Rationale-Guided Few-Shot Classification to Detect Abusive Language

Abusive language is a concerning problem in online social media. Past research on detecting abusive language covers different platforms, languages, demographies, etc. However, models trained using these datasets do not perform well in cross-domain evaluation settings. To overcome this, a common strategy is to use a few samples from the target domain to train models to get better performance in that domain (cross-domain few-shot training). However, this might cause the models to overfit the artefacts of those samples. A compelling solution could be to guide the models toward rationales, i.e., spans of text that justify the text's label. This method has been found to improve model performance in the in-domain setting across various NLP tasks. In this paper, we propose RGFS (Rationale-Guided Few-Shot Classification) for abusive language detection. We first build a multitask learning setup to jointly learn rationales, targets, and labels, and find a significant improvement of 6% macro F1 on the rationale detection task over training solely rationale classifiers. We introduce two rationale-integrated BERT-based architectures (the RGFS models) and evaluate our systems over five different abusive language datasets, finding that in the few-shot classification setting, RGFS-based models outperform baseline models by about 7% in macro F1 scores and perform competitively to models finetuned on other source domains. Furthermore, RGFS-based models outperform LIME/SHAP-based approaches in terms of plausibility and are close in performance in terms of faithfulness.Comment: 11 pages, 14 tables, 3 figures, The code repository is https://github.com/punyajoy/RGFS_ECA

Review on Analysis of Low Pass Finite Impulse Response Filter Using Window functions

Finite impulse response (FIR) filter plays a pivotal role in digital signal processing, multirate signal processing and speech analysis in the communication field. Implementation of the FIR filter employing MATLAB simulation tool can ease the computational complexity and enhance the filter performance to a greater extent. This review paper is based on the analysis of low pass FIR (Finite Impulse Response) filter using different windowing techniques. Rectangular window, Hamming window and Kaiser windows are basically considered for our simulation work . MATLAB programming tools are used to characterize the magnitude and phase response of low pass FIR filter and then analyze the input and output signal in frequency domain as well as time domain for the three window functions under consideration

A successful pregnancy occurred after isolating the offending antibody(s) and choosing appropriate sperm donor of similar phenotype

Sensitization against Rh(D )is the most common cause of haemolytic disease of fetus and newborn (HDFN). Now a days, a widespread use of antenatal and postnatal Rh immunoglobulin has resulted in marked decrease in prevalence of Rh(D) alloimmunization. Fetal loss due to other red cell antigens gain importance as there are no prophylactic immunoglobulin are available. Here, we present a case of primary infertility associated with non Rh(D) alloimmunization which was detected in a 30 year old housewife during her ongoing infertility treatment. The antibody identification workup showed patient is having multiple alloantibodies , probable anti-c, and anti-Fya. The extended phenotype shows that the husband is mismatched with the wife's phenotype in “c” and Fya. Also the probable antibody in the mother's serum are anti-c and anti-Fya which are noted to cause HDFN as per literature