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The purpose of this paper is to propose an extensible framework for extracting data set usage from research articles.

The framework uses a training set of manually labeled examples to identify word features surrounding data set usage references. Using the word features and general entity identifiers, candidate data sets are extracted and scored separately at the sentence and document levels. Finally, the extracted data set references can be verified by the authors using a web-based verification module.

This paper successfully addresses a significant gap in entity extraction literature by focusing on data set extraction. In the process, this paper: identified an entity-extraction scenario with specific characteristics that enable a multiphase approach, including a feasible author-verification step; defined the search space for word feature identification; defined scoring functions for sentences and documents; and designed a simple web-based author verification step. The framework is successfully tested on 178 articles authored by researchers from a large research organization.

Whereas previous approaches focused on completely automated large-scale entity recognition from text snippets, the proposed framework is designed for a longer, high-quality text, such as a research publication. The framework includes a verification module that enables the request validation of the discovered entities by the authors of the research publications. This module shares some similarities with general crowdsourcing approaches, but the target scenario increases the likelihood of meaningful author participation.

The purpose of this paper is to address the problem of content immutability and integrity of online scientific periodicals on the sites of small publishers that can be violated not only by the external hack of the publisher’s site but also by publisher’s and author’s misconduct or by submitting different versions of a periodical to different sites.

The authors defined a list of requirements that verify online scientific publications immutability and integrity. Then, the authors analyzed existing projects and recently emerged information on security technologies and identified challenges met during the development and testing. The use of the public blockchain network Ethereum as a secure storage location for data was explained.

The authors developed the method of checking online scientific periodicals for immutability and presented ecosystem architecture to control immutability and integrity of data. On the example of the online periodical “Istoriya”, it was demonstrated how the immutability of online scientific publication has been verified with the use of the public blockchain over a six-month period. First, operating results were evaluated; challenges hampering the implementation of the suggested ecosystem on Ethereum now were identified; and potential advantages of the suggested approach as compared to similar projects were discovered.

The considered prototype is not a ready-to-use system, but in future providing higher transparency and the development of general distributed ecosystem small publishers will have new opportunities for development given that the issues of scalability, reliability and operating speed on a public blockchain will be addressed. Introduction of the described ecosystem may even provoke some changes on such conservative market as that of publishing of academic papers.

This research is one of the first attempts to expand digital object identifier technology with the use of additional verifications based on the data storage and search in the public blockchain. The suggested idea is the example of “blockchainified science” that was brought to implementation in a real online journal. This method has some advantages compared to Crossmark service.

Currently, in the health-care sector, information security and privacy are increasingly important issues. The improvement in information security is highlighted in adopting digital patient records based on regulation, providers’ consolidation, and the growing need to exchange information among patients, providers, and payers.

Big data on health care are likely to improve patient outcomes, predict epidemic outbreaks, gain valuable insights, prevent diseases, reduce health-care costs and improve analysis of the quality of life.

In this paper, the big data analytics-based cybersecurity framework has been proposed for security and privacy across health-care applications. It is vital to identify the limitations of existing solutions for future research to ensure a trustworthy big data environment. Furthermore, electronic health records (EHR) could potentially be shared by various users to increase the quality of health-care services. This leads to significant issues of privacy that need to be addressed to implement the EHR.

This framework combines several technical mechanisms and environmental controls and is shown to be enough to adequately pay attention to common threats to network security.

The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckman et al. (2010).

The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation.

As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models.

In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis.

Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems.

It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system.

While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.

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.

Drawing on information foraging theory and the SERVQUAL model, this study built a research model to investigate the roles of middle-aged and elderly short-video creators' online attributes in attracting short-video viewers to be their followers.

Taking Douyin (a famous short-video platform in China) as an example, this study used a sequential triangulation mixed-methods approach (quantitative → qualitative) to examine the proposed model by investigating both creators and viewers.

Viewers who clicked the “like” button for the middle-aged and elderly creators' videos are more likely to follow the creators. Viewers will believe that middle-aged and elderly creators who received more likes are more popular. Thus, middle-aged and elderly creators with more likes usually have more followers. Viewers usually believe that middle-aged and elderly creators who more frequently publish professional and high-quality videos have invested more effort and who have official verification also have a high level of authority and are recognized by the platform. Thus, middle-aged and elderly creators with more professional videos and verification usually have more followers. Moreover, verification, the number of videos and the professionalism of videos can enhance the transformation of viewers who liked middle-aged and elderly creators' videos into their followers, and thus strengthen the positive relationship between the number of likes and the number of followers; however, the number of bio words will have an opposite effect.

These findings have implications for platform managers, middle-aged and elderly creators and the brands aiming to develop a “silver economy” by attracting more followers.

This study researches short-video platforms by using a mixed-methods approach to develop an understanding of viewers' decision-making when following middle-aged and elderly creators based on information foraging theory and the SERVQUAL model from the perspectives of both short-video creators and viewers.

Know your customer (KYC), accounting standards, issuance, clearing, and trade settlement became the major barrier to implement accounting, accountability and assurance process in supply chain finance (SCF). Blockchain technology features have the potential to solve accounting problems. This research focuses on exploring how blockchain technology provides solutions to overcome the barriers of accounting process in SCF. The benefits, opportunities, costs and risks related to blockchain adoption are also explored.

Multi-case study and qualitative methods are used with a framework based on blockchain role to overcome the accounting process barriers. Ten blockchain projects in SCF and 29 interviews of participants as a unit of analysis are considered.

The findings indicate that blockchain technology offers solutions to solve accounting, accountability and assurance problems in SCF. Validity, verification, smart contracts, automation and enduring data on trade transactions potentially solve those barriers. However, it is also necessary to consider costs such as implementation, technology, education and integration costs. Then there are possible risks such as regulatory compliance, operational, code development and scalability risk. This finding reflects the current status of blockchain technology roles in SCF.

This study unveils blockchain's SCF accounting potential, emphasizing multi-case method limitations and future research prospects. Diverse contexts challenge findings' applicability, warranting cross-industry studies for deeper insights. Addressing selection bias and integrating quantitative measures can enhance understanding of blockchain's accounting impact.

Accounting professionals can get an idea of the future direction and impact of blockchain technology on accounting, accountability and assurance processes.

This study provides initial findings on the potential, costs and risks of blockchain that is beneficial for parties involved in SCF, especially for banks and insurance underwriters. In addition, the findings also provide direction for the contribution of blockchain technology to accounting theory in the future.

This paper furthers research into the potential contribution of pollutant databases for corporate accountability. We evaluate the quality of corporate and government mercury reporting via the Australian National Pollutant Inventory (NPI), which underpins Australia's reporting under the Minamata Convention, a global agreement to reduce mercury pollution.

The qualitative characteristics of accounting information are used as a theoretical frame to analyse ten interviews with thirteen interviewees as well as 54 submissions to the 2018 governmental enquiry into the NPI.

While Australian mercury accounting using the NPI is likely sufficient to meet the expected Minamata reporting requirements (especially in comparison to developing countries), we find significant limitations in relation to comparability, accuracy, timeliness and completeness. These limitations primarily relate to government (as opposed to industry) deficiencies, caused by insufficient funding. The findings suggest that multiple factors are required to realise the potential of pollutant databases for corporate accountability, including appropriate rules, ideological commitment and resourcing

The provision of additional funding would enable the NPI to be considerably improved (for mercury as well as other pollutants), particularly in relation to the measurement and reporting of emissions from diffuse sources.

Whilst there have been prior reviews of the NPI, none have focused on mercury, whilst conversely prior studies which have discussed mercury information have not focused on the NPI. In addition, no prior NPI studies have utilised interviews nor have engaged directly with NPI regulators. There has been little prior engagement with pollutant databases in social and environmental accounting (SEA) research.

This paper aims to explore patterns of business financing and adoption of blockchain technology in the agricultural industry. The adoption of blockchain technology in terms of recording, storing, validating and securing data can solve a variety of agricultural problems such as agricultural business financing. If the banking and insurance industries are connected in real-time to activity data in the agricultural industry, they can create better credit ratings and profile models. So, finally, all parties in the agricultural industry have a greater chance to get business financing from banks.

This paper uses a case study research approach with a framework of analysis of the theory of adoption of technology, organization and environment (TOE) and the theory of “mindfulness of adoption”. The case study method has advantages when verification is still questioned or the application of certain theories in practice as phenomena and contexts that occur in the field in accordance with the application of blockchain technology into a relatively new business, both technically and practically in the field.

The findings indicate that there are no barriers to the availability of blockchain technology for technology adoption. The characteristics of this technology are very suitable for solving financing and supply chain business problems in the agricultural industry. However, the adoption of blockchain technology in agriculture shows that there is complexity in the organizational context involving internal and external organizations. The number of organizations and small parties involved in the agricultural process challenges the adoption of blockchain technology as new technology. Then, the external environment of technology, especially government regulations in developing countries, is still an obstacle to the adoption of blockchain technology.

This study faces several limitations, namely, the limited case of implementation of the blockchain technology due to the novelty of technology and government regulation. So that further research related to the adoption of blockchain technology needs to be done using field data such as surveys. Research related to the connectivity of the banking industry and other financial institutions also needs to be explored further, especially in creating a data-based credit risk model of the blockchain system.

On the practical side, case studies of technology adoption and its relationship with the financing of agricultural business are still little explored so this study contributes to exploring the application of blockchain technology in the agricultural industry. The adoption of blockchain technology has an impact not only on farmers but also on all parties involved in the supply chain including banks, insurance and other financial institutions. In addition, the distributed data exchange business model using blockchain technology is a new business model in the agriculture industry.

It is difficult for corporate sustainability reporting (CSR) to provide accountability to stakeholders. This paper assesses whether accountability-based CSR systems can be created through the application of Spotlight Accounting and WikiRate as a hybrid forum.

The current paper explores the utility of Spotlight Accounting for CSR through assessing its application to a hybrid forum, WikiRate. This process involved engaging student researchers to collect CSR data from the United Nations Global Compact's (UNGC) corporate action group (CAG) and recording this information into the WikiRate platform. Aggregate analysis was conducted to assess the limitations and challenges of the data to inform decision-making.

Spotlight Accounting exposes challenges within traditional applications of CSR. These challenges impact comparability, decision usefulness and accountability of CSR data for stakeholders.

This paper provides recommendations to enhance the accessibility and relevance of company information to assist in the provision of Spotlight Accounting. In doing so, it highlights the usefulness of CSR to leverage greater accountability between corporations and society.

This paper applies the emerging practices of Spotlight Accounting and presents it as an alternative way to research and conceptualise external accounts, reporting and accountability. This form of accounting has the potential to enhance communications and partnerships between companies and society as well as challenge dominate power dynamics held by corporations.