Social media has become a preferential place for sharing information. However, some users may create multiple accounts and manipulate them to deceive legitimate users. Most previous studies utilize verbal or behavior features based methods to solve this problem, but they are only designed for some particular platforms, leading to low universalness.In this paper, to support multiple platforms, we construct interaction tree for each account based on their social interactions which is common characteristic of social platforms. Then we propose a new method to calculate the social interaction entropy of each account and detect the accounts which are controlled by the same user. Experimental results on two real-world datasets show that the method has robust superiority over state-of-the-art methods.

Digital photography visiometer system (DPVS), that is video visibility instrument, is a new visibility instrument. It adopts the advanced digital photographic technology, and entirely imitates the principle of manual visibility observation with human eye. It carries on the manual visibility observation method, and overcomes the defects of the optical visibility instrument. This paper introduces the basic principles of DPVS, and shows the comparative results and observation differences between DPVS, forward scattering visibility instrument (Vaisala FD12) and manual visibility observation in complicated weather conditions. The comparative results show that DPVS, Vaisala FD12, and manual observation share the same change trend. However, in rainy days and other complicated weather conditions, the observation results of DPVS differ from that of the FD12 to some extent, while are much closer to that of manual observation. In the end of the paper, a concrete theoretical analytic conclusion on observation differences is provided.

Taklimakan Desert, located in southwest Xinjiang Uyghur Autonomous Region, is one of the predominant dust origin in China. Dust is one of the main types of atmospheric aerosol in this region. Emerging remote sensing imagery from geostationary meteorological satellite undeniably becomes an ideal mean for monitoring large regional distribution and intensity of dust storms. Among them, Indian National Satellite (INSAT 3A) is suitable for dust aerosol retrieval and dust storm detection for Xinjiang region as it can provide high spatiotemporal earth observation with a Charge Couple Device (CCD) camera. The camera contains three bands with a spatial resolution of 1km, which are very applicable for AOD retrieval. However, there is still no mature algorithm for the retrieval of AOD over land using INSAT 3A data, though some work have been done with other geostationary satellites [1-4].

Breast cancer is one of the leading causes of female mortality. Since the causes are unknown, breast cancer cannot be prevented. Microcalcifications and masses are the earliest signs of breast carcinomas and their detection is one of the key issues for breast cancer control. It is difficult for radiologists to provide accurate and uniform evaluations of the enormous number of mammograms generated in widespread screening. Computer-aided detection of microcalcifications and masses is an important and challenging task. The paper presents a novel approach for detecting microcalcification clusters (MCCs). First, mammograms are normalized. Then, fuzzy set theory and the fuzzy entropy principle are used to fuzzify the mammograms and the fuzzified images are enhanced. Finally, scale-space and Laplacian-of-a-Gaussian filter techniques are used to determine the sizes and locations of microcalcifications. A free-response operating characteristic (FROC) curve is used to evaluate the performance. The major advantage of the proposed system is its ability to detect microcalcifications even in mammograms of very dense breasts. A data set of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications was studied. Microcalcifications can be accurately and efficiently detected using the proposed approach.