The popularity of social networks has rapidly increased over the past few years. Social networks provide many kinds of services and benefits to their users like helping them to communicate, click, view and share contents that reflect their opinions or interests. Detecting important contents defined as the most visited posts and users whom disseminate them can provide some interesting insights from cyberspace user’ s activities. In this paper, a framework for discovering important posts (most popular posts by views count) and influential users is introduced. The proposed framework employed on Telegram instant messaging service in this study but it is also applicable to other social networks such as Instagram and Twitter. This framework continuously works in a real social network analysis system named Zekavat to find daily important posts and influential users. The effectiveness of this framework was shown in experiments. The accuracy achieved in the advertisement detection model is 89%. Text-based clustering part of the framework was tested based on the human factor verification and clustering time is less than linear. Graph creation based on publishing relationships is more effective than mention relationship and in this process influential users can be identified in a precise manner.

IntroductionThe study area is located in Semirom sub zone (between Zagros and Sanandaj-Sirjan zones). Considering the main Zagros zonation, Navai and Mehdi Zadeh Tehrani (1986) used satellite data and tectonic studies, other than the two main structural zone of Zagros and Sanandaj-Sirjan, and introduced another subzone for Semirom. This sub zone, based on stratigraphy and folding, is different than Zagros zone. Therefore, different facies can be formed based on different movements of the independent blocks in the basement. These blocks with differential vertical movements have changed physical and chemical conditions of the sedimentary basins. In the current paper, a detailed microfacies, pale environmental analyses, depositional models and sequence stratigraphic approaches are used to understand the relationship between pale environmental parameters and change of sedimentary facies…

In order to determine the microfacies and sedimentary environment of the Tarbur Formation, one stratigraphic section in 15 Km SW of Semirom was sampled and studied.In the studied area the Tarbur Formation with 804 m thickness, composed of carbonate and terrigenous rocks, which deposited during Maastrichtian. The lower boundary with Amiran Formation is conformable and the upper boundary with Kashkan Formation is marked by an erosional surface.According to the lithological and petrographic characteristics of the Tarbur Formation five lithofacies units were recognized in Semirom area.Field studies and obtained data from microscopic thin sections led to recognition of 7 carbonate facies attributed to lagoon (L1, L2, L3), bar (B1, B2) and open marine (O1, O2) subenvironments and 4 terrigenous facies interpreted as shallow (LSH) to deep marine (Osh) setting and meandring river (Fss, Fsh) system.The Tarbur Formation was formed in a carbonate platform (epicontinental sea) in a homoclinal ramp. presence of rudist debris indicates a photozone assemblage and suggests a tropical condition for deposition of the carbonate sediments.

Face recognition has become a crucial topic in recent decades, which offers important opportunities for applications in security surveillance, human-computer interaction, and forensics. However, it poses challenges, including uncontrolled environments, large datasets, and insufficiency of training data. In this paper, a face recognition system is proposed to iron out the above problems with a new framework based on a hashing function in a three-stage filtering approach. At the first stage, candidate subjects are chosen using the Locality-Sensitive Hashing (LSH) function. We employ a voting system to select candidates via disregarding a large number of dissimilar identities considering their local features. At the second stage, a robust image hashing based on Discrete Cosine Transform (DCT) coefficients is used to further refine the candidate images in terms of global visual information. Finally, the test image is recognized among selected identities using other visual information, resulting in further accuracy gains. Extensive experiments on FERET, AR, and ORL datasets show that the proposed method outperforms with a significant improvement in accuracy over the state-of-the-art methods.