On the size of computationally complete hybrid networks of evolutionary processors

A hybrid network of evolutionary processors (an HNEP) is a graph where each node is associated with an evolutionary processor (a special rewriting system), a set of words, an input filter and an output filter. Every evolutionary processor is given with a finite set of one type of point mutations (an insertion, a deletion or a substitution of a symbol) which can be applied to certain positions of a string over the domain of the set of these rewriting rules. The HNEP functions by rewriting the words that can be found at the nodes and then re-distributing the resulting strings according to a communication protocol based on a filtering mechanism. The filters are defined by certain variants of random-context conditions. HNEPs can be considered as both language generating devices (GHNEPs) and language accepting devices (AHNEPs). In this paper, by improving the previous results, we prove that any recursively enumerable language can be determined by a GHNEP and an AHNEP with 7 nodes. We also show that the families of GHNEPs and AHNEPs with 2 nodes are not computationally complete.     

The role of evolutionary operations in accepting hybrid networks of evolutionary processors

In this paper, we investigate the role of evolutionary operations in accepting hybrid networks of evolutionary processors (AHNEP for short) in the following way. We consider AHNEPs with all the nodes specialized in only one evolutionary operation (substitution, insertion, or deletion) or in two operations out of these three. The considered variants differ in two respects: filters that are used to control the exchange of information (we use random context conditions and regular languages as filters) and the way of accepting the input word (at least one output node or all output nodes are non-empty at some moment in the computation). The computational power of all these variants is studied.     

自适应并行进化策略在配电网络重构中的应用

针对目前利用计算机技术进行中压电网运行方案优化问题,构造了网络运行方案优化的数学模型,提出了自适应多种群并行进化策略模型,采用多种群连接节点逐步扩展法求解问题线路对应的串位进化变异,并对算法进行了实现。算例分析证明,这种策略能够极大地降低网损,提高经济效益。     

指纹加密在网络传输中的应用

该文讨论了目前计算机网络通信中广泛使用的密码体制的某些缺陷,提出了将个人指纹信息与随机密钥相结合的方法,从而进一步提高网络传输中信息的安全性。     

同态加密隐私保护数据挖掘方法综述*

如何保护私有信息或敏感知识在数据挖掘过程中不被泄露,同时能得到较为准确的挖掘结果,是隐私保护中面临的重大挑战。近年来国内外学者对隐私保护数据挖掘（privacy-preserving data mining,PPDM）进行了大量研究,适时地对研究成果进行总结,能够明确研究方向。从分类挖掘、关联规则挖掘、聚类挖掘和安全多方计算等几个方面,总结了现有的基于同态加密技术的算法,分析了其基本原理和特点,并在此基础上指出了PPDM技术今后发展的方向。     

混沌参数调制下RSA数据加密算法研究

随着网络信息通讯技术的发展,信息数据的通讯安全成为信息化网络信息数据通讯过程中的重要问题。面对大量侵入程序与数据漏洞的威胁,一套强有效的数据加密算法成为数据研究领域的研究方向。RSA数据加密算法作为如今常用的安全性最高的算法,在大数据动态数据节点混沌排列的条件下,无法有效保证数据的加密安全。混沌参数下,出现加密逻辑断裂、溢出、数列逆排等严重的算法漏洞。对此,提出混沌参数调制下RSA数据加密算法研究,采用混沌参数特征处理单元、特征序列逻辑控制单元与混沌FIE-RSA算法,对传统RSA加密算法存在的问题进行针对性解决。通过仿真实验证明,提出的混沌参数调制下RSA数据加密算法研究中,采用的一系列方法具有加密处理响应速度快、处理运算时间短、加密安全度高、反破解性能强等特点。     

无线传感器网络中虚假数据过滤方案

传统虚假数据过滤算法不能有效地应用于无线传感器网络,因而需要研究适合无线传感器网络的虚假数据过滤方案。在介绍无线传感器网络与虚假数据过滤的概念和特点后,提出了虚假数据过滤方案的分类方法,探讨了各种虚假数据过滤策略,着重分析了当前一些较为重要的虚假数据过滤策略,并指出了这些策略的优缺点。最后分析了当前亟待解决的问题,展望了其未来的发展趋势。     

一种用于进化算法历史计算数据的高效利用方法

进化算法由于其强大的系统建模能力和空间搜索能力已被广泛应用于许多实际问题的求解中。然而,在算法进化的过程中存在个体适应值重复计算的问题,尤其在解决实际工程中的复杂问题时,适应值的计算会消耗大量时间。为此,利用哈希表的高速存取能力,将哈希表用于存取适应值的历史计算数据,从而避免优化过程中适应值的重复计算,并且对优化结果没有任何影响。仿真实验结果验证了此方法的有效性。     

Rethinking the architecture design of data center networks

In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore’s law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidimensional characteristics. The huge amount of data brings many challenges on the management, transportation, and processing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. However, how to build a good data center remains an open challenge. Concurrently, the architecture design, which significantly affects the total performance, is of great research interest. This paper surveys advances in data center network design. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today’s data center network architectures. In the third part, we present some up-to-date data center frameworks and make a comprehensive comparison of them. During the comparison, we observe that there is no so-called optimal data center and the design should be different referring to the data placement, replication, processing, and query processing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions.     

The parallel computation of eigenvalues and eigenvectors of large Hermitian matrices using the AMT DAP 510

The solution of the algebraic eigenvalue problem is an important component of many applications in science and engineering. With the advent of novel architecture machines, much research effort is now being expended in the search for parallel algorithms for the computation of eigensystems which can gainfully exploit the processing power which these machines provide. Among important recent work References 1-4 address the real symmetric eigenproblem in both its dense and sparse forms, Reference 5 treats the unsymmetric eigenproblem, and Reference 6 investigates the solution of the generalized eigenproblem. In this paper two algorithms for the parallel computation of the eigensolution of Hermitian matrices on an array processor are presented. These algorithms are based on the Parallel Orthogonal Transformation algorithm (POT) for the solution of real symmetric matrices[7,8]. POT was developed to exploit the SIMD parallelism supported by array processors such as the AMT DAP 510. The new algorithms use the highly efficient implementation strategies devised for use in POT. The implementations of the algorithms permit the computation of the eigensolution of matrices whose order exceeds the mesh size of the array processor used. A comparison of the efficiency of the two algorithms for the solution of a variety of matrices is given.     

用并行计算从基因表达数据构建大规模基因调控网络

为解决大规模基因调控网络构建算法精度不高、计算时间过长的问题,提出一种从基因表达数据分析出发,并行计算和阈值限定相结合的新算法来构建大规模基因调控网络。该算法中基因间交互强度值采用条件互信息值度量,并行计算采用GPU与CPU相结合的CUDA与Open MP架构。综合数据集的运行结果证明该算法较新的构建算法(如贝叶斯模型算法和微分方程模型算法)相比,在构建大规模基因调控网络时有更高的运算精度和更短的运行时间。     

Ultra DMA模式下硬盘数据加密系统的设计及FPGA实现

设计了基于FPGA芯片的硬盘数据加密系统。该加密系统运行在Ultra DMA传输模式下,其加密核支持常用对称密码算法（AES、DES、3DES）和用户自主开发的各种对称密码算法。测试结果表明,在Ultra DMA模式2下系统运行速度为32 Mbps,对计算机正常运行没有任何影响。     

Comparison of encrypted control approaches and tutorial on dynamic systems using Learning With Errors-based homomorphic encryption

Encrypted control has been introduced to protect controller data by encryption at the stage of computation and communication, by performing the computation directly on encrypted data. In this article, we first review and categorize recent relevant studies on encrypted control. Approaches based on homomorphic encryption, multi-party computation, and secret sharing are introduced, compared, and then discussed with respect to computational complexity, communication load, enabled operations, security, and research directions. We proceed to discuss a current challenge in the application of homomorphic encryption to dynamic systems, where arithmetic operations other than integer addition and multiplication are limited. We also introduce a Learning With Errors based homomorphic cryptosystem called “Gentry-Sahai-Waters” scheme and discuss its benefits that allow for recursive multiplication of encrypted dynamic systems, without use of computationally expensive bootstrapping techniques.     

Constructing gene regulatory networks from microarray data using GA/PSO with DTW

Recently, many methods have been proposed for constructing gene regulatory networks (GRNs). However, most of the existing methods ignored the time delay regulatory relation in the GRN predictions. In this paper, we propose a hybrid method, termed GA/PSO with DTW, to construct GRNs from microarray datasets. The proposed method uses test of correlation coefficient and the dynamic time warping (DTW) algorithm to determine the existence of a time delay relation between two genes. In addition, it uses the particle swarm optimization (PSO) to find thresholds for discretizing the microarray dataset. Based on the discretized microarray dataset and the predicted types of regulatory relations among genes, the proposed method uses a genetic algorithm to generate a set of candidate GRNs from which the predicted GRN is constructed. Three real-life sub-networks of yeast are used to verify the performance of the proposed method. The experimental results show that the GA/PSO with DTW is better than the other existing methods in terms of predicting sensitivity and specificity.     

基于社会网络特性的双混沌互反馈加密算法研究

社会网络的数据获取已经成为社会网络分析的重要基石,虽然大多数社会媒体提供给开发者官方接口以供数据获取,但是在调用频次、权限、内容等方面都有严格的限制,难以获取全面的数据。因此,基于用户模拟登录的数据获取方法显得尤为重要,然而目前大多数社会媒体的登录过程存在较大的安全隐患,其登录密码均采用明文传输,严重威胁到用户的隐私安全。详细分析了Twitter登录过程中客户端与服务器间的交互过程,并且在流量层面解析POST请求时,发现Twitter的登录密码采用明文传输。为此,提出一种基于社会网络特性的双混沌互反馈加密算法。该算法利用登录用户的ID、创建时间、关注数作为加密函数的初始值与参数,并通过Logistic映射和Tent映射两个混沌系统交互式运算,得出密钥序列。由于输入参数的特殊性,使得密文具有不可预测性。实验表明,该算法取得了较好的加密和解密效果,同时加密与解密均处于毫秒级,可以做到用户的无感操作。此外,该算法拥有初始条件极度敏感、密钥空间大、加密强度高等特点。该算法能有效地防止攻击者使用相图、穷举、统计等方法进行密码破解,具有广阔的应用前景。     

Development of CCITT standards for packet data networks in the period 1980–1984

The rapid growth of the communications industry has produced the need for a programme of rationalization of international computing and networking standards. The work done at CCITT in this field during 1980–1984 period is reviewed, particular attention being given to the progress made in the standards for packet data networks: X.25, X.3, X.28, X.29 and X.32. Some aspects of the OSI model that are related to the network layer and to X.25 are also discussed.     

基于XML和本体的物联网数据交换标准体系研究

针对当前物联网应用层中相关数据交换标准纷繁复杂,相关标准主体各行其是的问题,从XML(可扩展标记语言)和本体的角度出发,从互联网、语义网和物联网的关系入手,提出了建立物联网数据交换标准体系的思路,即标准应以XML为语法格式,以标准化的本体为语义共识;标准体系应以顶级本体为基础,以纵向的领域本体和横向的任务本体为支撑,建立起各种不同的应用本体标准。文章最后通过一个应用实例分析了所述标准体系发展的若干关键要点。     

链式数据重组与神经网络在经济预测中的应用

建立经济模型和基于模型对宏观经济进行预测,是经济运行质量评价、仿真、制定发展规划等所必不可少的.针对宏观经济预测的特殊性:样本少、时变性,提出了反向传播(BP)神经网络的链式数据重组训练方法,并用于实际经济预测.和原数据用于预测的结果相比,达到了较高的预测精度.同时,解决了BP神经网络难以确定隐结点数的问题.