Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as presidential election. However, progress on stance detection has been hampered by the absence of large annotated datasets. In this paper, we present P-STANCE, a large stance detection dataset in the political domain, which contains 21,574 labeled tweets. We provide a detailed description of the newly created dataset and develop deep learning models on it. Our best model achieves a macro-average F1-score of 80.53%, which we improve further by using semi-supervised learning. Moreover, our P-STANCE dataset can facilitate research in the fields of cross-domain stance detection such as cross-target stance detection where a classifier is adapted from a different but related target. We publicly release our dataset and code. 1

No abstract

This paper presents the design and analysis of permanent magnet (PM) thrust bearing made up of three ring pairs for five degrees of freedom of the inner rings (rotor rings). The arrangement pattern of rings in PM bearing is considered in two ways: conventional structure and Halbach structure. The simplified three dimensional (3D) mathematical models employing Coulombian approach and vector method are used to design the bearing. MATLAB codes are written to evaluate the axial force, stiffness and moments in both the structures for five degrees of freedom, thereby the effect of axial, radial and angular displacements of the rotor on the aforementioned characteristics is addressed. The results of the mathematical model are validated by the results of 3D Finite Element Analysis (FEA) and experiments. It is observed that, the conventional structure seems to be more sensitive to the angular displacement, as the percentage decrease in force and stiffness is more with respect to angular displacement than the Halbach structure. The effect of angular displacement of the rotor on the performance of bearing in both the structures is crucial.

No abstract

The number of marine debris is excellent in understanding the diagnosis of debris from all oceans of the world and the identification of the highest levels of waste disposal that is most necessary for the removal of waste. Currently, the standard for floating waste management requires the use of a manta trawl. Techniques that require manta trawls (or similar ground-collection devices) that use the physical removal of marine debris as a first step and then analyze the collected samples as a second step. The need for pre-analysis removal is very costly and requires significant oversight - preventing the safe transfer of marine waste monitoring services to all Earth's marine bodies. Without better monitoring methods and samples, the overall impact of water pollution on the entire environment. This study revealed an unusual flow of activity that used images taken from aquatic debris as roots. Produces quantification of marine plastic or waste incorporated into photographs to perform accurate quantification and body removal. This model is trained in the ImageNet Large Visual Recognition Challenge using the 2012 data and can distinguish between many different classes such as cardboard, glass, metal, paper, and plastic. This program uses the transfer of learning from the existing model and then returns it to separate a new set of images. Workflow involves creating and processing domain-specific information, building an object acquisition model using a deep neural network.