Naïve Bayes Model for Analysis of Voting Rate Failure in Election System

These days several exercises are striking the engineering perspectives on non-engineering domain and it is major challenging too for the engineering community. One such non-engineering domain is prediction of the results in election system. So many factors like literacy, rainfall, population and so on may affect the results in election systems. Certainly there are some parameters which are relatively dependent on each other. In this connection, this paper tried to design a probabilistic model to analyze failure voting rates in election system by involving one of the strongest baye’s model called as Naïve Bayes Model

Social media crowdsourcing for rapid damage assessment following sudden-onset earthquakes

Rapid appraisal of damages related to hazard events is important to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections, and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, principally Twitter postings, is investigated to make approximate but rapid early assessments of damages following earthquake disasters. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a text-based damage assessment scale for earthquake damages and then develops a text classification model for rapid damage assessment. The 2019 Ridgecrest, California earthquake sequence is mainly investigated as the case study. Results reveal that Twitter users rapidly responded to this sudden-onset event, and the damage estimation shows temporal and spatial characteristics. The generalization ability of the model is validated through the investigation of damage assessment for another five earthquake events. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks

Challenges and perspectives of hate speech research

This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding