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Tian D.-G.,University of Shanghai for Science and Technology | Huang Y.-Q.,Shanghai Stock Communication Co.
Journal of Electrical and Computer Engineering | Year: 2012

The computation of channel capacity is a classical issue in information theory. We prove that algorithms based on self-concordant functions can be used to deal with such issues, especially when constrains are included. A new algorithm to compute the channel capacity per unit cost is proposed. The same view is suited to the computation of maximum entropy. All the algorithms are of polynomial time. Copyright © 2012 Da-gang Tian and Yi-qun Huang. Source


Huang Y.,Shanghai Stock Communication Co. | Chen J.,Shanghai Stock Communication Co. | Pei L.,Shanghai Stock Communication Co. | Shi Y.,Shanghai Stock Communication Co.
International Journal of Advancements in Computing Technology | Year: 2011

In this paper, we investigate the effective strategy which can enhance scale-free networks robustness to both random failures and intentional attacks while keeping the average connectivity 〈k〉 per node constant. The numerical results indicate that the strategy, which protects the highest degree nodes, is not effective. In spite of about 3% highest nodes are protected, scale-free networks' robustness only enhance about 0.1%. While the strategy that adding more edges to make the minimum degree of scale-free networks increase is more effective. Source


Huang T.,Shanghai Stock Communication Co. | Shi Y.,Shanghai Stock Communication Co. | Huang Y.,Shanghai Stock Communication Co. | Rong W.,Shanghai Stock Communication Co.
Advances in Information Sciences and Service Sciences | Year: 2011

A good personalization strategy can increase sales by improving customer conversion ratio, enhance customer loyalty by improving relationship with customers, or in other words, increase revenue and profit. The heart of personalization is to serve individual customers unique needs. However, customers needs are hard to pin-down; every customer is unique and so are his or her needs. With the constraints of traditional collaborative filtering (CF) methods, the heat conduction process is introduced into the CF method to enhance the personalization service performance. By introducing a new user similarity formulation base on the heat conduction process, we introduce a modified collaborative filtering algorithm (MCF), which has remarkably higher accuracy than the standard collaborative filtering. Furthermore, by introducing a tunable parameter l, the degree effects of the termination nodes on the performance are investigated. The numerical simulation results on a benchmark data set shows that the algorithmic accuracy of the MCF, measured by the ranking score, is further improved by 6.3% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that when the recommendation list L = 50 the diversity of the presented algorithm is improved 14.3%. There has long been a trade-off between satisfying more customers and better meeting the needs of every individual customer. This work may shed some light on improving the personalization service performance. Source

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