Search results

This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment.

Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions.

This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution.

Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

Errors are inevitable, resulting from the human condition itself, system failures and the interaction of both. It is essential to know how to deal with their occurrence, managing them. However, the negative tone associated with them makes it difficult for most organizations to talk about mistakes clearly and transparently, for fear of being harmed, preventing their detection, treatment and recovery. Consequently, errors are not managed, remaining accumulated in the system, turning into successive failures. Organizations need to recognize the inevitability of errors, making the system robust, through leadership and an organizational culture of error management. This study aims to understand the role of these influencing variables in an error management approach.

In this paper, the authors applied the methodology of a quantitative nature based on a questionnaire survey that analyses error management, leadership and the organizational culture of error management of 380 workers in Portuguese companies.

The results demonstrate that leadership directly influences error management and indirectly through the organizational culture of error management, giving this last variable a mediating role.

The study covers companies from different sectors of activity on a topic that is little explored in Portugal, but part of the daily life of organizations, which should deserve greater attention from directors and managers, as they assume a privileged position to promote and develop error management mechanisms. Error management must be the daily work of leaders. This study contributes to theoretical knowledge and business practice on error management.

This study aims to analyze the imbalances in the public finance structure of Pakistan’s economy and highlight the need for comprehensive reforms. Specifically, it aims to contribute to the empirical literature by analyzing the relationship between fiscal vulnerability, financial stress and macroeconomic policies in Pakistan’s economy between 1971 and 2020.

The study develops an index of fiscal vulnerability, an index of financial stress and an index of macroeconomic policies. The fiscal vulnerability index is based on the patterns of fiscal indicators resulting from past trends of the selected variables in Pakistan’s economy. The financial stress in Pakistan is caused from the financial disorders that are acknowledged in the composite index, which is based on variables with the potential to indicate periods of stress stemming from the foreign exchange market, the securities market and the monetary policy components. The macroeconomic policies index is developed to analyze the mechanism through which fiscal vulnerability and financial stress have influenced macroeconomic policies in Pakistan. The causal association between fiscal vulnerability, financial stress and macroeconomic policies is analyzed using the auto-regressive distributive lags approach.

There exists a long-run relationship between the three indices, and a bi-directional causality between fiscal vulnerability and macroeconomic policies.

This study contributes to the development of a fiscal monitoring mechanism, which has the basic purpose of analyzing the refinancing risk of public liabilities. Moreover, it focuses on fiscal vulnerability from a macroeconomic perspective. The study tries to develop a framework to assess fiscal vulnerability in light of “The Risk Octagon” theory, which focuses on three risk components: fiscal variables, macroeconomic-disruption-associated shocks and non-fiscal country-specific variables. The initial contribution of this work to the literature is to develop a framework (a fiscal vulnerability index, financial stress index and macroeconomic policies index) for effective and result-oriented macro-fiscal surveillance. Moreover, empirical literature emphasized and advised developing countries to develop their own capacity mechanisms to assess their fiscal vulnerability in light of the IMF guidelines regarding vulnerability assessments. This study thus attempts to fulfill the said gap identified in literature.

The purpose of this paper is to review possibilities of implementing ceramic additive manufacturing (AM) into electronic device production, which can enable great new possibilities.

A short introduction into additive techniques is included, as well as primary characterization of structuring capabilities, dielectric performance and applicability in the electronic manufacturing process.

Ceramic stereolithography (SLA) is suitable for microchannel manufacturing, even using a relatively inexpensive system. This method is suitable for implementation into the electronic manufacturing process; however, a search for better materials is desired, especially for improved dielectric parameters, lowered sintering temperature and decreased porosity.

Relatively inexpensive ceramic SLA, which is now available, could make ceramic electronics, currently restricted to specific applications, more available.

Ceramic AM is in the beginning phase of implementation in electronic technology, and only a few reports are currently available, the most significant of which is mentioned in this paper.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This paper aims to understand real earnings management behavior in the context of a parent–subsidiary relationship. It explores the differences between business groups and firms that do not have controlled subsidiaries and provides potential explanations for any measured difference.

The study uses the random-effects generalized least squares (GLS) estimation to find the difference between the real earnings management behavior of business groups, represented by the ultimate parent firms and the nonparent firms from 73 countries.

The results show that ultimate parent firms have lower abnormal production costs and abnormal discretionary expenses than nonparent firms. In contrast, parent firms have higher abnormal cash flow from operations (CFO) than nonparent firms. The results are unexpected because abnormal production costs usually have a dominant direct relationship with abnormal CFO. The results indicate that business groups use a route different from manipulating production costs and discretionary expenses.

The results reveal that parent firms use a route different from manipulating production costs and discretionary expenses. The results can be used to extend the discussion to specific business group cases, such as tracing the route or allocation of real earnings management (REM) pressure from a parent firm to its listed and private subsidiaries, and if the consolidation of minority voting rights and the transitivity of control affect the behavior in its subsidiaries.

Instead of the degree of diversification or affiliation, this paper investigates REM behavior based on the parent firm's control of its subsidiaries. With this approach, the study argues that business groups prefer a route other than manipulating production costs and discretionary expenses. The results may redirect the attention of regulators to the activities of parent firms that need more policing.

Along with the various beneficial uses of artificial intelligence (AI), there are various unsavory concomitants including the inscrutability of AI tools (and the opaqueness of their mechanisms), the fragility of AI models under adversarial settings, the vulnerability of AI models to bias throughout their pipeline, the high planetary cost of running large AI models and the emergence of exploitative surveillance capitalism-based economic logic built on AI technology. This study aims to document these harms of AI technology and study how these technologies and their developers and users can be made more accountable.

Due to the nature of the problem, a holistic, multi-pronged approach is required to understand and counter these potential harms. This paper identifies the rationale for urgently focusing on human-centered AI and provide an outlook of promising directions including technical proposals.

AI has the potential to benefit the entire society, but there remains an increased risk for vulnerable segments of society. This paper provides a general survey of the various approaches proposed in the literature to make AI technology more accountable. This paper reports that the development of ethical accountable AI design requires the confluence and collaboration of many fields (ethical, philosophical, legal, political and technical) and that lack of diversity is a problem plaguing the state of the art in AI.

This paper provides a timely synthesis of the various technosocial proposals in the literature spanning technical areas such as interpretable and explainable AI; algorithmic auditability; as well as policy-making challenges and efforts that can operationalize ethical AI and help in making AI accountable. This paper also identifies and shares promising future directions of research.

Excessive use of work-related information technology (IT) devices can lead to major performance and well-being concerns for organizations. Extant research has provided evidence of the incidence of such problematic IT use in organizations. We extend the understanding of problematic IT use by examining its individual (proximal) and organizational (distal) antecedents.

Drawing from the self-worth theory and the concept of fear of being left behind, we address proximal antecedents that lead to problematic IT use. Drawing from the concept of autonomy paradox, we address distal antecedents of problematic IT use through a positive association with the two proximal antecedents. We report the results of a field study involving 846 individuals who use IT for work. Structural equation modeling was employed to analyze the data.

The results indicate that the proximal antecedents (IT insecurity and fear of missing out) are positively associated with problematic IT use. The distal antecedents (IT use autonomy and involvement facilitation) are positively associated with the proximal antecedents except for the relationship between IT use autonomy and IT insecurity, which was found statistically non-significant. Furthermore, fear of missing out fully mediates the effect of IT use autonomy on problematic IT use, whereas IT insecurity and fear of missing out fully mediate the effects of involvement facilitation on problematic IT use.

The paper theoretically extends the understanding of problematic IT use and identifies novel its proximal and distal antecedents.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.