Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation

The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model. Its improved translation performance on low resource languages hints at potential cross-lingual transfer capability for downstream tasks. In this paper, we evaluate the cross-lingual effectiveness of representations from the encoder of a massively multilingual NMT model on 5 downstream classification and sequence labeling tasks covering a diverse set of over 50 languages. We compare against a strong baseline, multilingual BERT (mBERT), in different cross-lingual transfer learning scenarios and show gains in zero-shot transfer in 4 out of these 5 tasks

Molecular cloning and expression analysis of Human TLK1B-Kinase Domain construct in Escherichia coli

by Shashank Raman, Siddhant Bhoir and Sivapriya Kirubakara

An animation-and-chirplet based approach to intruder classification using PIR sensing

The development of a Passive Infra-Red (PIR) sensing based intrusion detection system is presented here having the ability to reject vegetative clutter and distinguish between human and animal intrusions. This has potential application to reducing human-animal conflicts in the vicinity of a wildlife park. The system takes on the form of a sensor-tower platform (STP) and was developed in-house. It employs a sensor array that endows the platform with a spatial-resolution capability. Given the difficulty of collecting data involving animal motion, a simulation tool was created with the aid of Blender and OpenGL software that is capable of quickly generating streams of human and animal-intrusion data. The generated data was then examined to identify a suitable collection of features that are useful in classification. The features selected corresponded to parameters that model the received signal as the superimposition of a fixed number of chirplets, an energy signature and a cross-correlation parameter. The resultant feature vector was then passed on to a Support Vector Machine (SVM) for classification. This approach to classification was validated by making use of real-world data collected by the STP which showed both STP design as well as classification technique employed to be quite effective. The average classification accuracy with both real and simulated data was in excess of 94%