A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

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.

Off-site trimer superfluid on a one-dimensional optical lattice

The Bose-Hubbard model with an effective off-site three-body tunneling,characterized by jumps towards one another,between one atom on a site and a pair atoms on the neighborhood site,is studied systematically on a one-dimensional（1D） lattice,by using the density matrix renormalization group method.The off-site trimer superfluid,condensing at momentum k = 0,emerges in the softcore Bose-Hubbard model but it disappears in the hardcore Bose-Hubbard model.Our results numerically verify that the off-site trimer superfluid phase derived in the momentum space from[Phys.Rev.A81,011601（R）（2010）]is stable in the thermodynamic limit.The off-site trimer superfluid phase,the partially off-site trimer superfluid phase and the Mott insulator phase are found,as well as interesting phase transitions,such as the continuous or first-order phase transition from the trimer superfluid phase to the Mott insulator phase.Our results are helpful in realizing this novel off-site trimer superfluid phase by cold atom experiments.

Application of Intelligent Power Consumption Management and Control Technology in Enterprise Off-site Enforcement

Regarding the current difficulties in SME(small and medium enterprise)supervision,especially considering the problem that the emission reduction task cannot be effectively implemented under the off-site daily supervision and the new normal of epidemic prevention with control of heavily polluted weather,a new type of intelligent power management technology has been proposed.Power information collection equipment and intelligent data collection and transmission terminal are installed to collect power consumption information of enterprise production facilities and pollution control facilities,and their operating conditions are monitored in 24 h.Abnormal operation alarm and closed-loop disposal management are provided through the software platform.The practical application of power monitoring technology in Rizhao City proves that the system alarms accurately and monitors efficiently.On the one hand,it has improved the efficiency of daily supervision of the ecological environment department,and can accurately investigate and correct corporate pollution control violations.On the other hand,it has enriched environmental supervision methods to accurately control the implementation of emergency emission reduction measures.The application of this technology has realized the transformation from civil defense to technical defense,from random law enforcement to precise law enforcement,and from on-site law enforcement to off-site law enforcement in the supervision of polluting enterprises in the jurisdiction,creating a new mode of off-site law enforcement for enterprises.

Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

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.

Using Hybrid Penalty and Gated Linear Units to Improve Wasserstein Generative Adversarial Networks for Single-Channel Speech Enhancement

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.

生育的幸福效应——兼论其对低生育率的支撑

生育的幸福效应是理解低生育率社会中个体生育决策的一个重要维度。利用中国家庭追踪调查2010—2018年数据,考察孩子出生前后父母幸福感轨迹。研究发现,个人在主观幸福感方面对生育事件有积极的预期收益,主观幸福感在生育事件发生的当年达到顶峰,然后随着时间的推移逐渐下降。生育事件前后的主观幸福感轨迹表明,生育事件对个人的心理和生活感知有积极的促进作用,同时也证实了适应效应的存在。就业性质对生育的幸福效应具有调节作用,并且存在显著的性别差异,表明当父亲和母亲的工作允许他们有更多时间照顾孩子时,他们都会增加亲子互动和沟通并从中获得更高水平的幸福感。生育的这种幸福效应具有超经济性,并借助个人的生育决策筑成低生育率的底部。

基于改进蚁狮算法的家庭用电优化调度

为了充分挖掘用户侧需求响应能力,平抑用户的负荷波动及降低用户的用电成本,提出了一种基于改进蚁狮优化算法的家庭用电优化调度方法。首先构建了家庭用电优化调度模型,并将峰均比和平均等待时间作为惩罚量引入用电成本得到综合成本函数。其次采用差分进化机制改进了传统的蚁狮算法。最后基于实时电价和临界峰值电价进行仿真,结果表明,改进的蚁狮算法可以有效的降低用户的用电成本,维持用电舒适度,并与粒子群算法、遗传算法和蚁狮算法进行对比,验证了所提算法有较高的稳定性,更强的全局搜索能力,收敛效果更好。

数字平台“二选一”行为的反垄断法公共规制

2021年以来,数字平台排他性限定交易的“二选一”行为逐渐成为反垄断执法重点,“阿里巴巴集团垄断案”为考察平台排他性限定交易行为提供了典型范本。规制具有市场支配地位的数字平台排他性交易行为,私法规范显著不足,为公共规制提供了契机;在公共规制体系,《反垄断法》相比《电子商务法》具备更大的制度张力,依循反垄断法规制逻辑可逐一破解相关难题。察究典型案例,“相关市场界定—市场支配地位认定—滥用行为识别—竞争效果分析”四步骤分析方法,依然是当下及未来判断平台商业交易行为是否滥用市场支配地位的共识型“黄金范式”;围绕典型案例的行政处罚决定书展开分析,学界对于法律责任承担部分仍有较多分歧。为有效规制数字平台“二选一”行为,执法机构除了肯定过往严谨的分析范式、完备的程序保障和多元的治理措施等经验以外,还需在贯彻反垄断法的规范适用、破解反垄断法双重规制路径的困扰、坚持反垄断执法与企业合规建设协同推进诸方面进行精细化升级,更好助力数字市场公平竞争和数字经济高质量发展。

刑事合规与单位犯罪立法的完善

在肯定法人犯罪的基础上,法人犯罪立法中的关键问题主要集中在法人犯罪的范围、法人犯罪的归责基础以及法人犯罪中法人与相关法人成员的关系。在刑法分则中挑出部分罪名进行立法的方式,固然有助于控制法人犯罪的范围,但还存在着罪名稳定性缺乏、选择标准模糊等难以解决的问题,法人犯罪的成立范围宜采取普遍性的立法。由于替代责任与刑法责任主义原则相悖,容易导致法人责任认定过于宽泛,故法人犯罪的归责基础应采用组织体责任,法人成员的意思一旦转化为单位整体意志,该意志即成为单位犯罪的意志。基于组织体责任,立法应明确企业事前合规对法人犯罪成立的阻却作用。同时,在两罚制视野下,法人犯罪中法人与法人成员属于两个不同的犯罪主体,法人成员的犯罪并不依附于法人,立法应统一规定,法人成员的犯罪按自然人犯罪定罪量刑。

食品经营企业行政责任主体双罚制分离构想

研究以《食品安全法》第75条为基础,深入探讨了食品经营企业行政责任主体双罚制的分离构想。首先,分析现行行政责任主体双罚制的主要问题和挑战。然后,提出了3个创新的制度设计,包括科技驱动的监管、弹性罚款制度和负责任的供应链管理。在科技驱动的监管部分,探索了如何利用大数据、AI和区块链等现代科技实现更精确、及时和有效的食品安全监管。在弹性罚款制度部分,设计一种罚款制度,其中罚款的大小与违规行为的严重性、企业的规模和财务状况等因素挂钩,以实现更公平和有效的惩罚。在负责任的供应链管理部分,强调了企业应建立全面的食品安全供应链管理制度,以防止供应链中的食品安全问题。

刑罚变更执行共识算法研究

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.

无参数自适应罚函数的高效代理模型优化设计方法

在飞行器气动外形优化设计中,复杂约束条件导致设计空间可行域呈现不连续的特征,且理想解大多靠近约束边界,传统高效代理模型方法难以适用。研究了参考点对优化设计的影响,提出了一种考虑约束的参考点选择机制;对于最优解靠近边界的问题,罚函数法更加有效,但惩罚因子的设置对于罚函数方法影响很大,不合适的惩罚因子反而会损害优化效率,分析了优化过程中罚函数方法对惩罚因子的要求,提出了一种无参数自适应罚函数的代理模优化设计方法,引入基于样本分析的惩罚项,结合归一化目标值和约束值,在优化过程中动态调整惩罚因子,使优化能够尽可能地聚焦于可行域内,迅速收敛到最优解,实现样本的高效配置。通过带约束的函数算例和翼型优化算例证实,所提方法可以大幅提高飞行器气动外形优化设计效率。

体系化治理视域下我国轻罪治理实践检视与进路

对犯罪治理,我国已进入轻罪治理时代。轻罪化治理能够在一定程度上提升社会文明程度,但也容易引发犯罪标签泛化、犯罪人口比例提升而加剧社会治安隐患。有鉴于此,应建立一般违法与轻罪、重罪,定罪与量刑,实体与程序之衔接体系。这具体体现为在规制社会失范行为时,首先应诉诸非法律手段,若无法达到预期效果,进而采取民事法、行政法、经济法等法域的制裁措施,最后在迫不得已的情况下适用刑法上的轻罪。

《吕刑》“德刑”思想钩沉

《尚书·吕刑》主旨,《书序》说是吕侯为穆王训夏赎刑,然全篇并无夏朝痕迹,所记为从蚩尤、三苗、伯夷以来,社会从暴政走向刑法、德刑的大脉络,当属周人所述先秦司法史。篇名犹言“吕侯说刑”,前半篇论刑之渊起,揭示三苗“德刑”之伪。后半篇论如何建设真德刑,涉及德之缘起、学理、结构、价值等大本大原问题,以及审案程序、疑赦从宽、典狱守则等操作规范,足见周人德刑思想的体系已在理论与制度层面上成立,其深意则是为刑安装“准星”,使之能精准除恶,故反复论德,而未渲染如何用刑。《吕刑》所谓德刑,并非儒家之德与法家之刑的简单相加,而是“德之刑”,犹同篇所言“祥刑”。各国皆有刑,而以德字加诸刑字之上,强调刑必须在德的指导下使用,则为中国传统司法文化所特有。

京津多机场系统容量损失影响分析

全球范围内多机场系统数量和规模都在不断增长。由于终端区内有限资源的限制,邻近机场的运行存在一定的相关性,这导致多机场系统运行效率低下。为了估计多机场运行产生的限制,以北京终端区为例提出一种统计分析框架对多机场系统的容量损失进行整体分析。首先,根据理论容量模型分别计算出北京终端区各机场的理论容量,并进一步确定各机场独立运行下的实际容量;其次,以北京终端区的历史运行数据为基础,估计出3个机场及终端区系统的具有鲁棒性的Pareto容量包络曲线;最后,基于独立运行容量与相关运行容量量化出终端区各机场及多机场系统的实际容量损失。为进一步提高多机场终端区的运行效率提供建议与参考。

面向联盟链的共识算法研究

实用拜占庭容错算法(Practical Byzantine Fault Tolerance)广泛用于理论研究和实际生产中,但存在着选取主节点随机、缺乏惩罚机制等缺点.针对以上问题,(1)提出一种基于复杂网络角度的区块链共识算法,通过引入时间感知PageRank算法,建立区块链网络节点的影响力评分机制,并建立共识群组;(2)为了减少共识攻击的发生,建立节点惩罚机制,增加节点作恶的成本,有效提高攻击代价,降低攻击概率;(3)实验表明,基于时间感知PageRank的共识算法在扩展性和可靠性上高于实用拜占庭容错算法和基于信用分级的拜占庭容错算法(Credit-Layered Byzantine Fault Tolerance);在公平性和在延迟性方面,均取得良好效果.

税务“首违不罚”的困境与制度重构

修订后的《行政处罚法》第33条仅对“首违不罚”制度作出原则性规定,《税收征管法》对于该制度处于规范空白状态,国家税务总局发布的《税务行政处罚“首违不罚”事项清单》以及各地的税务裁量基准对此规定不一,导致“首违不罚”制度内涵、“首次违法”的适用范围、适用周期、“不予处罚”等概念均存在界定不清的状况,致使司法实践中存在不一致的状况。应当依效力层级建立相关法规体系,完善“清单”制度,从实体与程序两个层面构建税收领域的“首违不罚”制度。

论《反垄断法》行政法律责任体系的二维扩张

《反垄断法》的实施为我国加速推进经济转轨创造了良好的制度环境。然而,近年来频发的垄断现象也反映出法律规范力有不逮,行政法律责任畸轻造成的威慑不足与预防效果不彰尤为明显。2022年修正的《反垄断法》试图对此予以回应,却未能提出完备的革新方案。在纵向维度上,双罚制的妥协性适用使得部分垄断行为的责任主体出现缺位;在横向维度上,处罚种类的局限性又对综合评价法律责任造成掣肘。未来可以考虑全面适用双罚制,并扩展财产罚的内涵,增加行为罚和人身罚,建构起《反垄断法》更具威慑力和灵活性的行政法律责任体系。

可信的图神经网络节点分类方法

为了研究节点特征表示的不确定性对节点分类的影响,提出一种可信的图神经网络节点分类方法。算法使用径向基函数计算节点间距离,得到各类节点质心后,根据距离分配与未标记节点最近质心的类别标签提高节点分类性能,同时定义未标记节点和质心之间的距离为模型输出的不确定性,并使用梯度惩罚损失加强输入变化的可检测性,可以有效地检测分布外节点样本。在Cora、Citeseer和Pubmed这3个公开网络数据集上的结果表明:模型在分类任务的AUROC指标分别达到81.5%、76.2%和74.6%,在分布外样本检测任务中AUROC指标分别达到83.6%、72.8%和70.6%,证明了所提算法在提高节点分类性能的同时,可以有效检测分布外的节点样本,提高了节点分类的可信性。

考虑时间紧迫度的应急救援车辆路径问题建模与优化

考虑时间紧迫度的应急救援车辆路径优化,具有重要的理论价值与实际意义。以总费用(早到惩罚费用+延时惩罚费用-时间窗内送达奖励费用)最小为目标构建模型,并针对车辆在时间窗内送达以及早到、晚到的三种不同情形,定义时间紧迫度,设计精确算法A求解,证明了算法A的时间复杂度为O(ln^(3)),其中l、n分别为配送车辆和受灾点的个数,以决策应急救援车辆的行驶路径。最后以上海嘉定区疫情防控应急物资配送为例,对模型和算法的有效性进行了证明,可为政府部门应急救援路径选择提供有效理论依据。