Entities In Context Research Articles

T-SRE: Transformer-based semantic Relation extraction for contextual paraphrased plagiarism detection

Plagiarism has become a pervasive issue in academics and professionals to safeguard academic integrity and intellectual property rights. The escalating sophistication of plagiarized content through semantic manipulation and structural reorganization poses significant challenges to existing detection systems that rely primarily on lexical similarity measures. The proposed T-SRE (Transformer-based Semantic Relation Extraction), a novel framework addresses the limitations of traditional n-gram and string-matching approaches by leveraging deep semantic analysis. The proposed framework combines Dependency Parsing (DP) for syntactic relationship mapping and Named Entity Recognition (NER) for contextual entity identification, augmented by a transformer-based neural network that captures long-range contextual dependencies. This learning methodology incorporates three key components: a position-aware word reordering algorithm, Levenshtein distance metric for structural similarity, and contextual word embeddings for semantic preservation detection. The proposed T-SRE enhances text structure recognition by combining position-aware reordering with semantic preservation through ensemble learning. The system implements a hierarchical classification scheme that quantifies plagiarism severity through a four-tier taxonomy: heavy, low, non-plagiarized and verbatim copy. The Udacity benchmark dataset showcases the model’s superior detection capabilities, achieving 92% precision, 89% recall, and an F1-score of 90.5%, particularly in lightweight textual modifications.The framework achieves a granularity score of 1.28, outperforming existing approaches.

DELIBERATIVE DEMOCRACY AND GENDER EQUALITY: CASE STUDY OF THE 2023 WOMEN'S SOCCER CUP IN SOCIAL AUDIT AND CITIZEN TRANSPARENCY

This article aims to analyze deliberative democracy, based on Jürgen Habermas' theory, applying it to the context of public and private entities, with a focus on gender equality in the 2023 Women's Football World Cup. The article will present a replicable model of social auditing, integrating Habermas' communicative rationality with the guidelines of the Brazilian Courts of Auditors. A connection will also be established between communicative rationality and administrative practices for the management of complex systems, since they share foundations of validity, correctness, sincerity, and truthfulness. The questions are based on the reports produced by the international media during the mega-event, using gender inequality as a category. The central argument lies in measuring the permeability of democratic civic common sense and identifying barriers to communication and effective engagement. The article seeks to contribute to citizen education and the integration of theory and practice in the face of the challenges of participatory democracy. The research presents two important results, the first confirms recent research on gender inequality in sport, as media coverage demonstrated disparities in women's football, and the second is the failure to comply with the rules of citizen transparency of sports entities.

Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction

Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a Knowledge Graph (KG) with distinct benefits: 1) Our approach amalgamates entity context and document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on benchmark datasets - DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based Knowledge Graph link prediction techniques can enhance the performance of document-level relation extraction models.

Hierarchical Context-Based Emotion Recognition With Scene Graphs.

For a better intention inference, we often try to figure out the emotional states of other people in social communications. Many studies on affective computing have been carried out to infer emotions through perceiving human states, i.e., facial expression and body posture. Such methods are skillful in a controlled environment. However, it often leads to misestimation due to the deficiency of effective inputs in unconstrained circumstances, that is, where context-aware emotion recognition appeared. We take inspiration from the advanced reasoning pattern of humans in perceived emotion recognition and propose the hierarchical context-based emotion recognition method with scene graphs. We propose to extract three contexts from the image, i.e., the entity context, the global context, and the scene context. The scene context contains abstract information about entity labels and their relationships. It is similar to the information processing of the human visual sensing mechanism. After that, these contexts are further fused to perform emotion recognition. We carried out a bunch of experiments on the widely used context-aware emotion datasets, i.e., CAER-S, EMOTIC, and BOdy Language Dataset (BoLD). We demonstrate that the hierarchical contexts can benefit emotion recognition by improving the accuracy of the SOTA score from 84.82% to 90.83% on CAER-S. The ablation experiments show that hierarchical contexts provide complementary information. Our method improves the F1 score of the SOTA result from 29.33% to 30.24% (C-F1) on EMOTIC. We also build the image-based emotion recognition task with BoLD-Img from BoLD and obtain a better emotion recognition score (ERS) score of 0.2153.

DATA CONFIDENTIALITY AND INTEGRITY: A REVIEW OF ACCOUNTING AND CYBERSECURITY CONTROLS IN SUPERANNUATION ORGANIZATIONS

In an era dominated by digital transformation, superannuation organizations face unprecedented challenges in safeguarding the confidentiality and integrity of sensitive financial data. This review explores the intricate relationship between accounting practices and cybersecurity controls within the context of superannuation entities. By examining the existing literature, regulatory frameworks, and industry best practices, this paper synthesizes the key considerations essential for ensuring robust data protection. The study delves into the critical role of accounting systems in managing financial information and the subsequent implications for data confidentiality. It investigates how evolving accounting standards and practices intersect with cybersecurity protocols to fortify the integrity of financial records within superannuation organizations. The dynamic nature of cyber threats necessitates a comprehensive analysis of technological safeguards, risk management frameworks, and compliance measures to uphold data confidentiality. Furthermore, the review underscores the imperative for a multidimensional approach to cybersecurity in the superannuation sector. It discusses the integration of advanced technologies such as encryption, blockchain, and anomaly detection alongside traditional accounting controls to create a resilient defense against emerging threats. The exploration extends to the examination of employee training programs, incident response strategies, and third-party risk assessments as integral components of a comprehensive cybersecurity posture. As superannuation organizations navigate the complex landscape of data management, a holistic understanding of the interplay between accounting and cybersecurity controls becomes paramount. This review contributes to the existing body of knowledge by providing insights into the challenges and opportunities presented by the evolving technological landscape, offering practitioners and policymakers a foundation for enhancing data confidentiality and integrity in the superannuation sector.
 Keywords: Data Confidentiality; Accounting; Cybersecurity; Superannuation Organization; Data Integrity.

The impact of procurement agility and procurement sustainability on organizational performance in UAE’s entities: The mediating role of corporate governance

Today, there is a need to develop businesses procurement activities through adopting agile and sustainable procurement practices. The key aim of this research is to identify the influence of both procurement agility and sustainability on organizational performance. The study creates a relationship between the procurement agility and procurement sustainability in the organizational performance with the mediating role of corporate governance. The research framework involves two critical predictors including procurement agility and procurement sustainability. The data was collected with a quantitative cross-sectional methodology within UAE’s business entities context through adaptation of a well-designed questionnaire that was distributed to different businesses like Aviation, Hospitality and Telecommunication (320 responses). The study findings supported a hypothesized model with a significant influence of procurement agility and procurement sustainability on organizational performance. In addition, corporate governance mediated the relationship between procurement agility and organizational performance. The study concluded with the growing non-traditional procurement activities for better organizational outcomes.

Fusion of SoftLexicon and RoBERTa for Purpose-Driven Electronic Medical Record Named Entity Recognition

Recently, researchers have extensively explored various methods for electronic medical record named entity recognition, including character-based, word-based, and hybrid methods. Nonetheless, these methods frequently disregard the semantic context of entities within electronic medical records, leading to the creation of subpar-quality clinical knowledge bases and obstructing the discovery of clinical knowledge. In response to these challenges, we propose a novel purpose-driven SoftLexicon-RoBERTa-BiLSTM-CRF (SLRBC) model for electronic medical records named entity recognition. SLRBC leverages the fusion of SoftLexicon and RoBERTa to incorporate the word lexicon information from electronic medical records into the character representations, enhancing the model’s semantic embedding representations. This purpose-driven approach helps achieve a more comprehensive representation and avoid common segmentation errors, consequently boosting the accuracy of entity recognition. Furthermore, we employ the classical BiLSTM-CRF framework to capture contextual information of entities more effectively. In order to assess the performance of SLRBC, a series of experiments on the public datasets of CCKS2018 and CCKS2019 were conducted. The experimental results demonstrate that SLRBC can efficiently extract entities from Chinese electronic medical records. The model attains F1 scores of 94.97% and 85.40% on CCKS2018 and CCKS2019, respectively, exhibiting outstanding performance in the extraction and utilization efficiency of clinical information.

MPTN: A message-passing transformer network for drug repurposing from knowledge graph

Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.

THEORETICAL AND EXPERIMENTAL STUDY OF THE ECONOMIC BEHAVIOR IRRATIONAL COMPONENT OF UKRAINIAN SOCIETY IN SPECIFIC MARKETS

The subject matter of the article is the theoretical-methodical and applied principles of behavioural economics and their implementation in Ukrainian society. The goal of the work is to analyse the theory of irrationality in the economic context to find out what its character is in modern Ukrainian conditions, as well as to confirm the main paradoxes inherent in the individual’s decision-making behaviour. The following tasks were solved in the article: highlighting important aspects of the theory of irrationality for experimental analysis; determination of the methodology of experiments based on internationally recognized works; proposing a hypothesis regarding Ukrainian realities; conducting experiments according to the proposed methodology to test the proposed hypotheses and systematize the obtained results. The following methods are used: analytical and inductive methods for determining the set of behavioural experiments and building hypotheses regarding their results; experimental method and method of mathematical processing to check the presence of selected behavioural deviations in Ukraine. The following results were obtained: it was determined that in Ukrainian society there is a difference between the degree of individualistic attitudes in different age groups; determined change in the perception of information of different generations as a result of more significant digitization of the young population; a higher tendency of children to risk for certain conditions is determined; the similarity of the obtained results of the experiments with the global ones are established and the impact of technologies on the economic behaviour of individuals and the peculiarities caused by the historical context and expanded access to information, in general, are determined. Conclusions: the use of analytical and inductive methods in combination with an experimental approach confirmed the existence of some of the classic behavioural patterns for modern Ukrainian society, in particular: the Allais paradox, the effect of bounded rationality, the effect of joining the majority, the effect of ownership and asymmetric dominance. In addition, based on the obtained results, it was determined that for Ukraine there is a significant difference between the nature of irrationality in different age groups, however, the postulation of this statement beyond the selected target groups requires additional research, as well as consideration of the context of other market entities.

Device Fingerprinting for Cyber-Physical Systems: A Survey

The continued growth of the cyber-physical system (CPS) and Internet of Things technologies raises device security and monitoring concerns. For device identification, authentication, conditioning, and security, device fingerprint/fingerprinting (DFP) is increasingly used. However, finding the correct DFP features and sources to establish a unique and stable fingerprint is challenging. We present a state-of-the-art survey of DFP techniques for CPS device applications. We investigate the numerous DFP features, their origins, characteristics, and applications. Additionally, we discuss the DFP characteristics and their sources in detail, taking into account the physical contexts of various entities (i.e., machines, sensors, networks, and computational devices), as well as their software and applications for the CPS. We believe that this article will provide researchers and developers with insights into the DFP and its applications, sources, aggregation methods, and factors affecting its use in CPS domains.

Multimodal Fake News Detection Incorporating External Knowledge and User Interaction Feature

With the development of online social media, the number of various news has exploded. While social media provides an information platform for news release and dissemination, it also makes fake news proliferate, which may cause potential social risks. How to detect fake news quickly and accurately is a difficult task. The multimodal fusion fake news detection model is the current research focus and development trend. However, in terms of content, most existing methods lack the mining of background knowledge hidden in the news content and ignore the connection between background knowledge and existing knowledge system. In terms of the propagation chain, the research tends to emphasize only the single chain from the previous communication node, ignoring the intricate communication chain and the mutual influence relationship among users. To address these problems, this paper proposes a multimodal fake news detection model, A-KWGCN, based on knowledge graph and weighted graph convolutional network (GCN). The model fully extracted the features of the content and the interaction between users of the news dissemination. On the one hand, the model mines relevant knowledge concepts from the news content and links them with the knowledge entities in the wiki knowledge graph, and integrates knowledge entities and entity context as auxiliary information. On the other hand, inspired by the “similarity effect” in social psychology, this paper constructs a user interaction network and defines the weighted GCN by calculating the feature similarity among users to analyze the mutual influence of users. Two public datasets, Twitter15 and Twitter16, are selected to evaluate the model, and the accuracy reaches 0.905 and 0.930, respectively. In the comparison experiments, A-KWGCN model has more significant advantages than the other six comparison models in four evaluation indexes. Also, ablation experiments are conducted to verify that knowledge module and weighted GCN module play the significant role in the detection of fake news.

Entity Linking based on RoFormer-Sim for Chinese Short Texts

Entity linking is an important means to identify named entities in text and a key technology for constructing knowledge graphs, playing an important role in fields such as intelligent question answering and information retrieval. However, existing entity linking methods for short texts have low accuracy due to the lack of rich contextual information, informal expression, and incomplete grammar structures. Therefore, this paper proposes a short-text entity linking model based on the RoFormer-Sim pre-training model. Firstly, entity context features are extracted by the RoFormer-Sim pre-training model, and then text similarity calculation and sorting are performed with candidate entity description texts to obtain the corresponding entity in the knowledge base with the disambiguated entity. The experimental results show that the RoFormer-Sim model can provide prior knowledge for entity linking, and the proposed model in this paper has an F1 value of 0.8851, which is better than other entity linking models based on other pre-training models.

A neuro-symbolic system over knowledge graphs for link prediction

Neuro-Symbolic Artificial Intelligence (AI) focuses on integrating symbolic and sub-symbolic systems to enhance the performance and explainability of predictive models. Symbolic and sub-symbolic approaches differ fundamentally in how they represent data and make use of data features to reach conclusions. Neuro-symbolic systems have recently received significant attention in the scientific community. However, despite efforts in neural-symbolic integration, symbolic processing can still be better exploited, mainly when these hybrid approaches are defined on top of knowledge graphs. This work is built on the statement that knowledge graphs can naturally represent the convergence between data and their contextual meaning (i.e., knowledge). We propose a hybrid system that resorts to symbolic reasoning, expressed as a deductive database, to augment the contextual meaning of entities in a knowledge graph, thus, improving the performance of link prediction implemented using knowledge graph embedding (KGE) models. An entity context is defined as the ego network of the entity in a knowledge graph. Given a link prediction task, the proposed approach deduces new RDF triples in the ego networks of the entities corresponding to the heads and tails of the prediction task on the knowledge graph (KG). Since knowledge graphs may be incomplete and sparse, the facts deduced by the symbolic system not only reduce sparsity but also make explicit meaningful relations among the entities that compose an entity ego network. As a proof of concept, our approach is applied over a KG for lung cancer to predict treatment effectiveness. The empirical results put the deduction power of deductive databases into perspective. They indicate that making explicit deduced relationships in the ego networks empowers all the studied KGE models to generate more accurate links.

An analysis of comment letters on a new, stand-alone standard for audits of less complex entities’ financial statements

PurposeThe objective of this paper is to extend the debate on audit quality in the less complex entity (LCE) context by analyzing comment letters submitted to the International Auditing and Assurance Standards Board (IAASB). The IAASB has drafted a new, stand-alone standard for audits of LCEs’ financial statements.Design/methodology/approachThe Gioia method is utilized to conduct the qualitative analysis. This enables the material to shine and provide a comprehensive picture of the important aspects of the comment letters about the International Standard on Auditing (ISA) for LCEs. A content analysis of the 145 comment letters is conducted to identify the extent of the support for and the arguments against the new, stand-alone draft standard for audits of LCEs’ financial statements. In addition, this study considers how the comment letters describe the respondents’ views on audit quality in relation to the new standard. Finally, the tone of the comment letters and audit quality arguments is investigated.FindingsThe findings provide a useful framework of the most frequently used arguments supporting and opposing the ISA for LCEs. Within the themes identified, a wide variety of issues and concerns are discussed. The results reveal that the arguments in the comment letters are contradictory. For instance, when discussing audit quality, those interest groups that perceived many positive opportunities in the adoption of the ISA for LCEs thought that the audit quality would increase. Conversely, those interest groups that were skeptical about the success of the ISA for LCEs argued that the audit quality could be compromised by the general prejudice that the ISA for LCEs might be perceived as a lower-quality audit with fewer procedures.Originality/valueThis paper is, to the best of the authors’ knowledge, the first to examine the content of comment letters in the context of a new, stand-alone standard for audits of LCEs. The international audience can utilize the results in the context of the widely discussed issue of reducing LCEs’ auditing obligations. This study aims to contribute to the two streams of accounting literature concerning audit quality and political lobbying.

The Sense of Belonging in the Context of Migration: Development and Trajectories Regarding Portuguese Migrants in Luxembourg.

The sense of belonging can be defined as a highly subjective and dynamic feeling of acceptance, inclusion, and connectedness to a specific contextual entity. Perceiving belongingness to others is positively related to psychological well-being, happiness, or higher self-esteem. The present contribution examined how the sense of belonging to spatial, social, and cultural entities evolves over the migration process of Portuguese first-generation migrants and their second-generation offspring residing in Luxembourg. The current study drew on the qualitative content analysis of ten semi-structured interviews, carried out with ten Portuguese migrant family dyads (one parent and one adult child per dyad). The results affirmed that the sense of belonging showed to be a complex and multi-faceted concept and highly shaped by specific contexts. The initially unfamiliar Luxembourgish context became a familiar setting and even a "home" over time. While the older generation presented noticeable belongingness to Luxembourg as a homeland, their affiliation to the Luxembourgish community and culture remained rather low. Simultaneously, they preserved a high connectedness to the Portuguese culture as well as to fellow Portuguese migrants living in the Grand Duchy. The younger generation expressed a much more pronounced attachment to Luxembourg, since they perceived belongingness to the Luxembourgish spatial, social, and (multi)cultural milieu. In addition to this, a certain affiliation to the Portuguese culture and language could be discerned. Although some factors, which might have contributed to this evolution, could be identified in the present study, one can assume that there are significantly more that have not been addressed yet.

FuAlign: Cross-lingual entity alignment via multi-view representation learning of fused knowledge graphs

Cross-lingual entity alignment is essential in knowledge graph completion. Recently, knowledge representation learning (KRL) has gained remarkable achievements in cross-lingual entity alignment. Most existing methods first learn entity representations of different knowledge graphs and transform them into a unified space based on a seed set of pre-aligned entity pairs. However, the transformation is error-prone due to the distribution differences between the seed set and the whole entity set. This paper presents FuAlign, a novel cross-lingual entity alignment framework based on multi-view KRL of a pre-fused knowledge graph. FuAlign first fuses two matching knowledge graphs based on the given seed set. Then, it exploits multi-view representation learning to map the fused knowledge graph into a unified space. FuAlign improves the performance from two aspects. First, it represents entities in a unified embedding space, thus avoiding the error-prone transformation between different embedding spaces. Second, the proposed multi-view representation learning model captures different kinds of information such as semantics, entity context, and long-term entity dependency in knowledge graphs. We conduct extensive experiments to evaluate the performance of the proposed method. Experimental results show that FuAlign outperforms most baseline methods and achieves comparable performance with the Bert-based model.

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping.

In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained using an entity annotation system. A topology-based data fusion method is also designed based on the hierarchical relationship between WordNet and DBpedia to fuse the knowledge obtained from image visualization and text description mining. Existing neural network-based entity linking methods ignore the semantic gap between the context of sequential entity denotative items and the context of graph-structured entities, thus affecting the accuracy of entity linking. It is observed that the importance of words in the context of entity denotative items is different, and the importance of content in the entity context is also different. To solve the above problems, this paper proposes an entity linking method that combines a common attention mechanism with a graph convolutional neural network. Secondly, based on the basic theory of value assessment, the characteristics of classical asset valuation methods and their inapplicability to the valuation of carbon assets are analyzed, and thus the real option valuation method and its two classical models are introduced; after demonstrating the real option characteristics of carbon assets of power enterprise projects, a real option model-based carbon asset valuation model for power enterprise projects is constructed and its applicability is verified with case studies. Through analyzing the current situation and problems of carbon asset valuation work in power enterprises, targeted practical suggestions are put forward to further strengthen and enhance the carbon asset valuation work in power enterprises in the future.

Learning attention-based representations from multiple patterns for relation prediction in knowledge graphs

Knowledge bases, and their representations in the form of knowledge graphs (KGs), are naturally incomplete. Since scientific and industrial applications have extensively adopted them, there is a high demand for solutions that complete their information. Several recent works tackle this challenge by learning embeddings for entities and relations, then employing them to predict new relations among the entities. Despite their aggrandizement, most of those methods focus only on the local neighbors of a relation to learn the embeddings. As a result, they may fail to capture the KGs’ context information by neglecting long-term dependencies and the propagation of entities’ semantics. In this manuscript, we propose ÆMP (Attention-based Embeddings from Multiple Patterns), a novel model for learning contextualized representations by: (i) acquiring entities context information through an attention-enhanced message-passing scheme, which captures the entities local semantics while focusing on different aspects of their neighborhood; and (ii) capturing the semantic context, by leveraging the paths and their relationships between entities. Our empirical findings draw insights into how attention mechanisms can improve entities’ context representation and how combining entities and semantic path contexts improves the general representation of entities and the relation predictions. Experimental results on several large and small knowledge graph benchmarks show that ÆMP either outperforms or competes with state-of-the-art relation prediction methods.

Learning Context-based Embeddings for Knowledge Graph Completion

Abstract Purpose Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one embedding vector. This study aims to propose a polysemous embedding approach, named KG embedding under relational contexts (ContE for short), for missing link prediction. Design/methodology/approach ContE models and infers different relationship patterns by considering the context of the relationship, which is implicit in the local neighborhood of the relationship. The forward and backward impacts of the relationship in ContE are mapped to two different embedding vectors, which represent the contextual information of the relationship. Then, according to the position of the entity, the entity's polysemous representation is obtained by adding its static embedding vector to the corresponding context vector of the relationship. Findings ContE is a fully expressive, that is, given any ground truth over the triples, there are embedding assignments to entities and relations that can precisely separate the true triples from false ones. ContE is capable of modeling four connectivity patterns such as symmetry, antisymmetry, inversion and composition. Research limitations ContE needs to do a grid search to find best parameters to get best performance in practice, which is a time-consuming task. Sometimes, it requires longer entity vectors to get better performance than some other models. Practical implications ContE is a bilinear model, which is a quite simple model that could be applied to large-scale KGs. By considering contexts of relations, ContE can distinguish the exact meaning of an entity in different triples so that when performing compositional reasoning, it is capable to infer the connectivity patterns of relations and achieves good performance on link prediction tasks. Originality/value ContE considers the contexts of entities in terms of their positions in triples and the relationships they link to. It decomposes a relation vector into two vectors, namely, forward impact vector and backward impact vector in order to capture the relational contexts. ContE has the same low computational complexity as TransE. Therefore, it provides a new approach for contextualized knowledge graph embedding.

Exploring the role of human resource development functions on crisis management: The case of Dubai-UAE during Covid-19 crisis.

Employee welfare represents a critical element of success for companies to remain competitive. Human resources increasingly encompass the management of critical situations that affect the employees' wellbeing. This research analyzes the effect of Human Resource Development (HRD), functions on the effectiveness of crisis management. It is an attempt to include HRD in the theory of Crisis management. Using Structural Equation Models-Partial Least Squares (SEM-PLS) analysis, the study analyzes how training, leadership, organizational strategy, and organizational culture directly positively impact the efficiency of Crisis management (CM) during the Covid-19 crisis in the public entities of Dubai-UAE. In particular, training showed to be the best predictor, followed by the Organizational culture. Organizational structure, Values and uniqueness show no impact on CM within the context of public entities of Dubai-UAE.