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This paper aims to discuss the significance of teacher authorship (jissen kiroku) developed during jugyo kenkyu. Specifically, it explores the structural conditions of jugyo kenkyu that enabled the flourishing of jissen kiroku.

To find how jissen kiroku developed in jugyo kenkyu, this paper settled triad of authors-text-readers as the analytical perspective. Disputes through 1960s–1980s are adequate to inquire because it can elucidate how readers read jissen kiroku, which is typically challenging to observe.

Jissen kiroku is a powerful tool for semantically preserving, reconstructing and consolidating professional values and knowledge in jugyo kenkyu with deepening connoisseurship. Voluntary educational research associations (VERAs) encourage teachers to write and read jissen kiroku to develop their professionalism, which also helped develop exclusive semantics within the field. These developments were possible due to the public nature of jissen kiroku, disseminated to lesson study (LS) actors, thereby strengthening discussions both inside and outside VERAs.

The paper proposes shift in views on educational science and emphasizes authorship as authority in that professionalism of teaching can be protected and elevated through authoring.

The significant roles of writing practice have not been explored enough. This paper finds the value of authorship in terms of public nature and openness to all teachers which enable the enhancement of professionalism of the LS field.

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

Successful implementation of a building information modeling (BIM) for building operation and maintenance (O&M) requires purposeful, early-design identification of end-user-specific model exchange requirements. This paper aims to provide a semantic data-rich classification system for model objects to convey facilities management (FM) requirements in BIM guidelines in support of efficient FM-BIM data workflows.

A modularized, repeatable and technical solution for semantic requirements of BIM exchange objects was developed through ontology-based data mapping of the industry foundation classes. The proposed solution further contextualizes syntax per the buildingSMART Data Dictionary schema and provides an implementation agreement to address the quality issues of discipline BIMs and establish consistent modeling and naming conventions to facilitate automated BIM data workflow.

The level of semantics (LOS) development framework and the results of LOS implementation focusing on a building mechanical system case project are presented and discussed to showcase the increased efficiency resulting from its implementation throughout the BIM data management workflows.

This study represents a pioneering effort to create and implement the LOS schema as a modularized solution in support of automatic BIM data creation, adjustment, verification and transition across the design, construction and O&M workflows of a large owner organization in the Midwest USA.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

The purpose of this paper was to develop a quantitative classroom observation method that is able to analyse the school day to identify Time-on-Task losses comprehensively and systematically, at a level of detail that can be used by teachers and principals to stimulate and focus practical improvement efforts.

The novel Time-on-Task Analysis (TOTA) model was developed by triangulating the conceptual framework of the Overall Equipment Effectiveness metric with the semantics and structure of the target domain. Once developed, the model was tested structurally against a time-series classroom observation data set, after which the resulting TOTA was presented to a sample of 52 education stakeholders, who then gave their perspectives of the analysis in a structured survey.

The ontological model was found to be accurate, complete and without conceptual incongruencies, and its output novel and useful by the sample of education stakeholders. Of the participants, 90.3% found the analysis to provide a new perspective, 94.2% reported that the analysis triggered improvement ideas and 80.8% thought that their school(s) could benefit from a TOTA study.

The TOTA model introduces a time-loss-focused perspective to the field of quantitative classroom observation studies, which is dominated by more sociologic- and pedagogic-focused topics. Its grounding in Overall Equipment Effectiveness also gives it a more detailed and systematic approach than the few Time-on-Task studies done to date, resulting in a model made for the “Gemba”: the school classroom.

This conceptual paper presents a proposal for improving a performance measurement (PM) system implementation process based on enterprise engineering (EE) guidelines, which gives the process a sense of completeness.

This paper analyzes a well-known process for PM systems implementation organized in two phases: identifying, designing and implementing the top-level performance measures; and cascading the top-level measures and identify appropriate lower-level performance measures. The proposed improvements to the studied process derive from the EE guidelines, which establish a basis for the structure of an organizational management system, the formalization and synchronization of processes, performance expectations, exception handling and change management.

The study reveals that not all EE guidelines are covered by the analyzed process, with four of them having no evidence of being adopted: involvement of people in process design and implementation; ensuring interoperability between different systems in the information structure; addressing of all possible exceptions; coherence and consistency of semantics across all processes.

By the lens of EE guidelines, this paper advances a how-to-guide. This paper can support managers and researchers on PM system design and implementation, given the importance and relevance of EE recommendations having a consistent and well-structured procedure.

This article explores how the Danish public sector, over time, has followed different temporal strategies in order to extend the present and handle the system's increasing complexity, thereby counteracting a tendency towards entropy. It proposes that historical changes in the public sector's understandings of the concepts of “time” and “change” can be seen as the answer to the sector's enduring problem of ever-increasing complexity.

The authors conduct second-order observations of how the Danish public sector, in the period from 1900 until 2020, observes “time” and “change”. More specifically, they first observe how issues over time are temporalized in different forms, before employing the guiding distinction, operation/temporalization, to analyse the differences between temporalities.

The authors show that, today, the Danish public sector deals with the problems of complexity and entropy through, what is called, potentialization. Potentialization entails operations that aim to increase potentialities, rather than realize possibilities within a given potentiality. It works by extending the present, drawing on a particular temporality which is split into a present present and a future future.

The paper offers managers insights into the implications of their own observations of time and change, including how they might draw on different temporal semantics, through which managerial situations emerge differently. The paper also reveals that issues of transformation are not always about transformation, rather they concern the question of how to handle an increasing internal complexity.

The article shows that potentialization and its temporal semantic of “transformation” also comes with a price – namely that it dissolves the certainties of structures, which results in conflicting expectations.

The paper draws on systems theory, including its notions of time and entropy, to analyse the evolution of public administration and management. It thereby produces a diagnosis of the present which offers insights into contemporary conditions for public management.

This study examines the relationship between streamers' product descriptions, customer comments and online sales and focuses on the moderating effect of streamer–viewer relationship strength.

Between June 2021 and April 2022, the structured data of 965 livestreaming and unstructured text data of 42,956,147 characters from two major live-streaming platforms were collected for the study. Text analysis and regression analysis methods were employed for data analysis.

First, the authors' analysis reveals an inverted U-shaped relationship between comment length and product sales. Notably, comment volume and comment emotion positively influence product sales. Furthermore, the semantic richness, emotion and readability of streamers' product descriptions also positively influence product sales. Secondly, the authors find that the strength of streamer–viewer relationship weakens the positive effects of comment volume and comment emotion without moderating the inverted U-shaped effect of comment length. Lastly, the strength of streamer–viewer relationship also diminishes the positive effects of emotion, semantics and readability of streamers' product descriptions on product sales.

This study is the first to concurrently examine the direct and interactive effects of user-generated content (UGC) and marketer-generated content (MGC) on consumer purchase behaviors in livestreaming e-commerce, offering a novel perspective on individual decision-making and cue utilization in the social retail context.

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.

This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.

The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.

The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.