电子商务中的安全技术

系统介绍了电子商务采用的对称加密、非对称加密的加密技术 ;基于数字信封、数字证书、数字签名的网络信息安全传输技术 ;SSL ,SET两种主要协议和 CA中心的应用。并对 EDI的安全进行了描述。结合国内情况提出了安全地开展电子商务的相关事宜     

基于自适应遗传算法的智能组卷的研究

智能组卷是智能计算机辅助教学中一个关键的问题,针对组卷中带约束的多目标优化问题,提出了一种自适应遗传算法,并采用新的编码方式、交叉算子和变异算子,建立了数学模型。实验结果表明,该算法能有效地解决智能组卷问题,具有较好的实用效果。     

基于小生境遗传算法的人工拣货路径优化研究

以某配送中心双区型仓库的拣货作业为研究对象,首先将其人工拣货路径优化问题转化为TSP问题,以此建立拣货路径优化模型。随后采用基于适应值共享的小生境遗传算法,对拣货路径问题进行优化研究。最后采用Matlab仿真证明小生境遗传算法可以有效缩短拣货路径节约拣货时间。     

Distinguishing between stochastic and deterministic behavior in high frequency foreign exchange rate returns: Can non-linear dynamics help forecasting?

This paper investigates the dynamic properties of high frequency foreign exchange rate returns. Using hourly data for four exchanges rates, the British Pound, the Deutschemark, the Japanese Yen and Swiss Franc, we attempt to differentiate between stochastic and deterministic behavior in hourly rates of returns. While the autocorrelation coefficients and the Brock-Dechert-Scheinkman test point to the presence of some non-linear dependence, correlation dimension estimates reveal little evidence in favor of low-dimensional chaos. The analysis appears to support the view that although it is not possible to exploit deterministic non-linear dependence in exchange rate time series in order to improve short-term forecasting, non-linear stochastic models can be used for conditional volatility forecasts.     

Aspects of statistical computing, past, present and future

The various stages in the history of statistical computing are illustrated by personal experiences. The stages include batch processing, interactive working, and the consultative mode. Statistical aspects associated with one or more of these stages include the development of general algorithms, procedures for model checking, and data-driven non-parametric modelling.     

Performance-Based Reward Distribution Methods for Anonymous Decision-Making Groups

Research has shown that both the support of anonymity and the use of appropriate incentives can lead to improved group performance. Anonymity enables a more open discussion resulting in a more critical analysis of a problem. Rewards can motivate individuals to cooperate, giving them the incentive to share valuable information with the group. Unfortunately, these two mechanisms are both dependent on the ability to identify the contributor. Anonymity hides the identity of the contributor, while the support of individualized, performance-based rewards requires the rewarding agent to be able to determine the identity of the contributor. This contradictory requirement has prevented the simultaneous used of anonymity and performance-based rewards in decision making. Using group decision support systems as a basis, this work identifies procedures to simultaneously support participant anonymity and performance-based rewards. Mechanisms based on public key encryption technologies are presented which make it possible to distribute individual rewards to anonymous contributors, guarantee that only the contributor can claim a reward for her contribution, verify that a reward has been distributed, and be able to deliver this reward in such a way that the identity of the anonymous contributor is protected. This is accomplished without the rewarding agent ever knowing the identity of the recipient.     

从混沌看SDN系统的演变

多功能开放型企业供需网(简称SDN)是应对复杂多变的混沌环境下的企业管理模式,其形成与发展充满了混沌性,符合现代市场与企业发展规律的需要。以图论的观点分析SDN的复杂结构,并以节点的可达路径构造出的最大信息熵的确定方法,从而建立SDN系统的混沌模型,并模拟出SDN系统的非线性演化规律。这对进一步研究SDN系统有一定的理论参考价值,其混沌演化也映射出了企业的非线性生长规律。     

A GRASP algorithm for the container stowage slot planning problem

This work presents a generalization of the Slot Planning Problem which raises when the liner shipping industry needs to plan the placement of containers within a vessel (stowage planning). State-of-the-art stowage planning relies on a heuristic decomposition where containers are first distributed in clusters along the vessel. For each of those clusters a specific position for each container must be found. Compared to previous studies, we have introduced two new features: the explicit handling of rolled out containers and the inclusion of separations rules for dangerous cargo. We present a novel integer programming formulation and a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. The approach is able to find high-quality solution within 1 s. We also provide comparison with the state-of-the-art on an existing and a new set of benchmark instances.     

Predicting demand for air taxi urban aviation services using machine learning algorithms

This research focuses on predicting the demand for air taxi urban air mobility (UAM) services during different times of the day in various geographic regions of New York City using machine learning algorithms (MLAs). Several ride-related factors (such as month of the year, day of the week and time of the day) and weather-related variables (such as temperature, weather conditions and visibility) are used as predictors for four popular MLAs, namely, logistic regression, artificial neural networks, random forests, and gradient boosting. Experimental results suggest gradient boosting to consistently provide higher prediction performance. Specific locations, certain time periods and weekdays consistently emerged as critical predictors.     

Análisis de portafolio por sectores mediante el uso de algoritmos genéticos: caso aplicado a la Bolsa Mexicana de Valores

The type of industry, size of company, number of employees, etc. are variables that are considered as control variables in a large number of articles. In this research we consider the sector variable as a determinant of financial performance (Baird et al. 2012) and the risk (Artikis and Nifora, 2011) rather than as a control variable. This paper analyzes six sectors of the Mexican economy divided according to the Mexican Stock Exchange: industrial, basic consumer products, materials, non basic consumer products, telecommunications and financial services. The sample consists of Mexican companies, that is, 30 companies in the 2007-2012 period. To measure portfolio performance two classic indicators are used: (1) Jensen alpha and (2) Sharpe ratio, and also conditional metrics are used that measures the number of times the portfolio return exceeds the market average. The goal is to find a portfolio that maximizes these parameters and compare the results between the different sectors under study. Due to a nonlinear programming problem, genetic algorithms are used to obtain the optimal portfolio that maximizes these metrics. The results show a better risk-adjusted financial performance in the field of materials and financial services and a lower performance in such sectors as the industrial and telecommunications ones.