“算法信用时代”供应链金融业务法律风险研究

本文在具体分析供应链金融具体业务模式中各主体间的法律关系,探讨各个业务模式中所存在的法律风险的基础上,提出转变供应链金融法律风险的管控思维,在“算法信用”逐渐成为一种有效评价机制的时代背景下,利用区块链、大数据融合平台等信息技术来弥补相关法规的滞后、加强法规的实施、辅助供应链金融的监管,从而实现供应链金融基础业务中核心法律风险的有效管控。

C2C电子商务网站信用评价模型及算法研究

信用问题是困扰电子商务发展的主要瓶颈问题,C2C电子商务网站所提供的信用评价体系,在一定程度上保证了网络交易的安全,促进了人们进行网上购物的意愿。对C2C电子商务网站信用评价模型及算法进行了研究,分析了现有评价模型的缺点,建立了改进的C2C电子商务网站信用评价模型;基于改进的模型,提出了新的信用评价算法,通过权衡考虑交易对方的信用度和交易次数、交易金额来计算被评用户的信用加权平均分和信用度,再确定被评用户的信用等级。

基于特征分组和遗传算法的学生信用特征选择

随着互联网金融的发展,消费信贷已经走入高校学生的生活中。本文在高校学生消费信用数据缺失的情况下,根据学生在学校中的图书馆借阅记录、图书馆门禁记录、一卡通消费记录以及学习成绩等数据进行分析,为高校学生信用模型的建立筛选相关特征变量。本文中采用特征分组与遗传算法相结合的方法,筛选出了与学生信用相关性最大的特征变量,为高校学生征信提供了重要的参考价值。

基于C2C模式的电子商务信用评价算法研究--以淘宝网为例

电子商务飞速发展，信用评价问题成为了影响其发展的一个重要因素。在电子商务环境下建立有效信用评价体系能够保障电子商务市场诚信经营环境，通过信用评价规范网商的经营行为，能有效地促使商家进行诚信经营。本文针对淘宝网现有的信用评价算法存在的问题，分别对买家和卖家的信用评价算法进行改进，以更好地反映买家和卖家的信用状况，促进C2C电子商务的发展。

基于动态博弈分析的C2C电子商务信用模式研究

通过分析现有的C2C信用评价模型,指出其存在的缺陷,并运用"静态博弈"和基于"复制动态"思想的演化博弈,分别对电子交易的成交和评价阶段买卖双方的策略选择展开研究。结果表明:现存评价机制中的买家诚信、卖家诚信、第三方平台投诉机制、惩罚机制等因素会对电子交易中买卖双方的策略选择产生影响。为了解决C2C信用危机、促进C2C电子商务的可持续发展,有必要利用交易金额、卖家信用度、买家信用度三项指标建立一个新的信用评价体系。在此基础上,引入一种新的基于加权平均的信用评价算法,最终形成一个更加科学、可行的动态C2C信用评价模型。

基于C2C模式的电子商务信用评价模型研究

在C2C电子商务高速发展的同时,隐藏在其中的消费者信任问题也逐渐暴露出来,成为阻碍其发展的主要问题之一。C2C电子商务网站现有提供的信用评价体系,在一定程度上保证了网络交易的安全,促进了人们进行网上购物的意愿。但仍存在不足之处,对此本文提出了新的信用评价算法,基于这些算法,建立改进的个人评价信用模型,通过权衡考虑交易对方的信用度、交易金额等因素来计算被评用户的加权平均分和信用度,再确定被评用户的信用等级。

基于C2C电子商务信用评价体系的探讨

C2C电子商务在中国的电子商务体系中占有很重要的地位,但是信用问题越来越为人们所关注,信用的评价也就成了电子商务网站的一项很重要的工作。结合实例对几个代表网站的信用评价体系进行分析,找出其优缺点。提出改进的意见,对不足之处加以完善,利用初始静态信用评价、加权平均、构建信用度函数图等方法,来更加科学合理的计算信用度,使之呈现的更加直观。

物流货运交易网站信用评价模型研究

信用问题是物流行业发展所面临的重要问题,也是C2C模式网上货运交易的主要瓶颈之一。首先对目前大多数货运交易网站所采用的信用评价模型进行了研究,指出现有模型所存在的三个主要问题:没有考虑用户自身信用水平的影响、缺少对交易风险大小的区分、对交易欺骗惩罚不足。然后,针对已有模型的不足,提出了改进的交易信用评价模型并设计了评分算法。最后,通过在模拟环境中与原模型进行对比,验证了改进模型和算法的有效性。

算法嵌入公共信用评价:内在逻辑、风险检视与规制路径

公共信用评价是信用监管的核心制度。随着算法技术的迭代,其在公共信用评价中的优势也日渐显现。“数据+算法”的智能融合是算法嵌入公共信用评价的技术逻辑,推动政府治理现代化和提升社会信用水平则分别构成算法嵌入公共信用评价的实践逻辑和价值逻辑。然而,算法在发挥技术优势的同时,也可能在公共信用评价中引发三重风险:技术风险,主要包括算法黑箱破坏评价公正性和数据质量影响评价准确性;法律风险,主要包括算法权力助推评价权力滥用和数据滥用危及隐私与信息安全;伦理风险,主要包括算法至上引发评价自主性困境和算法偏见破坏基于信用的分配正义。针对上述风险,在选择规制路径时宜采取“整体性视角”和“基于过程的方法”。在事前阶段,需要制定算法信用评价技术标准、建立算法信息公开制度、完善算法影响评估制度;在事中阶段,需要运用行政法治原则钳制行政权力、以信用及个人信息权利制衡算法权力;在事后阶段,需要完善公共信用评价异议申诉制度,同时强化对公共信用评价的算法审计和司法审查。

A Precise Information Extraction Algorithm for Lane Lines

Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.

Web multimedia information retrieval using improved Bayesian algorithm

The main thrust of this paper is application of a novel data mining approach on the log of user' s feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author' s expression and the user' s understanding and expectation. User spacemodel was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the au-thors' proposed algorithm was efficient.

SERVICE COMMUNITY CONSTRUCTION METHOD OF INTERNET OF THINGS BASED ON SEMANTIC SIMILARITY

Internet of Things (IoT) as an important and ubiquitous service paradigm is one of the most important issues in IoT applications to provide terminal users with effective and efficient services based on service community. This paper presents a semantic-based similarity algorithm to build the IoT service community. Firstly, the algorithm reflects that the nodes of IoT contain a wealth of semantic information and makes them to build into the concept tree. Then tap the similarity of the semantic information based on the concept tree. Finally, we achieve the optimization of the service community through greedy algorithm and control the size of the service community by adjusting the threshold. Simulation results show the effectiveness and feasibility of this algorithm.

APO-Based Parallel Algorithm of Channel Allocation for Cognitive Networks

This article investigates channel allocation for cognitive networks, which is difficult to obtain the optimal allocation distribution. We first study interferences between nodes in cognitive networks and establish the channel allocation model with interference constraints. Then we focus on the use of evolutionary algorithms to solve the optimal allocation distribution. We further consider that the search time can be reduced by means of parallel computing, and then a parallel algorithm based APO is proposed. In contrast with the existing algorithms, we decompose the allocation vector into a number of sub-vectors and search for optimal allocation distribution of sub-vector in parallel. In order to speed up converged rate and improve converged value, some typical operations of evolutionary algorithms are modified by two novel operators. Finally, simulation results show that the proposed algorithm drastically outperform other optimal solutions in term of the network utilization.

Adaptive and Dynamic Mobile Phone Data Encryption Method

To enhance the security of user data in the clouds,we present an adaptive and dynamic data encryption method to encrypt user data in the mobile phone before it is uploaded.Firstly,the adopted data encryption algorithm is not static and uniform.For each encryption,this algorithm is adaptively and dynamically selected from the algorithm set in the mobile phone encryption system.From the mobile phone's character,the detail encryption algorithm selection strategy is confirmed based on the user's mobile phone hardware information,personalization information and a pseudo-random number.Secondly,the data is rearranged with a randomly selected start position in the data before being encrypted.The start position's randomness makes the mobile phone data encryption safer.Thirdly,the rearranged data is encrypted by the selected algorithm and generated key.Finally,the analysis shows this method possesses the higher security because the more dynamics and randomness are adaptively added into the encryption process.

ADAPTIVE BLIND ESTIMATION ALGORITHM FOR OFDM-MIMO RADIO SYSTEMS OVER MULTIPATH CHANNELS

This paper investigates the blind algorithm for channel estimation of Orthogonal Frequency Division Multiplexing-Multiple Input Multiple Output (OFDM-MIMO) wireless communication system using the subspace decomposition of the channel received complex baseband signals and proposes a new two-stage blind algorithm. Exploited the second-order cyclostationarity inherent in OFDM with cyclic prefix and the characteristics of the phased antenna, the practical HIPERLAN/2 standard based OFDM-MIMO simulator is established with the sufficient consideration of statistical correlations between the multiple antenna channels under wireless wideband multipath fading environment, and a new two-stage blind algorithm is formulated using rank reduced subspace channel matrix approximation and adaptive Constant Modulus (CM)criterion. Simulation results confirm the theoretical analysis and illustrate that the proposed algorithm is capable of tracking matrix channel variations with fast convergence rate and improving acceptable overall system performance over various common wireless and mobile communication links.

Energy Efficient Switch Policy for Small Cells

Switch policy is essential for small cells to properly serve variable number of users in an energy efficient way.However,frequently switching small cell base stations(SBSs) may increase the network operating cost,especially when there is an nonnegligible start-up energy cost.To this end,by observing the variety of user number,we focus on the design of a switch policy which minimize the cumulative energy consumption.A given user transmission rate is guaranteed and the capability of SBSs are limited as well.According to the knowledge on user number variety,we classify the energy consumption problem into two cases.In complete information case,to minimize the cumulative energy consumption,an offline solution is proposed according to critical segments.A heuristic algorithm for incomplete information case(HAIIC) is proposed by tracking the difference of cumulative energy consumption.The upper bound of the Energy Consumption Ratio(ECR) for HAIIC is derived as well.In addition,a practical Q-learning based probabilistic policy is proposed.Simulation results show that the proposed HAIIC algorithm is able to save energy efficiently.

Context-Aware Scheduling Algorithm in Smart Home System

The main idea of pervasive computing is to make computing exist everywhere in the physical world.The smart home system is an important realisation of pervasive computing whose aim is to provide system users with an intelligent life experience.The key technique used to realise this is context awareness.Contexts in the living space can provide large amounts of information regarding users’behaviours and habits.Together with an information system,it can automatically execute many common operations of applications,instead of users,and can make the applications"smart".However,since contexts in the environment are diverse and sensitive,it is difficult to choose the ones that are most useful to the users’current activity.A proper scheduling strategy should first consider the users’demand.This paper proposes a context-aware scheduling algorithm that is based on correlation,with the purpose of improving the utilization rate of context collections.Experiments show that with the priority based on correlation in low-level contexts,the scheduling of reasoning tasks can reduce the cost of transmission.

Application of interacting multi-model algorithm in gyro signal processing

There is one problem existing in gyroscope signal processing,which is that single models can＇ t adapt to change of carrier maneuvering process.Since it is difficult to identify the angular motion state of gyroscope carriers,interacting multiple model （IMM） is employed here to solve the problem.The Kalman filter-based IMM （IMMKF） algorithm is explained in detail and its application in gyro signal processing is introduced.And with the help of the Singer model,the system model set of gyro outputs is constructed.In order to demonstrate the effectiveness of the proposed approach,static experiment and dynamic experiment are carried out respectively.Simulation analysis results indicate that the IMMKF algorithm is excellent in eliminating gyro drift errors,which could adapt to the change of carrier maneuvering process well.

Research on Tourism E-commerce based on Data Mining

This paper describes in detail the web data mining technology, analyzes the relationship between the data on the web site to the tourism electronic commerce （including the server log, tourism commodity database, user database, the shopping cart）, access to relevant user preference information for tourism commodity. Based on these models, the paper presents recommended strategies for the site registered users, and has had the corresponding formulas for calculating the current user of certain items recommended values and the corresponding recommendation algorithm, and the system can get a recommendation for user.

数字时代失信惩戒法治化的新进路:从制裁失信到管控风险

将失信惩戒纳入行政处罚体系是当前失信惩戒法治化的主流进路。这无法解决将违约、违法行为视为失信行为并加以公法制裁的合法化难题,又不能充分救济相对人,也难以充分承担社会信用制度的功能期待,还有碍于社会信用制度的体系化。从管控资源配置风险出发,信用是基于信用数据的交易可信度评判工具,失信惩戒是为了管控资源配置风险,而非对失信行为进行法律制裁。实施以风险管控为目的的失信惩戒,既符合行政机关的资源配置主体身份,也符合社会治理创新的需要与大数据时代社会治理革新的趋势。在大数据时代,失信惩戒法治化框架之建构当以信用算法的规制为中心,以风险管控原则统领信用算法,以个人信息权益与相应国家保障义务为具体抓手。