Knowledge Model Research Articles

Measurement of Employee Information Security Awareness: A Case Study of National Civil Service Agency

National Civil Service Agency is a State institution tasked with the role and function of overseeing and implementing national civil servant management using information technology. There are 4.2 million civil servant data distributed throughout Indonesia that must be safeguarded by BKN. As the utilization of information systems grows, it also leads to an increase in information security risks. Based on the reports from Id-SIRTII/CC and BKN's internal report, there has been an increase in cyber attacks targeting BKN. In addition, there are other types of attacks that occur, such as online defacement, phishing, DDOS, and employee data theft, as well as the presence of employees who are still indifferent to information security. Based on this, the objective of this research is to measure the level of information security awareness among BKN employees and identify the factors that influence it. The Human Aspects of Information Security Questionnaire (HAIS-Q) using the Knowledge, Attitude, and Behavior (KAB) model was selected for measurement, with an additional focus on the Management of Information Systems/Technology Assets, consisting of a total of 75 statements. The quantitative measurements conducted yielded a result of 88.80% for the level of information security awareness among BKN employees, categorized as good. Furthermore, there is a significant influence on information security awareness from the dimensions of knowledge towards attitude, attitude towards behavior, and knowledge towards behavior.

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition.

Audio-visual video recognition (AVVR) integrates audio and visual cues to accurately categorize videos. While current methods using provided datasets achieve satisfactory results, they face challenges in retaining historical class knowledge when new classes appear in real-world situations. There are no dedicated methods to address this issue, prompting this paper to explore Class Incremental Audio-Visual Video Recognition (CIAVVR). CIAVVR aims to preserve historical knowledge contained in stored data and learned models to prevent catastrophic forgetting. Audio-visual data and models inherently have hierarchical structures, where the model contains both low-level and high-level semantic information, and data includes snippet-level, video-level, and distribution-level spatial information. It is crucial to fully exploit these hierarchical structures for data knowledge preservation and model knowledge preservation. However, existing image class incremental learning methods do not explicitly consider these hierarchical structures. Therefore, we introduce Hierarchical Augmentation and Distillation (HAD), which includes the Hierarchical Augmentation Module (HAM) and Hierarchical Distillation Module (HDM). These modules efficiently utilize the hierarchical structure of data and models. Specifically, HAM uses a novel augmentation strategy, segmental feature augmentation, to preserve hierarchical model knowledge. Simultaneously, HDM employs newly designed hierarchical logical distillation (video-distribution) and hierarchical correlative distillation (snippet-video) to maintain intra-sample and inter-sample hierarchical knowledge. Evaluations on four benchmarks (AVE, AVK-100, AVK-200, and AVK-400) show that HAD effectively captures hierarchical information, enhancing the preservation of historical class knowledge and performance. We also provide a theoretical analysis to support the segmental feature augmentation strategy.

Transductive Learning With Prior Knowledge for Generalized Zero-Shot Action Recognition

It is challenging to achieve generalized zero-shot action recognition. Different from the conventional zero-shot tasks which assume that the instances of the source classes are absent in the test set, the generalized zero-shot task studies the case that the test set contains both the source and the target classes. Due to the gap between visual feature and semantic embedding as well as the inherent bias of the learned classifier towards the source classes, the existing generalized zero-shot action recognition approaches are still far less effective than traditional zero-shot action recognition approaches. Facing these challenges, a novel transductive learning with prior knowledge (TLPK) model is proposed for generalized zero-shot action recognition. First, TLPK learns the prior knowledge which assists in bridging the gap between visual features and semantic embeddings, and preliminarily reduces the bias caused by the visual-semantic gap. Then, a transductive learning method that employs unlabeled target data is designed to overcome the bias problem in an effective manner. To achieve this, a target semantic-available approach and a target semantic-free approach are devised to utilize the target semantics in two different ways, where the target semantic-free approach exploits prior knowledge to produce well-performed semantic embeddings. By exploring the usage of the aforementioned prior-knowledge learning and transductive learning strategies, TLPK significantly bridges the visual-semantic gap and alleviates the bias between the source and the target classes. The experiments on the benchmark datasets of HMDB51 and UCF101 demonstrate the effectiveness of the proposed model compared to the state-of-the-art methods. The source code of this work can be found in https://mic.tongji.edu.cn.

INTEGRAÇÃO DAS TDIC NA FORMAÇÃO DE PROFESSORES: APROXIMAÇÕES ENTRE O MODELO TPACK E A ABORDAGEM SOCIOCULTURAL

ABSTRACT: We aimed to conduct a theoretical discussion of the elements that enable reading of the Pedagogical and Content Technological Knowledge (TPACK) model from the perspective of the sociocultural approach. To this end, we present the conceptual structures of the TPACK model and introduce information and communication technologies (ICT) as cultural and symbolic tools to mediate the teacher's pedagogical action. By assuming the concepts of mediation, mastery, and appropriation, arising from the sociocultural approach, we establish a dialogue with the TPACK model to highlight the possible contributions of this reinterpretation to teacher training today. Finally, we discuss the implications of this approach for initial teacher education for and with digital technologies. We hope we will contribute to the discussions underlying teacher research and training, supporting conceptualization that is useful for the research into the modes of interaction mediated by ICT, as well as to the discussion about the factors involved in the ways teachers access, capture, and build professional knowledge in the interface with digital technologies.

Activity-Based Detection of (Anti-)Patterns: An Embedded Case Study of the Fire Drill

Background: Nowadays, expensive, error-prone, expert-based evaluations are needed to identify and assess software process anti-patterns. Process artifacts cannot be automatically used to quantitatively analyze and train prediction models without exact ground truth. Aim: Develop a replicable methodology for organizational learning from process (anti-)patterns, demonstrating the mining of reliable ground truth and exploitation of process artifacts. Method: We conduct an embedded case study to find manifestations of the Fire Drill anti-pattern in n=15 projects. To ensure quality, three human experts agree. Their evaluation and the process’ artifacts are utilized to establish a quantitative understanding and train a prediction model. Results: Qualitative review shows many project issues. (i) Expert assessments consistently provide credible ground truth. (ii) Fire Drill phenomenological descriptions match project activity time (for example, development). (iii) Regression models trained on approx. 12–25 examples are sufficiently stable. Conclusion: The approach is data source-independent (source code or issue-tracking). It allows leveraging process artifacts for establishing additional phenomenon knowledge and training robust predictive models. The results indicate the aptness of the methodology for the identification of the Fire Drill and similar anti-pattern instances modeled using activities. Such identification could be used in post mortem process analysis supporting organizational learning for improving processes.

USER: Unified Semantic Enhancement With Momentum Contrast for Image-Text Retrieval.

As a fundamental and challenging task in bridging language and vision domains, Image-Text Retrieval (ITR) aims at searching for the target instances that are semantically relevant to the given query from the other modality, and its key challenge is to measure the semantic similarity across different modalities. Although significant progress has been achieved, existing approaches typically suffer from two major limitations: (1) It hurts the accuracy of the representation by directly exploiting the bottom-up attention based region-level features where each region is equally treated. (2) It limits the scale of negative sample pairs by employing the mini-batch based end-to-end training mechanism. To address these limitations, we propose a Unified Semantic Enhancement Momentum Contrastive Learning (USER) method for ITR. Specifically, we delicately design two simple but effective Global representation based Semantic Enhancement (GSE) modules. One learns the global representation via the self-attention algorithm, noted as Self-Guided Enhancement (SGE) module. The other module benefits from the pre-trained CLIP module, which provides a novel scheme to exploit and transfer the knowledge from an off-the-shelf model, noted as CLIP-Guided Enhancement (CGE) module. Moreover, we incorporate the training mechanism of MoCo into ITR, in which two dynamic queues are employed to enrich and enlarge the scale of negative sample pairs. Meanwhile, a Unified Training Objective (UTO) is developed to learn from mini-batch based and dynamic queue based samples. Extensive experiments on the benchmark MSCOCO and Flickr30K datasets demonstrate the superiority of both retrieval accuracy and inference efficiency. For instance, compared with the existing best method NAAF, the metric R@1 of our USER on the MSCOCO 5K Testing set is improved by 5% and 2.4% on caption retrieval and image retrieval without any external knowledge or pre-trained model while enjoying over 60 times faster inference speed. Our source code will be released at https://github.com/zhangy0822/USER.

Semantic-Oriented Visual Prompt Learning for Diabetic Retinopathy Grading on Fundus Images.

Diabetic retinopathy (DR) is a serious ocular condition that requires effective monitoring and treatment by ophthalmologists. However, constructing a reliable DR grading model remains a challenging and costly task, heavily reliant on high-quality training sets and adequate hardware resources. In this paper, we investigate the knowledge transferability of large-scale pre-trained models (LPMs) to fundus images based on prompt learning to construct a DR grading model efficiently. Unlike full-tuning which fine-tunes all parameters of LPMs, prompt learning only involves a minimal number of additional learnable parameters while achieving a competitive effect as full-tuning. Inspired by visual prompt tuning, we propose Semantic-oriented Visual Prompt Learning (SVPL) to enhance the semantic perception ability for better extracting task-specific knowledge from LPMs, without any additional annotations. Specifically, SVPL assigns a group of learnable prompts for each DR level to fit the complex pathological manifestations and then aligns each prompt group to task-specific semantic space via a contrastive group alignment (CGA) module. We also propose a plug-and-play adapter module, Hierarchical Semantic Delivery (HSD), which allows the semantic transition of prompt groups from shallow to deep layers to facilitate efficient knowledge mining and model convergence. Our extensive experiments on three public DR grading datasets demonstrate that SVPL achieves superior results compared to other transfer tuning and DR grading methods. Further analysis suggests that the generalized knowledge from LPMs is advantageous for constructing the DR grading model on fundus images.

Early concurrent engineering in the aerospace industry supported by a Digital Thread framework

The traditional sequential aircraft design process is reaching its limits in an increasingly complex environment. Therefore, it has become an industrial necessity to implement radical change in working methods. This paper focuses on the conceptual design phase, where concurrent engineering offers the greatest potential to improve the costs, quality, and development time of a new aircraft. It pays particular attention to the knowledge management and modeling activities used to provide global perspectives that are inherently lacking in early phases. A digital thread framework is presented linking model-based systems engineering artifacts from product and manufacturing systems, coupled with knowledge capture methods. This framework aims to enable early trade-of analysis to support concurrent engineering during the conceptual design phase.

On cultivation of cybersecurity and safety talents and responsible developers

To address the serious imbalance between the supply and demand of the cybersecurity workforce, this paper proposes to embrace the latest trend of a fundamental shift in the “underlying dynamics of the digital ecosystem”, focusing on a shared liability for cybersecurity between the application side and the manufacturing side. Assuming that product providers shall take more responsibility by implementing secure defaults, this paper explores the establishment of an S&S talent cultivation system to strike the right balance of cybersecurity liabilities by nurturing more responsible developers. This paper proposes a Knowledge, Skill, and Awareness (KSA) model for Security and Safety (S&S) talent cultivation, proves the feasibility of this model by analyzing the theoretical, disciplinary, methodological, practical, and societal foundations of S&S talent cultivation. Additionally, this paper proposes principles and strategies for building a S&S talent cultivation system based on its unique characteristics and patterns. It gives a talent cultivation scheme, supported by an “Independent Knowledge System, Education and Cultivation System, Practice and Training system, Evaluation and Certification system, and Awareness Popularization System”. Finally, this paper puts forward a proposal for coordinating efforts and adopting multiple measures to accelerate the cultivation of S&S talents.

Actors Impacting Teachers’ Behavioral Intention to Use the Interactive Whiteboard in Hebei, China

The present study aimed to investigate factors impacting teachers’ Behavioral Intention (BI) to use the Interactive Whiteboard (IWB) based on the theoretical foundations of Technological Pedagogical Content Knowledge (TPACK) and Technology Acceptance Model (TAM). Specifically, it examined the effect of TPACK on teachers’ BI to use the IWB and mediating effects of Perceived Usefulness (PU) and Perceived Ease of Use (PEU) in the relationship between the teachers’ TPACK and BI to use this educational tool. To achieve these objectives, a quantitative research design was employed. Data were collected using a questionnaire, involving a total of 794 high school teachers who participated in this study. Structural Equation Modeling (SEM) was used for the data analysis, whereby the results revealed that TPACK moderately influenced teachers’ BI to use the IWB. Additionally, PU and PEU were influential mediators in the relationship between TPACK and teachers’ BI to use this tool. Accordingly, the findings provided empirical evidence on the use of IWB and important input for educational authorities and institutions on further improvements with regard to the IWB and its use in schools. Moreover, the findings indicated a need for training to enhance teachers’ skills in using the IWB within the classroom setting. Finally, research limitations and suggestions for future studies were discussed.

“NADA SOBRE NÓS SEM CORPO COMUM”: OUTRO PARADIGMA PARA INCLUSÃO NO ENSINO SUPERIOR?

ABSTRACT: This article problematizes the ethical dimension ignored by the knowledge on disability and the epistemes used to designate unmeasured power in the body in which it is imprinted, even with the achievements obtained by its political movements in recent decades and its capture by current devices of inclusion in Brazilian higher education. To this end, we resort to the genealogical method and the ethical problematic of the “indignity of speaking for others” enunciated by Michel Foucault to situate, historically, at what moment the political movements of people with disabilities assume this statement in some way and what are the limits of its effects of power in Brazilian higher education. We aim to critically analyze the internal tensions of such movements, the signs of evasion from the medical norm and social normality, intending to problematize the models of knowledge that were based on them, as well as highlighting the emerging powers of a common body like any other in their struggles for inclusive education and social justice. In this sense, we debate for the formation of a “common body” in higher education, constituted from the encounters of knowledge and differences, syndicating for the practice of radical alterity and the mobilization of the becoming -minority of the “people lacking” as a possibility of the emergence of another paradigm of inclusion.

Presenting a Commercialization Model of Knowledge Emphasizing the Role of Science and Technology Parks in the Relationship between University and Industry (A Case Study of Knowledge-Based Companies in the Central Province Science and Technology Park)

Objective: The commercialization of knowledge and technology is an important part of the innovation process. Commercialization implies scaling up from prototype to mass production and gaining access to more resources. Methodology: This research, by nature, is exploratory and was conducted using a descriptive-analytical method based on grounded theory, aiming to identify the components of knowledge commercialization with an emphasis on the role of science and technology parks in Iran. In the qualitative part, purposive sampling was employed with 12 individuals, where this type of sampling involves selecting units of study based on the research goal rather than at random. In the quantitative part, stratified random sampling was used, and samples were selected for each of the groups mentioned above proportionally to their population relative to the entire statistical population, using the Krejcie and Morgan table, resulting in the use of 300 questionnaires and ultimately, 232 questionnaires were collected and analyzed. For data analysis, factor loadings, Cronbach's alpha tests, composite reliability, and average variance extracted (AVE), the PLS algorithm was utilized. Findings: The findings indicated that ten dimensions have been identified as components of knowledge commercialization based on the role of science and technology parks: educational-scientific dimension, financial-economic dimension, management dimension, process structure dimension, communicational dimension, legal dimension, socio-cultural dimension, support dimension, opportunities dimension, and threats dimension. Conclusion: The results show that the cultural and communicational dimensions are the most effective components, and the threats dimension is the least effective component of knowledge commercialization.

ИССЛЕДОВАНИЕ МЕТОДОВ И АЛГОРИТМОВ РЕДУКЦИИ НЕЧЕТКИХ ПРАВИЛ В МОДЕЛЯХ ОЦЕНКИ ДИСКРЕТНОГО СОСТОЯНИЯ ОБЪЕКТОВ

The article describes the stages and results of the study of methods and genetic algorithms used to reduce fuzzy rules in models for assessing the discrete state of objects. Such models are fuzzy knowledge bases and fuzzy logical inference algorithms. A study was conducted of the influence of the parameter values of genetic algorithms on the results of reduction of the initial and intermediate knowledge bases (on the reduction of the number of rules in the knowledge bases and on the values of classification quality metrics). The following stages of research were implemented: selection of data sets from publicly available sources, formation of research knowledge bases, study of the influence of genetic algorithm parameter values on the results of knowledge base reduction, analysis of research results and development of recommendations for choosing optimal values of genetic algorithm parameters to achieve the best reduction results. Data for analysis was obtained from the public source UCI Machine Learning Repository. The selected sets are: Iris, Banknote Authentication and User Knowledge Modeling. To create research knowledge bases, the software package “Neuro-fuzzy system for generating fuzzy models for assessing the discrete state of objects” was used. Based on the analysis of the Iris data set, 3 research knowledge bases were formed, including 269, 339 and 542 fuzzy rules. For the Banknote Authentication data set, corresponding knowledge bases with 335, 643 and 1038 fuzzy rules were generated. For the User Knowledge Modeling set - with 877, 2094 and 2797 rules. For each of the knowledge bases, the values of the classification quality metrics Accuracy, Precision, Recall and F1-Score were calculated on the training and test samples. The reduction of knowledge bases was carried out in two successive stages, at each of which the corresponding genetic algorithm was used. Based on the results of the research, recommendations were developed for choosing the optimal values of the parameters of each of the genetic algorithms. The reduction of research knowledge bases based on the developed methods and algorithms made it possible to significantly reduce the number of their fuzzy rules (by an average of 85%) and at the same time slightly increase the generalizing ability of fuzzy models (by an average of 1.5%). In general, this made it possible to reduce the complexity of fuzzy models and their constituent knowledge bases, increase the accuracy of estimating the discrete state of objects and improve the interpretability of the generated solutions.

Applying artificial intelligence in teaching music for primary education students at Tan Trao University

Artificial Intelligence (AI) is driving broader and deeper changes in higher education. The application of AI in university teaching includes learner data collection, building learner models, constructing domain knowledge models, and developing teaching models. However, there are still some challenges in applying AI in teaching higher education, primarily concerning the understanding of the relationship between AI and humans, establishing educational goals in AI applications, and ensuring data safety and risks. The article is based on the theory of music teaching and the results of applying AI in teaching to highlight the current situation of music education for primary education students at Tan Trao University. Through an analysis of the current situation, we propose five solutions to apply AI in music education and organize pedagogical experiments to verify feasibility, with the aim of innovating teaching methods and enhancing teaching quality.

Informationization Construction of Civic Education in Colleges Based on Big Data Technology

Abstract This paper explores the application of big data technology in daily civic education in colleges, and visualizes the data with heterogeneous systems, on which the result is based to design the education evaluation data model. Calculating the total evaluation value of the teachers in the field of Civics and Political Science, combining with the sector area ratio division algorithm to realize the uniform display of scientific research achievements in the field of Civics and Political Science, and completing the construction of the knowledge model of Civics and Political Science education in colleges with above procedures. The appropriate learning path of Civics informatization for the learners is projected with the ant colony algorithm, which is based on the adaptive test ability value of the learners generated from item response theory. The construction of civic education information in colleges is done using big data technology. The results show that after a period of learning in the mode of informatization of Civic and Political Education, the mean value of the students in the experimental class scores 10 points higher than that of the control class learning. Overall students are more agreeable to the role of informatization of Civic and Political Education in colleges and the construction of informatization of Civic and Political Education in colleges has achieved good results.

A knowledge-driven framework and methodology for precision assembly process decision making

Precision assembly process planning technology is one of the major challenges in the field of assembly. Compared with general assembly process planning, precision assembly process planning needs to consider a more complex assembly process, higher precision requirements, and more difficult process control. For this reason, it is of great significance to implement an intelligent assembly process planning and decision-making method for the development of precision assembly technology. This paper realizes the intelligent planning of precision assembly processes through the decision-making route of knowledge modeling and knowledge reasoning. Firstly, a theoretical system of a knowledge-driven precision assembly process decision-making framework is proposed. Secondly, a key precision assembly process decision-making technology is described in detail, which introduces a new approach for knowledge updates based on matching rules. Finally, the effectiveness of the knowledge update method is demonstrated through experimental cases. The knowledge-driven decision-making method combines the accuracy of domain experts’ empirical knowledge with the efficiency of intelligent reasoning algorithms. The knowledge update method ensures the currency of knowledge used for decision making. Therefore, the proposed precision assembly decision-making system framework and knowledge updating techniques in this paper are of great significance for knowledge-driven precision assembly process planning.

Sustaining hybrid teaching as effective pedagogy at higher education institutions: Students' voices.

There is a dire need to ensure higher education institutions employ effective pedagogy in their educational programs. Hybrid teaching has received increased attention in recent decades due to its effectiveness in improving learning outcomes. There is a lack of studies exploring students' views on hybrid teaching in acupuncture programs in the South African context. Therefore, this study explored acupuncture students' views of hybrid teaching in the acupuncture program. The Technological, Pedagogical, and Content Knowledge Model was anchored in this study as a theoretical lens. The author employed a qualitative single case study design within an interpretivist paradigm. A purposive sampling technique was adopted to recruit six participants for this study from an identified public university in South Africa. Participants were interviewed between March 2023 and May 2023. Data were analyzed through thematic analysis. The findings of this study highlight positive student views of the hybrid model, emphasizing its benefits in optimizing the teaching and learning process. However, participants also identify barriers such as poor infrastructure and the absence of computer skills. The study underscores the need for substantial investment in technological facilities and faculty development programs to enhance lecturers' digital pedagogical competencies. While offering valuable insights into the specific context studied, limitations include the single-case design and the focus on a particular student cohort. Despite these limitations, the research contributes to the ongoing discourse on effective teaching methodologies in the dynamic landscape of higher education. Recommendations are proposed to address the identified challenges and foster an inclusive and technologically advanced learning environment in South African HEIs.

COVID-19 Pandemic: From Population Need to Professional Nursing Competence.

Nursing has proven to be an essential healthcare profession, especially in the face of the COVID-19 pandemic crisis. In this chapter, it shows the essential aspects of the discipline of care and its application in the face of the pandemic from an Informatics Nursing approach. The conceptual bases include the conception of care and its historical evolution. Thus, the Personal Care Knowledge Model, the clinical care sequence and its standardized languages allow Taxonomic Triangulation to be developed. Taxonomic Triangulation is a technique created by nurses that allows managing information and that served to extract knowledge from documents and clinical experiences. The application of this vision of care and its knowledge management models have been tested in different situations: from the identification of care diagnoses in a World Health Organization clinical guide to the design of a care plan manual in a hospital. On the other hand, a secondary result is the resilience shown by the nurses. A resilience based on theoretical models centered on the person and on a language that can represent life from care. In addition, nursing includes a comprehensive perspective that addresses the emotional and spiritual area. In conclusion, nurses and their specialization with skills in knowledge management allow giving visibility to care. A professional care whose purpose is to improve health systems through solutions based on care so that people can achieve their best health situation.

Developing and Validating the Contextual Technology Andragogy/Pedagogy Entrepreneurship Work Content Knowledge Model: A Framework for Vocational Education

Developing and Validating the Contextual Technology Andragogy/Pedagogy Entrepreneurship Work Content Knowledge Model: A Framework for Vocational Education