基于邻居决策的协同过滤推荐算法

协同过滤技术应用于个性化推荐系统中,稀疏性问题和可扩展性问题成为亟需解决的问题。针对传统方法的不足,提出一种凭借邻居数做决策的方法,比较各个待测位置的用户邻居数和项目邻居数,由数量多的一方作预测,同时对预测值判定给出一种合理而有效的度量方法。实验结果表明,该方法能够提高推荐质量。     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

网格环境下可靠的可分割作业调度机制

针对传统可分割作业多路调度算法不能适应动态网格环境的不足,基于统一多路(Uniform Multi-Round:UMR)算法,提出一种可靠的可分割作业调度机制.系统动态地监控网格资源的变化,当资源发生变化时,通过性能预测与评估,及时地对剩余作业进行再调度.实验表明,较之传统的多路调度算法,该机制在动态的网格环境下,降低了作业完成时间,有效地利用了网格资源,提高了作业调度的可靠性.     

基于共享近邻的自适应谱聚类

谱聚类是一种极具竞争力的聚类算法.相似度定义对谱聚类算法的性能有至关重要的影响.本文用两点的共享近邻数目表征局部密度,从而获知隐含的簇结构信息.将这一信息与自调节的高斯核函数结合,提出了基于共享近邻的自适应相似度及相应的谱聚类算法.它满足聚类假设的要求,具有局部密度的自适应性,能有效识别数据点之间的内在联系.典型人工和真实数据集上的实验结果证明了算法的有效性.     

数控系统现场总线通信效率的改进

根据数控系统对现场总线通信的可靠性和效率需求,在分析传统通信机制的实时性和效率问题及其产生根源的基础上,提出一种基于硬件链路层重发机制的高效可靠通信机制,将其应用到数控系统中,消除了层间协议转换和操作系统的中断延迟等开销。实验结果表明,该机制具有较高的实时性和通信效率,能够满足高档数控系统的应用需求。     

基于相似负载的iSCSI透明可靠多播

提出一种针对iSCSI网络计算系统的I/O加速策略——基于相似负载的iSCSI透明可靠多播。通过对I/O请求数据块进行相似负载判定以及对原有iSCSI协议进行扩展,在iSCSI网络计算系统中构建数据传输双路径——iSCSI协议数据包路径和相似负载的多播路径,以提高网络计算数据的加载速度。测试结果表明,该策略能有效提升多个客户主机的并发I/O性能。     

Implementing uniform reliable broadcast with binary consensus in systems with fair-lossy links

When implementing multivalued consensus using binary consensus, previous algorithms assume the availability of uniform reliable broadcast, which is not implementable in systems with fair-lossy links. In this paper, we show that with binary consensus we can implement uniform reliable broadcast directly in systems with fair-lossy links, and thus the separate assumption of the availability of uniform reliable broadcast is not necessary. We further prove that, when binary consensus instances are available only as black boxes, any implementation of uniform reliable broadcast in the fair-lossy link model requires the invocation of an infinite number of binary consensus instances even if no process ever broadcasts any messages, and this is true even when multivalued consensus is used.     

Geno-mathematical identification of the multi-layer perceptron

In this paper, we will focus on the use of the three-layer backpropagation network in vector-valued time series estimation problems. The neural network provides a framework for noncomplex calculations to solve the estimation problem, yet the search for optimal or even feasible neural networks for stochastic processes is both time consuming and uncertain. The backpropagation algorithm—written in strict ANSI C—has been implemented as a standalone support library for the genetic hybrid algorithm (GHA) running on any sequential or parallel main frame computer. In order to cope with ill-conditioned time series problems, we extended the original backpropagation algorithm to a K nearest neighbors algorithm (K-NARX), where the number K is determined genetically along with a set of key parameters. In the K-NARX algorithm, the terminal solution at instant t can be used as a starting point for the next t, which tends to stabilize the optimization process when dealing with autocorrelated time series vectors. This possibility has proved to be especially useful in difficult time series problems. Following the prevailing research directions, we use a genetic algorithm to determine optimal parameterizations for the network, including the lag structure for the nonlinear vector time series system, the net structure with one or two hidden layers and the corresponding number of nodes, type of activation function (currently the standard logistic sigmoid, a bipolar transformation, the hyperbolic tangent, an exponential function and the sine function), the type of minimization algorithm, the number K of nearest neighbors in the K-NARX procedure, the initial value of the Levenberg–Marquardt damping parameter and the value of the neural learning (stabilization) coefficient α. We have focused on a flexible structure allowing addition of, e.g., new minimization algorithms and activation functions in the future. We demonstrate the power of the genetically trimmed K-NARX algorithm on a representative data set.     

基于流媒体的隐蔽通信可靠传输机制的研究

首先分析了原有流媒体隐蔽通信系统模型在传输机制上存在的问题.在此基础上改进了原有的传输方案,提出了一种新的基于流媒体的隐蔽通信的可靠传输机制,并进行实现和实际测试.实验结果表明,对于只有当载体满足一定条件时才可以嵌入的隐藏算法,新的传输机制也能保证可靠传输,性能较以前的方法也有长足的改善.     

无线传感器网络中能量均衡可靠路由度量方法

针对无线传感器网络的资源限制以及对传输可靠性的特殊要求,提出一种能量均衡路由度量方法EBETX。以ETX值作为路由度量的同时,兼顾网络中节点能量消耗情况,保证网络各节点能量均衡消耗,延长网络的生存时间,实现高效路由与能量均衡的有效权衡。通过NS2仿真实验验证了该方法的可行性。