Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limite
d. In this work, we take this research direction to the opposite and study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.
In recent years pre-trained language models (PLM) such as BERT have proven to be very effective in diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question Answering. Trained with massive general-domain text, these pre-traine
d language models capture rich syntactic, semantic and discourse information in the text. However, due to the differences between general and specific domain text (e.g., Wikipedia versus clinic notes), these models may not be ideal for domain-specific tasks (e.g., extracting clinical relations). Furthermore, it may require additional medical knowledge to understand clinical text properly. To solve these issues, in this research, we conduct a comprehensive examination of different techniques to add medical knowledge into a pre-trained BERT model for clinical relation extraction. Our best model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset.
In recent years, few-shot models have been applied successfully to a variety of NLP tasks. Han et al. (2018) introduced a few-shot learning framework for relation classification, and since then, several models have surpassed human performance on this
task, leading to the impression that few-shot relation classification is solved. In this paper we take a deeper look at the efficacy of strong few-shot classification models in the more common relation extraction setting, and show that typical few-shot evaluation metrics obscure a wide variability in performance across relations. In particular, we find that state of the art few-shot relation classification models overly rely on entity type information, and propose modifications to the training routine to encourage models to better discriminate between relations involving similar entity types.
Since Electroencephalogram (EEG) signals have very small magnitude, it's very hard
to capture these signals without having noise (produced by surrounding artifacts) affect the
real EEG signals, so it is necessary to use Filters to remove noise.
Th
is work proposes a design of an electronic circuit using a microcontroller, an
instrumentation amplifier and an operational amplifier able to capture EEG signals, convert
the captured signals from analog state to digital one and send the converted signal (digital
signal) to a group of three digital filters.
This paper gives a design of three digital elliptic filters ready to be used in real time
filtering of EEG signals (which preliminary represents the condition of the brain) making
the software part which complements the hardware part in the EEG signals capturing
system.
Finally we are going to show the way of using the designed electronic circuit with
the three designed digital filters, demonstrate and discuss the results of this work.
We have used Eagle 6.6 software to design and draw the circuit, CodeVision AVR
3.12 software to write the program downloaded on the microcontroller, Mathworks
MATLAB 2014a software to design the three digital filters and Mathworks MATLAB
2014a Simulink tool to make the appropriate experiments and get the results.
يلْجأُ عادة لتحسين أداء نظم الزمن الحقيقي استخدام ما يسمى بتشارك الحمولة، و ذلك لرفـع
نسبة ضمان المهام المقدمة للشبكة المكونة من عدة عقد تعمل بتعاون و انسـجام مدروسـين.
تقسم المهام الواردة للعقدة إلى: ١ – مهام مضمونة: يمكن ضمان تنفيذ هذه المهام خلا
ل زمنها
الحرج على العقدة نفسها. ٢ – مهام غير مضمونة: لا يمكن ضمان تنفيذ هذه المهام خلال
زمنها الحرج على العقدة نفسها. لتنسيق عمل مجموعة عقد الشبكة يتم توصيل جميع هذه العقد
إلى المشرف العمومي، الذي يقوم بتحصيل معلومات حالة العقد بشكل دوري و دائم.
يقوم المشرف العمومي بالإشراف على نقل المهام غير المضمونة إلى عقد أخرى قادرة على
ضمان تنفيذها، مما يؤدي إلى رفع نسبة ضمان المهام على الشبكة و هي البارامتر الأهم فـي
الدراسة.
