Toxic Spans Detection(TSD) task is defined as highlighting spans that make a text toxic. Many works have been done to classify a given comment or document as toxic or non-toxic. However, none of those proposed models work at the token level. In this
paper, we propose a self-attention-based bidirectional gated recurrent unit(BiGRU) with a multi-embedding representation of the tokens. Our proposed model enriches the representation by a combination of GPT-2, GloVe, and RoBERTa embeddings, which led to promising results. Experimental results show that our proposed approach is very effective in detecting span tokens.
This study aims is to analyze the effect of spatial accuracy of the control points on the
images geometric correction accuracy, and this is done by applying tests on the same
image (IKONOS), where polynomial transformations were applied using sets
of control
points, each with absolute accuracy different from the other. These points were
extrapolated from a 1/1000 topographic map and from a georeferenced MOMS satellite
image with geometric accuracy of 2m and measured by GPS. The study showed that it is
possible to obtain the most accurate geometric correction by using control points with
absolute accuracy close to the spatial resolution of the image. It also showed that the use of
more precise control points would not ameliorate the accuracy of the geometric correction,
because the measurement of these points on the image is limited by its spatial resolution.
This paper presents a new technique to extract the features of a
common case of images of the iris called off-angle iris which
taken for persons identification system. The main problem when
using biological iris measurements to identify the persons is the
difficulty of identifying and extracting features of the iris. This
problem increasing when dealing with off-angle iris and it leading
to decrease system accuracy and increase system rate error.
Information extraction is the task of finding structured information
from unstructured or semi-structured text. It is an important task in
text mining and has been extensively studied in various research
communities including natural language proc
essing, information
retrieval and Web mining. It has a wide range of applications in
domains such as biomedical literature mining and business
intelligence. Two fundamental tasks of information extraction are
named entity recognition and relation extraction. The former refers to
finding names of entities such as people, organizations and
locations. The latter refers to finding the semantic relations between
entities.
في هذا البحث ، تم اقتراح طريقة جديدة منخفضة التكلفة للتعرف على لوحات ترخيص المركبات (LPR) والتي يمكن استخدامها بسهولة في لوحات أخرى.
تم استخدام تقنية تجزئة LP جديدة مع ثلاث مجموعات من نواقل الميزات مع مطابقة القالب لتشكيل الوحدتين الرئيسيتين: وحدة
توطين لوحة الترخيص ووحدة LPR.
تم اختبار هذه الطريقة على أكثر من 238 صورة مركبة مأخوذة من مشاهد مختلفة بخطوط وخلفيات مختلفة من دولتين عربيتين. كانت دقة التجزئة للنظام المنفذ 97.5٪ مع دقة التعرف على 99٪ للصور المشوهة إلى حد ما. يوضح النموذج المقدم أنه على الرغم من التأثير السلبي للظلال والشقوق والأوساخ وفصل الشخصيات ، أظهر النظام معدل نجاح إجمالي بنسبة 92٪ في توطين الألواح و 95٪ لتجزئة اللوحات و 92٪ للتعرف على البلد والمدينة و 99 ٪ لتجزئة الرقم والتمييز.
أدى الجمع بين جميع المعدلات إلى دقة نظام كلية بلغت 93٪. مقارنة بالعديد من أنظمة LPR المتطورة ، يستخدم هذا النظام المطور حديثًا 3 مجموعات تدريب صغيرة تقلل من أوقات تشغيل الحل المقترح إلى أقل من 5 ثوانٍ باستخدام MATLAB R2008A الذي يعمل على Compaq 8510W مع ذاكرة وصول عشوائي (RAM) 4 جيجا. النتائج قابلة للمقارنة ، وفي بعض الحالات تكون أفضل مع ظروف مقيدة مثل مكان الانحراف وحجم اللوحة والإضاءة والخلفية.
There are many Qoranic interpretations that do not fulfill the objective conditions ,obviously
many false ideas had been put into them .
So we have to confront and clarify them for more knowledge.
As many studies did ,but the originated researches
are still relatively few .
This research is basically done to put the out lines to differentiate between authorized and
none authorized ideas in explaining the Holy Qoraan. . ..
