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
How to allocate a resource efficiently and fairly attracts the attention of both researchers and practitioners. Data envelopment analysis（DEA） has been brought to bear on its solution. The existing literature applie...How to allocate a resource efficiently and fairly attracts the attention of both researchers and practitioners. Data envelopment analysis（DEA） has been brought to bear on its solution. The existing literature applies Gini coefficient to measure the fairness in the resource allocation process. However, the Gini coefficient is inapplicable in many applications. This paper proposes a novel centralized resource allocation model based on DEA that considers both the efficiency and the fairness. This paper adopts a notion of fairness, namely α-fairness that is well studied in welfare economics and is of practical significance. The new model integratesα-fairness with DEA to support resource allocation decisions. It aids decision makers in making a trade-off between the efficiency and the fairness. An illustrative application is used to validate the proposed approach.展开更多
Different from conventional cellular networks, a maritime communication base station(BS) has to cover a much wider area due to the limitation of available BS sites. Accordingly the performance of users far away from t...Different from conventional cellular networks, a maritime communication base station(BS) has to cover a much wider area due to the limitation of available BS sites. Accordingly the performance of users far away from the BS is poor in general. This renders the fairness among users a challenging issue for maritime communications. In this paper, we consider a practical massive MIMO maritime BS with hybrid digital and analog precoding. Only the large-scale channel state information at the transmitter(CSIT) is considered so as to reduce the implementation complexity and overhead of the system. On this basis, we address the problem of fairness-oriented precoding design. A max-min optimization problem is formulated and solved in an iterative way. Simulation results demonstrate that the proposed scheme performs much better than conventional hybrid precoding algorithms in terms of minimum achievable rate of all the users, for the typical three-ray maritime channel model.展开更多
In this paper, proportional fairness(PF)-based energy-efficient power allocation is studied for multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) systems. In our schemes, statistical channel st...In this paper, proportional fairness(PF)-based energy-efficient power allocation is studied for multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) systems. In our schemes, statistical channel state information(CSI) is utilized for perfect CSI is impossible to achieve in practice. PF is used to balance the transmission efficiency and user fairness. Energy efficiency(EE) is formulated under basic data rate requirements and maximum transmitting power constraints. Due to the non-convex nature of EE, a two-step algorithm is proposed to obtain sub-optimal solution with a low complexity. Firstly, power allocation is determined by golden section search for fixed power. Secondly total transmitting power is determined by fractional programming method in the feasible regions. Compared to the performance of MIMO-NOMA without PF constraint, fairness is obtained at expense of decreasing of EE.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
Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows m...Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows multiple terminals to share the same resource unit at the same time.The receiver usually needs to configure successive interference cancellation(SIC).The receiver eliminates co-channel interference(CCI)between users and it can significantly improve the system throughput.In order to meet the demands of users and improve fairness among them,this paper proposes a new power allocation scheme.The objective is to maximize user fairness by deploying the least fairness in multiplexed users.However,the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker(KKT)constraints.Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.展开更多
In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottlene...In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottleneck.However,how fair an allocation algorithm is remains an urgent issue.In this paper,we propose Dynamic Evaluation Framework for Fairness(DEFF),a framework to evaluate the fairness of an resource allocation algorithm.In our framework,two sub-models,Dynamic Demand Model(DDM) and Dynamic Node Model(DNM),are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment.Combining Fairness on Dominant Shares and the two sub-models above,we finally obtain DEFF.In our experiment,we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.展开更多
This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an S...This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting(EH) relay. We present a power splitting(PS)-based two-way relaying(PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signalto-noise ratio(SNR), the performance loss is relatively small. Another observation is that, in relatively high SNR regime, PS-TWR outperforms time switching-based two-way relaying(TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.展开更多
Non-orthogonal multiple access(NOMA)is one of the leading technologies for 5G communication.User pairing(UP)and power allocation(PA)are the key controlling mechanisms for the optimization of the performance of NOMA sy...Non-orthogonal multiple access(NOMA)is one of the leading technologies for 5G communication.User pairing(UP)and power allocation(PA)are the key controlling mechanisms for the optimization of the performance of NOMA systems.This paper presents a novel UP and PA(UPPA)technique for capacity and fairness maximization in NOMA called(CFM-UPPA).The impact of the power allocation coefficient and the ratio between the channel gains of the paired users on the sum-rate capacity and the fairness in NOMA is firstly investigated.Then,based on this investigation,the PA and UP algorithms of the CFM-UPPA technique are proposed.The power allocation coefficient of the proposed PA is formulated as an exponentially decaying function of the ratio between the channel gains of the paired users to maximize the capacity and the fairness,and its maximum value is adjusted to guarantee the successive interference cancellation(SIC)constraints.The proposed UP is based on selecting the user that has the highest channel gain per subcarrier as the strong user to maximize the capacity and selecting the user that has the closest lower channel gain to the strong user’s channel gain as the weak user to improve the fairness and capacity.The performance evaluation of the proposed CFM-UPPA technique in terms of capacity,fairness,and outage probability demonstrates that its performance significantly outperforms that of the orthogonal multiple access(OMA)system and that of the NOMA system with random UP.Also,the simulation results demonstrate the efficiency of the proposed PA in improving the performance of other UP algorithms,such as the random UP algorithm.展开更多
Purpose:This paper proposes a discrimination index method based on the Jain’s fairness index to distinguish researchers with the same H-index.Design/methodology/approach:A validity test is used to measure the correla...Purpose:This paper proposes a discrimination index method based on the Jain’s fairness index to distinguish researchers with the same H-index.Design/methodology/approach:A validity test is used to measure the correlation of D-offset with the parameters,i.e.H-index,the number of cited papers,the total number of citations,the number of indexed papers,and the number of uncited papers.The correlation test is based on the Saphiro-Wilk method and Pearson’s product-moment correlation.Findings:The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset(D-offset),with a range of D-offset from 0.00 to 0.99.The result of the correlation value between the D-offset and the number of uncited papers is 0.35,D-offset with the number of indexed papers is 0.24,and the number of cited papers is 0.27.The test provides the result that it is very unlikely that there exists no relationship between the parameters.Practical implications:For this reason,D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index.The H-index for researchers can be written with the format of“H-index:D-offset”.Originality/value:D-offset is worthy to be considered as a complement value to add the H-index value.If the D-offset is added in the H-index value,the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.展开更多
The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development.The data used in most of the existing assessment methods comes from statistical yea...The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development.The data used in most of the existing assessment methods comes from statistical yearbooks or field survey sampling.These statistics are generally based on administrative areas and are difficult to support a fine-grained evaluation model.In response to these problems,the evaluation method proposed in this paper is based on the query statistics of the geographic grid of the target area,which are more accurate and efficient.Based on the query statistics of hot words in the geographic grids,this paper adopts the maximum likelihood estimation method to estimate the population in the grid region.Then,according to the statistical yearbook data of Hunan province,the estimated number and actual number of hospitals in each grid are analyzed and compared to measure the fairness of health resource allocation in the target region.Experiments show that the geographical grid population assessment based on hot words is more accurate and close to the actual value.The estimated average error is only about 17.8 percent.This method can assess the fairness of health resource allocation in any scale,and is innovative in data acquisition and evaluation methods.展开更多
This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism call...This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism called Dominant Resource with Bottlenecked Fairness(DRBF), which generalizes Bottleneck-aware Allocation(BAA) to the settings of Dominant Resource Fairness(DRF). We classify users into different queues by their dominant resources. The goals are to ensure that users in the same queue receive allocations in proportion to their fair shares while users in different queues receive allocations that maximize resource utilization subject to well-studied fairness properties such as those in DRF. Under DRBF, no user 1) is worse off sharing resources than dividing resources equally among all users; 2) prefers the allocation of another user; 3) can improve their own allocation without reducing other users' allocations; and(4) can benefit by misreporting their resource demands. Experiments demonstrate that the proposed allocation policy performs better in terms of high resource utilization than does DRF.展开更多
Quantum entanglement has emerged as a new resource to enhance cooperation and remove dilemmas.This paper aims to explore conditions under which full cooperation is achievable even when the information of payoff is inc...Quantum entanglement has emerged as a new resource to enhance cooperation and remove dilemmas.This paper aims to explore conditions under which full cooperation is achievable even when the information of payoff is incomplete.Based on the quantum version of the extended classical cash in a hat game,we demonstrate that quantum entanglement may be used for achieving full cooperation or avoiding moral hazards with the reasonable profit distribution policies even when the profit is uncertain to a certain degree.This research further suggests that the fairness of profit distribution should play an important role in promoting full cooperation.It is hopeful that quantum entanglement and fairness will promote full cooperation among distant people from various interest groups when quantum networks and quantum entanglement are accessible to the public.展开更多
Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions,device monitoring, and collection of information. Due to limited energy...Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions,device monitoring, and collection of information. Due to limited energy resourcesavailable at SN, the primary issue is to present an energy-efficient framework andconserve the energy while constructing a route path along with each sensor node.However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routingwith minimal energy consumption in WSN. This paper presents an energy-efficientrouting system called Energy-aware Proportional Fairness Multi-user Routing(EPFMR) framework in WSN. EPFMR is deployed in the WSN environment usingthe instance time. The request time sent for the route discovery is the foremost stepdesigned in the EPFMR framework to reduce the energy consumption rate. Theproportional fairness routing in WSN selects the best route path for the packet flowbased on the relationship between the periods of requests between different SN.Route path discovered for packet flow also measure energy on multi-user route pathusing the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR developsnode dependent energy-efficient localized route path, improving the throughput.The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing,Throughput, RST, node density and average energy per packet in WSN. The RouteSearching Time (RST) is reduced using the Boltzmann Distribution (BD), and as aresult, the energy is minimized on multi-user WSN. Finally, GIFM applies aninstance time difference-based route searching on WSN to attain an optimal energyminimization system. Experimental analysis shows that the EPFMR framework canreduce the RST by 23.47% and improve the throughput by 6.79% compared withthe state-of-the-art works.展开更多
In this paper, we propose a flexible and fairness-oriented packet scheduling approach for 3GPP UTRAN long term evolution (LTE) type packet radio systems, building on the ordinary proportional fair (PF) scheduling prin...In this paper, we propose a flexible and fairness-oriented packet scheduling approach for 3GPP UTRAN long term evolution (LTE) type packet radio systems, building on the ordinary proportional fair (PF) scheduling principle and channel quality indicator (CQI) feedback. Special emphasis is also put on practical feedback reporting mechanisms, including the effects of mobile measurement and estimation errors, reporting delays, and CQI quantization and compression. The performance of the overall scheduling and feedback re-porting process is investigated in details, in terms of cell throughput, coverage and resource allocation fairness, by using extensive quasistatic cellular system simulations in practical OFDMA system environment with frequency reuse of 1. The performance simulations show that by using the proposed modified PF ap-proach, significant coverage improvements in the order of 50% can be obtained at the expense of only 10-15% throughput loss, for all reduced feedback reporting schemes. This reflects highly improved fairness in the radio resource management (RRM) compared to other existing schedulers, without essentially com-promising the cell capacity. Furthermore, we demonstrate the improved functionality increase in radio re-source management for UE’s utilizing multi-antenna diversity receivers.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 National Natural Science Foundation of China(7117118171301155)+1 种基金the Fundamental Research Fundsfor the Central Universities(WK2040160008J2014HGBZ0172)
文摘How to allocate a resource efficiently and fairly attracts the attention of both researchers and practitioners. Data envelopment analysis（DEA） has been brought to bear on its solution. The existing literature applies Gini coefficient to measure the fairness in the resource allocation process. However, the Gini coefficient is inapplicable in many applications. This paper proposes a novel centralized resource allocation model based on DEA that considers both the efficiency and the fairness. This paper adopts a notion of fairness, namely α-fairness that is well studied in welfare economics and is of practical significance. The new model integratesα-fairness with DEA to support resource allocation decisions. It aids decision makers in making a trade-off between the efficiency and the fairness. An illustrative application is used to validate the proposed approach.
基金supported in part by the National Science Foundation of China under grant No. 91638205,grant No. 61771286, and grant No. 61701457, and grant No. 61621091
文摘Different from conventional cellular networks, a maritime communication base station(BS) has to cover a much wider area due to the limitation of available BS sites. Accordingly the performance of users far away from the BS is poor in general. This renders the fairness among users a challenging issue for maritime communications. In this paper, we consider a practical massive MIMO maritime BS with hybrid digital and analog precoding. Only the large-scale channel state information at the transmitter(CSIT) is considered so as to reduce the implementation complexity and overhead of the system. On this basis, we address the problem of fairness-oriented precoding design. A max-min optimization problem is formulated and solved in an iterative way. Simulation results demonstrate that the proposed scheme performs much better than conventional hybrid precoding algorithms in terms of minimum achievable rate of all the users, for the typical three-ray maritime channel model.
基金supported by the National Natural Science Foundation of China (No. 61671252)
文摘In this paper, proportional fairness(PF)-based energy-efficient power allocation is studied for multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) systems. In our schemes, statistical channel state information(CSI) is utilized for perfect CSI is impossible to achieve in practice. PF is used to balance the transmission efficiency and user fairness. Energy efficiency(EE) is formulated under basic data rate requirements and maximum transmitting power constraints. Due to the non-convex nature of EE, a two-step algorithm is proposed to obtain sub-optimal solution with a low complexity. Firstly, power allocation is determined by golden section search for fixed power. Secondly total transmitting power is determined by fractional programming method in the feasible regions. Compared to the performance of MIMO-NOMA without PF constraint, fairness is obtained at expense of decreasing of EE.
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.
文摘Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows multiple terminals to share the same resource unit at the same time.The receiver usually needs to configure successive interference cancellation(SIC).The receiver eliminates co-channel interference(CCI)between users and it can significantly improve the system throughput.In order to meet the demands of users and improve fairness among them,this paper proposes a new power allocation scheme.The objective is to maximize user fairness by deploying the least fairness in multiplexed users.However,the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker(KKT)constraints.Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.
基金supported in part by Program for Changjiang Scholars and Innovative Research Team in University No.IRT1078The Key Program of NSFC-Guangdong Union Foundation No.U1135002The Fundamental Research Funds for the Central Universities No.JY0900120301
文摘In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottleneck.However,how fair an allocation algorithm is remains an urgent issue.In this paper,we propose Dynamic Evaluation Framework for Fairness(DEFF),a framework to evaluate the fairness of an resource allocation algorithm.In our framework,two sub-models,Dynamic Demand Model(DDM) and Dynamic Node Model(DNM),are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment.Combining Fairness on Dominant Shares and the two sub-models above,we finally obtain DEFF.In our experiment,we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.
基金supported by the National Natural Science Foundation of China ( No . 61602034 )the Beijing Natural Science Foundation (No. 4162049)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (No. 2014D03)the Fundamental Research Funds for the Central Universities Beijing Jiaotong University (No. 2016JBM015)the NationalHigh Technology Research and Development Program of China (863 Program) (No. 2015AA015702)
文摘This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting(EH) relay. We present a power splitting(PS)-based two-way relaying(PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signalto-noise ratio(SNR), the performance loss is relatively small. Another observation is that, in relatively high SNR regime, PS-TWR outperforms time switching-based two-way relaying(TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.
基金This research was supported by Taif University Researchers Supporting Project Number(TURSP-2020/147),Taif University,Taif,Saudi Arabia.
文摘Non-orthogonal multiple access(NOMA)is one of the leading technologies for 5G communication.User pairing(UP)and power allocation(PA)are the key controlling mechanisms for the optimization of the performance of NOMA systems.This paper presents a novel UP and PA(UPPA)technique for capacity and fairness maximization in NOMA called(CFM-UPPA).The impact of the power allocation coefficient and the ratio between the channel gains of the paired users on the sum-rate capacity and the fairness in NOMA is firstly investigated.Then,based on this investigation,the PA and UP algorithms of the CFM-UPPA technique are proposed.The power allocation coefficient of the proposed PA is formulated as an exponentially decaying function of the ratio between the channel gains of the paired users to maximize the capacity and the fairness,and its maximum value is adjusted to guarantee the successive interference cancellation(SIC)constraints.The proposed UP is based on selecting the user that has the highest channel gain per subcarrier as the strong user to maximize the capacity and selecting the user that has the closest lower channel gain to the strong user’s channel gain as the weak user to improve the fairness and capacity.The performance evaluation of the proposed CFM-UPPA technique in terms of capacity,fairness,and outage probability demonstrates that its performance significantly outperforms that of the orthogonal multiple access(OMA)system and that of the NOMA system with random UP.Also,the simulation results demonstrate the efficiency of the proposed PA in improving the performance of other UP algorithms,such as the random UP algorithm.
基金This research was financially supported by the Ministry of Research and Technology,Republic of Indonesia through Fundamental Research Grant No.225-98/UN7.6.1/PP/2020.
文摘Purpose:This paper proposes a discrimination index method based on the Jain’s fairness index to distinguish researchers with the same H-index.Design/methodology/approach:A validity test is used to measure the correlation of D-offset with the parameters,i.e.H-index,the number of cited papers,the total number of citations,the number of indexed papers,and the number of uncited papers.The correlation test is based on the Saphiro-Wilk method and Pearson’s product-moment correlation.Findings:The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset(D-offset),with a range of D-offset from 0.00 to 0.99.The result of the correlation value between the D-offset and the number of uncited papers is 0.35,D-offset with the number of indexed papers is 0.24,and the number of cited papers is 0.27.The test provides the result that it is very unlikely that there exists no relationship between the parameters.Practical implications:For this reason,D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index.The H-index for researchers can be written with the format of“H-index:D-offset”.Originality/value:D-offset is worthy to be considered as a complement value to add the H-index value.If the D-offset is added in the H-index value,the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.
文摘The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development.The data used in most of the existing assessment methods comes from statistical yearbooks or field survey sampling.These statistics are generally based on administrative areas and are difficult to support a fine-grained evaluation model.In response to these problems,the evaluation method proposed in this paper is based on the query statistics of the geographic grid of the target area,which are more accurate and efficient.Based on the query statistics of hot words in the geographic grids,this paper adopts the maximum likelihood estimation method to estimate the population in the grid region.Then,according to the statistical yearbook data of Hunan province,the estimated number and actual number of hospitals in each grid are analyzed and compared to measure the fairness of health resource allocation in the target region.Experiments show that the geographical grid population assessment based on hot words is more accurate and close to the actual value.The estimated average error is only about 17.8 percent.This method can assess the fairness of health resource allocation in any scale,and is innovative in data acquisition and evaluation methods.
基金financial support of the Oversea Study Program of the Guangzhou Elite Project(GEP)supported by the National Natural Science Foundation of China under Grant 61471173Guangdong Science Technology Project(no:2017A010101027)
文摘This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism called Dominant Resource with Bottlenecked Fairness(DRBF), which generalizes Bottleneck-aware Allocation(BAA) to the settings of Dominant Resource Fairness(DRF). We classify users into different queues by their dominant resources. The goals are to ensure that users in the same queue receive allocations in proportion to their fair shares while users in different queues receive allocations that maximize resource utilization subject to well-studied fairness properties such as those in DRF. Under DRBF, no user 1) is worse off sharing resources than dividing resources equally among all users; 2) prefers the allocation of another user; 3) can improve their own allocation without reducing other users' allocations; and(4) can benefit by misreporting their resource demands. Experiments demonstrate that the proposed allocation policy performs better in terms of high resource utilization than does DRF.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61673389,61273202,and 61134008)
文摘Quantum entanglement has emerged as a new resource to enhance cooperation and remove dilemmas.This paper aims to explore conditions under which full cooperation is achievable even when the information of payoff is incomplete.Based on the quantum version of the extended classical cash in a hat game,we demonstrate that quantum entanglement may be used for achieving full cooperation or avoiding moral hazards with the reasonable profit distribution policies even when the profit is uncertain to a certain degree.This research further suggests that the fairness of profit distribution should play an important role in promoting full cooperation.It is hopeful that quantum entanglement and fairness will promote full cooperation among distant people from various interest groups when quantum networks and quantum entanglement are accessible to the public.
文摘Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions,device monitoring, and collection of information. Due to limited energy resourcesavailable at SN, the primary issue is to present an energy-efficient framework andconserve the energy while constructing a route path along with each sensor node.However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routingwith minimal energy consumption in WSN. This paper presents an energy-efficientrouting system called Energy-aware Proportional Fairness Multi-user Routing(EPFMR) framework in WSN. EPFMR is deployed in the WSN environment usingthe instance time. The request time sent for the route discovery is the foremost stepdesigned in the EPFMR framework to reduce the energy consumption rate. Theproportional fairness routing in WSN selects the best route path for the packet flowbased on the relationship between the periods of requests between different SN.Route path discovered for packet flow also measure energy on multi-user route pathusing the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR developsnode dependent energy-efficient localized route path, improving the throughput.The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing,Throughput, RST, node density and average energy per packet in WSN. The RouteSearching Time (RST) is reduced using the Boltzmann Distribution (BD), and as aresult, the energy is minimized on multi-user WSN. Finally, GIFM applies aninstance time difference-based route searching on WSN to attain an optimal energyminimization system. Experimental analysis shows that the EPFMR framework canreduce the RST by 23.47% and improve the throughput by 6.79% compared withthe state-of-the-art works.
文摘In this paper, we propose a flexible and fairness-oriented packet scheduling approach for 3GPP UTRAN long term evolution (LTE) type packet radio systems, building on the ordinary proportional fair (PF) scheduling principle and channel quality indicator (CQI) feedback. Special emphasis is also put on practical feedback reporting mechanisms, including the effects of mobile measurement and estimation errors, reporting delays, and CQI quantization and compression. The performance of the overall scheduling and feedback re-porting process is investigated in details, in terms of cell throughput, coverage and resource allocation fairness, by using extensive quasistatic cellular system simulations in practical OFDMA system environment with frequency reuse of 1. The performance simulations show that by using the proposed modified PF ap-proach, significant coverage improvements in the order of 50% can be obtained at the expense of only 10-15% throughput loss, for all reduced feedback reporting schemes. This reflects highly improved fairness in the radio resource management (RRM) compared to other existing schedulers, without essentially com-promising the cell capacity. Furthermore, we demonstrate the improved functionality increase in radio re-source management for UE’s utilizing multi-antenna diversity receivers.
