Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Automated Traffic Light Signal Violation Detection System Using Convolutional Neural Network(Springer, 2020) Bordia, B.; Nishanth, N.; Patel, S.; Anand Kumar, M.; Rudra, B.Automated traffic light violation detection system relies on the detection of traffic light color from the video captured with the CCTV camera, detection of the white safety line before the traffic signal and vehicles. Detection of the vehicles crossing traffic signals is generally done with the help of sensors which get triggered when the traffic signal turns red or yellow. Sometimes, these sensors get triggered even when the person crosses the line or some animal crossover or because of some bad weather that gives false results. In this paper, we present a software which will work on image processing and convolutional neural network to detect the traffic signals, vehicles and the white safety line present in front of the traffic signals. We present an efficient way to detect the white safety line in this paper combined with the detection of traffic lights trained on the Bosch dataset and vehicle detection using the TensorFlow object detection SSD model. © 2020, Springer Nature Singapore Pte Ltd.Item Overview of the track on HASOC-offensive Language Identification-DravidianCodeMix(CEUR-WS, 2020) Chakravarthi, B.R.; Anand Kumar, M.; Mccrae, J.P.; Premjith, B.; Padannayil, K.P.; Mandl, T.We present the results and main findings of the HASOC-Offensive Language Identification on code mixed Dravidian languages. The task featured two tasks. Task 1 is about offensive language identification in Malayalam language where the comment were written in both native script and Latin script. Task 2 is about offensive language identification in Tamil and Malayalam languages where the comments were written in Latin script (non-native script). For both the task, given a comment the participants should develop a system to classify the text into offensive or not-offensive. In total 96 participants participated and 12 participants submitted the papers. In this paper, we present the task, data, the results and discuss the system submission and methods used by participants. © 2020 Copyright for this paper by its authors.Item NITK NLP at FinCausal-2020 Task 1 Using BERT and Linear models.(Association for Computational Linguistics (ACL), 2020) LekshmiAmmal, R.L.; Anand Kumar, M.FinCausal-2020 is the shared task which focuses on the causality detection of factual data for financial analysis. The financial data facts don’t provide much explanation on the variability of these data. This paper aims to propose an efficient method to classify the data into one which is having any financial cause or not. Many models were used to classify the data, out of which SVM model gave an F-Score of 0.9435, BERT with specific fine-tuning achieved best results with F-Score of 0.9677. © 2020 FNP-FNS 2020 - 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, Proceedings. All rights reserved.Item Leveraging multimodal behavioral analytics for automated job interview performance assessment and feedback(Association for Computational Linguistics (ACL), 2020) Agrawal, A.; George, R.A.; Ravi, S.S.; Kamath S․, S.; Anand Kumar, M.Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee’s suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Therefore, candidates need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee’s facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results. © 2017 Association for Computational LinguisticsItem Machine learning approach to manage adaptive push notifications for improving user experience(Association for Computing Machinery, 2020) Madhusoodanan, A.; Anand Kumar, M.; Fraser, K.; Yousuf, B.In this modern connected world mobile phone users receive a lot of notifications. Many of the notifications are useful but several cause unwanted distractions and stress. Managing notifications is a challenging task with the large influx of notifications users receive on a daily basis. This paper proposes a machine learning approach for notification management based upon the context of the user and his/her interactions with the mobile device. Since the proposed idea is to generate personalised notifications there is no ground truth data hence performance metrics such as accuracy cannot be used. The proposed solution measures the diversity score, the click through rate score and the enticement score. © 2020 ACM.Item Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German(Association for Computing Machinery, 2020) Mandl, T.; Modha, S.; Anand Kumar, M.; Chakravarthi, B.R.This paper presents the HASOC track and its two parts. HASOC is dedicated to evaluate technology for finding Offensive Language and Hate Speech. HASOC is creating test collections for languages with few resources and English for comparison. The first track within HASOC has continued work from 2019 and provided a testbed of Twitter posts for Hindi, German and English. The second track within HASOC has created test resources for Tamil and Malayalam in native and Latin script. Posts were extracted mainly from Youtube and Twitter. Both tracks have attracted much interest and over 40 research groups have participated as well as described their approaches in papers. In this overview, we present the tasks, the data and the main results. © 2020 ACM.Item Clustering Enhanced Encoder–Decoder Approach to Dimensionality Reduction and Encryption(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Mukesh, B.R.; Madhumitha, N.; Aditya, N.P.; Vivek, S.; Anand Kumar, M.Dimensionality reduction refers to reducing the number of attributes that are being considered, by producing a set of principal variables. It can be divided into feature selection and feature extraction. Dimensionality reduction serves as one of the preliminary challenges in storage management and is useful for effective transmission over the Internet. In this paper, we propose a deep learning approach using encoder–decoder networks for effective (almost-lossless) compression and encryption. The neural network essentially encrypts data into an encoded format which can only be decrypted using the corresponding decoders. Clustering is essential to reduce the variation in the dataset to ensure overfit. Using clustering resulted in a net gain of 1% over the standard encoder architecture over three MNIST datasets. The compression ratio achieved was 24.6:1. The usage of image datasets is for visualization only and the proposed pipeline could be applied for textual and visual data as well. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item NITK_NLP at CheckThat! 2021: Ensemble transformer model for fake news classification(CEUR-WS, 2021) LekshmiAmmal, R.L.; Anand Kumar, M.Social media has become an inevitable part of our life as we are primarily dependent on them to get most of the news around us. However, the amount of false information propagated through it is much higher than the genuine ones, thus becoming a peril to society. In this paper, we have proposed a model for Fake News Classification as a part of CLEF2021 Checkthat! Lab1 shared task, which had Multi-class Fake News Detection and Topical Domain Classification of News Articles. We have used an ensemble model consisting of pre-trained transformer-based models that helped us achieve 4tℎ and 1st positions on the leaderboard of the two tasks. We achieved an F1-score of 0.4483 against a top score of 0.8376 in one task and a score of 0.8813 in another. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Item Classification of Censored Tweets in Chinese Language using XLNet(Association for Computational Linguistics (ACL), 2021) Ahmed, S.S.; Anand Kumar, M.In the growth of today’s world and advanced technology, social media networks play a significant role in impacting human lives. Censorship is the overthrowing of speech, public transmission, or other details that play a vast role in social media. The content may be considered harmful, sensitive, or inconvenient. Authorities like institutes, governments, and other organizations conduct Censorship. This paper has implemented a model that helps classify censored and uncensored tweets as a binary classification. The paper describes submission to the Censorship shared task of the NLP4IF 2021 workshop. We used various transformer-based pre-trained models, and XLNet outputs a better accuracy among all. We fine-tuned the model for better performance and achieved a reasonable accuracy, and calculated other performance metrics. © 2021 Association for Computational Linguistics.Item Findings of the Shared Task on Machine Translation in Dravidian languages(Association for Computational Linguistics (ACL), 2021) Chakravarthi, B.R.; Priyadharshini, R.; Banerjee, S.; Saldanha, R.; Mccrae, J.P.; Anand Kumar, M.; Krishnamurthy, P.; Johnson, M.This paper presents an overview of the shared task on machine translation of Dravidian languages. We presented the shared task results at the EACL 2021 workshop on Speech and Language Technologies for Dravidian Languages. This paper describes the datasets used, the methodology used for the evaluation of participants, and the experiments’ overall results. As a part of this shared task, we organized four sub-tasks corresponding to machine translation of the following language pairs: English to Tamil, English to Malayalam, English to Telugu and Tamil to Telugu which are available at https://competitions.codalab.org/competitions/27650. We provided the participants with training and development datasets to perform experiments, and the results were evaluated on unseen test data. In total, 46 research groups participated in the shared task and 7 experimental runs were submitted for evaluation. We used BLEU scores for assessment of the translations. ©2021 Association for Computational Linguistics