The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this d...The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.展开更多
SOUTH Africa＇s invitation to the BRICS club back in December 2010- arguably President Jacob Zuma＇s greatest foreign policy success - has by association positioned the country as a ＂leading＂ emerging market economy...SOUTH Africa＇s invitation to the BRICS club back in December 2010- arguably President Jacob Zuma＇s greatest foreign policy success - has by association positioned the country as a ＂leading＂ emerging market economy~ While some still question the inclusion based on economic merit, South Africa has become an active member looking to shape the grouping. South Africa is looking to leverage the advantages of this association with particular focus on collaboration opportunities for Africa＇s development.展开更多
In late October,at the invitation of friendly organizations in Cambodia and Laos,a Chinese NGO Delegation visited the two countries.The delegation was made up of 6 members from the Chinese Association for Internationa...In late October,at the invitation of friendly organizations in Cambodia and Laos,a Chinese NGO Delegation visited the two countries.The delegation was made up of 6 members from the Chinese Association for International Understanding,Chinese People’s Association for Peace and Disarmament,China NGO Network for International Exchanges and China Foundation for Peace and Development respectively.Though the visit展开更多
Based on the latest macro financial data, this paper estimates China＇ s overall leverage ratio and sector-specific leverage ratios for households, non-financial enterprises, government and financial institutions. It ...Based on the latest macro financial data, this paper estimates China＇ s overall leverage ratio and sector-specific leverage ratios for households, non-financial enterprises, government and financial institutions. It is noted with particular emphasis that the tendency of non-financial enterprises to increase leverage has further intensified instead of abated, which warrants our great attention. Considering that increasing leverage of government sector represents a basic international trend since the eruption of global financial crisis, we simulate the paths of dynamic evolution of China＇s debt-to-GDP ratio on the basis of different scenarios of the difference between real economic growth rate and real interest rate, together with the NPL ratio of banks. Result indicates that in the coming two decades, the leverage ratio of China＇s government sector will continue to rise and will not converge. Hiking leverage ratio, growing debt burden and rising non-performing assets present major financial risks facing China for a certain period of time in the future. Under the premise of supply-side structural reforms and in tandem with the efforts of the real economy to reduce overcapacity, inventory and eliminate zombie firms, we suggest that China focus on disposing of non-performing assets and steadily deleverage through the implementation of integrated strategies to prevent debt problems from triggering systemic financial crisis.展开更多
Dividing aggregate liabilities by GDP is not an appropriate method for calculating the leverage ratio, and may mislead deleveraging operations. In terms of an intrinsic mechanism, an appropriate measure of leverage ra...Dividing aggregate liabilities by GDP is not an appropriate method for calculating the leverage ratio, and may mislead deleveraging operations. In terms of an intrinsic mechanism, an appropriate measure of leverage ratio should be the liability/asset ratio. In their business operations, it is inevitable for real-economy enterprises to incur liabilities arising from business and financial transactions. Therefore, the significance of deleveraging operations is to reduce the leverage ratio below a certain threshold to effectively prevent risks arising from an excessive leverage ratio, rather than to reduce the liability ratio of real-economy enterprises to zero. For real-economy enterprises, a key question is how to adjust their debt structure by reducing current liabilities and increasing mid-and long-term liabilities. The debt structure of real-economy enterprises is primarily determined by their financial structure. Hence, it is essential to adjust the financial structure in order to improve the debt structure of real-economy enterprises and increase the share of direct finance. Various risks exist in the combination of shares and bonds within the banking system, investment-lending linkage and market-based debt-to-equity operations, which are options in reducing the leverage ratio for real-economy enterprises. From the standpoint of giving play to capital market functions, it is advisable to increase the issuance of midand long-term corporate bonds and preferred stock, restrict non-financial listed companies from engaging in financial operations and the shareholders of listed companies from selling shares, encourage equity investment institutions to enhance equity investment in realeconomy enterprises, and further develop the financing function of the stock market.展开更多
Using province-level data in China for the period of 1999-2015,we examine the mechanisms through which sectoral differences in leverage ratio and productivity affect macro leverage ratios.The state-owned sector undert...Using province-level data in China for the period of 1999-2015,we examine the mechanisms through which sectoral differences in leverage ratio and productivity affect macro leverage ratios.The state-owned sector undertakes a large number of public services and plays an irreplaceable role in solving market failures and providing public goods.However,in the case of information asymmetry and incentive incompatibility,these policy burdens affect the leverage optimization and productivity improvement of the state-owned sector.From the perspective of sectoral differences,we therefore decompose the change in macro leverage ratio into leverage ratio structure effect and productivity structure effect,and then substantiate the impact mechanisms of these two effects on macro leverage ratios.Overall,our conclusions provide theoretical support and empirical evidence for structural deleveraging in China.展开更多
This study aims to analyze the influence of leverage,firm size,firm value,and managerial ownership on tax aggressiveness in technology sector companies listed on the Indonesia Stock Exchange.Tax aggressiveness has bec...This study aims to analyze the influence of leverage,firm size,firm value,and managerial ownership on tax aggressiveness in technology sector companies listed on the Indonesia Stock Exchange.Tax aggressiveness has become a critical issue in corporate financial management practices,particularly in the context of optimizing tax burdens through strategies that remain within legal boundaries.The study adopts a quantitative approach using panel data regression methods.Data processing and analysis were conducted using EViews version 12.The research sample consists of 13 technology sector companies selected through purposive sampling,with an observation period spanning five years(2019-2023),resulting in a total of 65 observations.The analysis results indicate that leverage and firm value have a negative effect on tax aggressiveness.Conversely,managerial ownership is found to have a positive effect on tax aggressiveness.Meanwhile,firm size does not show a significant influence on tax aggressiveness.展开更多
Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recogn...Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also utilized. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.展开更多
Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation ...Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation to continue in the presence of fault measurement.Affected by satellite geometry, the leverage of each measurement in position solution may differ greatly.However, the conventional RAIM FI methods are generally based on maximum likelihood of ranging error for different measurements, thereby causing a major decrease in the probability of correct identification for the fault measurement with high leverage.In this paper, the impact of leverage on the fault identification is analyzed.The leveraged RAIM fault identification（L-RAIM FI） method is proposed with consideration of the difference in leverage for each satellite in view.Furthermore,the theoretical probability of correct identification is derived to evaluate the performance of L-RAIM FI method.The experiments in various typical scenarios demonstrate the effectiveness of L-RAIM FI method over conventional FI methods in the probability of correct identification for the fault with high leverage.展开更多
Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time....Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.展开更多
This article discusses the problem of the detection of influential cases in nonlinear reproductive dispersion models (NRDM). A diagnostic based on case\|deletion approach in estimating equations is proposed. The relat...This article discusses the problem of the detection of influential cases in nonlinear reproductive dispersion models (NRDM). A diagnostic based on case\|deletion approach in estimating equations is proposed. The relationships between the generalized leverage defined by Wei et al. in 1998, statistical curvature, and the local influence of the response vector perturbations are investigated in NRDM. Two numerical examples are given to illustrate the results.展开更多
This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) ...This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.展开更多
基金This work was supported partially by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)Support Program(IITP-2024-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.
文摘SOUTH Africa＇s invitation to the BRICS club back in December 2010- arguably President Jacob Zuma＇s greatest foreign policy success - has by association positioned the country as a ＂leading＂ emerging market economy~ While some still question the inclusion based on economic merit, South Africa has become an active member looking to shape the grouping. South Africa is looking to leverage the advantages of this association with particular focus on collaboration opportunities for Africa＇s development.
文摘In late October,at the invitation of friendly organizations in Cambodia and Laos,a Chinese NGO Delegation visited the two countries.The delegation was made up of 6 members from the Chinese Association for International Understanding,Chinese People’s Association for Peace and Disarmament,China NGO Network for International Exchanges and China Foundation for Peace and Development respectively.Though the visit
文摘Based on the latest macro financial data, this paper estimates China＇ s overall leverage ratio and sector-specific leverage ratios for households, non-financial enterprises, government and financial institutions. It is noted with particular emphasis that the tendency of non-financial enterprises to increase leverage has further intensified instead of abated, which warrants our great attention. Considering that increasing leverage of government sector represents a basic international trend since the eruption of global financial crisis, we simulate the paths of dynamic evolution of China＇s debt-to-GDP ratio on the basis of different scenarios of the difference between real economic growth rate and real interest rate, together with the NPL ratio of banks. Result indicates that in the coming two decades, the leverage ratio of China＇s government sector will continue to rise and will not converge. Hiking leverage ratio, growing debt burden and rising non-performing assets present major financial risks facing China for a certain period of time in the future. Under the premise of supply-side structural reforms and in tandem with the efforts of the real economy to reduce overcapacity, inventory and eliminate zombie firms, we suggest that China focus on disposing of non-performing assets and steadily deleverage through the implementation of integrated strategies to prevent debt problems from triggering systemic financial crisis.
文摘Dividing aggregate liabilities by GDP is not an appropriate method for calculating the leverage ratio, and may mislead deleveraging operations. In terms of an intrinsic mechanism, an appropriate measure of leverage ratio should be the liability/asset ratio. In their business operations, it is inevitable for real-economy enterprises to incur liabilities arising from business and financial transactions. Therefore, the significance of deleveraging operations is to reduce the leverage ratio below a certain threshold to effectively prevent risks arising from an excessive leverage ratio, rather than to reduce the liability ratio of real-economy enterprises to zero. For real-economy enterprises, a key question is how to adjust their debt structure by reducing current liabilities and increasing mid-and long-term liabilities. The debt structure of real-economy enterprises is primarily determined by their financial structure. Hence, it is essential to adjust the financial structure in order to improve the debt structure of real-economy enterprises and increase the share of direct finance. Various risks exist in the combination of shares and bonds within the banking system, investment-lending linkage and market-based debt-to-equity operations, which are options in reducing the leverage ratio for real-economy enterprises. From the standpoint of giving play to capital market functions, it is advisable to increase the issuance of midand long-term corporate bonds and preferred stock, restrict non-financial listed companies from engaging in financial operations and the shareholders of listed companies from selling shares, encourage equity investment institutions to enhance equity investment in realeconomy enterprises, and further develop the financing function of the stock market.
基金supported financially by the National Social Science Foundation of China(No.21FJYB005)the China Postdoctoral ScienceFoundation(No.2019M663758).
文摘Using province-level data in China for the period of 1999-2015,we examine the mechanisms through which sectoral differences in leverage ratio and productivity affect macro leverage ratios.The state-owned sector undertakes a large number of public services and plays an irreplaceable role in solving market failures and providing public goods.However,in the case of information asymmetry and incentive incompatibility,these policy burdens affect the leverage optimization and productivity improvement of the state-owned sector.From the perspective of sectoral differences,we therefore decompose the change in macro leverage ratio into leverage ratio structure effect and productivity structure effect,and then substantiate the impact mechanisms of these two effects on macro leverage ratios.Overall,our conclusions provide theoretical support and empirical evidence for structural deleveraging in China.
文摘This study aims to analyze the influence of leverage,firm size,firm value,and managerial ownership on tax aggressiveness in technology sector companies listed on the Indonesia Stock Exchange.Tax aggressiveness has become a critical issue in corporate financial management practices,particularly in the context of optimizing tax burdens through strategies that remain within legal boundaries.The study adopts a quantitative approach using panel data regression methods.Data processing and analysis were conducted using EViews version 12.The research sample consists of 13 technology sector companies selected through purposive sampling,with an observation period spanning five years(2019-2023),resulting in a total of 65 observations.The analysis results indicate that leverage and firm value have a negative effect on tax aggressiveness.Conversely,managerial ownership is found to have a positive effect on tax aggressiveness.Meanwhile,firm size does not show a significant influence on tax aggressiveness.
文摘Increasing demand for a fast and reliable face recognition technology has obliged researchers to try and examine different pattern recognition schemes. But until now, Genetic Programming (GP), acclaimed pattern recognition, data mining and relation discovery methodology, has been neglected in face recognition literature. This paper tries to apply GP to face recognition. First Principal Component Analysis (PCA) is used to extract features, and then GP is used to classify image groups. To further improve the results, a leveraging method is also utilized. It is shown that although GP might not be efficient in its isolated form, a leveraged GP can offer results comparable to other Face recognition solutions.
基金supported by the National Basic Research Program of China (No.2011CB707004)the National Natural Science Foundation of China (No.61179054)
文摘Receiver autonomous integrity monitoring（RAIM） provides integrity monitoring of global positioning system（GPS） for safety-of-life applications.In the process of RAIM, fault identification（FI） enables navigation to continue in the presence of fault measurement.Affected by satellite geometry, the leverage of each measurement in position solution may differ greatly.However, the conventional RAIM FI methods are generally based on maximum likelihood of ranging error for different measurements, thereby causing a major decrease in the probability of correct identification for the fault measurement with high leverage.In this paper, the impact of leverage on the fault identification is analyzed.The leveraged RAIM fault identification（L-RAIM FI） method is proposed with consideration of the difference in leverage for each satellite in view.Furthermore,the theoretical probability of correct identification is derived to evaluate the performance of L-RAIM FI method.The experiments in various typical scenarios demonstrate the effectiveness of L-RAIM FI method over conventional FI methods in the probability of correct identification for the fault with high leverage.
文摘Hydrates always are considered as a threat to petroleum industry due to the operational problems it can cause.These problems could result in reducing production performance or even production stoppage for a long time.In this paper, we were intended to develop a LSSVM algorithm for prognosticating hydrate formation temperature(HFT) in a wide range of natural gas mixtures. A total number of 279 experimental data points were extracted from open literature to develop the LSSVM. The input parameters were chosen based on the hydrate structure that each gas species form. The modeling resulted in a robust algorithm with the squared correlation coefficients(R^2) of 0.9918. Aside from the excellent statistical parameters of the model, comparing proposed LSSVM with some of conventional correlations showed its supremacy, particularly in the case of sour gases with high H_2S concentrations, where the model surpasses all correlations and existing thermodynamic models. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach was performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for HFT prediction and almost all the data points are in the applicability domain of the model.
文摘This article discusses the problem of the detection of influential cases in nonlinear reproductive dispersion models (NRDM). A diagnostic based on case\|deletion approach in estimating equations is proposed. The relationships between the generalized leverage defined by Wei et al. in 1998, statistical curvature, and the local influence of the response vector perturbations are investigated in NRDM. Two numerical examples are given to illustrate the results.
基金Project (No. 28-05-03-03) supported by the Yildiz Technical University Research Fund, Turkey
文摘This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.
