新媒体时代新闻众筹在中国的发展与对策

新闻众筹作为一种在全球范围内方兴未艾的众筹类型,在中国刚刚崭露头角.本文介绍了新闻众筹的概念及影响,提出了新闻众筹的发展瓶颈,最后针对新闻众筹的持续性发展提出了建议.     

基于JSP的众筹平台设计与实现

通过对众筹平台的背景、现状和基本概念进行研究,了解众筹的相关信息,采用JSP技术,以SSM为开发框架,以Eclipse为开发工具,以MySQL为后台数据库,设计并实现了基于JSP的众筹平台.系统主要实现了会员注册、项目发布、项目投资、项目审核、充值提现审核、项目催还及数据统计等功能.     

考虑消费者利他偏好的贫困农民扶贫参与策略

基于众筹扶贫模式,探讨缺乏生产资金的风险规避型农民在面对市场不确定性时的最优定价决策及扶贫参与模式选择问题.通过构建农民在传统扶贫模式和众筹扶贫模式下的决策模型,得到农民的最优定价策略.通过对比发现:1)当利他型消费者规模较小时,农民在众筹模式下只能采取低价策略,且最优参与模式的选择与利他型消费者比例和利他主义动机影响强度相关;而当利他型消费者规模较大时,农民是否参与众筹只与利他主义动机影响强度相关,此时利他型消费者比例只影响众筹模式下的定价策略.2)农民的风险规避程度和贷款利率较低、需求波动性较小时,农民更适合选择传统扶贫模式.3)消费者参与众筹的风险程度越低,农民越倾向于选择众筹模式.     

基于区块链的众筹智能合约设计

众筹是当前热门的一种互联网融资模式，目前的众筹平台存在违规经营、公信力不足、监管不善等诸多问题。智能合约是一种由事件驱动的、具有状态的代码合约和算法合同，随着区块链技术的深入发展而受到广泛关注和研究。基于区块链的智能合约技术具有去中心化、自治化、可观察、可验证、可信息共享等特点，可以有效构建可编程金融，为解决众筹平台的问题提供了新的途径。在区块链和智能合约理论的基础上，搭建了众筹区块链（Crowdfunding Blockchain，CBC），并基于以太坊实验环境开发了众筹合约系统，将传统的众筹合约文本代码化，合约的存储与执行均在区块链上进行，这不仅保证了众筹项目的自治化和可靠性，而且提高了项目可信性和公信力。     

众筹项目推荐:面向隐式反馈的深度学习协同过滤

目前,在推荐系统研究中,用户的隐式反馈,以及极度稀疏的数据,已成为影响协同过滤推荐效果的主要问题.针对这一现象,本文提出了深度学习协同过滤算法,先利用卷积神经网络,对用户-项目矩阵的隐层特征进行学习,再结合协同过滤,对用户-项目的交互信息进行建模,并将两种特征融合预测推荐列表.以众筹平台的数据为实验对象,比较模型中各参数对推荐效果的影响,并设计与基线方法的对比实验.实验结果表明:均匀采集负反馈,并在一定卷积层数的网络中,数据稀疏度越高,效果越好;对比基线方法,本文提出的算法在公开数据集(Yahoo!Movie)上取得了最好的推荐结果.本文提出的算法有助于提高众筹平台的融资成功率,同时也丰富了推荐系统的研究体系.     

基于动静态表征的众筹协同预测方法

众筹是一个新兴的互联网金融平台,项目的发起者可以通过使用互联网,征求大量平台用户的资金来资助他们的项目.但是由于众筹平台所具有的独特规则,只有在特定时间内收集了足够的资金,项目的筹资才会成功进行交易.为了防止项目发起者和投资者在可能失败的项目上浪费时间和精力,动态追踪众筹项目的筹资过程以及估算其融资成功概率便极为重要.然而,现有的一些工作既没有针对动态预测跟踪机制的研究,也没有考虑平台上的项目发起者和投资者之间的动态行为交互.为了解决这些问题,基于长短期记忆网络设计了一种新颖的动静态协同预测模型.该模型着重分析了用户行为,包括评论的情绪倾向以及融资过程中的动态增量信息,从而将融资项目与投资人之间的交互行为进行深度挖掘分析.首先,针对平台上的静态特征和动态用户行为数据,通过不同的Embedding方法得到他们的深度表征.在此基础上,进一步设计了基于注意力机制的协同预测模型,以便了解项目融资的时序信息对最终结果的影响程度.最后,在真实的众筹数据集上进行的大量实验结果表明,所提出的动静态表征预测方法相比其他预测方法更为有效.     

试论众筹模式下的财会管理

从最初艺术家们为创作筹集资金的一个方法，到如今成为个人和企业为项目筹资的一种渠道。借助互联网蓬勃发展，互联网金融迅速蔓延的态势，众筹这一新兴模式已逐渐被大众所接受。为了更好地发挥众筹的力量，财会工作应针对不同的模式，作出更适宜的管理。     

基于区块链智能合约的代币系统

针对传统代币中存在的公信力不足、监管困难等问题,结合区块链和智能合约技术,设计了一种去中心化的区块链代币系统。以此区块链代币系统为基础,通过部署代币合约、众筹合约,构建了一个基于区块链的众筹系统,实现了基本的众筹功能。同时设计了资金管理合约,实现了众筹成功后对发起者的监督和资金的管理。最后通过以太坊平台进行系统测试,分析了该代币系统的功能性、安全性以及时间性能。结果表明,基于区块链的代币系统在实现代币基本功能的同时,能够有效提升代币的安全性和功能性。     

双层网络效应下众筹平台上新产品扩散

基于创新扩散理论分析在由线下网络与线上网络构成的双层网络效应下,众筹支持者中充当“创新代理人”的群体如何利用簇群形成帮助众筹发起者实现新产品从早期少数采纳者到早期多数者的跨越扩散过程.同时基于传染病模型,建立众筹平台的双层网络O_SCI$_2$R模型,并运用Matlab对“创新代理人”的数量和影响力对线上/线下创新扩散的影响、“创新代理人”对簇内创新扩散的影响以及簇对线上/线下创新扩散的影响进行模拟仿真.研究结论显示:“创新代理人”数量越多,线上/线下创新扩散越快;“创新代理人”影响力越大,线上/线下创新扩散越快;“创新代理人”不仅促进了簇内的创新扩散,也扩大了簇群间的创新扩散;围绕“创新代理人”形成的簇在最初短时间内促进了线上/线下创新扩散,但并不能决定后期的创新扩散.     

通过Selfstarter建立开源众筹平台

如果你想搭建一个类似Kickstarter的众筹平台,利用GitHub上的开源项目Selfstarter便可轻松实现。     

FuzzyTree crossover for multi-valued stock valuation

Stock valuation is very important for fundamental investors in order to select undervalued stocks so as to earn excess profits. However, it may be difficult to use stock valuation results, because different models generate different estimates for the same stock. This suggests that the value of a stock should be multi-valued rather than single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming (GP). In our fuzzy GP model the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is scant literature available on the crossover operator for our fuzzy trees, except for the vanilla subtree crossover. This study generalizes the subtree crossover in order to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated. These experimental results indicate that the fuzzy tree (FuzzyTree) crossover is more effective than a subtree (SubTree) crossover in terms of expression tree complexity and run time. Secondly, shorter training periods produce a better return of investment (ROI), indicating that long-term financial statements may distort the intrinsic value of a stock. Finally, the return of a multi-valued fuzzy trading strategy is better than that of single-valued and buy-and-hold strategies.     

Query Optimization in Multidatabase Systems

Global query execution in a multidatabase system can be done parallelly, as all the local databases are independent. In this paper, a cost model that considers parallel execution of subqueries for a global query is developed. In order to obtain maximum parallelism in query execution, it is required to find a query execution plan that is represented in the form of a bushy tree and this query tree should be balanced to the maximal possible extent with respect to execution time. A new bottom up approach called Agglomerative Approach (AA) is proposed to construct balanced bushy trees with respect to execution time. By the deterministic nature of this approach, it generates local optimal solutions. This local minima problem will be severe in the case of graph queries, i.e., queries that are represented with a graph structure. A Simulated annealing Approach (SA) is employed to obtain a (near) optimal solution. These approaches (AA and SA) are suitable for handling on-line and off-line queries respectively. A Hybrid Approach (HA), that is an integration of AA and SA, is proposed to optimize queries for which the estimated time to be spent on optimization is known a priori. Results obtained with AA and SA on both tree and graph structured queries are presented.     

基于遗传算法和模糊决策树的时间序列预测模型

股票价格预测总是投资者和技术分析者感兴趣的一个主题.然而,决定买卖股票的最好时间仍然是困难的,因为有很多因素可能影响股票价格.通过改进模糊决策树建立了一个新型金融时间序列数据预测模型.该预测模型融合数据聚类技术,模糊决策树及遗传算法来构建基于历史数据和技术指标的一个决策系统.提出的GAFDT模型在与各种股票的其它方法相比较时有平均预测准确率为0.82的最好绩效.     

Pricing real abandonment options on several R&;D investment projects

Much previous research has shown that the R&D investments can be evaluated by real growth options approach. But few studies have been done on real abandonment options for R&D projects which may not succeed. The contribution of this paper is not only to derive a more general closed-form solution for evaluating real abandonment options, but to put backup project consideration into our model for reality. We show that both Black-Scholes’s and Stulz’s models are special cases of our model under some specifications of parameters. From the simulation results, we explore that the higher the percentage of recovering salvage value, the more investment projects should be carried out. We hope that the results in this study could provide a useful reference for the manager, to make better decisions regarding backup projects.     

考虑附加情绪的两阶段投资组合前景决策模型

针对前景模型在投资情绪偏好决策的不足,基于BW理论从偏好角度对投资者情绪的定义,建立附加情绪的投资决策模型.首先根据从众心理与收益追踪对投资者的影响,定义情绪转变的概率分布;然后构建附加二元非线性参数的价值函数,对可选投资项目进行偏好排序;最后根据偏好决策及风险差异将收益约束下的最大信息熵作为风险规避者的目标函数,将熵约束下的最大均值收益率作为风险偏好者的目标函数,分别进行迭代计算求得投资比例.仿真与算例分析结果表明:收益波动超过临界值后,持悲观态度的投资者占比更大;乐观投资者的初始占比一定程度上会改变个体偏好决策;风险偏好者愿意对高收益高风险的项目给予更大的投资权重;经济市场有序化有利于投资者作出投资决策.     

An O(log n) algorithm for parallel update of minimum spanning trees

Parallel algorithms are presented for updating a minimum spanning tree when the cost of an edge changes or when a new node is inserted in the underlying graph. Our model of computation is a parallel random access machine which allows simultaneous reads but prohibits simultaneous writes into the same memory location. The algorithms described in this paper for updating a minimum spanning tree require O(log n) time and O(n²) processors. These algorithms are efficient when compared to previously known algorithms for initial construction of a minimum spanning tree that require O(log²n) time and use O(n²) processors. The two main contributions of this paper are: (i) usage of an inverted tree for updating minimum spanning trees, and (ii) discovery of an interesting property of minimum spanning trees that we exploit to develop a novel algorithm for vertex insertion update.     

On the effectiveness of early life cycle defect prediction with Bayesian Nets

Standard practice in building models in software engineering normally involves three steps: collecting domain knowledge (previous results, expert knowledge); building a skeleton of the model based on step 1 including as yet unknown parameters; estimating the model parameters using historical data. Our experience shows that it is extremely difficult to obtain reliable data of the required granularity, or of the required volume with which we could later generalize our conclusions. Therefore, in searching for a method for building a model we cannot consider methods requiring large volumes of data. This paper discusses an experiment to develop a causal model (Bayesian net) for predicting the number of residual defects that are likely to be found during independent testing or operational usage. The approach supports (1) and (2), does not require (3), yet still makes accurate defect predictions (an R ² of 0.93 between predicted and actual defects). Since our method does not require detailed domain knowledge it can be applied very early in the process life cycle. The model incorporates a set of quantitative and qualitative factors describing a project and its development process, which are inputs to the model. The model variables, as well as the relationships between them, were identified as part of a major collaborative project. A dataset, elicited from 31 completed software projects in the consumer electronics industry, was gathered using a questionnaire distributed to managers of recent projects. We used this dataset to validate the model by analyzing several popular evaluation measures (R ², measures based on the relative error and Pred). The validation results also confirm the need for using the qualitative factors in the model. The dataset may be of interest to other researchers evaluating models with similar aims. Based on some typical scenarios we demonstrate how the model can be used for better decision support in operational environments. We also performed sensitivity analysis in which we identified the most influential variables on the number of residual defects. This showed that the project size, scale of distributed communication and the project complexity cause the most of variation in number of defects in our model. We make both the dataset and causal model available for research use.     

Iterative design and testing within the software development life cycle

The activity of testing begins during system development and spans all subsequent phases. Some system development lifecycles describe testing which is performed after the coding phase, but this may cause the software to be delivered without sufficient testing. In this paper, we present a software system development lifecycle model, called the Test design Stages Processed model (TSP model), in which we emphasize that iterative test design stages should be incorporated at each phase of the software development lifecycle. When a phase is completed, testing of the phase should also be completed at that time. Within this paper we have added unit, integration and system testing processes into BoochÕs micro–design process to generate a new designs and test model. This shows the process of iterative and incremental software development. Comparing this with our model, we explain how the TSP model can be used for developing and testing an object-oriented software system.     

基于元胞自动机的人工金融市场及其仿真研究

该文通过对金融市场复杂性的分析,并基于元胞自动机和争当少数者模型提出了一个开放的金融预测模型。模型中的投资者相当于争当少数者模型里的agent,每个投资者都必须从初始时各自定义的策略集合中选择最成功的作为每一步的预测策略。同样,投资者也相仿于元胞自动机中的元胞,而选定的预测策略就作为元胞的局部规则,每个元胞都根据局部信息和局部规则做出估价。模型的最终预测值就是由数千个这样的投资者决定的。此模型是开放性的,因为可以通过扩充或重构其预测策略库来获得更精确的预测结果。数值实验显示,虽然策略库比较简单,但其预测的平均相对误差仅为1．73％。     

Capital investment appraisal: a new risk premium model

Net Present Value (NPV) is the principal valuation model of the financial literature. Firms are accordingly directed, as a matter of good practice, to adopt the model for selecting investment projects, yet questionnaire surveys show that the adoption rate has been very slow and the quality of usage questionable. In particular, alternative risk measures are popular amongst practitioners. In this paper we remodel the treatment of risk in the NPV model based on assumptions that seem realistic in an organizational or operational, as opposed to a personal, investment context. We derive formulas for calculating: the appropriate discount rate, a 'risk horizon' (where the risk premium exceeds the expected value), and a maximum default hazard point for projects. These measures provide a rationale for non-NPV approaches to risk measurement in questionnaire responses and offer a practical benefit to investors.