In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boo...In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.展开更多
As a generative model,Latent Dirichlet Allocation Model,which lacks optimization of topics' discrimination capability focuses on how to generate data,This paper aims to improve the discrimination capability throug...As a generative model,Latent Dirichlet Allocation Model,which lacks optimization of topics' discrimination capability focuses on how to generate data,This paper aims to improve the discrimination capability through unsupervised feature selection.Theoretical analysis shows that the discrimination capability of a topic is limited by the discrimination capability of its representative words.The discrimination capability of a word is approximated by the Information Gain of the word for topics,which is used to distinguish between "general word" and "special word" in LDA topics.Therefore,we add a constraint to the LDA objective function to let the "general words" only happen in "general topics" other than "special topics".Then a heuristic algorithm is presented to get the solution.Experiments show that this method can not only improve the information gain of topics,but also make the topics easier to understand by human.展开更多
The first step in speechmaking is choosing a topic,which will decide the success of the speech.In the paper,we are go ing to discuss how to select a topic in public speaking,and we'll mainly talk about the followi...The first step in speechmaking is choosing a topic,which will decide the success of the speech.In the paper,we are go ing to discuss how to select a topic in public speaking,and we'll mainly talk about the following:the speaker,the occasion and the audience,selecting a topic,specific methods for choosing a topic and available topics.展开更多
How to select a research topic that is appropriate for a clinician and that can lead to a peer-reviewed publication?In this essay,I will provide 5 tips:keep it interesting,keep it relevant,keep it inclusive,keep it si...How to select a research topic that is appropriate for a clinician and that can lead to a peer-reviewed publication?In this essay,I will provide 5 tips:keep it interesting,keep it relevant,keep it inclusive,keep it simple,and keep it trying.Keep it展开更多
Dissertation is one of the most important indicators to evaluate the training quality of postgraduate students.As the first step of writing dissertations,topic selection directly influences the dissertation quality.By...Dissertation is one of the most important indicators to evaluate the training quality of postgraduate students.As the first step of writing dissertations,topic selection directly influences the dissertation quality.By utilizing the intelligent retrieval system of the National Science and Technology Library(NSTL),we have selected MTCSOL(Master of Teaching Chinese to Speakers of Other Languages)dissertation topics during the last 10 years,divided them into four stages and statistically analyzed the general tendency and crucial problems in these dissertations.MTCSOL dissertations have increased greatly in quantity and become more diversified and multidisciplinary.However,they are still too concentrated on similar topics with narrow scopes;and researches lack of creativity,pioneering spirits and practicality.It concludes that MTCSOL students should expand their research scopes and improve the quality of their dissertations with the historic mission of international Chinese language teaching to support the Belt and Road Initiative and build a harmonious community with shared future for humanity in the new era always in their minds.展开更多
In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces ...In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features.In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features.Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.展开更多
基金supported by National Natural Science Foundation of China(Nos.71131002,71071045,71231004 and 71201042)
文摘In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.
基金supported by National Nature Science Foundation of China under Grant No.60905017,61072061National High Technical Research and Development Program of China(863 Program)under Grant No.2009AA01A346+1 种基金111 Project of China under Grant No.B08004the Special Project for Innovative Young Researchers of Beijing University of Posts and Telecommunications
文摘As a generative model,Latent Dirichlet Allocation Model,which lacks optimization of topics' discrimination capability focuses on how to generate data,This paper aims to improve the discrimination capability through unsupervised feature selection.Theoretical analysis shows that the discrimination capability of a topic is limited by the discrimination capability of its representative words.The discrimination capability of a word is approximated by the Information Gain of the word for topics,which is used to distinguish between "general word" and "special word" in LDA topics.Therefore,we add a constraint to the LDA objective function to let the "general words" only happen in "general topics" other than "special topics".Then a heuristic algorithm is presented to get the solution.Experiments show that this method can not only improve the information gain of topics,but also make the topics easier to understand by human.
文摘The first step in speechmaking is choosing a topic,which will decide the success of the speech.In the paper,we are go ing to discuss how to select a topic in public speaking,and we'll mainly talk about the following:the speaker,the occasion and the audience,selecting a topic,specific methods for choosing a topic and available topics.
文摘How to select a research topic that is appropriate for a clinician and that can lead to a peer-reviewed publication?In this essay,I will provide 5 tips:keep it interesting,keep it relevant,keep it inclusive,keep it simple,and keep it trying.Keep it
基金the key national social science foundation project“Overseas Dissemination and Influence of Ancient Chinese Classic Primers”(17AZS012)the TCSOL Professional Degree Postgraduate Education Research Project 2017:Research on the Intercultural Communication Competence Cultivation System for MTCSOL(HGJ201713)2018 Jiangsu Province Postgraduate Practice and Innovation Plan(SJCX18_0719):The Dissemination and Influence of Confucius’s View of Human Nature in Singapore.
文摘Dissertation is one of the most important indicators to evaluate the training quality of postgraduate students.As the first step of writing dissertations,topic selection directly influences the dissertation quality.By utilizing the intelligent retrieval system of the National Science and Technology Library(NSTL),we have selected MTCSOL(Master of Teaching Chinese to Speakers of Other Languages)dissertation topics during the last 10 years,divided them into four stages and statistically analyzed the general tendency and crucial problems in these dissertations.MTCSOL dissertations have increased greatly in quantity and become more diversified and multidisciplinary.However,they are still too concentrated on similar topics with narrow scopes;and researches lack of creativity,pioneering spirits and practicality.It concludes that MTCSOL students should expand their research scopes and improve the quality of their dissertations with the historic mission of international Chinese language teaching to support the Belt and Road Initiative and build a harmonious community with shared future for humanity in the new era always in their minds.
基金supported by National Science Foundation of China (No. 62176055)China University S&T Innovation Plan Guided by the Ministry of Education。
文摘In multi-dimensional classification(MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features.In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features.Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension′s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard k NN, weighted k NN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.
