Overview of the HASOC track at FIRE 2020: Hate speech and offensive content identification in Indo-European languages

With the growth of social media, the spread of hate speech is also increasing rapidly. Social media are widely used in many countries. Also Hate Speech is spreading in these countries. This brings a need for multilingual Hate Speech detection algorithms. Much research in this area is dedicated to English at the moment. The HASOC track intends to provide a platform to develop and optimize Hate Speech detection algorithms for Hindi, German and English. The dataset is collected from a Twitter archive and pre-classified by a machine learning system. HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of the best classification algorithms for task A are F1 measures of 0.51, 0.53 and 0.52 for English, Hindi, and German, respectively. For task B, the best classification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 for English, Hindi, and German, respectively. This article presents the tasks and the data development as well as the results. The best performing algorithms were mainly variants of the transformer architecture BERT. However, also other systems were applied with good success

Amperometric hydrogen peroxide and cholesterol biosensors designed by using hierarchical curtailed silver flowers functionalized graphene and enzymes deposits

Novel flower-like silver particles with triangular plates as building block along with functionalized graphene (straggled sheets) and enzymes horseradish peroxidase (HRP) or cholesterol oxidase (ChOx), were obtained on graphite electrode by galvanostatic electrodeposition method. The morphology of the electrodeposits has been characterized using scanning electron microscopy and energy-dispersive analysis of X-ray. The resulting biosensors named Nf/(HRP-f-graphene-Ag)/Gr and Nf/(ChOx-f-graphene-Ag)/Gr were evaluated for electrochemical activity using cyclic voltammetry (CV), differential pulse voltammetry (DPV) and chronoamperometry. Optimization of the interdependent experimental parameters such as pH and temperature were achieved and maintained constant throughout the experiments. An activation energy of 2.5 kJ mol -1 was obtained for Nf/(HRP-f-graphene-Ag)/Gr electrode while Nf/(ChOx-f-graphene-Ag)/Gr showed an activation energy of 2.06 and 3.12 kJ mol-1. Furthermore, the former electrode demonstrated a good linear range of 25 μM to 19.35 mM with rapid response time of 3 s and detection limit of 5 μM for hydrogen peroxide. Similarly, the Nf/(ChOx-f-graphene-Ag)/ Gr electrode revealed a linear range of 0.1-4.5 mM with rapid response time of 3 s and an excellent detection limit of 0.514 mM for cholesterol. Besides this, the Nf/(HRP-f-graphene-Ag)/Gr and Nf/(ChOx-f-graphene-Ag)/Gr electrodes displayed a Michaelis-Menten constant of 0.26 and 0.57 mM, respectively, suggesting high affinity and enzymatic activity. The enhanced performance of biosensors towards detection of substrate and rejection of interferents, provided an evidence for its high anti-interference ability. Additionally the biosensors exhibit long term storage stability and reproducibility with antifouling properties. © 2013 Springer-Verlag Berlin Heidelberg.