In recent years, large amounts of uncertain data are emerged with the widespread employment of the new technologies, such as wireless sensor networks, RFID and privacy protection. According to the features of the unce...
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Access control is essential to computer security, especially in an open, distributed, networked communication environment. Modern access control model such as UCON aims at accommodating general requirements. Tradition...
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Access control is essential to computer security, especially in an open, distributed, networked communication environment. Modern access control model such as UCON aims at accommodating general requirements. Traditional one such as BLP focuses on specific properties, e.g. confidentiality. Both of these two realms have their limitations. Taking UCON and BLP as case study, this paper explores mutual support of modern and traditional access control models. It investigates BLP's adaptable characteristic in the UCON perspective. First, it constructs properties in the UCON language to manifest the BLP adaptability, which shows that the BLP adaptability can be ensured to function correctly by the UCON framework. Further, it proposes a formal specification for the BLP adaptability under the UCON framework with the Temporal Logic of Actions, which demonstrates that the BLP adaptability is in good consistency with the UCON model. The significance of the paper is twofold. On the one hand, it exhibits that adaptable quality of the traditional BLP model may be ensured theoretically by the philosophy of modern access control. On the other hand, it enriches the real sense of modern access control models by strengthening the power of traditional access control models.
Automatic analysis of sentiments expressed in large scale online reviews is very important for intelligent business applications. Sentiment classification is the most popular task of sentiment analysis, which is more ...
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There are hundreds or thousands of web data sources providing data of relevance to a particular domain on the Web, so how to find a suitable set of sources quickly to integrate from a number of sources is becoming mor...
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On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, ...
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On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, and often redundant lawyer information, we propose a new information integration method using multi-source information cross-validation. Based on the unified integrated data, a lawyer recommendation system is built. Several key technologies are presented and evaluated, including the multi-source information acquisition and validation. Experimental results indicate the key techniques used in the system are effective for lawyer information integration and recommendation.
Big data analysis is a main challenge we meet recently. Cloud computing is attracting more and more big data analysis applications, due to its well scalability and fault-tolerance. Some aggregation functions, like SUM...
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Big data analysis is a main challenge we meet recently. Cloud computing is attracting more and more big data analysis applications, due to its well scalability and fault-tolerance. Some aggregation functions, like SUM, can be computed in parallel, because they satisfy distributive law of addition. Unfortunately, some of statistical functions are not naturally parallelizable. That means they do not satisfy distributive law of addition. In this paper, we focus on percentile computing problem. We proposed an iterative-style prediction-based parallel algorithm in a distributed system. Prediction is done through a sampling technique. Experiment results verify the efficiency of our algorithm.
Recommender systems have been accepted as a vital application on the web by offering product advice or information that users might be interested in. Despite its success, similarity-based collaborative filtering suffe...
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