＂Global economic imbalance＂ and ＂global economic rebalancing＂ have aroused great interest among international economic and.financial research circles. As the global financial crisis begins to abate, some Western c...＂Global economic imbalance＂ and ＂global economic rebalancing＂ have aroused great interest among international economic and.financial research circles. As the global financial crisis begins to abate, some Western countries have used ＂global economic rebalancing＂ as an excuse for trade protectionism and restricting the foreign economic development of developing nations. As the basic theoretical justification for the ＂global economic imbalance,＂ the theory of international trade equilibrium is wrong both in theory and in practice, because it has never been proven in the 200 years of history since the Industrial Revolution. ＂Global economic rebalancing＂ contains serious policy traps and does not generate any winners. The exchange rate is only one of the）actors which affect international trade;furthermore, it is not the fundamental mechanism. With the U.S. dollar retaining its status as the key currency of the international monetary system, it is impossible for the United States to achieve long-term foreign economic and trade equilibrium. The United States＇ trade deficit is an inevitable result of the dollar＇s status as an international currency.展开更多
We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)strategies.PR refers to the process of adjusting the relative weight of asse...We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)strategies.PR refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals,whereas TR is a process of setting allocation limits for portfolios and rebalancing when portfolios exceed a specific percentage of deviation from the target allocation.The HFRA is constructed as an integration of pairs trading and a threshold-based rebalancing strategy,and the profitability of the HFRA is examined to determine the optimal portfolio size.The HFRA is applied to a dataset of real price series from cryptocurrency exchange markets across various trends and volatility regimes.Using cointegrated price data,it is shown that increasing the number of assets in a portfolio supports the profitability of the HFRA in an up-trend and reduces the potential loss of the HFRA in a down-trend in a high-volatility environment.For low-volatility regimes,although increasing portfolio size marginally enhances the HFRA’s profitability,the profits of portfolios of varied sizes do not significantly differ.It is demonstrated that when volatility is relatively high and the trend is upward,the HFRA can yield a substantial return via portfolios of large sizes.Moreover,the profitability of the HFRA is compared with that of the PR and TR strategies for long-term application.The HFRA is more profitable than the PR and TR strategies.This achievement of the HFRA is also validated statistically using the Fisher–Pitman permutation test.展开更多
One of the greatest challenges China faces is reshaping its heavily investment-driven mode of economic growth. By investigating how the rebalancing of Japan＇s economic growth mode was realized in the 1970s, we indica...One of the greatest challenges China faces is reshaping its heavily investment-driven mode of economic growth. By investigating how the rebalancing of Japan＇s economic growth mode was realized in the 1970s, we indicate that it is essential in rebalancing to correct the distortions in factor cost （labor cost and capital cost） in a harmonious way. In addition, we refer to Japan＇s experience to indicate that rebalancing of domestic growth does not necessarily lead to external rebalancing.展开更多
With the development of the bike-sharing system(BSS)and the introduction of green and low carbon development,the environmental impacts of BSS had received increasing attention in recent years.However,the emissions fro...With the development of the bike-sharing system(BSS)and the introduction of green and low carbon development,the environmental impacts of BSS had received increasing attention in recent years.However,the emissions from the rebalancing of BSS,where fossil-fueled vehicles are commonly used,are usually neglected,which goes against the idea of green travel in a sharing economy.Previous studies on the bike-sharing rebalancing problem(BRP),which is considered NP-hard,have mainly focused on algorithm innovation instead of improving the solution model,thereby hindering the application of many existing models in large-scale BRP.This study then proposes a method for optimizing the CO_(2)emissions from BRP and takes the BSS of Beijing as a demonstration.We initially analyze the spatial and temporal characteristics of BSS,especially the flow between districts,and find that each district can be independently rebalanced.Afterward,we develop a rebalancing optimization model based on a partitioning strategy to avoid deciding the number of bikes being loaded or unloaded at each parking node.We then employ the tabu search algorithm to solve the model.Results show that(i)due to over launch and lack of planning in rebalancing,the BSS in Beijing shows great potential for optimization,such as by reducing the number of vehicle routes,CO_(2)emissions,and unmet demands;(ii)the CO_(2)emissions of BSS in Beijing can be reduced by 57.5%by forming balanced parking nodes at the end of the day and decreasing the repetition of vehicle routes and the loads of vehicles;and(iii)the launch amounts of bikes in specific districts,such as Shijingshan and Mentougou,should be increased.展开更多
The crisis of 2008 has shown the unsustainability of the global imbalances centered on the US-China symbiotic relationship that characterized the previous decade. This has revived the so-called growth-rebalancing deb...The crisis of 2008 has shown the unsustainability of the global imbalances centered on the US-China symbiotic relationship that characterized the previous decade. This has revived the so-called growth-rebalancing debate. In particular, the new emerging consensus calls for a re-orientation of the US economy away from consumption and toward exports, and for policy shifts that can help China to reduce its dependence on external demandand inefficiently high rates of capital accumulation. We discuss the economic and political feasibility of the proposed patterns of re-adjustment by focusing on the short-term and long-term trade-offs faced by the policy-makers. We argue that the rebalancing will be gradual and partial because of the costs associated with a radical shift in the growth models adopted by both countries. We believe that this scenario will be consistent with a world economy expanding at lower rates than over the past decade.展开更多
Multi-class change detection can make various ground monitoring projects more efficient and convenient.With the development of deep learning,the multi-class change detection methods have introduced Deep Neural Network...Multi-class change detection can make various ground monitoring projects more efficient and convenient.With the development of deep learning,the multi-class change detection methods have introduced Deep Neural Network(DNN)to improve the accuracy and efficiency of traditional methods.The class imbalance in the image will affect the feature extraction effect of DNN.Existing deep learning methods rarely consider the impact of data on DNN.To solve this problem,this paper proposes a class rebalancing algorithm based on data distribution.The algorithm iteratively trains the SSL model,obtains the distribution of classes in the data,then expands the original dataset according to the distribution of classes,and finally trains the baseline SSL model using the expanded dataset.The trained semantic segmentation model is used to detect multi-class changes in two-phase images.This paper is the first time to introduce the image class balancing method in the multi-class change detection task,so a control experiment is designed to verify the effectiveness and superiority of this method for the unbalanced data.The mIoU of the class rebalancing algorithm in this paper reaches 0.4615,which indicates that the proposed method can effectively detect ground changes and accurately distinguish the types of ground changes.展开更多
This paper considers interactions between and their global implications. We present China ＂s domestic and external imbalances scenarios detailing how a rebalancing of China＇s growth pattern from investment-driven gr...This paper considers interactions between and their global implications. We present China ＂s domestic and external imbalances scenarios detailing how a rebalancing of China＇s growth pattern from investment-driven growth towards more consumption-driven growth may occur in practice. Using input-output tables for 2012, we illustrate the knife-edged nature of Chinese rebalancing, the linkages between expenditure-side and production-side rebalancing, and how an internal rebalancing could exacerbate external imbalances. A policy implication for China is that for rebalancing to be fast, consumption must be exceptionally resilient and the efficiency of investment must increase sharply. If rebalancing is too slow, the capital-to-output ratio will rise to potentially unsustainable levels and consumption will fail to attain levels of contemporary upper middle-income economies by 2030. Global input-output tables （1995-2011） suggest that the patterns of Chinese rebalancing considered in our scenarios may generate substantial headwinds for exports to China by its trading partners.展开更多
China’s economic rebalancing is in the global spotlight because it has far-reaching implications for both China and the world. What triggered the imbalance? How should China push forward the adjustment?
Having persevered amid the trials of the financial crisis, the Chinese economy has arrived at the crossroads. As the country looks for more sustainable growth, the issue of economic rebalancing and structural adjust-
As China fights its way out of the financial downturn, the developed world, primarily the United States and Europe, is still reeling from the recession. Despite the divergence in levels of recovery, there are common l...As China fights its way out of the financial downturn, the developed world, primarily the United States and Europe, is still reeling from the recession. Despite the divergence in levels of recovery, there are common lessons that need to be learned. The key point to展开更多
This study proposes a rebalancing method for a dockless e-micromobility sharing system,employing both trucks and users.Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping techno...This study proposes a rebalancing method for a dockless e-micromobility sharing system,employing both trucks and users.Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping technology.In addition,some users intending to rent an e-micromobility vehicle are offered incentives to end their trips in defined locations to assist with rebalancing.The integrated formulation of rebalancing and recharging accounts for each e-micromobility vehicle's characteristics,such as location and charge level.The problem is formulated as a mixed binary problem,which minimizes operational costs and total unmet demand while maximizing the system's profit.To solve the optimization problem,a Branch and Bound method is employed.Rebalancing decisions and routing plans of each truck are obtained by solving the optimization problem.We simulate an on-demand shared e-micromobility system with the proposed integrated rebalancing method and conduct numerical studies.The results indicate that the proposed method enhances system performance and user travel times.展开更多
Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER...Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER)models,which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy.A novel FER model is introduced to address these issues.It integrates rebalancing mechanisms to regulate attention consistency and focus,offering enhanced efficacy.Our approach proposes the following improvements:(i)rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart;(ii)coefficient factors are incorporated into the standard cross entropy loss function,and rebalancing weights are incorporated to fine-tune the loss adjustment.Experimental results indicate that the FER model outperforms the current leading algorithm,MEK,achieving 0.69%and 2.01%increases in overall and average recognition accuracies,respectively,on the RAF-DB dataset.The model exhibits accuracy improvements of 0.49%and 1.01%in the AffectNet dataset and 0.83%and 1.23%in the FERPlus dataset,respectively.These outcomes validate the superiority and stability of the proposed FER model.展开更多
The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses ...The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.展开更多
Deterrence and coercion are two kinds of strategies,the latter being more aggressive than the former.The U.S.Asia-Pacific Rebalancing strategy is an important diplomatic legacy of Obama's administration.For the is...Deterrence and coercion are two kinds of strategies,the latter being more aggressive than the former.The U.S.Asia-Pacific Rebalancing strategy is an important diplomatic legacy of Obama's administration.For the issues involving the Diaoyu Islands,the South China Sea,cyber security,DPRK's nuclear program,and Iran's nuclear program,the U.S.has carried out military deterrence and non-force coercion against China.But generally,these are low-level coercive measures and distinct from the severe economic sanction and diplomatic isolation imposed by the U.S.on Russia,Syria,DPRK and Iran in recent years.Concerning issues where the U.S.and China hold distinct views,there would be less strategic leeway for the two countries.If the U.S.is to strengthen deterrence and coercion towards China,China can respond more actively and effectively,but it will be more difficult to build a new model of China-U.S.major-country relationship.展开更多
Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA me...Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA methods have acquired great success when labels in the source domain are pure.However,even the acquisition of scare clean labels in the source domain needs plenty of costs as well.In the presence of label noise in the source domain,the traditional UDA methods will be seriously degraded as they do not deal with the label noise.In this paper,we propose an approach named Robust Self-training with Label Refinement(RSLR)to address the above issue.RSLR adopts the self-training framework by maintaining a Labeling Network(LNet)on the source domain,which is used to provide confident pseudo-labels to target samples,and a Target-specific Network(TNet)trained by using the pseudo-labeled samples.To combat the effect of label noise,LNet progressively distinguishes and refines the mislabeled source samples.In combination with class rebalancing to combat the label distribution shift issue,RSLR achieves effective performance on extensive benchmark datasets.展开更多
By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems,cyber attackers can mislead the control center into taking wrong actions.From the viewpoint of machine learning,norm...By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems,cyber attackers can mislead the control center into taking wrong actions.From the viewpoint of machine learning,normal and malicious synchrophasors may exhibit different spatial distribution characteristics when mapped into a latent space.Hence,a malicious synchrophasor detector can be acquired by training a classification model with instances derived from historical operational synchrophasor data.However,malicious synchrophasors occur infrequently in practice.It is likely to incur a great deal of effort and may even introduce inevitable experience errors when extracting and labeling a sufficient number of malicious synchrophasors from historical operational data for training.For most existing detectors,if they are directly trained with highly imbalanced datasets,their performances may severely deteriorate.In this paper,a novel type of malicious synchrophasor detector is developed based on a combinatorial use of data rebalancing,Bagging-based ensemble learning,and the widely recognized eXtreme Gradient Boosting(XGBoost)classifier.Experiments show that although fewer malicious instances are provided,the proposed detector is still capable of detecting malicious synchrophasors.展开更多
Since 2014, capital inflows into China have turned into capital outflows, reversing the gradual appreciation path of the renminbi against the US dollar into an erratic depreciation path. The paper explains the current...Since 2014, capital inflows into China have turned into capital outflows, reversing the gradual appreciation path of the renminbi against the US dollar into an erratic depreciation path. The paper explains the current capital ouows by comparing China and Japan with respect to the impact of exchange rate expectations on speculative capital flows. It is argued that both in China and Japan, given benign liquidity conditions in the USA, policy-induced appreciation expectations have generated capital inflows that have contributed to overinvestment and financial market bubbles. The current reversal of capital flows is seen as a signal that the bubble in China has burst. To stabilize growth in China and to discourage speculative capital ouows a fixed exchange rate to the dollar is recommended. Given Japan＇s experience and given that China＇s foreign assets remain high, the depreciation pressure on the Chinese renminbi can be expected to abate.展开更多
For a long time, China s impressive growth performance has been driven by investment and high productivity gains. Based on a discussion of possible overcapacities and overinvestment in China, this paper investigates t...For a long time, China s impressive growth performance has been driven by investment and high productivity gains. Based on a discussion of possible overcapacities and overinvestment in China, this paper investigates the sustainability of China s investment and export-driven growth model. Since the turn of the millennium, buoyant capital inflows and low interest rates have been at the root of overinvestment and misallocation of capital, which necessitated export subsidies to clear markets. The overinvestment boom is argued to have ended around 2014. Since then, the overcapacities have weakened China's bargaining position in the US-Chinese trade conflict and have tempted Chinese authorities to postpone the restructuring of the Chinese economy by providing low-interest credit. The gradual reemergence of quasi-soft budget constraints is seen to undermine China s long-term growth potential.展开更多
文摘＂Global economic imbalance＂ and ＂global economic rebalancing＂ have aroused great interest among international economic and.financial research circles. As the global financial crisis begins to abate, some Western countries have used ＂global economic rebalancing＂ as an excuse for trade protectionism and restricting the foreign economic development of developing nations. As the basic theoretical justification for the ＂global economic imbalance,＂ the theory of international trade equilibrium is wrong both in theory and in practice, because it has never been proven in the 200 years of history since the Industrial Revolution. ＂Global economic rebalancing＂ contains serious policy traps and does not generate any winners. The exchange rate is only one of the）actors which affect international trade;furthermore, it is not the fundamental mechanism. With the U.S. dollar retaining its status as the key currency of the international monetary system, it is impossible for the United States to achieve long-term foreign economic and trade equilibrium. The United States＇ trade deficit is an inevitable result of the dollar＇s status as an international currency.
文摘We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)strategies.PR refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals,whereas TR is a process of setting allocation limits for portfolios and rebalancing when portfolios exceed a specific percentage of deviation from the target allocation.The HFRA is constructed as an integration of pairs trading and a threshold-based rebalancing strategy,and the profitability of the HFRA is examined to determine the optimal portfolio size.The HFRA is applied to a dataset of real price series from cryptocurrency exchange markets across various trends and volatility regimes.Using cointegrated price data,it is shown that increasing the number of assets in a portfolio supports the profitability of the HFRA in an up-trend and reduces the potential loss of the HFRA in a down-trend in a high-volatility environment.For low-volatility regimes,although increasing portfolio size marginally enhances the HFRA’s profitability,the profits of portfolios of varied sizes do not significantly differ.It is demonstrated that when volatility is relatively high and the trend is upward,the HFRA can yield a substantial return via portfolios of large sizes.Moreover,the profitability of the HFRA is compared with that of the PR and TR strategies for long-term application.The HFRA is more profitable than the PR and TR strategies.This achievement of the HFRA is also validated statistically using the Fisher–Pitman permutation test.
文摘One of the greatest challenges China faces is reshaping its heavily investment-driven mode of economic growth. By investigating how the rebalancing of Japan＇s economic growth mode was realized in the 1970s, we indicate that it is essential in rebalancing to correct the distortions in factor cost （labor cost and capital cost） in a harmonious way. In addition, we refer to Japan＇s experience to indicate that rebalancing of domestic growth does not necessarily lead to external rebalancing.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.71871022,71828401 and 71521002)the Joint Development Program of Beijing Municipal Commission of Education,the Fok Ying Tung Education Foundation(Grant No.161076)+1 种基金the National Key R&D Program(Grant No.2016YFA0602603)the National Program for Support of Top-notch Young Professionals.
文摘With the development of the bike-sharing system(BSS)and the introduction of green and low carbon development,the environmental impacts of BSS had received increasing attention in recent years.However,the emissions from the rebalancing of BSS,where fossil-fueled vehicles are commonly used,are usually neglected,which goes against the idea of green travel in a sharing economy.Previous studies on the bike-sharing rebalancing problem(BRP),which is considered NP-hard,have mainly focused on algorithm innovation instead of improving the solution model,thereby hindering the application of many existing models in large-scale BRP.This study then proposes a method for optimizing the CO_(2)emissions from BRP and takes the BSS of Beijing as a demonstration.We initially analyze the spatial and temporal characteristics of BSS,especially the flow between districts,and find that each district can be independently rebalanced.Afterward,we develop a rebalancing optimization model based on a partitioning strategy to avoid deciding the number of bikes being loaded or unloaded at each parking node.We then employ the tabu search algorithm to solve the model.Results show that(i)due to over launch and lack of planning in rebalancing,the BSS in Beijing shows great potential for optimization,such as by reducing the number of vehicle routes,CO_(2)emissions,and unmet demands;(ii)the CO_(2)emissions of BSS in Beijing can be reduced by 57.5%by forming balanced parking nodes at the end of the day and decreasing the repetition of vehicle routes and the loads of vehicles;and(iii)the launch amounts of bikes in specific districts,such as Shijingshan and Mentougou,should be increased.
文摘The crisis of 2008 has shown the unsustainability of the global imbalances centered on the US-China symbiotic relationship that characterized the previous decade. This has revived the so-called growth-rebalancing debate. In particular, the new emerging consensus calls for a re-orientation of the US economy away from consumption and toward exports, and for policy shifts that can help China to reduce its dependence on external demandand inefficiently high rates of capital accumulation. We discuss the economic and political feasibility of the proposed patterns of re-adjustment by focusing on the short-term and long-term trade-offs faced by the policy-makers. We argue that the rebalancing will be gradual and partial because of the costs associated with a radical shift in the growth models adopted by both countries. We believe that this scenario will be consistent with a world economy expanding at lower rates than over the past decade.
基金supported by Guizhou Science and Technology Cooperation Program:[Grant Number QKH[2016]5103].
文摘Multi-class change detection can make various ground monitoring projects more efficient and convenient.With the development of deep learning,the multi-class change detection methods have introduced Deep Neural Network(DNN)to improve the accuracy and efficiency of traditional methods.The class imbalance in the image will affect the feature extraction effect of DNN.Existing deep learning methods rarely consider the impact of data on DNN.To solve this problem,this paper proposes a class rebalancing algorithm based on data distribution.The algorithm iteratively trains the SSL model,obtains the distribution of classes in the data,then expands the original dataset according to the distribution of classes,and finally trains the baseline SSL model using the expanded dataset.The trained semantic segmentation model is used to detect multi-class changes in two-phase images.This paper is the first time to introduce the image class balancing method in the multi-class change detection task,so a control experiment is designed to verify the effectiveness and superiority of this method for the unbalanced data.The mIoU of the class rebalancing algorithm in this paper reaches 0.4615,which indicates that the proposed method can effectively detect ground changes and accurately distinguish the types of ground changes.
文摘This paper considers interactions between and their global implications. We present China ＂s domestic and external imbalances scenarios detailing how a rebalancing of China＇s growth pattern from investment-driven growth towards more consumption-driven growth may occur in practice. Using input-output tables for 2012, we illustrate the knife-edged nature of Chinese rebalancing, the linkages between expenditure-side and production-side rebalancing, and how an internal rebalancing could exacerbate external imbalances. A policy implication for China is that for rebalancing to be fast, consumption must be exceptionally resilient and the efficiency of investment must increase sharply. If rebalancing is too slow, the capital-to-output ratio will rise to potentially unsustainable levels and consumption will fail to attain levels of contemporary upper middle-income economies by 2030. Global input-output tables （1995-2011） suggest that the patterns of Chinese rebalancing considered in our scenarios may generate substantial headwinds for exports to China by its trading partners.
文摘China’s economic rebalancing is in the global spotlight because it has far-reaching implications for both China and the world. What triggered the imbalance? How should China push forward the adjustment?
文摘Having persevered amid the trials of the financial crisis, the Chinese economy has arrived at the crossroads. As the country looks for more sustainable growth, the issue of economic rebalancing and structural adjust-
文摘As China fights its way out of the financial downturn, the developed world, primarily the United States and Europe, is still reeling from the recession. Despite the divergence in levels of recovery, there are common lessons that need to be learned. The key point to
文摘This study proposes a rebalancing method for a dockless e-micromobility sharing system,employing both trucks and users.Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping technology.In addition,some users intending to rent an e-micromobility vehicle are offered incentives to end their trips in defined locations to assist with rebalancing.The integrated formulation of rebalancing and recharging accounts for each e-micromobility vehicle's characteristics,such as location and charge level.The problem is formulated as a mixed binary problem,which minimizes operational costs and total unmet demand while maximizing the system's profit.To solve the optimization problem,a Branch and Bound method is employed.Rebalancing decisions and routing plans of each truck are obtained by solving the optimization problem.We simulate an on-demand shared e-micromobility system with the proposed integrated rebalancing method and conduct numerical studies.The results indicate that the proposed method enhances system performance and user travel times.
基金support from the National Natural Science Foundation of China(Grant Number 62477023).
文摘Facial expression datasets commonly exhibit imbalances between various categories or between difficult and simple samples.This imbalance introduces bias into feature extraction within facial expression recognition(FER)models,which hinders the algorithm’s comprehension of emotional states and reduces the overall recognition accuracy.A novel FER model is introduced to address these issues.It integrates rebalancing mechanisms to regulate attention consistency and focus,offering enhanced efficacy.Our approach proposes the following improvements:(i)rebalancing weights are used to enhance the consistency between the heatmaps of an original face sample and its horizontally flipped counterpart;(ii)coefficient factors are incorporated into the standard cross entropy loss function,and rebalancing weights are incorporated to fine-tune the loss adjustment.Experimental results indicate that the FER model outperforms the current leading algorithm,MEK,achieving 0.69%and 2.01%increases in overall and average recognition accuracies,respectively,on the RAF-DB dataset.The model exhibits accuracy improvements of 0.49%and 1.01%in the AffectNet dataset and 0.83%and 1.23%in the FERPlus dataset,respectively.These outcomes validate the superiority and stability of the proposed FER model.
文摘The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.
文摘Deterrence and coercion are two kinds of strategies,the latter being more aggressive than the former.The U.S.Asia-Pacific Rebalancing strategy is an important diplomatic legacy of Obama's administration.For the issues involving the Diaoyu Islands,the South China Sea,cyber security,DPRK's nuclear program,and Iran's nuclear program,the U.S.has carried out military deterrence and non-force coercion against China.But generally,these are low-level coercive measures and distinct from the severe economic sanction and diplomatic isolation imposed by the U.S.on Russia,Syria,DPRK and Iran in recent years.Concerning issues where the U.S.and China hold distinct views,there would be less strategic leeway for the two countries.If the U.S.is to strengthen deterrence and coercion towards China,China can respond more actively and effectively,but it will be more difficult to build a new model of China-U.S.major-country relationship.
基金supported by the National Key R&D Program of China(2022ZD0114801)the National Natural Science Foundation of China(Grant No.61906089)the Jiangsu Province Basic Research Program(BK20190408).
文摘Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA methods have acquired great success when labels in the source domain are pure.However,even the acquisition of scare clean labels in the source domain needs plenty of costs as well.In the presence of label noise in the source domain,the traditional UDA methods will be seriously degraded as they do not deal with the label noise.In this paper,we propose an approach named Robust Self-training with Label Refinement(RSLR)to address the above issue.RSLR adopts the self-training framework by maintaining a Labeling Network(LNet)on the source domain,which is used to provide confident pseudo-labels to target samples,and a Target-specific Network(TNet)trained by using the pseudo-labeled samples.To combat the effect of label noise,LNet progressively distinguishes and refines the mislabeled source samples.In combination with class rebalancing to combat the label distribution shift issue,RSLR achieves effective performance on extensive benchmark datasets.
基金This work was supported in part by the National Natural Science Foundation of China(No.51777081).
文摘By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems,cyber attackers can mislead the control center into taking wrong actions.From the viewpoint of machine learning,normal and malicious synchrophasors may exhibit different spatial distribution characteristics when mapped into a latent space.Hence,a malicious synchrophasor detector can be acquired by training a classification model with instances derived from historical operational synchrophasor data.However,malicious synchrophasors occur infrequently in practice.It is likely to incur a great deal of effort and may even introduce inevitable experience errors when extracting and labeling a sufficient number of malicious synchrophasors from historical operational data for training.For most existing detectors,if they are directly trained with highly imbalanced datasets,their performances may severely deteriorate.In this paper,a novel type of malicious synchrophasor detector is developed based on a combinatorial use of data rebalancing,Bagging-based ensemble learning,and the widely recognized eXtreme Gradient Boosting(XGBoost)classifier.Experiments show that although fewer malicious instances are provided,the proposed detector is still capable of detecting malicious synchrophasors.
文摘Since 2014, capital inflows into China have turned into capital outflows, reversing the gradual appreciation path of the renminbi against the US dollar into an erratic depreciation path. The paper explains the current capital ouows by comparing China and Japan with respect to the impact of exchange rate expectations on speculative capital flows. It is argued that both in China and Japan, given benign liquidity conditions in the USA, policy-induced appreciation expectations have generated capital inflows that have contributed to overinvestment and financial market bubbles. The current reversal of capital flows is seen as a signal that the bubble in China has burst. To stabilize growth in China and to discourage speculative capital ouows a fixed exchange rate to the dollar is recommended. Given Japan＇s experience and given that China＇s foreign assets remain high, the depreciation pressure on the Chinese renminbi can be expected to abate.
文摘For a long time, China s impressive growth performance has been driven by investment and high productivity gains. Based on a discussion of possible overcapacities and overinvestment in China, this paper investigates the sustainability of China s investment and export-driven growth model. Since the turn of the millennium, buoyant capital inflows and low interest rates have been at the root of overinvestment and misallocation of capital, which necessitated export subsidies to clear markets. The overinvestment boom is argued to have ended around 2014. Since then, the overcapacities have weakened China's bargaining position in the US-Chinese trade conflict and have tempted Chinese authorities to postpone the restructuring of the Chinese economy by providing low-interest credit. The gradual reemergence of quasi-soft budget constraints is seen to undermine China s long-term growth potential.
