This study constructed a moderated mediation model to examine how the social support received by teachers is associated with their work pay fairness perception in relation to their job satisfaction and job performance...This study constructed a moderated mediation model to examine how the social support received by teachers is associated with their work pay fairness perception in relation to their job satisfaction and job performance.Data were collected from 2411 preschool teachers in China(female=98.01%;mean age=29.12 years,SD=6.28 years).These data were analyzed using structural equation modelling,bootstrapping and latent moderate structural equations.The results indicated that teachers’perception of pay fairness is directly associated with self-rated job performance.Additionally,pay fairness perceptions have an indirect effect on higher job performance through job satisfaction.The social support that teachers perceive moderates the relationship between pay fairness perception and job satisfaction:the more social support teachers receive,the weaker the impact of pay fairness perception on job satisfaction.Thesefindings suggest that teachers’perception of pay fairness is related to their sense of quality of work life,as indicated by their job satisfaction and performance.展开更多
Love of peace and fairness has been a focus since ancient times,for domestic development as well as international exchanges.WHAT does fairness mean?In English,the original definition of the word“fair”includes the me...Love of peace and fairness has been a focus since ancient times,for domestic development as well as international exchanges.WHAT does fairness mean?In English,the original definition of the word“fair”includes the meaning of“beauty”and“harmony,”obviously referring to people’s longing for good prospects in life.In Arabic,the word for“fair”includes the meaning of“balance”and“integrity.”展开更多
As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improveme...As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.展开更多
Fairness is a fundamental value in human societies,with individuals concerned about unfairness both to themselves and to others.Nevertheless,an enduring debate focuses on whether self-unfairness and other-unfairness e...Fairness is a fundamental value in human societies,with individuals concerned about unfairness both to themselves and to others.Nevertheless,an enduring debate focuses on whether self-unfairness and other-unfairness elicit shared or distinct neuropsychological processes.To address this,we combined a three-person ultimatum game with computational modeling and advanced neuroimaging analysis techniques to unravel the behavioral,cognitive,and neural patterns underlying unfairness to self and others.Our behavioral and computational results reveal a heightened concern among participants for self-unfairness over other-unfairness.Moreover,self-unfairness consistently activates brain regions such as the anterior insula,dorsal anterior cingulate cortex,and dorsolateral prefrontal cortex,spanning various spatial scales that encompass univariate activation,local multivariate patterns,and whole-brain multivariate patterns.These regions are well-established in their association with emotional and cognitive processes relevant to fairness-based decision-making.Conversely,other-unfairness primarily engages the middle occipital gyrus.Collectively,our findings robustly support distinct neurocomputational signatures between self-unfairness and other-unfairness.展开更多
Fairness is an emerging consideration when assessing the segmentation per-formance of machine learning models across various demographic groups.During clinical decision-making,an unfair segmentation model exhibits ris...Fairness is an emerging consideration when assessing the segmentation per-formance of machine learning models across various demographic groups.During clinical decision-making,an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups,resulting in severe consequences for patients and society.In medical artificial intelligence(AI),the fairness of multi-organ segmentation is imperative to augment the integration of models into clinical practice.As the use of multi-organ segmentation in medical image analysis expands,it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity.However,comprehensive studies assessing the problem of fairness in multi-organ segmentation remain lacking.This study aimed to provide an overview of the fairness problem in multi-organ segmentation.We first define fairness and discuss the factors that lead to fairness problems such as individual fairness,group fairness,counterfactual fairness,and max–min fairness in multi-organ segmentation,focusing mainly on datasets and models.We then present strategies to potentially improve fairness in multi-organ segmentation.Additionally,we highlight the challenges and limita-tions of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi-organ segmentation.展开更多
To satisfy different service requirements of multiple users in the orthogo nal frequency division multiple access wireless local area network OFDMA-WLAN system downlink transmission a resource allocation algorithm bas...To satisfy different service requirements of multiple users in the orthogo nal frequency division multiple access wireless local area network OFDMA-WLAN system downlink transmission a resource allocation algorithm based on fairness and quality of service QoS provisioning is proposed. Different QoS requirements are converted into different rate requirements to calculate the QoSs atisfaction level.The optimization object is revised as a fairness-driven resource optimization function to provide fairness. The complex resource allocation problem is divided into channel allocation and power assignment sub-problems. The sub-problems are solved by the bipartite graph matching and water-filling based method.Compared with other algorithms the proposed algorithm sacrifices less data rate for higher fairnes and QoS satisfaction.The sim ulation results show that the proposed algorithm is capableo fp rovi ding QoS and fairness and performs better in a tradeoff among QoS fairness and data rate.展开更多
To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SO...To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.展开更多
With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) ...With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) as a subjective assessment is usually adopted for accurate and convincing reflection of user perceived quality.In this paper,we consider the effect of the burst transmission of best effort(BE) traffic on the uses with real time video traffic in the same cell.We extend the rate scaling process which was initially used to shape burstiness of BE users as interference to handle the scenario that BE users act as resource competitors with video users.A power reallocation strategy between the two types of users is presented and an algorithm further improving the fairness of BE users is proposed.The simulation results demonstrate that the proposed algorithm can not only promote the QoE of both types of users,but also guarantee the fairness among users.展开更多
Measuring the impact of AI systems The recent 2025 AI Index Report from Stanford University revealed that skepticism about the ethical conduct of AI companies is growing,and trust in fairness is shrinking.There is als...Measuring the impact of AI systems The recent 2025 AI Index Report from Stanford University revealed that skepticism about the ethical conduct of AI companies is growing,and trust in fairness is shrinking.There is also less confidence that personal data will be protected and fewer people believe AI systems are unbiased and free of discrimination.Trust and transparency are essential for AI to deliver on its promises in a safe and responsible way.Governments are stepping up with new AI-related regulations,and international standards such as ISO/IEC 42001 have been developed to support them,but a lot more needs to be done to reduce potential risks and address societal concerns.展开更多
茶香缱绻,文化悠长,健康时尚与历史在杯中交融。Tea,the world’s second most popular drink after water,has a rich history and cultural significance.In 2019,the United Nations set up International Tea Day,which is held eve...茶香缱绻,文化悠长,健康时尚与历史在杯中交融。Tea,the world’s second most popular drink after water,has a rich history and cultural significance.In 2019,the United Nations set up International Tea Day,which is held every year on May 21.It’s a day to honor tea’s economic,social,and health contributions.In 2025,the event will highlight women in tea production,emphasizing fair treatment for female workers globally.展开更多
三年前,我们的团队还带着点“火药味”,如今,伙伴们不仅和谐相处,更是团结协作——这三年的变化,藏在五个“带组锦囊”里。锦囊一:“Love and fairness”,爱与公平。去年冬天,小吴老师刚休完产假回来,总在课间偷偷抹眼泪。我趁她没课拉...三年前,我们的团队还带着点“火药味”,如今,伙伴们不仅和谐相处,更是团结协作——这三年的变化,藏在五个“带组锦囊”里。锦囊一:“Love and fairness”,爱与公平。去年冬天,小吴老师刚休完产假回来,总在课间偷偷抹眼泪。我趁她没课拉她到楼梯间喝咖啡,她才说:“怕跟不上进度,怕学生不喜欢我,晚上失眠。”展开更多
基金funded by the National Social Science Fund of China,grant number CHA200267.
文摘This study constructed a moderated mediation model to examine how the social support received by teachers is associated with their work pay fairness perception in relation to their job satisfaction and job performance.Data were collected from 2411 preschool teachers in China(female=98.01%;mean age=29.12 years,SD=6.28 years).These data were analyzed using structural equation modelling,bootstrapping and latent moderate structural equations.The results indicated that teachers’perception of pay fairness is directly associated with self-rated job performance.Additionally,pay fairness perceptions have an indirect effect on higher job performance through job satisfaction.The social support that teachers perceive moderates the relationship between pay fairness perception and job satisfaction:the more social support teachers receive,the weaker the impact of pay fairness perception on job satisfaction.Thesefindings suggest that teachers’perception of pay fairness is related to their sense of quality of work life,as indicated by their job satisfaction and performance.
文摘Love of peace and fairness has been a focus since ancient times,for domestic development as well as international exchanges.WHAT does fairness mean?In English,the original definition of the word“fair”includes the meaning of“beauty”and“harmony,”obviously referring to people’s longing for good prospects in life.In Arabic,the word for“fair”includes the meaning of“balance”and“integrity.”
基金National Natural Science Foundation of China,Grant/Award Number:62272114Joint Research Fund of Guangzhou and University,Grant/Award Number:202201020380+3 种基金Guangdong Higher Education Innovation Group,Grant/Award Number:2020KCXTD007Pearl River Scholars Funding Program of Guangdong Universities(2019)National Key R&D Program of China,Grant/Award Number:2022ZD0119602Major Key Project of PCL,Grant/Award Number:PCL2022A03。
文摘As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.
基金supported by the National Natural Science Foundation of China (32271126 and 31920103009)the Natural Science Foundation of Guangdong Province (2021A1515010746)+1 种基金the Major Project of National Social Science Foundation (20&ZD153)Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (2023SHIBS0003).
文摘Fairness is a fundamental value in human societies,with individuals concerned about unfairness both to themselves and to others.Nevertheless,an enduring debate focuses on whether self-unfairness and other-unfairness elicit shared or distinct neuropsychological processes.To address this,we combined a three-person ultimatum game with computational modeling and advanced neuroimaging analysis techniques to unravel the behavioral,cognitive,and neural patterns underlying unfairness to self and others.Our behavioral and computational results reveal a heightened concern among participants for self-unfairness over other-unfairness.Moreover,self-unfairness consistently activates brain regions such as the anterior insula,dorsal anterior cingulate cortex,and dorsolateral prefrontal cortex,spanning various spatial scales that encompass univariate activation,local multivariate patterns,and whole-brain multivariate patterns.These regions are well-established in their association with emotional and cognitive processes relevant to fairness-based decision-making.Conversely,other-unfairness primarily engages the middle occipital gyrus.Collectively,our findings robustly support distinct neurocomputational signatures between self-unfairness and other-unfairness.
基金Shanghai Municipal Science and Technology Major Project,Grant/Award Number:2023SHZD2X02A05National Natural Science Foundation of China,Grant/Award Number:62331021Shanghai Sailing Program,Grant/Award Numbers:20YF1402400,22YF1409300。
文摘Fairness is an emerging consideration when assessing the segmentation per-formance of machine learning models across various demographic groups.During clinical decision-making,an unfair segmentation model exhibits risks in that it can pose inappropriate diagnoses and unsuitable treatment plans for underrepresented demographic groups,resulting in severe consequences for patients and society.In medical artificial intelligence(AI),the fairness of multi-organ segmentation is imperative to augment the integration of models into clinical practice.As the use of multi-organ segmentation in medical image analysis expands,it is crucial to systematically examine fairness to ensure equitable segmentation performance across diverse patient populations and ensure health equity.However,comprehensive studies assessing the problem of fairness in multi-organ segmentation remain lacking.This study aimed to provide an overview of the fairness problem in multi-organ segmentation.We first define fairness and discuss the factors that lead to fairness problems such as individual fairness,group fairness,counterfactual fairness,and max–min fairness in multi-organ segmentation,focusing mainly on datasets and models.We then present strategies to potentially improve fairness in multi-organ segmentation.Additionally,we highlight the challenges and limita-tions of existing approaches and discuss future directions for improving the fairness of AI models for clinically oriented multi-organ segmentation.
基金The National Science and Technology Major Project(No.2012ZX03004005-003)the National Natural Science Foundationof China(No.61171081,61201175)the Science and Technology Support Program of Jiangsu Province(No.BE2011187)
文摘To satisfy different service requirements of multiple users in the orthogo nal frequency division multiple access wireless local area network OFDMA-WLAN system downlink transmission a resource allocation algorithm based on fairness and quality of service QoS provisioning is proposed. Different QoS requirements are converted into different rate requirements to calculate the QoSs atisfaction level.The optimization object is revised as a fairness-driven resource optimization function to provide fairness. The complex resource allocation problem is divided into channel allocation and power assignment sub-problems. The sub-problems are solved by the bipartite graph matching and water-filling based method.Compared with other algorithms the proposed algorithm sacrifices less data rate for higher fairnes and QoS satisfaction.The sim ulation results show that the proposed algorithm is capableo fp rovi ding QoS and fairness and performs better in a tradeoff among QoS fairness and data rate.
基金supported by the 863 Program (2015AA01A705)NSFC (61271187)
文摘To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.
基金supported by China National S&T Major Project 2013ZX03003002003Beijing Natural Science Foundation No.4152047+1 种基金the 863 project No.2014AA01A701111 Project of China under Grant B14010
文摘With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) as a subjective assessment is usually adopted for accurate and convincing reflection of user perceived quality.In this paper,we consider the effect of the burst transmission of best effort(BE) traffic on the uses with real time video traffic in the same cell.We extend the rate scaling process which was initially used to shape burstiness of BE users as interference to handle the scenario that BE users act as resource competitors with video users.A power reallocation strategy between the two types of users is presented and an algorithm further improving the fairness of BE users is proposed.The simulation results demonstrate that the proposed algorithm can not only promote the QoE of both types of users,but also guarantee the fairness among users.
文摘Measuring the impact of AI systems The recent 2025 AI Index Report from Stanford University revealed that skepticism about the ethical conduct of AI companies is growing,and trust in fairness is shrinking.There is also less confidence that personal data will be protected and fewer people believe AI systems are unbiased and free of discrimination.Trust and transparency are essential for AI to deliver on its promises in a safe and responsible way.Governments are stepping up with new AI-related regulations,and international standards such as ISO/IEC 42001 have been developed to support them,but a lot more needs to be done to reduce potential risks and address societal concerns.
文摘茶香缱绻,文化悠长,健康时尚与历史在杯中交融。Tea,the world’s second most popular drink after water,has a rich history and cultural significance.In 2019,the United Nations set up International Tea Day,which is held every year on May 21.It’s a day to honor tea’s economic,social,and health contributions.In 2025,the event will highlight women in tea production,emphasizing fair treatment for female workers globally.
文摘三年前,我们的团队还带着点“火药味”,如今,伙伴们不仅和谐相处,更是团结协作——这三年的变化,藏在五个“带组锦囊”里。锦囊一:“Love and fairness”,爱与公平。去年冬天,小吴老师刚休完产假回来,总在课间偷偷抹眼泪。我趁她没课拉她到楼梯间喝咖啡,她才说:“怕跟不上进度,怕学生不喜欢我,晚上失眠。”
