Faculty Publications
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Publications by NITK Faculty
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Item Bot and gender identification from twitter notebook for PAN at CLEF 2019(CEUR-WS ceurws@sunsite.informatik.rwth-aachen.de, 2019) Radarapu, R.; Vishwakarma, Y.; Sai Gopal, A.S.; Anand Kumar, M.The popularity of social media raises a concern about the quality of content over its platforms. The quality of data is important, especially for fair and considerable predictive analysis. If the quality of data is less, it may result in the prediction of wrong circumstances of an event. This causes misleading trending problems and more importantly, the sensitive stock price may fluctuate. The contents available on social media can be corrupted and overflowed by bots. There are a variety of bots available such as Spam Bots, Influence Bots, etc. Our target is to identify such bots on Twitter. Twitter data is mostly used by data analysts for applications related to scientific predictions or opinion analysis. This working note is capitalized on earlier approaches and Machine Learning (ML) approaches used to classify between a bot and human and find the gender further for interesting studies in crime detection etc. By sharing many attributes for user profiles, we have identified the pattern to find out that the given user is a bot or human based on the tweets posted. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CLEF 2019, 9-12 September 2019, Lugano, Switzerland.Item A Novel Approach for Video Captioning Based on Semantic Cross Embedding and Skip-Connection(Springer Science and Business Media Deutschland GmbH, 2021) Radarapu, R.; Bandari, N.; Muthyam, S.; Naik, D.Video Captioning is the task of describing the content of a video in simple natural language. Encoder-Decoder architecture is the most widely used architecture for this task. Recent works exploit the use of 3D Convolutional Neural Networks (CNNs), Transformers or by changing the structure of basic Long Short-Term Memory (LSTM) units used in Encoder-Decoder to improve the performance. In this paper, we propose the use of a sentence vector to improve the performance of the Encoder-Decoder model. This sentence vector acts as an intermediary between the video space and the text space. Thus, it is referred to as semantic cross embedding that bridges the two vector spaces, in this paper. The sentence vector is generated from the video and is used by the Decoder, along with previously generated words to generate a suitable description. We also employ the use of a skip-connection in the Encoder part of the model. Skip-connection is usually employed to tackle the vanishing gradients problem in deep neural networks. However, our experiments show that a two-layer LSTM with a skip-connection performs better than the Bidirectional LSTM, for our model. Also, the use of a sentence vector improves performance considerably. All our experiments are performed on the MSVD dataset. © 2021, Springer Nature Singapore Pte Ltd.Item Video summarization and captioning using dynamic mode decomposition for surveillance(Springer Science and Business Media B.V., 2021) Radarapu, R.; Gopal, A.S.S.; Nh, M.; Anand Kumar, M.Video surveillance has become a major tool in security maintenance. But analyzing in a playback version to detect any motion or any sort of movements might be tedious work because only for a short length of the video there would be any motion. There would be a lot of time wasted in analyzing the video and also it is impossible to always find the accurate frame where the transition has occurred. So there is a need in obtaining a summary video that captures any changes/motion. With the advancements in image processing using OpenCV and deep learning, video summarization is no longer an impossible work. Captions are generated for the summarized videos using an encoder–decoder captioning model. With the help of large, well-labeled video data sets like common objects in context, Microsoft video description, video captioning is a feasible task. Encoder–decoder models are used extensively to extract text from visual features with the arrival of long short term memory (LSTM). Attention mechanism has been widely used on decoder for the work of video captioning. Keyframes are obtained from very long videos using methods like dynamic mode decomposition, an algorithm in fluid dynamics, OpenCV’s absdiff(). We propose these tools for motion detection and video/image captioning for very long videos which are common in video surveillance. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.