Detection

In this article, we propose a joint semantic transfer network (JSTN) toward effective intrusion detection (ID) for large-scale scarcely labeled Internet of Things (IoT) domain. As a multisource heterogeneous domain adaptation (MS-HDA) method, the JSTN integrates a knowledge-rich network intrusion (NI) domain and another small-scale IoT intrusion (II) domain as source domains and preserves intrinsic semantic properties to assist target II domain ID. The JSTN jointly transfers the following three semantics to learn a domain-invariant and discriminative feature representation. The scenario semantic endows source NI and II domains with characteristics from each other to ease the knowledge transfer process via a confused domain discriminator and categorical distribution knowledge preservation. It also reduces the source–target discrepancy to make the shared feature space domain invariant. Meanwhile, the weighted implicit semantic transfer boosts discriminability via a fine-grained knowledge preservation, which transfers the source categorical distribution to the target domain. The source–target divergence guides the importance weighting during knowledge preservation to reflect the degree of knowledge learning. Additionally, the hierarchical explicit semantic alignment performs centroid-level and representative-level alignment with the help of a geometric similarity-aware pseudo-label refiner, which exploits the value of the unlabeled target II domain and explicitly aligns feature representations from a global and local perspective in a concentrated manner. Comprehensive experiments on various tasks verify the superiority of the JSTN against state-of-the-art comparing methods, on average a 10.3% of accuracy boost is achieved. The statistical soundness of each constituting component and the computational efficiency is also verified.

PROSES PEMBERDAYAAN EKONOMI KELUARGA MELALUI PROGRAM UMKM KERIPIK CIRENG RASAKU DI DESA CIBATU KECAMATAN CISAAT KABUPATEN SUKABUMI

The Covid-19 pandemic has caused many people to lose their jobs, this has an impact on increasing the number of unemployment and poverty in Indonesia, and can make the national economy experienced a crisis. The Family Welfare Empowerment Group should contribute to building the national economy by optimizing the family economy through Micro, Small, and Medium Enterprises (MSMEs) in the community.The method used in this research is qualitative research with a descriptive research type. Data collection techniques were carried out using interviews, observations, and documentation studies. The resource persons in this study were 8 members of the Cireng Rasaku Chips MSMEs. The results of this study are the process of empowering the family economy at the Cireng Rasaku Chips MSMEs. including the awareness process, the knowledge transfer process, and the enrichment process. Then, in this study, only three of the four indicators of empowerment were achieved, namely freedom of mobility, the ability to buy small commodities as well as economic security, and contribution to the family. The driving factor in this empowerment is the existence of a partnership program and adequate quality of human resources. The inhibiting factors include limited capital, limited marketing capabilities, limited access to business information, and selfish between members.

Community Engagement and Social Innovation Through Knowledge Transfer: Micro Evidence from Setiu Fishermen in Terengganu, Malaysia

The university’s involvement with the community cultivates the process of knowledge transfer. This knowledge transfer can be translated through social innovation, knowledge innovation, and technological innovation. Social innovation in the context of knowledge transfer can provide an appropriate platform for the exchange of creative and innovative ideas, research findings, experiences, and skills between university, industry, and society. The university’s involvement with the Setiu fishing community in collaboration with the Setiu Area Fishermen Association in Kampung Rhu Sepuluh, Setiu, has successfully changed the quality of life of Setiu fishermen through social innovation, where bonding, bridging, and linkages were introduced to the community. A study was conducted on the Setiu fishing community found that ‘community engagement’ and togetherness between the university and the community had brought a significant impact on the community’s lifestyle and living standard. The element of ‘togetherness’ throughout the project period was emphasised to ensure the participants to be independent in generating affordable income, improving their quality of life, and moving out of poverty. Supportive relationships and networking linkages from various stakeholders are needed to ensure their survival at a commendable level. This knowledge transfer project’s implications can be elaborated into four main components, namely new ideas, concepts, activities, services, and applications; meet social goals; improvement of results and output; and realising new collaborations with social values. In this study, an effective knowledge transfer of entrepreneurial skills was conducted, and the community had participated in sales competition during month of Ramadhan where community had increased of sales between 4 and 240%. In conclusion, a well-structured knowledge transfer of entrepreneurship had transformed community to be more resilient and competitive through bonding, bridging, and linkages of university-community-government partnership.

Orthogonal Transfer for Multitask Optimization

Knowledge transfer (KT) plays a key role in multitask optimization. However, most of the existing KT methods still face two challenges. First, the tasks may commonly have different dimensionalities (DDs), making the KT between heterogeneous search spaces very difficult. Second, the tasks may have different degrees of similarity in different dimensions, making that treating all dimensions with equal importance may be harmful to the KT process. To address these two challenges, this article proposes a novel orthogonal transfer (OT) method that is enabled by a cross-task mapping (CTM) strategy, which can achieve high-quality KT among heterogeneous tasks. For the first challenge, the CTM strategy maps the global best individual of one task from its original search space to the search space of the target task via an optimization process, which can handle the difference in task dimensionality. For the second challenge, the OT method is performed on the CTM-obtained individual and a random individual of the target task to find the best combination of different dimensions in these two individuals rather than treating all the dimensions equally, so as to achieve high-quality KT. To verify the effectiveness of the proposed OT method and the resulted OT-based multitask optimization (OTMTO) algorithm, this article not only uses the existing multitask optimization benchmark but also proposes a new benchmark test suite named multitask optimization problems (MTOPs) with DDs. Comprehensive experimental results on the existing and the proposed benchmarks show that the proposed OT method and the OTMTO algorithm are very advantageous in providing high-quality KT and in handling the heterogeneity of search space in MTOPs compared to the existing competitive evolutionary multitask optimization (EMTO) algorithms.

UPAYA PENINGKATAN PENGETAHUAN DAN PELATIHAN PENGOLAHAN KERIPIK DAN TEH KULIT BUAH PISANG MAS SEBAGAI PANGAN KAYA ANTIOKSIDAN DI KECAMATAN TAMANSARI KOTA TASIKMALAYA

Tamansari Village is one of the villages in Tasikmalaya City that occupies the highest case of Covid as of 2020. In 2020, knowledge has been increased by counseling on how to use vitamin C properly and correctly as well as processed herbs to increase immunity. As a target area, the knowledge transfer process was continued with training on the processing of banana mas peels in the form of chips and tea which had previously been researched on antioxidant activity in 2021. The target to be achieved is that cadres / communities can maintain immunity with healthy processed food and rich in antioxidants and be creative and can make processed products made from banana peels to be used as chips and tea as an opportunity for entrepreneurship so that it can increase family income and be able to analyze the feasibility of entrepreneurship in making snacks. The method of implementing community service activities is the provision of training and counseling on the process of making processed chips and banana mas peel tea. The implementation of community service activities will be carried out in 2022 with 4 meetings (exploration, implementation of phase I, implementation of phase II and evaluation). The results and outputs of service are the increase in public knowledge shown by an increase in post-test value by 80% and the creation of processed products of chips and banana peel tea shown by the existence of a video as an evaluation material for posyandu cadres in processing banana peels.

LAD: Layer-Wise Adaptive Distillation for BERT Model Compression.

Recent advances with large-scale pre-trained language models (e.g., BERT) have brought significant potential to natural language processing. However, the large model size hinders their use in IoT and edge devices. Several studies have utilized task-specific knowledge distillation to compress the pre-trained language models. However, to reduce the number of layers in a large model, a sound strategy for distilling knowledge to a student model with fewer layers than the teacher model is lacking. In this work, we present Layer-wise Adaptive Distillation (LAD), a task-specific distillation framework that can be used to reduce the model size of BERT. We design an iterative aggregation mechanism with multiple gate blocks in LAD to adaptively distill layer-wise internal knowledge from the teacher model to the student model. The proposed method enables an effective knowledge transfer process for a student model, without skipping any teacher layers. The experimental results show that both the six-layer and four-layer LAD student models outperform previous task-specific distillation approaches during GLUE tasks.

KONSEP BERKAH DALAM KONSUMSI MAKANAN HALAL: TINJAUAN AL-QUR’AN DAN SUNAH

Consuming halal products for Muslims is a condition for accepting acts of worship. In fact, it plays a role in shaping a person's behavior and personality, and has a major influence on the process of knowledge transfer. Ironically, the low of halal awareness makes people tend to ignore what they consume, so that blessings in life are lost. This study aims to explore the concept of blessing in halal food consumption activities using the perspective of the Qur'an and Sunna. This research is a literature study using a descriptive method with a qualitative approach. Explicitly, the Qur'an mentions God's commandment for humans to eat halal and tayyib food. This shows the relationship between the Qur'an and science, namely how nutritious and good food is to be consumed. On the other hand, Islam teaches the concept of blessing in consuming food so that it brings goodness to human life. The concept of blessing is widely discussed in the Qur'an and Sunna. This blessing is an important point in building an Islamic civilization

Public Response to General Election Dissemination on social media by the Indonesian General Election Commission

Socialization is a process of transferring knowledge through values and rules so that an individual can become a member of a group, and this knowledge transfer process can also be referred to as an ongoing learning process that impacts the formation of one's personality. Suppose the focus is on socialization by the Indonesian General Election Commission. In that case, the socialization efforts aim to form a community that understands the election process and the role of the community in the election stages. This effort is carried out by providing information containing the general election stages that are being carried out or will run. However, to what extent has the public responded to social media socialization efforts that the General Election Commission has carried out? This research will try to understand the public's response to the socialization that the General Election Commission carried out after the 2020 Simultaneous General Elections were held. The research data was obtained by conducting an online survey through online questionnaire media, distributed widely to research respondents from 16 February 2021 to 28 February 2021. The total number of research respondents reached 212 respondents from 24 provinces in Indonesia. Male respondents came to 144 people (64%), and the number of female respondents was 68 (32%). In processing data, this research uses Nvivo 12 Plus to assist researchers in processing qualitative data into more interactive data visualizations. The findings of this study indicate that the public's response to the dissemination of general elections through social media is very high; this suggests that social media has become the primary medium for the public to find information related to general elections. Furthermore, this research has also identified the tendency for the General Election Commission's social media account at the central level to provide the most information, namely the Indonesian General Election Commission. In addition, the public's assessment of account performance, the influence of socialization, and the quality of socialization have also been known. The last and no less important is the addition of public suggestions for socialization in the future.

Підвищення ефективності самостійної роботи здобувачів вищої освіти за умови дистанційної форми навчання

Independent work is an important component of professional training of specialists in the higher education system. The paper considers the importance o f independent work of higher éducation learners in the organization o f distance learning. It is aimed at mastering and consolidating the theoretical knowledge acquired by the student during his studies, deepening them, as well as acquiring and improving professional skills and abilities, competencies in accordance with the chosen specialty. Independent work maximally develops creative thinking, individual abilities, and research skills of higher education students, allows training of proactive specialists, develops scientific intuition, depth of thinking, creative approach to knowledge perception, and practical application in solving certain tasks. The transition to distance learning has significantly increased the role of independent work for higher education students. Distance learning is not just a tribute to fashion or a desire to copy the successful educational solutions of developed countries. It has been revealed that the teacher can create problem-based situations at all stages of the learning process, namely: when explaining, consolidating the material, during control of knowledge, when directing students to solve it. It has been found that the students' independent work obtains importance in distance learning mode. Distance education is not just a tribute to fashion or a desire to copy the successful educational decisions of developed countries. At the heart of the distance educational process is the independent cognitive activity of learners, self-education, the opportunity not only to instill the skills of independent acquisition of new knowledge, but, most importantly, their practical use. The popularity of distance education is due to the flexibility and availability of these technologies. Increasing the number of extracurricular hours encourages teachers to seek new forms of teaching material, to develop new teaching methods, as well as ways to manage self-independent cognitive activity of students, which are the basis of modern educational technologies. Distance education is a fundamentally new, high-tech approach to the process of knowledge transfer, a very flexible education system that helps to solve these problems. The advantages of distance learning systems of providing educational process have been identified. It is emphasized that the using of social networks in the organization of independent educational activity provides an opportunity to develop communication skills, enhance the information culture, exchange views during the performance of educational tasks. It is noted that for the effective organization of independent educational activities of higher education applicants must be developed appropriate methodological advices and documents.

Efficient Few-Shot Object Detection via Knowledge Inheritance.

Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recently. However, most existing methods ignore the efficiency issues, e.g., high computational complexity and slow adaptation speed. Notably, efficiency has become an increasingly important evaluation metric for few-shot techniques due to an emerging trend toward embedded AI. To this end, we present an efficient pretrain-transfer framework (PTF) baseline with no computational increment, which achieves comparable results with previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed. Within the KI initializer, we propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights. Finally, our approach not only achieves the SOTA results across three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits high efficiency with 1.8-100× faster adaptation speed against the other methods on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is the first work to consider the efficiency problem in FSOD. We hope to motivate a trend toward powerful yet efficient few-shot technique development. The codes are publicly available at https://github.com/Ze-Yang/Efficient-FSOD.

AN ANALYSIS OF THE KNOWLEDGE TRANSFER PARADOX IN PROJECTS WITH AN OPERATIONAL IMPROVEMENT OBJECTIVE IN MINES

The execution of operational improvement projects can create new knowledge that can be used to contribute to the success of future projects. A paradox exists, in that new knowledge is seldom transferred to future projects, and so the associated value-adding opportunities are lost. The objectives of this study are to determine whether lessons learned are captured during operational improvement projects, and to identify contextual factors that support or hinder the knowledge transfer processes. Through the use of interviews and subsequent data analysis, the research shows that the transfer of knowledge can be better facilitated by addressing the knowledge transfer inhibitors, recognising the value-adding potential of knowledge transfer during the execution of improvement projects, and creating a culture that is conducive to sharing knowledge – in particular, the lessons learned from mistakes and poor management practices.

Meta-GRS: A Graph Neural Network for Cross-Domain Recommender System via Meta-Learning

Despite the fact that Recommender Systems have been researched and developed for quite a while, they still hold some certain challenges, two of which are Cold Start and Data Sparsity. Being introduced as a savior, the Cross-domain Recommender system (CDR) owns the capacity to transfer knowledge across domains which makes it become the practical solution for the mentioned issues. In CDR approaches, the family of graph-based solutions is very effective, which builds graphs to illustrate the relationship between users, items, and other factors and learn their representations through graph representation learning. However, most graph-based approaches focus on extracting either domain-specific or domain-shared features and do not have the mechanism to prevent the transfer of private features, which can degrade the quality of vector representations. Moreover, the knowledge transfer process built on overlapping users makes the model biased toward these users, thus downgrading the performance on cold-start users. This paper proposes a meta-GRS framework that uses Graph Neural Network with Meta-Learning and Adversarial Learning for cross-domain recommendation. In meta-GRS, the representative user and item embeddings are improved thanks to the features extracted by the private and cross-domain graphs. Domain-shared features are learned under adversarial learning such that the domain discriminator is unable to determine whether the domain they came from can ensure the positive transfer. To optimize the model performance effectively, we use a Dynamic Weight Averaging algorithm to learn loss weights automatically. The model's parameters are optimized under the optimization-based meta-learning method provides our model the capability of generalization to the new users. Experiments on practical datasets illustrate that the meta-GRS leads the chart in the comparison of other state-of-the-art baselines in recommendation accuracy.

Automatic knowledge exchange between ontologies and semantic graphs

This article presents an innovative knowledge management approach for the validation and transfer of knowledge between semantic networks or ontologies and a target ontology represented in web ontology language (OWL) format. This process has been designed for addressing the quality improvement of ontologies automatically created by learning techniques. The knowledge transfer process is a semi-automatic computer aided method to assist the domain expert to improve the target ontology. To validate our proposal, we have used an automatically generated target ontology. We used knowledge transfer from the well-known Babelnet semantic graph and a manually generated ontology to improve the quality of the target ontology. Finally, to show the suitability of our proposal in the ontology-fixing process, we compare the improved target ontology resulting from the application of the proposed validation and knowledge transfer techniques with its original version. We developed an example of our proposal in our OntologyFixer tool, which is available on a GitHub repository ( https://github.com/gabyluna/OntologyFixer/tree/ontofixer_v2 ).

Rethinking the Knowledge Transfer Process Through the Use of Metaverse: A Qualitative Study of Organizational Learning Approach for Remote Workplace

Abstract This paper aims to explore the possibilities of implementing the tacit knowledge transfer process and practices in the metaverse and remote workplaces. Tacit knowledge transfer is crucial for organizational knowledge management and maintaining an organization's sustainable development. The research team believes that the use of the metaverse in remote workplaces is expected to revolutionize established organizational learning, knowledge management, and tacit knowledge transfer models. This research attempts to understand employees’ experiences from three perspectives: (1) the acceptance by both senior and junior staff of using the metaverse for training; (2) the experiences of tacit knowledge transfer over the metaverse; and (3) the role of immersion and interactivity during the knowledge transfer process. The significance of this research is the theoretical investigation of the tacit knowledge transfer model in the metaverse. This research is explorative in nature; therefore, this research is not going to generalize or prove the effectiveness of using the metaverse in the tacit knowledge transfer process, but rather explore the factors and deepen our understanding of these fundamental attributes. The findings of this research suggest a modification of the classical model SECI Matrix by adding a new component of “integration” in the transfer process.

Museumspädagogik im digitalen Raum und die Pandemiezeit ‒ ihre transitiven Prozesse und Rituale

As a specific pedagogical transformation of the above-mentioned cultural anthropological approach, the article attempts to answer the questions motivated by museum pedagogy. They are: a) What transitional processes are evident in the world of traditional museums because of digitalisation in the informal processes of museum knowledge transfer? b) How do these processes affect the behaviour of museum visitors and the rituals of the museum visit itself? The topicality of the research to be presented is that museums closed because of the COVID pandemic have been permanently transformed into virtual space, which has fundamentally changed the range of museum content and their museum education programmes. Research complementing the topic analyses the websites of Hungarian museums and examines the main characteristics of virtual visitors (need-based information, search, and user activities) and changes in visitor behaviour. It analyses a) how visitor rituals develop, b) how specific elements of these specific transitional changes appear in cyberspace. The research intervals were Spring quarantine 2020, reopening in summer and closure period from autumn 2020 to spring 2021.