Analytical Modeling of a Multi-queue Nodes Network Router

This paper presents the derivation of an analytical model for a multi-queue nodes network router, which is referred to as the multi-queue nodes （mQN） model. In this model, expressions are derived to calculate two performance metrics, namely, the queue node and system utilization factors. In order to demonstrate the flexibility and effectiveness of the mQN model in analyzing the performance of an mQN network router, two scenarios are performed. These scenarios investigated the variation of queue nodes and system utilization factors against queue nodes dropping probability for various system sizes and packets arrival routing probabilities. The performed scenarios demonstrated that the mQN analytical model is more flexible and effective when compared with experimental tests and computer simulations in assessing the performance of an mQN network router.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.

End-to-End Performance Evaluation of TCP Traffic under Multi-Queuing Networks

While Internet traffic is currently dominated by elastic data transfers, it is anticipated that streaming applications will rapidly develop and contribute a significant amount of traffic in the near future. Therefore, it is essential to understand and capture the relation between streaming and elastic traffic behavior. In this paper, we focus on developing simple yet effective approximations to capture this relationship. We study, then, an analytical model to evaluate the end-to-end performance of elastic traffic under multi-queuing system. This model is based on the fluid flow approximation. We assume that network architecture gives the head of priority to real time traffic and shares the remaining capacity between the elastic ongoing flows according to a specific weight.

RTS^TM Voice Over Network Card

RVON-8（RTS^TM Voice Over Network）卡可以直接安装入ADAM^TM矩阵内部通讯系统，它扩展了ADAM^TM矩阵内部通讯系统的连通性，有8个音频输入和输出通道，每个通道都有可配置的网络和带宽两种参数：能够追踪每个网络的功能特点，而且这些辅助资料能够用于通讯面板和中断设备控制：能支持所有规格的产品，

Credit Card Fraud Detection Using Improved Deep Learning Models

Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

安全智能存储卡(Secure Micro SDCard)研发与应用

VPN技术是一种非常方便实用的技术,可以实现中心资源的合理利用,通过VPN网络,企业可以以更低的成本连接远程办事机构、出差人员以及业务合作伙伴关键业务。所以针对市场的需求,在市场调研的基础上,我们提出安全智能储存卡的研发和应用,并针对该研究的相关问题展开阐述。

基于USB接口的5G数据转接卡设计与实现

为了解决5G模块在工业控制和物联网领域接口应用复杂性的问题,对5G通信模块接口的硬件设计和基于Linux操作系统的通用串行总线(USB)协议进行了深入研究。设计了基于USB接口的5G数据转接卡方案。为应对5G模块高速、大数据的通信特点,采用高性能ARM-CortexA7系列芯片和国产自主知识产权的5G通信模块进行硬件设计。为解决应用软件接口的兼容性问题,采用基于Linux操作系统的远程网络驱动接口规范(RNDIS)通信协议进行软件设计。测试结果表明,转接卡可以通过USB接口适配其他物联网设备,上、下行速率满足工业控制对数据传输的要求。该方案验证了USB通信接口与5G模块数据转接的可行性,为5G数据转接卡设计提供参考。该方案有助于推动5G通信在工业控制和物联网领域的应用。

Market Structure and Information in Payment Card Markets

This paper investigates the structure of the payment card market, with consumers and merchants basing their subscription decisions on different information sets. We find that the market structure depends crucially on the information set on which consumers and merchants base their subscription decisions. In the studied case, we observe that a market with few cards dominating only emerges when decisions are based on very limited information. Under the same conditions using a complete information set, all cards survive in the long run. The use of an agent-based model, focusing on the interactions between merchants and consumers, as a basis for subscription decisions allows us to investigate the dynamics of the market and the effect of the indirect network externalities rather than investigating only equilibrium outcomes.

Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network

Frauds don’t follow any recurring patterns.They require the use of unsupervised learning since their behaviour is continually changing.Fraud-sters have access to the most recent technology,which gives them the ability to defraud people through online transactions.Fraudsters make assumptions about consumers’routine behaviour,and fraud develops swiftly.Unsupervised learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other techniques.Building a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization pat-terns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony(ABC).Using real-time data and other datasets that are readily available,the ABC-Recurrent Neural Network(RNN)categorizes fraud behaviour and compares it to the current algorithms.When compared to the current approach,the findings demonstrate that the accuracy is high and the training error is minimal in ABC_RNN.In this paper,we measure the Accuracy,F1 score,Mean Square Error(MSE)and Mean Absolute Error(MAE).Our system achieves 97%accuracy,92%precision rate and F1 score 97%.Also we compare the simulation results with existing methods.

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

In recent years,deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields.In the diagnosis of sucker-rod pump working conditions,due to the lack of a large-scale dynamometer card data set,the advantages of a deep convolutional neural network are not well reflected,and its application is limited.Therefore,this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning,which is used to solve the problem of too few samples in a dynamometer card data set.Based on the dynamometer cards measured in oilfields,image classification and preprocessing are conducted,and a dynamometer card data set including 10 typical working conditions is created.On this basis,using a trained deep convolutional neural network learning model,model training and parameter optimization are conducted,and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards.The experimental results show that transfer learning is feasible,and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network.The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.

Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms

Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.

网络犯罪防治中的轻罪扩张及其限度

网络犯罪的防治目标导致刑法不断扩张。当前最为典型的问题是如何理性认识并调适帮助信息网络犯罪活动罪的防治功能及其限度。在帮助信息网络犯罪活动罪的起诉人数爆发式增长的背景下,应当认为本罪的出台和适用基本符合刑事正义,但需要做出一定的调适。其一,帮助信息网络犯罪活动罪的司法基本符合罪刑法定原则,被帮助的行为类型属于刑法上规定的行为类型即可,这平衡了应对网络社会风险和满足公民可预见性的需求,是在网络空间中贯彻罪刑法定原则的最新趋势。其二,根据罪刑相当理念,从一重罪论处不应当仅仅看重可能判处的刑罚,还应当考虑罪名标签是否适当、全面评价了帮助行为的性质和危害。在帮助多种犯罪行为类型时,认定为本罪更加适当、全面,但可能需要法定刑幅度的增设与匹配。其三,在程序正义上,客观罪量和主观“明知”可以通过严格的程序得以确认,但在打击“两卡”犯罪导致案件“井喷”的态势下,需要调适“两卡”犯罪线下帮助行为的入罪标准。

基于模糊神经网络的数字媒体数据自动化采集系统设计

为了改善数字媒体数据传输效果,确保系统安全、稳定运行,设计基于模糊神经网络的数字媒体数据自动化采集系统。构建数字媒体数据自动化采集系统框架,数据采集层利用数据采集卡获取不同渠道的数字媒体数据,由数据处理层的数据融合模块调用改进模糊神经网络算法完成数字媒体数据的融合处理后,通过数据传输层的分层自组织无线网络将其传输至存储应用层,实现数字媒体数据的存储、查询、显示与输出。实验结果表明:该系统采集的音频信号波形规律、曲线平滑、功率均值波动误差在(0,0.14),满足允许误差范围;数据传输效果优于采用DSR协议的单点传输方式,当数字媒体数据源为3时,平均峰值信噪比指标最高;具有数字媒体数据查询功能。

基于图注意力Transformer神经网络的信用卡欺诈检测模型

针对现有模型无法精准识别复杂多变的团伙诈骗模式的问题,提出一种新型实用的基于复杂交易图谱的信用卡反欺诈检测模型。首先,利用用户原始的交易信息构造关联交易图谱;随后,使用图自注意力Transformer神经网络模块直接从交易网络中挖掘团伙欺诈特征,无需构建繁冗的特征工程;最后,通过欺诈预测网络联合优化图谱中的拓扑模式和时序交易模式,实现对欺诈交易的高精度检测。在信用卡交易数据上的反欺诈实验结果表明,所提模型在全部评价指标上均优于7个对比的基线模型:在交易欺诈检测任务中,平均精度(AP)比基准图注意力神经网络(GAT)提升了20%,ROC曲线下方面积(AUC)平均提升了2.7%,验证了所提模型在信用卡欺诈交易检测中的有效性。

电信网络诈骗非法提供两卡行为司法治理研究

当前法律规范下,大量为电信网络诈骗非法提供两卡行为被以帮助信息网络犯罪活动罪认定并处之以刑罚,不仅难以实现刑法惩罚与保护的目的,反而可能影响社会秩序的稳定,造成刑事司法资源的浪费。对其中形式上符合犯罪构成要件但实质上不需要判处刑罚的案件,在司法裁判中作出罪处理不仅具有必要性,还能够从损害可弥补、被侵害的法益可修复等角度找到理论支撑,在我国现行实体、程序法框架中也是有据可依的。非法提供两卡构成犯罪案件,如果行为人的人身危险性和再犯罪可能性低、行为本身危害性小、行为人事后认罪悔罪并积极退赔退赃或有其他轻微情节,可以考虑在司法裁判时免予刑事处罚。

面向容器级多变体的Mimicveth网卡设计与实现

聚焦于容器级多变体执行(MVX)过程,针对容器级多变体系统分发时存在容器不能一对多通信、报文信息不匹配和TCP协议握手失败的问题,以及表决时存在报文时序乱序和报文分段分片问题,提出一种基于Mimicveth网卡的多变体系统网络输入输出(I/O)分发表决方案。该Mimicveth网卡为分发代理、执行体、表决器之间建立一对多双向传输通道。实验验证,依托该Mimicveth网卡能够有效实现容器级多变体系统分发和表决,改善容器级多变体系统可用性,为后续面向容器的多变体系统研究发展提供了原型基础。

基于国产芯片的MVB通信网卡设计与实现

多功能车辆总线技术作为列车通信网络的核心技术,为解决进口芯片对MVB产品设计及生产的制约,对国内芯片设计公司、芯片制造商以及市场占有率较高的芯片类型进行了深度调研,在国产化MVB通信产品的设计过程中,研制出以国产半导体为载体、FPGA技术实现的MVB协议控制器。通过一致性测试对国产化MVB通信网卡的功能和性能进行了验证。研究结果表明,基于国产化元器件实现的MVB通信网卡具有维修成本低、性能稳定等特点,与牵引系统、信号系统、制动系统匹配良好,满足了同类产品深度自主可控的需求,为实现国产化车辆实现提供了参考方案。

将一卡通校园网打造成智慧校园的核心系统

随着科技的不断发展,一卡通系统在校园网络中的深度融入与进化,已成为构筑智慧校园不可或缺的核心支撑体系之关键要素。通过追溯一卡通系统的发展历程,详尽阐述了其多样化的功能需求,在利用一卡通系统构建智慧校园核心支持系统的过程中,依托校园网络基础深入探讨了相关技术的最新进展、系统设计的精细步骤以及实施策略的有效方法,旨在为智慧校园的全面升级提供坚实的技术支撑与实现路径。

多目标意象驱动的梳棉机造型设计研究

目的 为满足用户对梳棉机的多维感性需求,多层次提升纺织机械的造型意象满意度和用户满意度,提出一种多目标意象驱动的梳棉机造型设计方法,并展开设计实践与实验研究。方法 首先,设计感性评价实验,通过焦点小组讨论和问卷调研获取用户对梳棉机的感性意象评价均值;其次,采用形态分析法,对梳棉机造型设计要素进行划分,并对代表性样本的造型类目特征进行编码;基于反向传播神经网络(Back Propagation Neural Network,BP-NN),建立梳棉机产品造型要素与用户感性意象评价均值之间的关联映射模型,建立用于造型推荐的样本库,获取单意象维度下的梳棉机造型设计策略;再次,应用层次分析法(Analytic Hierarchy Process,AHP)得到各目标意象维度的权重值,输出多目标意象下梳棉机造型设计策略;最后,结合梳棉机的造型设计实践及感性评价,进一步验证该方法的可靠性和有效性。结果 基于该方法展开设计实践,依据推荐的梳棉机造型设计策略得到的设计方案,在目标感性意象的整体评价得分优于对照样本。结论 该方法有较好的可靠性和有效性,能为企业的新产品开发及设计师的设计实践输出指向具体、操作性强的梳棉机多目标意象设计策略。

基于双RTU的田间滴灌工程远程监测系统的研究

为了节约水资源和实现田间灌溉自动化,提出基于双RTU的自动滴灌方案。两个RTU系统可以分别独立实施自动灌溉,且相互监督、备用。将水泵供水区域内的农田划片处理可以提升两个系统的利用率。针对水量要求比较严格的农作物,以及土壤墒情的反馈信息,采用智能灌溉控制系统实施自动灌溉。针对大片种植相同农作物的田地,使用低成本的刷卡灌溉控制系统实施灌溉。用户可通过IC卡刷卡或手机发送灌溉指令,并在RTU屏幕或手机上查看水费、电费等信息。同时,RTU会将灌溉信息发送给监控中心进行数据的处理。实践表明该系统在节水、省时省力方面具有重要的研究意义。