Methyl-CpG binding protein 2 （MeCP2） has a crucial role in transcriptional regulation and neural development （Ausi6 et al., 2014）. Loss of function mutations of MECP2 in human lead to Rett syndrome （RTT）, a seve...Methyl-CpG binding protein 2 （MeCP2） has a crucial role in transcriptional regulation and neural development （Ausi6 et al., 2014）. Loss of function mutations of MECP2 in human lead to Rett syndrome （RTT）, a severe neurodevelopmental disorders （Amir et al., 1999）, whereas individuals with the chromosomal duplications containing the MECP2 locus showed severe autism-like symptoms （Ramocki et al., 2009）.展开更多
Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how...Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processed by traditional static analysis approaches, and we can also derive the as-built behavior specifications and cover the entire behavior space of the malware samples. Although there have been several works focusing on malware behavior analysis, such research is far from mature, and no overviews have been put forward to date to investigate current developments and challenges. In this paper, we conduct a survey on malware behavior description and analysis considering three aspects： malware behavior description, behavior analysis methods, and visualization techniques. First, existing behavior data types and emerging techniques for malware behavior description are explored, especially the goals, prin- ciples, characteristics, and classifications of behavior analysis techniques proposed in the existing approaches. Second, the in- adequacies and challenges in malware behavior analysis are summarized from different perspectives. Finally, several possible directions are discussed for future research.展开更多
The existing search engines are lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. By analyzing user＇s dynamic s...The existing search engines are lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. By analyzing user＇s dynamic search behavior, the paper introduces a new method of using a keyword query graph to express user＇s dynamic search behavior, and uses Bayesian network to construct the prior probability of keyword selection and the migration probability between keywords for each user. To reflect the dynamic changes of the user＇s preference, the paper introduces non-lineal gradual forgetting collaborative filtering strategy into the personalized search recommendation model. By calculating the similarity between each two users, the model can do the recommendation based on neighbors and be used to construct the personalized search engine.展开更多
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB02050400)the National Natural Science Foundation of China(Grant No. #91432111) to Z.Qiu
文摘Methyl-CpG binding protein 2 （MeCP2） has a crucial role in transcriptional regulation and neural development （Ausi6 et al., 2014）. Loss of function mutations of MECP2 in human lead to Rett syndrome （RTT）, a severe neurodevelopmental disorders （Amir et al., 1999）, whereas individuals with the chromosomal duplications containing the MECP2 locus showed severe autism-like symptoms （Ramocki et al., 2009）.
基金Project supported by the National Natural Science Foundation of China(No.61472437)
文摘Behavior-based malware analysis is an important technique for automatically analyzing and detecting malware, and it has received considerable attention from both academic and industrial communities. By considering how malware behaves, we can tackle the malware obfuscation problem, which cannot be processed by traditional static analysis approaches, and we can also derive the as-built behavior specifications and cover the entire behavior space of the malware samples. Although there have been several works focusing on malware behavior analysis, such research is far from mature, and no overviews have been put forward to date to investigate current developments and challenges. In this paper, we conduct a survey on malware behavior description and analysis considering three aspects： malware behavior description, behavior analysis methods, and visualization techniques. First, existing behavior data types and emerging techniques for malware behavior description are explored, especially the goals, prin- ciples, characteristics, and classifications of behavior analysis techniques proposed in the existing approaches. Second, the in- adequacies and challenges in malware behavior analysis are summarized from different perspectives. Finally, several possible directions are discussed for future research.
基金supported by the National Natural Science Foundation of China (60432010)the National Basic Research Program of China (2007CB307103)+1 种基金the Fundamental Research Funds for the Central Universities (2009RC0507)Important Science & Technology Specific Project of Guizhou Province (【2007】6017)
文摘The existing search engines are lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. By analyzing user＇s dynamic search behavior, the paper introduces a new method of using a keyword query graph to express user＇s dynamic search behavior, and uses Bayesian network to construct the prior probability of keyword selection and the migration probability between keywords for each user. To reflect the dynamic changes of the user＇s preference, the paper introduces non-lineal gradual forgetting collaborative filtering strategy into the personalized search recommendation model. By calculating the similarity between each two users, the model can do the recommendation based on neighbors and be used to construct the personalized search engine.
