Popularity of patent commercialization policies: Iran as a case

Commercialization of technologies has attracted increasing attention over the past years due to its role in improving national competitiveness. Iran has not been an exception as the country's policy-makers have been actively engaged in crafting national policies to facilitate and promote commercialization of technologies. The popularity of the existing policies in this field, however, has not been properly researched. Our research, by following a survey study methodology, aims for the first time to address this gap. The study looks at popularity of governmental policies aimed at supporting commercialization of patented technologies in Iran, based on the careful study of 10 cases of successfully commercialized patented technologies in Iran. Our results indicate that the commercialization process of the studied patented technologies was very much affected by some company/technology-specific characteristics. The study shows that the two policy measures with the highest perceived popularity were the financial supports and the services offered by incubation centers. Our results also show that some of the existing supportive policy measures did not serve the intended purposes and some other measures need to be fine-tuned, as far as the studied cases are concerned, such as frequently held invention and innovation exhibitions and the government-supported media coverage.     

基于媒体用户访问行为偏好模型的代理缓存算法

目前,代理缓存技术广泛应用于改善流媒体传输的服务质量.文章从实际用户日志文件的分析出发,利用发现的用户浏览流媒体对象时的行为分布模型,提出了一种新的视频流媒体缓存算法.仿真结果证明,该算法可以通过记录很少的用户访问信息获取较高的性能表现.     

Proactive replication for rare objects in unstructured peer-to-peer networks

Unstructured peer-to-peer (P2P) networks have become a very popular architecture for content distribution in large-scale and dynamic environments. The search efficiency problem in unstructured P2P networks has not been adequately addressed so far, especially concerning search for rare objects. In this paper, we propose a proactive replication strategy to improve search efficiency for rare objects. It uses an object-probing technique for peers to decide whether or not to establish replications for their objects when they join the network. This strategy can effectively increase the popularity of rare objects in order to enhance search efficiency. We also present a rare object search algorithm to reduce the overhead caused by the replication strategy. When a peer forwards a search request, the forward probability is calculated according to its neighbors' degrees and the number of neighbors' objects. Therefore, the search request is forwarded to the peers more likely containing target objects. Simulations show that our proactive replication strategy greatly improves search efficiency for rare objects with moderate communication overhead. The rare object search algorithm not only improves search efficiency for rare objects, but also achieves load balance in search.     

On the fit of models to covariances and methodology to the Bulletin..

Notes that 7 of the 10 top-cited articles in the Psychological Bulletin deal with methodological topics. One of these is the P. M. Bentler and D. G. Bonett (1980) article on the assessment of fit in covariance structure models. Some context is provided on the popularity of this article. In addition, a citation study of methodology articles appearing in the Bulletin since 1978 was carried out. It verified that publications in design, evaluation, measurement, and statistics continue to be important to psychological research. Some thoughts are offered on the role of the journal in making developments in these areas more accessible to psychologists. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于传播模拟的消息流行度预测

社交网络中的消息流行度预测问题对于信息推荐和病毒式营销等应用具有重要意义。该文提出了一种基于传播模拟的消息流行度预测方法,首先使用最大熵模型学习并预测用户转发消息的概率,然后使用独立级联传播模型在真实的社会网络上模拟消息的传播过程,从而完成消息流行度的预测。该方法的优点在于更充分的利用了社会网络的结构和用户特征信息。该文在Twitter数据集上的实验结果表明,相对于基准方法,该文提出的方法具有更高的准确率和稳定性。     

融合内容特征和时序信息的深度注意力视频流行度预测模型

针对视频流行度动态变化过程中的时序信息难以捕捉的问题,提出一种融合内容特征和时序信息的深度注意力视频流行度预测模型（DAFCT）。首先,根据用户的反馈信息,构建基于注意力机制的长短期记忆网络（Attention-LSTM）模型来捕捉流行趋势并挖掘时序信息;然后,采用神经网络因子分解机（NFM）处理多模态的内容特征,并采用嵌入技术对稀疏的高维特征进行降维处理,从而降低模型的计算复杂性;最后,采用concatenate方法融合时序信息和内容特征,并设计了一种深度注意力视频流行度预测（DAVPP）算法来求解DAFCT。实验结果表明,与Attention-LSTM模型和NFM模型相比,DAFCT的召回率分别提高了10.82和3.31个百分点,F1分数分别提高了9.80和3.07个百分点。     

基于因子分析和Elman网络的舆情关键词热度预测

从宏观角度研究基于关键词的网络舆情热度有助于相关机构把握目标群体的整体舆情动态,从而实现精准施策,提升舆论引导水平.本文以新浪微博数据为例,采用因子分析方法 (Factor Analysis, FA),挖掘舆情热度内在影响因素,并通过改进Elman网络结构,利用遗传算法(Genetic Algorithm, GA)优化初始参数来构建模型对网络舆情关键词热度进行分析预测.实验结果表明,所提出的方法相较于采用原始数据集和标准Elman网络的预测结果,具有更高的预测精度,可为相关研究提供决策支持.     

有章法的颠覆——论广告的创意与构思

从实用角度论述广告创意思维方式,着重对主题创意进行研究和探讨,分别对广告创意的基础,广告创意的思考方法,以及广告创意灵感的突破口这几个方面进行论述,以求取得创意提示,激发创意灵感,治疗创意萎缩。     

基于内容热度与节点介数的NDN网络缓存策略

缓存技术是数据命名网络（Named data networking,NDN）的关键技术之一. NDN传统的LCE缓存策略会造成较大的冗余. 改进的RCOne策略采用随机放置的方法,没有利用任何内容、节点信息,对网络缓存性能的提升有限. Betw策略只考虑到节点介数,导致高介数节点缓存更替频繁,当节点缓存容量远小于内容总量时,缓存性能下降. 为了解决这些问题,本文提出一种结合内容热度与节点介数的新型缓存策略HotBetw（Hot content placed on node with high Betweenness）,充分利用内容与节点信息选择最佳的位置放置缓存. 仿真实验表明相对于典型NDN缓存策略,HotBetw缓存策略在提高缓存命中率、降低平均跳数方面具有很好的效果.     

Firms’ involvement in Open Source projects: A trade-off between software structural quality and popularity

Open Source (OS) was born as a pragmatic alternative to the ideology of Free Software and it is now increasingly seen by companies as a new approach to developing and making business upon software. Whereas the role of firms is clear for commercial OS projects, it still needs investigation for projects based on communities. This paper analyses the impact of firms’ participation on popularity and internal software design quality for 643 SourceForge.net projects. Results show that firms’ involvement improves the ranking of OS projects, but, on the other hand, puts corporate constraints to OS developing practices, thus leading to lower structural software design quality.