Similarity-aware deep attentive model for clickbait detection

© Springer Nature Switzerland AG 2019. Clickbait is a type of web content advertisements designed to entice readers into clicking accompanying links. Usually, such links will lead to articles that are either misleading or non-informative, making the detection of clickbait essential for our daily lives. Automated clickbait detection is a relatively new research topic. Most recent work handles the clickbait detection problem with deep learning approaches to extract features from the meta-data of content. However, little attention has been paid to the relationship between the misleading titles and the target content, which we found to be an important clue for enhancing clickbait detection. In this work, we propose a deep similarity-aware attentive model to capture and represent such similarities with better expressiveness. In particular, we present the ways of either using similarity only or integrating it with other available quality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods

Identification of potential transcription factors that enhance human iPSC generation

Although many factors have been identified and used to enhance the iPSC reprogramming process, its efficiency remains quite low. In addition, reprogramming efficacy has been evidenced to be affected by disease mutations that are present in patient samples. In this study, using RNA-seq platform we have identified and validated the differential gene expression of five transcription factors (TFs) (GBX2, NANOGP8, SP8, PEG3, and ZIC1) that were associated with a remarkable increase in the number of iPSC colonies generated from a patient with Parkinson's disease. We have applied different bioinformatics tools (Gene ontology, protein–protein interaction, and signaling pathways analyses) to investigate the possible roles of these TFs in pluripotency and developmental process. Interestingly, GBX2, NANOGP8, SP8, PEG3, and ZIC1 were found to play a role in maintaining pluripotency, regulating self-renewal stages, and interacting with other factors that are involved in pluripotency regulation including OCT4, SOX2, NANOG, and KLF4. Therefore, the TFs identified in this study could be used as additional transcription factors that enhance reprogramming efficiency to boost iPSC generation technology.This study was supported by QBRI internal grant (QB16) and the Qatar University Student grant (QUST-2-CMED-2019-1)

Explaining the Components of Improving the Physical and Functional Quality of the Culture and ARTS CENTER to Meet Human Social Needs and Promote Social Interactions

Objective: The purpose of this study is to explain the components of improving the physical and functional quality of the Culture and Arts Center to meet human social needs and promote social interactions. Objective: The purpose of this study is to explain the components of improving the physical and functional quality of the Culture and Arts Center to meet human social needs and promote social interactions. Research method: The present research method is quantitative and the researcher-made questionnaire is the main tool for collecting information in this research. The main structures of the questionnaire are derived from theoretical foundations. For this purpose, 384 questionnaires were distributed among the statistical sample. Descriptive and inferential statistics were used to analyze the findings in SPSS software as well as Smart PLS. Findings: The findings of this study show that the problem of promoting social interactions and meeting human social needs in Qazvin Culture and Art Center depends on a series of interventions in the functional, aesthetic, identity, physical and environmental areas of space

ECG denoising and compression by sparse 2D separable transform with overcomplete mixed dictionaries

International audienceIn this paper, an algorithm for ECG denoising and compression based on a sparse separable 2-dimensional transform for both complete and overcomplete dictionaries is studied. For overcomplete dictionary we have used the combination of two complete dictionaries. The experimental results obtained by the algorithm for both complete and overcomplete transforms are compared to soft thresholding (for denoising) and wavelet db9/7 (for compression). It is experimentally shown that the algorithm outperforms soft thresholding for about 4dB or more and also outperforms Extended Kalman Smoother filtering for about 2dB in higher input SNRs. The idea of the algorithm is also studied for ECG compression, however it does not result in better compression ratios than wavelet compression