Contemporary liberal theory on moral rights argues that moral rights associated with personal liberty constitute a strong constraint on the boundaries of state power.Therefore,the core issue of the penalty justificati...Contemporary liberal theory on moral rights argues that moral rights associated with personal liberty constitute a strong constraint on the boundaries of state power.Therefore,the core issue of the penalty justification is not the purpose of the penalty,but the reason for the penalty to refrain from infringing on the moral rights of individuals.In order to justify the penal system,scholars have explored solutions such as limiting the content of rights,waiving rights,and finally rights forfeiture.However,the concept of rights forfeiture cannot be reasonably integrated into the framework of the liberal theory of moral rights.The failure of these attempts stems from the patchwork understanding of rights presupposed by the liberal theory of moral rights.There is another systematic way of understanding rights that offers a better justification.Individual rights are not an independent non-derivative moral justification,and both individual rights and the penal power of the state are only part of a specific(realistic or ideal)system of rules that collectively serve certain values.The real question of penalty justification is not why the punishment does not infringe on the moral rights of individuals,but whether the overall institutional arrangements,including the penal system,are justifiable for all citizens,including the punished.展开更多
With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detec...With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.展开更多
Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mob...Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.展开更多
Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as con...Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,etc.In this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech quality.Specifically,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated speech.The experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.展开更多
区块链技术为刑罚变更执行数据的真实可信提供支撑,并实现各主体间的互信和高效协同.对权威证明共识算法(proof of authority, PoA)存在的制约因素进行分析并提出一种改进的动态加权权威证明算法(dynamic weighted proof of authority, ...区块链技术为刑罚变更执行数据的真实可信提供支撑,并实现各主体间的互信和高效协同.对权威证明共识算法(proof of authority, PoA)存在的制约因素进行分析并提出一种改进的动态加权权威证明算法(dynamic weighted proof of authority, DWPoA),进行刑罚变更执行提请共识.构建联盟链,基于Tangle结构提出一种链上共识算法,进行刑罚变更执行主体间的链上共识;基于(t,n)门限签名提出一种分布式预言机链下共识算法进行链上、链下共识,并对带宽占用率和共识时长进行仿真实验,分布式预言机链下共识算法更具优势.展开更多
针对外卖配送电动自行车换电柜布局不合理带来的部分换电柜利用率低与部分换电需求得不到及时满足的供需矛盾问题,本文通过聚类POI(Point of Interest)数据确定外卖配送起止点,并通过仿真模拟外卖骑手配送路径预测外卖配送电动自行车换...针对外卖配送电动自行车换电柜布局不合理带来的部分换电柜利用率低与部分换电需求得不到及时满足的供需矛盾问题,本文通过聚类POI(Point of Interest)数据确定外卖配送起止点,并通过仿真模拟外卖骑手配送路径预测外卖配送电动自行车换电需求时空分布,构建换电柜运营商总成本最低和用户满意度最高的多目标换电柜选址定容模型,并以新乡市主城区为例,采用NSGA-II(Non-dominated Sorting Genetic Algorithm II)算法得到换电柜选址定容方案。研究结果表明:仿真模拟得出的换电需求时间分布预测值与实际值基本吻合,换电需求在11:00,14:00,17:00和20:00左右急剧增长,且11:00和14:00左右的换电需求量显著高于17:00和20:00左右的换电需求量,外卖骑手配送路径仿真模拟方法在换电需求预测上具有较高的预测精度;换电柜选址方案不能同时满足运营商和用户利益均为最优,用户满意度的提高需以增加运营商总成本为代价;同时,兼顾运营商和用户利益的新乡市主城区外卖配送电动自行车换电柜最佳建设数量为26,其中,容量为11的换电柜11个,容量为22的换电柜8个,容量为33的换电柜7个;新乡市主城区应按照备选点编号15-7-19-17依次新增换电柜至30个,此时,用户满意度最大,若继续增加换电柜建设数量,只会增加运营商总成本。展开更多
基金the phased achievement of the 2023university(college)-level research program of the Party School of the Central Committee of C.P.C(National Academy of Governance):Research on the Legal Foundation of Digital Human Rights and Its Legal Guarantee Approaches。
文摘Contemporary liberal theory on moral rights argues that moral rights associated with personal liberty constitute a strong constraint on the boundaries of state power.Therefore,the core issue of the penalty justification is not the purpose of the penalty,but the reason for the penalty to refrain from infringing on the moral rights of individuals.In order to justify the penal system,scholars have explored solutions such as limiting the content of rights,waiving rights,and finally rights forfeiture.However,the concept of rights forfeiture cannot be reasonably integrated into the framework of the liberal theory of moral rights.The failure of these attempts stems from the patchwork understanding of rights presupposed by the liberal theory of moral rights.There is another systematic way of understanding rights that offers a better justification.Individual rights are not an independent non-derivative moral justification,and both individual rights and the penal power of the state are only part of a specific(realistic or ideal)system of rules that collectively serve certain values.The real question of penalty justification is not why the punishment does not infringe on the moral rights of individuals,but whether the overall institutional arrangements,including the penal system,are justifiable for all citizens,including the punished.
基金This research was funded by Innovation and Entrepreneurship Training Program for College Students in Hunan Province in 2022(3915).
文摘With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.
文摘Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of applications.The main challenges are related to routing techniques that are generally employed in them.Mobile Adhoc system management,on the other hand,requires further testing and improvements in terms of security.Traditional routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two nodes.The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network.It is achieved by changing the residual energy of each network node.Also,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are proposed.Moreover,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters.The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.
基金supported by the National Science Foundation under Grant No.62066039.
文摘Recently,speech enhancement methods based on Generative Adversarial Networks have achieved good performance in time-domain noisy signals.However,the training of Generative Adversarial Networks has such problems as convergence difficulty,model collapse,etc.In this work,an end-to-end speech enhancement model based on Wasserstein Generative Adversarial Networks is proposed,and some improvements have been made in order to get faster convergence speed and better generated speech quality.Specifically,in the generator coding part,each convolution layer adopts different convolution kernel sizes to conduct convolution operations for obtaining speech coding information from multiple scales;a gated linear unit is introduced to alleviate the vanishing gradient problem with the increase of network depth;the gradient penalty of the discriminator is replaced with spectral normalization to accelerate the convergence rate of themodel;a hybrid penalty termcomposed of L1 regularization and a scale-invariant signal-to-distortion ratio is introduced into the loss function of the generator to improve the quality of generated speech.The experimental results on both TIMIT corpus and Tibetan corpus show that the proposed model improves the speech quality significantly and accelerates the convergence speed of the model.
文摘区块链技术为刑罚变更执行数据的真实可信提供支撑,并实现各主体间的互信和高效协同.对权威证明共识算法(proof of authority, PoA)存在的制约因素进行分析并提出一种改进的动态加权权威证明算法(dynamic weighted proof of authority, DWPoA),进行刑罚变更执行提请共识.构建联盟链,基于Tangle结构提出一种链上共识算法,进行刑罚变更执行主体间的链上共识;基于(t,n)门限签名提出一种分布式预言机链下共识算法进行链上、链下共识,并对带宽占用率和共识时长进行仿真实验,分布式预言机链下共识算法更具优势.
