Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success is focused only on the top 104 languages in Wikipedia it was trained on. In this paper, we propose a simple but effective approach to extend M-BERT (E-MBERT) so it can benefit any new language, and show that our approach aids languages that are already in M-BERT as well. We perform an extensive set of experiments with Named Entity Recognition (NER) on 27 languages, only 16 of which are in M-BERT, and show an average increase of about 6% F 1 on M-BERT languages and 23% F 1 increase on new languages. We release models and code at 1 .
The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified, 1 stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.
Today there is colossal advancement in virtual age that has achieved the improvement of various technology without trouble using contraptions and procedures specifically with in the side the fields of interchanges and measurements change to an allencompassing distance. The transmission of insights within the state of reports, pictures, voice and so on is presently available to all components of the general public and the contributions are minimal effort to a greater assortment of individuals. A basic factor is insights Compression and recovery and for that to be checked Image Compression and recovery, as pictures shape a greater a piece of measurements being traded over the net through long range informal communication and informing web locales and applications wherever on the planet. Among the entirety of the various sorts of insights pictures and films address the bulkiest measurements. Consequently, need for compacting the photo archives is a basic factor in insights correspondence.
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