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In the field of cryptography, authentication, secrecy and identification can be accomplished by use of secret keys for any computer-based system. The need to acquire certificates endorsed through CA to substantiate users for the barter of encoded communications is one of the most significant constraints for the extensive recognition of PKC, as the technique takes too much time and susceptible to error. PKC’s certificate and key management operating costs are reduced with IBC. IBE is a crucial primeval in IBC. The thought behind presenting the IBE scheme was to diminish the complexity of certificate and key management, but it also gives rise to key escrow and key revocation problem, which provides access to unauthorised users for the encrypted information.

This paper aims to compare the result of IIBES with the existing system and to provide security analysis for the same and the proposed system can be used for the security in federated learning.

Furthermore, it can be implemented using other encryption/decryption algorithms like elliptic curve cryptography (ECC) to compare the execution efficiency. The proposed system can be used for the security in federated learning.

As a result, a novel enhanced IBE scheme: IIBES is suggested and implemented in JAVA programming language using RSA algorithm, which eradicates the key escrow problem through eliminating the need for a KGC and key revocation problem by sing sub-KGC (SKGC) and a shared secret with nonce. IIBES also provides authentication through IBS as well as it can be used for securing the data in federated learning.

This study aims to investigate the opacity of bank assets because of the International Financial Reporting Standard (IFRS) 9 implementation. It highlights that the Asian-Pacific countries’ banking industries are experiencing economic volatility. In other words, it examines information asymmetries because of the standards requiring a mechanistic treatment. Thus, this focuses on the tragedy of the commons (ToTC) caused by the implementation of the standard.

This research selects a sample of banking firms in the Asia-Pacific region from 2010 to 2021. Furthermore, it examines the impacts of IFRS 9’s implementation on earnings forecasts and share-return conveyances. This research first uses the OLS regression for examining the bank assets’ opacities, which may affect future earnings and information conveyancing. Second, it arranges these opacities, earnings and stock returns with the 2-SLS regression to find the staging associations because of hierarchical relevances.

This study finds that bank assets’ opacity is caused by a standard’s implementation, which is a ToTC, and this study signifies its first occurrence. Simultaneously, it recognises an information asymmetry because of the implemented procedural calculation mandated by the standard. Furthermore, these opacities affect future earnings and information conveyancing that inherited information asymmetries, which have affected them as the second ToTC. Finally, current and future earnings as a consequent impact of asset opacity are recursively associated with stock return conveyancing as the third ToTC.

This study demonstrates hierarchical information about bank asset opacities, starting by recognising and measuring them in financial statements. Then, these recognised and measured asset opacities are associated with current and future earnings, ending on the ordinarily and staged influencing of stock return conveyancing. Moreover, it reveals hierarchical information in the direct-ordinarily and staged associations among bank asset opacities, earnings and return conveyances. Thus, these associations are valid and occur because of the mandates of the standard’s measurement.

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.

The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.

The authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.

The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.

This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

This study aims to investigate the dynamic capabilities of Indonesia’s local government internal auditors (LGIAs). LGIAs are the functional civil apparatus that is responsible for the main task of auditing local governments at the provincial, regency and municipal levels. Meanwhile, the LGIAs are also a spearhead in identifying and analysing errors, irregularities and fraudulent actions in the finance and development of local government.

The exploratory case study methodology was used, gathering insights from 18 individuals through interviews. In addition, the authors use a critical perspective of the LGIAs’ behaviours in enhancing their capabilities in compliance with the regulations. Moreover, the authors discuss the low motivation of LGIAs in terms of achievement in knowledge acquisition, a mechanistic curriculum creating a climate of low spirit, mental models in rooted ordinariness and behavioural anxiety in hierarchical systems of expertise.

This paper infers that the LGIAs reflect inertia in terms of capabilities because its curriculum, environment and organisation have pervasively changed the culture of the work environment. Consequently, although immorally convenient and practical, the LGIAs work with professional discipline and expedient behaviours. In addition, the LGIAs behave performativity, presenting task performances with undynamic capabilities. Lastly, these behaviours imply the need to enhance the LGIAs’ dynamic capabilities by structuring local governments’ adaptive environment. Hence, this adaptive environment, in turn, could facilitate LGIAs’ further being in high spirits in enhancing knowledge-based expertise.

This study firstly implies that the research findings indicate the need for environmental-, organisational- and curriculum-made transformations to change the capabilities and competencies of LGIAs in the future, facilitating them to increase assimilation-learning abilities. Furthermore, the research shows that mental models dominate LGIAs, resulting in low spirits and reluctance to develop their dynamic capabilities. The paper recommends creating a work culture where anxiety is not dominant and changing the flexibility of the professional structure for LGIAs so that they can be promoted from functional to structural officers.

LGIAs work in a cultural environment that is always structured to fulfil what the regulations require. So, this study’s first novelty is that it underlines the ordinary job practices of LGIAs and the low incentives to enhance their dynamic capabilities. Secondly, it is highlighted that the institution’s auspices do not facilitate LGIAs to advance their dynamic capabilities because of the static competency-based development curriculum. Thirdly, the research shows that the LGIAs are a civil apparatus whose employment system in Indonesia implies a no-dismissal culture and halo effect in measuring performance.

The demand for halal food products is increasing globally. However, fraudulent activities in halal products and certification are also rising. One strategy to ensure halal integrity in the food supply chain is applying halal blockchain technology. However, to date, a few studies have assessed the factors and variables that facilitate or hinder the adoption of this technology. Thus, this study aims to assess the significant factors and variables affecting the adoption of halal blockchain technology.

A Delphi-based approach, using semi-structured interviews, was conducted with three food companies (chicken slaughterhouses, milk processing plants and frozen food companies). The cognitive best–worst method determines the significant factors and variables to prioritise halal blockchain adoption decisions.

The results showed that the most significant factors were coercive pressure and halal strategy. Nineteen variables were identified to establish a valid hierarchical structure for halal blockchain adoption in the Indonesian food industry. The five significant variables assessed through the best–worst method were demand, regulator, supply side, sustainability of the company’s existence and main customers.

The proposed halal blockchain decision structure can assist food companies in deciding whether to adopt the technology.

This study proposes 19 variables that establish a valid hierarchical structure of halal blockchain adoption for the Indonesian food industry.

Referred data set produces reliable information about the network flows and common attacks meeting with real-world criteria. Accordingly, this study aims to focus on the use of imbalanced intrusion detection benchmark knowledge discovery in database (KDD) data set. KDD data set is most preferably used by many researchers for experimentation and analysis. The proposed algorithm improvised random forest classification with error tuning factors (IRFCETF) deals with experimentation on KDD data set and evaluates the performance of a complete set of network traffic features through IRFCETF.

In the current era of applications, the attention of researchers is immersed by a diverse number of existing time applications that deals with imbalanced data classification (ImDC). Real-time application areas, artificial intelligence (AI), Industrial Internet of Things (IIoT), etc. are dealing ImDC undergo with diverted classification performance due to skewed data distribution (SkDD). There are numerous application areas that deal with SkDD. Many of the data applications in AI and IIoT face the diverted data classification rate in SkDD. In recent advancements, there is an exponential expansion in the volume of computer network data and related application developments. Intrusion detection is one of the demanding applications of ImDC. The proposed study focusses on imbalanced intrusion benchmark data set, KDD data set and other benchmark data set with the proposed IRFCETF approach. IRFCETF justifies the enriched classification performance on imbalanced data set over the existing approach. The purpose of this work is to review imbalanced data applications in numerous application areas including AI and IIoT and tuning the performance with respect to principal component analysis. This study also focusses on the out-of-bag error performance-tuning factor.

Experimental results on KDD data set shows that proposed algorithm gives enriched performance. For referred intrusion detection data set, IRFCETF classification accuracy is 99.57% and error rate is 0.43%.

This research work extended for further improvements in classification techniques with multiple correspondence analysis (MCA); hierarchical MCA can be focussed with the use of classification models for wide range of skewed data sets.

The metrics enhancement is measurable and helpful in dealing with intrusion detection systems–related imbalanced applications in current application domains such as security, AI and IIoT digitization. Analytical results show improvised metrics of the proposed approach than other traditional machine learning algorithms. Thus, error-tuning parameter creates a measurable impact on classification accuracy is justified with the proposed IRFCETF.

Proposed algorithm is useful in numerous IIoT applications such as health care, machinery automation etc.

This research work addressed classification metric enhancement approach IRFCETF. The proposed method yields a test set categorization for each case with error reduction mechanism.

Along their journey to achieve exponential growth, startups must process a vast amount of information and make quick decisions, reevaluate and adjust strategies and simultaneously redesign their organization along with the venture lifecycle. This paper delineates the evolution of startups' organizational design and identifies the influencing factors in every phase of the lifecycle.

This study adopts an explorative qualitative approach using a multiple case study methodology for six Indonesian startups. Indonesia is chosen as an emerging country in Southeast Asia with tremendous growth in digital startup businesses.

The research findings suggest that, as they experience exponential growth, startups strive to manage the tension between being structured and being flexible and hence remain innovative by combining management-centric and employee-centric approaches. In particular, this study identified three main factors that potentially influence the evolution of startups' organizational design: founders, investors and the characteristics of business and market.

The present study focuses mainly on Indonesian digital startups and does not fully explain how the influencing factors work in each phase of the venture journey.

This study offers practical contributions for startups pursuing business growth by focusing on the importance of balancing the tension between structured and flexible organizational design and placing more attention on founders, investors and business-market characteristics.

This empirical study is among the first to delineate nuances of organizational design evolution during the startup lifecycle by adopting an explorative qualitative method.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

To better understand the characteristics of Indonesian Muslims, this study uses cluster analysis to group upper-middle-class Muslims based on psychographic variables related to participation in cash waqf for productive purposes.

This study used mixed methods to build and analyse the segmentation of upper-middle-class Muslims towards cash waqf and propose scenarios for a cash waqf model based on the findings.

This study identified six clusters for upper-middle-class Muslims related to the participation in cash waqf for productive purposes. All clusters show heterogeneous values of all factors. Although relatively few Muslims perform cash waqf for productive purposes, the high scores for the economic rational, family and community factors indicate great potential for the development of various cash waqf models for investment purposes. The next challenge will lie in reviewing the “one-fits-all strategy” in the development of program, education and socialisation. Based on the findings, this study proposes three scenarios of cash waqf participation: as wakif only (waqf donor), investor only (capital provider) and hybrid participation (waqf donor and capital provider).

The limitation of this study is the location and object of the sample are only Muslims in Indonesia who are categorised as upper-middle class in terms of their monthly income. Based on this study’s findings, other Muslim-majority countries worldwide have the potential to develop a cash waqf model that is integrated with financial instruments and involves the role of Islamic banking and other Islamic commercial institutions in future research development. Researchers can also attempt to include a simulation or experiment method to construct and validate the proposed cash waqf model based on this study’s findings and to explore other factors that have not been addressed.

The findings of this study can contribute as a foundation for the development of a cash waqf model and business-marketing strategy to increase the participation of upper-middle-class Muslims.

The findings of this study will support the acceleration of cash waqf collection for investment initiatives, which in turn will have a broader social and economic impact nationally.

To the best of the authors’ knowledge, this study constitutes the first attempt to specifically investigate upper-middle-class Muslim segmentation toward cash waqf participation for productive purposes. This study’s knowledge is helpful for various stakeholders such as academia, the Islamic banking industry, regulators and the Muslim community about customer segmentation to Islamic banking products and services related to cash waqf.

The purpose of this paper was to understand the difficulties related to asset management observed by experts in Brazilian organizations in light of the requirements outlined in the ISO 55001:2014 standard.

A survey was performed with asset management experts. The collected data were analyzed using frequency analysis, hierarchical cluster analysis and fuzzy technique for order preference by similarity to deal solution (TOPSIS).

Based on data analysis, the most critical difficulties observed were related to managing and controlling the impact of changes in the company that affect asset management objectives; to the committing to and supporting the asset management system by the top management of the organization; to manage the processes for dealing with risks and opportunities for the asset management system and plans, and correcting failures in asset performance; and to plan and conduct actions in an integrated manner to identify and minimize adverse impacts associated with the asset management system, and afterwards verifying their effectiveness.

The findings of this study have important theoretical and practical contributions, since they indicate the most critical points observed in asset management in Brazil, which can be used as a source for future research and by professionals to prioritize difficulties in future planning and develop action plans to overcome them. The step-by-step methodological approach presented in this study provides professionals and researchers with a replicable method of identifying potential asset management difficulties in a given specific reality.