Influential facilities placement over moving objects

Distributed and Parallel Databases - In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate...     

Texture compensation with multi-scale dilated residual blocks for image denoising

Neural Computing and Applications - Deep convolutional neural networks have achieved great success for image denoising recently. However, increasing the depth of the neural network cannot...     

一种基于Matlab图形用户界面的数字水印系统

数字水印技术是一种典型的信息网络安全有效保护措施。文章运用Maflab软件强大的GUI（图形用户界面）功能开发出一种基于小波变换的数字水印系统,该系统的构建是通过对载体彩色图像进行小波变换,然后将二值水印图像嵌入到小波变换后载体图像的低频子带中。实验结果证明,文章利用Matlab软件构建的数字水印系统在抗噪声攻击、JPEG压缩、小幅度剪切方面效果良好,稳健性强。     

An investigation of the effect of online consumer trust on expectation, satisfaction, and post-expectation

This study proposes a framework regarding the relationship between consumer trust, satisfaction, expectation, and post-expectation in the context of electronic commerce. In particular, the framework draws together from three theories: social exchange theory, expectation-confirmation theory, and post-acceptance model of IS continuance. Following the longitudinal pre-purchase and post-purchase stages, this study provides a theoretical framework combining trust, expectation, satisfaction, and post-expectation (i.e., perceived usefulness) and tests the proposed model empirically using Internet consumer behavior data collected via two rounds of Web surveys. The empirical findings suggest that both consumer’s trust and expectation have positive influences on consumer’s satisfaction; a significant and positive relationship is detected between consumer’s trust and expectation; customer’s satisfaction and perceived usefulness as post-expectation belief are important predictors of repurchase intention. In consequence, the study provides a framework explaining the subsequent relationships of trust, expectation, confirmation, satisfaction, post-expectation, and repurchase intention (i.e., consumer trust → expectation → confirmation → satisfaction → post-expectation → repurchase intention) across pre-purchase and post-purchase stages. Theoretical and practical implications are discussed.     

具有时变状态滞后2-D离散系统的稳定与镇定(英文)

针对一类由局部状态空间(LSS)Fornasini-Marchesini(FM)第二模型描述的,具有时变状态滞后的2-D离散系统,其中时变滞后项的上、下界均为正实数,研究了其稳定性和控制综合问题。首先,利用Lyapunov-Krasovski泛函方法,提出了系统的稳定性准则。再根据这一准则,分别设计状态反馈和动态输出反馈控制器保证系统的稳定性。状态反馈控制律和输出反馈矩阵可由线性矩阵不等式(LMI)求得。最后,通过数值算例说明所得结果的有效性。     

Customer knowledge discovery from online reviews

The explosive growth of Chinese electronic market has made it possible for companies to better understand consumers?? opinion towards their products in a timely fashion through their online reviews. This study proposes a framework for extracting knowledge from online reviews through text mining and econometric analysis. Specifically, we extract product features, detect topics, and identify determinants of customer satisfaction. An experiment on the online reviews from a Chinese leading B2C (Business-to-Customer) website demonstrated the feasibility of the proposed method. We also present some findings about the characteristics of Chinese reviewers.     

CLARO: modeling and processing uncertain data streams

Uncertain data streams, where data are incomplete and imprecise, have been observed in many environments. Feeding such data streams to existing stream systems produces results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the claro system that supports stream processing for uncertain data naturally captured using continuous random variables. claro employs a unique data model that is flexible and allows efficient computation. Built on this model, we develop evaluation techniques for relational operators by exploring statistical theory and approximation. We also consider query planning for complex queries given an accuracy requirement. Evaluation results show that our techniques can achieve high performance while satisfying accuracy requirements and outperform state-of-the-art sampling methods.     

A self-learning particle swarm optimizer for global optimization problems

Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.     

From ethnographic record to system design

This paper explores the issues involved in moving from ethnographic explorations of work in context to a practical contribution to system design. It does so using the example of an interdisciplinary research project involving sociologists and computer scientists in the domain of air traffic control systems. It forms a pair with another paper (Sommerville et al., 1992) exploring these questions from the perspective of our computer science partners. We characterise ethnography as a research method, and consider the differences between undertaking it for strictly sociological or anthropological purposes by contrast with interdisciplinary and design purposes. We summarise some of our results in ethnographic explications of the work of air traffic controllers, and the sociality which it manifests. We describe the dialogues involved in rendering these observations informative for systems design, and the mutual translations implied in attempting to reconcile sociological with software engineering questions about supporting the work. We conclude by specifying some features of cooperative work which an engineering approach is in danger of overlooking; the ways, and limits, in which ethnographers can form a bridge between users and designers; and some of the conflicts of interest entrained in generating technical change.The research is funded by the UK MRC/SERC/ESRC Cognitive Science/HCI Initiative with the title Social Analysis of Control Systems for HCI Design, grant number SPG 8931598. The computer science partners are Professor Ian Sommerville, Dr Tom Rodden, Dr Pete Sawyer and Mr Richard Bentley. Thanks are due to Val King, Kjeldt Schmidt, Ian Sommerville and Tom Rodden for very helpful comments on earlier drafts of this paper.     

数字图像合成技术综述

数字图像合成一直是图像处理中的研究热点,在图片编辑,平面设计,电影特效等领域有着广泛的应用。从原图像中准确地提取目标物体并将其无缝地合成到新背景下是图像合成的基本目标。按所使用的关键技术分类,现有的数字图像合成技术可分为基于α分量的图像合成,基于梯度场的图像合成和基于多分辨率模型的图像合成。首先详述了3类方法中的典型算法,并从合成质量、鲁棒性、运算效率等方面进行分析比较,然后对新的图像合成应用方式进行了扩展介绍,最后总结了现有图像合成方法的普遍局限性,并探讨了图像合成今后面临的挑战和发展方向。