全球移动增值业务发展现状

移动增值业务在全球的发展速度依然很快,非话音收入占电信总收入的比重越来越大.在移动增值业务的发展上,日本遥遥领先,韩国紧随其后.在中国,虽然短信业务收入开始下降,但无线应用协议(WAP)、交互式语音回应(lVR)等业务收入快速攀升,从而保证了移动增值业务在中国的发展速度.由于中国信息产业部整顿收费混乱的移动增值业务市场,使服务提供商/内容提供商(SP/CP)的暴利时代成为过去,增值业务市场进入精耕细作期.随着用户对移动增值业务新的需求的不断增长,整首音乐下载、移动E-mail以及手机电视等业务将成为未来全球最有前途的移动增值业务.     

Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

A nonlinear method named detrended fluctuation analysis （DFA） was utilized to investigate the scaling behavior of the human electroencephalogram （EEG） in three emotional music conditions （fear, happiness, sadness） and a rest condition （eyes-closed）. The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides α band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the ＇approach-withdrawal＇ model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.     

Practical algorithms for transposition-invariant string-matching

We consider the problems of (1) longest common subsequence (LCS) of two given strings in the case where the first may be shifted by some constant (that is, transposed) to match the second, and (2) transposition-invariant text searching using indel distance. These problems have applications in music comparison and retrieval. We introduce two novel techniques to solve these problems efficiently. The first is based on the branch and bound method, the second on bit-parallelism. Our branch and bound algorithm computes the longest common transposition-invariant subsequence (LCTS) in time O((m²+loglogσ)logσ) in the best case and O((m²+logσ)σ) in the worst case, where m and σ, respectively, are the length of the strings and the size of the alphabet. On the other hand, we show that the same problem can be solved by using bit-parallelism and thus obtain a speedup of O(w/logm) over the classical algorithms, where the computer word has w bits. The advantage of this latter algorithm over the present bit-parallel ones is that it allows the use of more complex distances, including general integer weights. Since our branch and bound method is very flexible, it can be further improved by combining it with other efficient algorithms such as our novel bit-parallel algorithm. We experiment on several combination possibilities and discuss which are the best settings for each of those combinations. Our algorithms are easily extended to other musically relevant cases, such as δ-matching and polyphony (where there are several parallel texts to be considered). We also show how our bit-parallel algorithm is adapted to text searching and illustrate its effectiveness in complex cases where the only known competing method is the use of brute force.     

利用Windows计划任务设计广播站自动化播出

研究利用Windows的计划任务来解决扩音机和电脑的自动开关及建立相关音乐曲库、自动播放任务，以此实现广播系统的自动化播出。     

液相色谱专家系统中柱系统推荐规则的建立和验证

把分子结构和分子、离子静电作用力作为影响溶质保留值和选择性的基本点,提出了液相色谱专家系统柱系统推荐的基本规则,由此可以从溶质所包含的基团或官能团的信息进行色谱柱系统推荐。为了验证这些规则的正确性,应用几类常用的或典型的样品从分子结构通道进行液相色谱柱系统推荐,获得满意的结果。     

音乐物联网的设计与研究

音乐物联网(Internet of musical things,IoMusT)是一个涉及多学科、多专业的综合工程,以往的研究都是单一的从某一个方面着手,缺乏一个统一的研究框架,完整的研究体系还不健全。研究重点是从工程的角度定义音乐事件,并结合当下互联网各个环节的技术特性,从工程的角度定义音乐事件,构造一个小范围的IoMusT生态系统,从软硬件方面测试IoMusT性能,进而验证IoMusT设计架构效。     

What installed mobile applications tell about their owners and how they affect users’ download behavior

The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. An experimental study was conducted through the use of a specifically designed mobile application, on users’ mobile phones. The goals were; first, to learn about the users’ personality and the applications they downloaded to their mobile phones, second to recommend applications to users via notifications through the use of experimental mobile application and learn about user behavior in mobile environment. The question of how the personality features of users affect their compliance to recommendations is explored in this study. It is found that conscientiousness is positively related with accepting recommended applications and being agreeable is related with the preference for the applications of editor’s choice. Furthermore, in this study, applications owned by the user and the composition of applications under categories and their relation with personality features are explored. It is shown that the number of user owned applications and their category differ according to gender and personality. Having similar applications and the number of applications owned under specific categories increase the probability of accepting recommended applications.     

An algorithm for friend-recommendation of social networking sites based on SimRank and ant colony optimization

We put forward an algorithm on friend-recommendation of social networking sites based on SimRank and ant colony optimization, which broadens the appliance of the algorithm in this academic question. The algorithm focuses on the existing relationships between the members as the initial measurement and constructs artificial ants’ completed routing graph. Finally, an ordered and limited list of personalized recommendations through recursive optimization is produced. In the end, we verify the algorithm's rationality and validity through simulation and the result shows that it can improve the precision of friend-recommendation.     

云环境下top-n推荐算法

Top-n推荐技术是近年来信息服务领域的一个研究重点和热点.针对云环境下的top-n推荐算法进行了深入研究,提出了适合top-n推荐的多层分布式存储架构MDSA(Multilayer Distributed Storage Architecture),并从降低网络传输代价出发,设计了基于MDSA架构的数据编码模式,进而利用map/reduce分布式编程模型来快速实现top-n推荐.此外,为了满足实际的需求,给出了三种top-n推荐的应用扩展.理论分析和实验结果表明,本文所提的方法具有有效性和实用性.     

一种社交网络群组间信息推荐的有效方法

群组间信息推荐是社交网络中人们传递和分享资讯的重要途径,然而获取精确的最优推荐方案需要指数级时间开销.为此,本文提出一种有效算法EAOORS(Efficient Algorithm for Obtaining Optimal Recommendation Solution),将该指数级时间开销问题等价归约为EST(Extended Steiner Tree,扩展Steiner树)问题,并在多项式时间复杂度内快速获取近似最优推荐方案.理论分析和仿真实验表明,本文所提的算法具有有效性和实用性.