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.     

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。     

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.     

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.     

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。     

Tweakable enciphering schemes using only the encryption function of a block cipher

A new construction of block cipher based tweakable enciphering schemes (TES) is described. The major improvement over existing TESs is that the construction uses only the encryption function of the underlying block cipher. Consequently, this leads to substantial savings in the size of hardware implementation of TES applications such as disk encryption. This improvement is achieved without loss in efficiency of encryption and decryption compared to previously known schemes. We further show that the same idea can also be used with a stream cipher which supports an initialization vector (IV) leading to the first example of a TES from such a primitive.     

Using mobile data collectors to improve network lifetime of wireless sensor networks with reliability constraints

In this paper, we focus on maximizing network lifetime of a Wireless Sensor Network (WSN) using mobile Data Collectors (DCs) without compromising on the reliability requirements. We consider a heterogeneous WSN which consists of a large number of sensor nodes, a few DCs, and a static Base Station (BS). The sensor nodes are static and are deployed uniformly in the terrain. The DCs have locomotion capabilities and their movement can be controlled. Each sensor node periodically sends sensed event packets to its nearest DC. The DCs aggregate the event packets received from the sensor nodes and send these aggregate event packets to the static BS. We address the following problem: the DCs should send the aggregate event packets to the BS with a given reliability while avoiding the hotspot regions such that the network lifetime is improved. Reliability is achieved by sending each aggregate event packet via multiple paths to the BS. The network lifetime is maximized by moving the DCs in such a way that the forwarding load is distributed among the sensor nodes. We propose both centralized and distributed approaches for finding a movement strategy of the DCs. We show via simulations that the proposed approaches achieve the required reliability and also maximize the network lifetime compared to the existing approaches.     

基于粗糙集和神经网络的上海最低工资标准研究

介绍了粗糙集基本理论和基于粗糙集的神经网络建模,根据粗糙集理论的属性约简步骤,提出了融合粗糙集对原有的神经网络模型加以改进的分析研究方法,并将此方法应用于对上海最低工资标准的分析研究,给出了应用此方法进行实证研究的过程和分析结果,并给出了用约简后的属性进行学习过程时的误差分析曲线.旨在保留重要信息的前提下,消除多余的属性数据,提高仿真的精度和速度,从而更好地为政府制定相应决策,提供更科学合理的依据.     

Comparison of the Prim-Dijkstra and kraskal algorithms on an associative parallel processor

With the help of a model of an associative parallel processor with vertical processing (STAR computer), Prim-Dijkstra and Kraskal algorithms for finding a minimal spanning tree of an undirected graph represented in the form of a list of edges and their weights are compared. A relatively simple representation of the Prim-Dijkstra algorithm is constructed in which the initial node is taken into account. The Kraskal algorithm is also presented and the possibility of eliminating the stage of preliminary sorting of edges by their weights is shown. Translated from Kibernetika i Sistemnyi Analiz, No. 2. pp. 19–27, March–April, 2000.     

Hierarchical parallel processing of large scale data clustering on a PC cluster with GPU co-processing

This paper presents an effective scheme for clustering a huge data set using a PC cluster system, in which each PC is equipped with a commodity programmable graphics processing unit (GPU). The proposed scheme is devised to achieve three-level hierarchical parallel processing of massive data clustering. The divide-and-conquer approach to parallel data clustering is employed to perform the coarse-grain parallel processing by multiple PCs with a message passing mechanism. By taking advantage of the GPU’s parallel processing capability, moreover, the proposed scheme can exploit two types of the fine-grain data parallelism at the different levels in the nearest neighbor search, which is the most computationally-intensive part of the data-clustering process. The performance of our scheme is discussed in comparison with that of the implementation entirely running on CPU. Experimental results clearly show that the proposed hierarchial parallel processing can remarkably accelerate the data clustering task. Especially, GPU co-processing is quite effective to improve the computational efficiency of parallel data clustering on a PC cluster. Although data-transfer from GPU to CPU is generally costly, acceleration by GPU co-processing is significant to save the total execution time of data-clustering.     

Self-organizing neural networks for the analysis and representation of data: Some financial cases

Many recent papers have dealt with the application of feedforward neural networks in financial data processing. This powerful neural model can implement very complex nonlinear mappings, but when outputs are not available or clustering of patterns is required, the use of unsupervised models such as self-organizing maps is more suitable. The present work shows the capabilities of self-organizing feature maps for the analysis and representation of financial data and for aid in financial decision-making. For this purpose, we analyse the Spanish banking crisis of 1977–1985 and the Spanish economic situation in 1990 and 1991, making use of this unsupervised model. Emphasis is placed on the analysis of the synaptic weights, fundamental for delimiting regions on the map, such as bankrupt or solvent regions, where similar companies are clustered. The time evolution of the companies and other important conclusions can be drawn from the resulting maps.Characters and symbols used and their meaning nx x dimension of the neuron grid, in number of neurons - ny y dimension of the neuron grid, in number of neurons - n dimension of the input vector, number of input variables - (i, j) indices of a neuron on the map - k index of the input variables - w _ijk synaptic weight that connects thek input with the (i, j) neuron on the map - W _ij weight vector of the (i, j) neuron - x _k input vector - X input vector - (t) learning rate - _o starting learning rate - _f final learning rate - R(t) neighbourhood radius - R₀ starting neighbourhood radius - R _f final neighbourhood radius - t iteration counter - t _rf number of iterations until reachingR _f - t _f number of iterations until reaching _f - h(·) lateral interaction function - standard deviation - for every - d (x, y) distance between the vectors x and y     

Integrated approach of cognitive maps and neural networks using qualitative information on the World Wide Web: the KBNMiner

Abstract: In this paper, we propose a method for integrating cognitive maps and neural networks to gain competitive advantage using qualitative information acquired from news information on the World Wide Web. We have developed the KBNMiner, which is designed to represent the knowledge of domain experts with cognitive maps, to search and retrieve news information on the Internet according to the knowledge and to apply the information to a neural network model. In addition, we investigate ways to train neural networks more effectively by separating the learning data into two groups on the basis of event information acquired from news information. To validate our proposed method, we applied 180,000 news articles to the KBNMiner. The experimental results are found to support our proposed method through tenfold cross‐validation.     

Numerical solution of a calculus of variations problem using the feedforward neural network architecture

It is demonstrated, through theory and numerical example, how it is possible to construct directly and noniteratively a feedforward neural network to solve a calculus of variations problem. The method, using the piecewise linear and cubic sigmoid transfer functions, is linear in storage and processing time. The L₂ norm of the network approximation error decreases quadratically with the piecewise linear transfer function and quartically with the piecewise cubic sigmoid as the number of hidden layer neurons increases. The construction requires imposing certain constraints on the values of the input, bias, and output weights, and the attribution of certain roles to each of these parameters.

All results presented used the piecewise linear and cubic sigmoid transfer functions. However, the noniterative approach should also be applicable to the use of hyperbolic tangents and radial basis functions.     

A self-organizing model of the British economy for control with optimal prediction using the balance-of-variables criterion

A set of self-organized linear differential equations describing the economic system of Britain in 1965 is developed using the GMDH (the group method of data handling). The characteristic variables of the system were taken from the work of Parks and Pyatt. For selection the new criterion of the balance of variables was used. This assures the minimum deviation of the solution on the prediction interval (from 1965 to 1969). The equations are used to define the predicted optimal control when the moving interval of prediction is equal to four years. To synthesize the optimum control on the moving prediction interval, a simplified variant of the maximum principle is used.     

Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method

A hybrid forecasting method is proposed which leverages from statistical and neural network techniques to perform multi-step ahead forecasting. The proposed method is based on the disaggregation of time series components, the prediction of each component individually and the reassembling of the extrapolations to obtain an estimation for the global data. The STL decomposition procedure from the literature [5] is implemented to obtain the seasonal, trend and irregular components of the time series, whilst Generalized Regression Neural Networks (GRNN) [12] are used to perform out-of sample extrapolations of the seasonal and residual components. The univariate Theta model is employed for the estimation of the directional component. The application of the GRNN is based on the dynamic calibration of the training process for each of the seasonal and irregular components individually. The proposed hybrid forecasting method is applied to 60 time series from the NN3 competition and 227 time series from the M1 Competition dataset, to obtain 18 out-of sample predictions. The results from the application demonstrate that the proposed method can outperform standard statistical techniques in the literature. One of the main contributions of the current research lies in the investigation of the strengths and weaknesses of the GRNN in extrapolating structural components of time series.     

Predicting the type of pregnancy using artificial neural networks and multinomial logistic regression: a comparison study

Some medical and epidemiological surveys have been designed to predict a nominal response variable with several levels. With regard to the type of pregnancy there are four possible states: wanted, unwanted by wife, unwanted by husband and unwanted by couple. In this paper, we have predicted the type of pregnancy, as well as the factors influencing it using two different models and comparing them. Regarding the type of pregnancy with several levels, we developed a multinomial logistic regression and a neural network based on the data and compared their results using three statistical indices: sensitivity, specificity and kappa coefficient. Based on these three indices, neural network proved to be a better fit for prediction on data in comparison to multinomial logistic regression. When the relations among variables are complex, one can use neural networks instead of multinomial logistic regression to predict the nominal response variables with several levels in order to gain more accurate predictions.     

Exploring approaches to improve the performance of autonomous monitoring with imperfect data in location-aware wireless sensor networks

In recent years, information and sensing technologies have been applied to the construction industry to collect and provide rich information to facilitate decision making processes. One of the applications is using location data to support autonomous crane safety monitoring (e.g., collision avoidance and dangerous areas control). Several location-aware wireless technologies such as GPS (Global Positioning System), RFID (Radio-frequency identification), and Ultra-Wide Band sensors, have been proposed to provide location information for autonomous safety monitoring. However, previous studies indicated that imperfections (errors, uncertainty, and inconsistency) exist in the data collected from those sensors and the data imperfections have great impacts on autonomous safety monitoring system performance. This paper explores five computationally light-weight approaches to deal with the data imperfections, aiming to improve the system performance. The authors built a scaled autonomous crane safety monitoring testbed with a mounted localization system to collect location data and developed five representative test cases based on a live construction jobsite. Seven hundred and sixty location readings were collected at thirty-eight test points from the sensors. Those location data was fed into the reasoning mechanisms with five approaches to generate the safety decisions at those thirty-eight test points and evaluate system performance in terms of precision, recall and accuracy. The results indicate that system performance can be improved if at least ten position readings from sensors can be collected at small intervals at any location along the moving path. However, by including additional data such as velocity and acceleration that may be read from devices mounted on workers, localization error may be significantly reduced. These findings represent a path forward to improve localization accuracy by mixing imperfect data from the sensed environment with supplemental input.