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Based on detailed analysis of the inversion tectonics in each of the secondary tectonic units of the East China Sea Shelf Basin (ECSSB), this paper suggests that the Cenozoic positive inversion tectonics in the basin are typically anticlines of various shapes associated with thrusts. Four stages of tectonic inversions are recognized in the basin related to the Oujiang Movement (T80), the Yuquan Movement (T30), the Huagang Movement (T20) and the Longjing Movement (T12), and these four inversion stages are temporally and spatially distinct. Temporally, the tectonic inversion in the West Depression Group tended to weaken, relatively, from the Oujiang Movement (T80) to the Huagang Movement (T20), while the Longjing Movement (T12) does not show an obvious inversion signature. Spatially, the Longjing Movement tectonic inversion in the Xihu and Diaobei sags of the East Depression Group shows a pronounced reduction in intensity from north to south. The tectonic inversion in the East Depression Group tended to intensify, relatively, from the Yuquan Movement (T30), through the Huagang Movement (T20) to the Longjing Movement (T12). The intensity distribution, migration and evolution of Cenozoic inversions in the ECSSB are local responses to the synthetic effect of the convergence and subduction rates, and direction changes between the Pacific and Eurasian plates and/or between the Indian and Eurasian plates. In particular, the eastward migration of positive inversion tectonics in the ECSSB is closely and successively related to the formation of the East Depression Group and the opening of the Okinawa Trough. Copyright © 2016 John Wiley & Sons, Ltd.

The East China Sea Shelf Basin (ECSSB) is located on the southeastern continental margin of the Eurasian Plate. The basement of the ECSSB is an extension of the Cathaysian Block as well as an important part of the West Pacific or East Asian Continental Margin tectonic domain. By the analysis of global plate tectonic evolution, the ECSSB is situated in the eastern part of the West Pacific triangle region, as a huge convergence zone between the Indian‐Australian, Pacific and Eurasian plates, as well as a global convergence centre. The ECSSB is closely related to the evolution of the Tethyan and West Pacific tectonic domains. Overall, the ECSSB is a pull‐apart basin under the transtensional tectonic setting of a continental margin, which is contributed to by the combination of subduction retreat and back‐arc spreading between the Eurasian Plate and the Pacific Plate, and the far‐field effect of the collision and extrusion between the Indian‐Australian and Eurasian plates, and deep mantle dynamics. The formation mechanism of the ECSSB is under passive extension. The eastward mantle flow and the asthenospheric upwelling in the deep Earth are the main driving forces of the basin jumping and eastward tectonic migration. Copyright © 2016 John Wiley & Sons, Ltd.

Social networks can be analyzed to discover important social issues; however, it will cause privacy disclosure in the process. The edge weights play an important role in social graphs, which are associated with sensitive information (e.g., the price of commercial trade). In the paper, we propose the MB-CI (Merging Barrels and Consistency Inference) strategy to protect weighted social graphs. By viewing the edge-weight sequence as an unattributed histogram, differential privacy for edge weights can be implemented based on the histogram. Considering that some edges have the same weight in a social network, we merge the barrels with the same count into one group to reduce the noise required. Moreover,k-indistinguishability between groups is proposed to fulfill differential privacy not to be violated, because simple merging operation may disclose some information by the magnitude of noise itself. For keeping most of the shortest paths unchanged, we do consistency inference according to original order of the sequence as an important postprocessing step. Experimental results show that the proposed approach effectively improved the accuracy and utility of the released data.

Sentiment analysis is widely studied to extract opinions from user generated content (UGC), and various methods have been proposed in recent literature. However, these methods are likely to introduce sentiment bias, and the classification results tend to be positive or negative, especially for the lexicon-based sentiment classification methods. The existence of sentiment bias leads to poor performance of sentiment analysis. To deal with this problem, we propose a novel sentiment bias processing strategy which can be applied to the lexicon-based sentiment analysis method. Weight and threshold parameters learned from a small training set are introduced into the lexicon-based sentiment scoring formula, and then the formula is used to classify the reviews. In this paper, a completed sentiment classification framework is proposed. SentiWordNet (SWN) is used as the experimental sentiment lexicon, and review data of four products collected from Amazon are used as the experimental datasets. Experimental results show that the bias processing strategy reduces polarity bias rate (PBR) and improves performance of the lexicon-based sentiment analysis method.

Analyzing massive user-generated microblogs is very crucial in many fields, attracting many researchers to study. However, it is very challenging to process such noisy and short microblogs. Most prior works only use texts to identify sentiment polarity and assume that microblogs are independent and identically distributed, which ignore microblogs are networked data. Therefore, their performance is not usually satisfactory. Inspired by two sociological theories (sentimental consistency and emotional contagion), in this paper, we propose a new method combining social context and topic context to analyze microblog sentiment. In particular, different from previous work using direct user relations, we introduce structure similarity context into social contexts and propose a method to measure structure similarity. In addition, we also introduce topic context to model the semantic relations between microblogs. Social context and topic context are combined by the Laplacian matrix of the graph built by these contexts and Laplacian regularization are added into the microblog sentiment analysis model. Experimental results on two real Twitter datasets demonstrate that our proposed model can outperform baseline methods consistently and significantly.

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In the existing centralized location services system structure, the server is easily attracted and be the communication bottleneck. It caused the disclosure of users’ location. For this, we presented a new distributed collaborative recommendation strategy that is based on the distributed system. In this strategy, each node establishes profiles of their own location information. When requests for location services appear, the user can obtain the corresponding location services according to the recommendation of the neighboring users’ location information profiles. If no suitable recommended location service results are obtained, then the user can send a service request to the server according to the construction of a k-anonymous data set with a centroid position of the neighbors. In this strategy, we designed a new model of distributed collaborative recommendation location service based on the users’ location information profiles and used generalization and encryption to ensure the safety of the user’s location information privacy. Finally, we used the real location data set to make theoretical and experimental analysis. And the results show that the strategy proposed in this paper is capable of reducing the frequency of access to the location server, providing better location services and protecting better the user’s location privacy.