A synergistic strategy for combining thesaurus-based and corpus-based approaches in building ontology for multilingual search engines

In this article we illustrate a methodology for building cross-language search engine. A synergistic approach between thesaurus-based approach and corpus-based approach is proposed. First, a bilingual ontology thesaurus is designed with respect to two languages: English and Spanish, where a simple bilingual listing of terms, phrases, concepts, and subconcepts is built. Second, term vector translation is used – a statistical multilingual text retrieval techniques that maps statistical information about term use between languages (Ontology co-learning). These techniques map sets of t f id f term weights from one language to another. We also applied a query translation method to retrieve multilingual documents with an expansion technique for phrasal translation. Finally, we present our findings.     

改进HSR-FCN的服装图像识别分类算法研究

目前网络上的服装图像数量增长迅猛，对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络（Region-Based Fully Convolutional Networks，R-FCN）引入到服装图像识别中，针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题，提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合，改变图片特征学习方式，使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络，对输入服装图像和特征图进行了空间变换及对齐，加强了对多角度服装和形变服装的特征学习。实验结果表明，改进后的HSR-FCN模型有效地加强了对形变服装图像的学习，且在训练时间更短的情况下，比原来的网络模型R-FCN平均准确率提高了大约3个百分点，达到96.69%。     

面向应用的无线传感器网络

结合具体的应用环境分析了无线传感器网络发展到第二阶段需要考虑的重点问题；探讨了基于上述应用需求的无线传感器网络的协议栈结构和今后的研究方向。     

A call admission and rate control scheme for multimedia support over IEEE 802.11 wireless LANs

Quality of service (QoS) support for multimedia services in the IEEE 802.11 wireless LAN is an important issue for such WLANs to become a viable wireless access to the Internet. In this paper, we endeavor to propose a practical scheme to achieve this goal without changing the channel access mechanism. To this end, a novel call admission and rate control (CARC) scheme is proposed. The key idea of this scheme is to regulate the arriving traffic of the WLAN such that the network can work at an optimal point. We first show that the channel busyness ratio is a good indicator of the network status in the sense that it is easy to obtain and can accurately and timely represent channel utilization. Then we propose two algorithms based on the channel busyness ratio. The call admission control algorithm is used to regulate the admission of real-time or streaming traffic and the rate control algorithm to control the transmission rate of best effort traffic. As a result, the real-time or streaming traffic is supported with statistical QoS guarantees and the best effort traffic can fully utilize the residual channel capacity left by the real-time and streaming traffic. In addition, the rate control algorithm itself provides a solution that could be used above the media access mechanism to approach the maximal theoretical channel utilization. A comprehensive simulation study in ns-2 has verified the performance of our proposed CARC scheme, showing that the original 802.11 DCF protocol can statically support strict QoS requirements, such as those required by voice over IP or streaming video, and at the same time, achieve a high channel utilization. Hongqiang Zhai received the B.E. and M.E. degrees in electrical engineering from Tsinghua University, Beijing, China, in July 1999 and January 2002 respectively. He worked as a research intern in Bell Labs Research China from June 2001 to December 2001, and in Microsoft Research Asia from January 2002 to July 2002. Currently he is pursuing the PhD degree in the Department of Electrical and Computer Engineering, University of Florida. He is a student member of IEEE. Xiang Chen received the B.E. and M.E. degrees in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 1997 and 2000, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Florida, Gainesville, in 2005. He is currently a Senior Research Engineer at Motorola Labs, Arlington Heights, IL. His research interests include resource management, medium access control, and quality of service (QoS) in wireless networks. He is a Member of Tau Beta Pi and a student member of IEEE. Yuguang Fang received a Ph.D degree in Systems and Control Engineering from Case Western Reserve University in January 1994, and a Ph.D degree in Electrical Engineering from Boston University in May 1997. From June 1997 to July 1998, he was a Visiting Assistant Professor in Department of Electrical Engineering at the University of Texas at Dallas. From July 1998 to May 2000, he was an Assistant Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology. In May 2000, he joined the Department of Electrical and Computer Engineering at University of Florida where he got the early promotion with tenure in August 2003 and has been an Associate Professor since then. He has published over one hundred (100) papers in refereed professional journals and conferences. He received the National Science Foundation Faculty Early Career Award in 2001 and the Office of Naval Research Young Investigator Award in 2002. He is currently serving as an Editor for many journals including IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and ACM Wireless Networks. He is also actively participating in conference organization such as the Program Vice-Chair for IEEE INFOCOM’2005, Program Co-Chair for the Global Internet and Next Generation Networks Symposium in IEEE Globecom’2004 and the Program Vice Chair for 2000 IEEE Wireless Communications and Networking Conference (WCNC’2000).     

用于机器人轨迹定位纠正系统的回声状态神经网络研究

循环神经网络(RecurrentNeuralNetworks)是人工神经网络(ArtificialNeuralNetworks)中重要的分支,与前馈神经网络(ForwardNeuralNetworks)相比具有更好的时间序列学习能力。但长期以来其学习法一直不能脱离前馈神经网络而自成一体,回声状态神经网络(EchoStateNetworks(ESN))是打破这一局面的全新学习方法。其独特的结构,良好的短期记忆能力,方便的学习方法,不俗的非线性特性是以前循环神经网络所不可比的。本文在介绍了回声状态神经网络之后将其用于四轮机器人的位置测量系统中,有良好的表现。     

A Monte Carlo simulation study of the mechanical and conformational properties of networks of helical polymers. I. General concepts

We study the mechanical and conformational properties of networks of helical polymers with a combination of Monte Carlo simulations based on the Wang-Landau algorithm and the three-chain model. We find that the stress-strain behavior of these networks has novel features not observed in typical networks made of synthetic polymers. In particular, we find that as these networks are stretched they first strengthen, then soften and finally strengthen again. This non-monotonic behavior of the stress correlates with the one of the helical content and is rationalized by the elongation-induced formation and melting of the helical structure of the polymer. We complement these results with a study of other conformational properties of the polymer strands that clarify the molecular mechanisms behind the mechanical behavior of these networks. Finally, we present a qualitative comparison of some of our results with the theoretical ones recently reported by Kutter and Terentjev.     

On parity problems and the functional-link artificial neural network

This paper contains the proof of a theorem on the capability of functional-link artificial neural networks both to represent and to learn the n-dimensional parity problem. The result is obtained by an embedding of the problem into a space of dimension 2ⁿ — 1. It is shown that the Volterra expansion of the data in n-dimensions provides the necessary transformation. By computing the parity function, it is shown that a suitable set of neural network weights can be deduced. Finally, it is demonstrated that 2ⁿ — 1 is the minimum embedding dimension for the problem.The contribution of A Zuderell of the University of Innsbruck is acknowledged.     

Experiments of Fast Learning with High Order Boltzmann Machines

This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.     

Sustaining Rural Landscapes: the role of integrated tourism

The aim of a European Union-funded research project entitled 'Supporting and Promoting Integrated Tourism in Europe's Lagging Rural Regions' (SPRITE) is to analyse and develop the potential for better-integrated tourism in the rural landscapes of Europe. Integrated tourism can be defined as that which is explicitly linked to the localities in which it takes place and, in practical terms, has clear connections with local resources, activities, products, production and service industries, and a participatory local community. Enhancing the sustainable economic potential of rural landscapes requires partnerships among rural people and the sustainable yet productive use of rural resources. A 'culture economy' approach to integrated tourism is proposed, which emphasizes the importance of local identity, the strategic commodification of resources and of place, and the importance of extra-local forces in enabling local activities. It is suggested that this approach may represent a shift beyond more traditional approaches to rural development.     

Integrating neural network technology and noise analysis

The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check valves and bearings illustrate the usefulness of the methodology developed.