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Register a new usermining GPS data to determine interesting locations
استكشاف الاماكن المثيرة للاهتمام من خلال تحليل بيانات الGPS
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Damascus University حلقة بحث
Publication date
2019
fields
Informatics Engineering
and research's language is
العربية
Authors
jihad( طالب )
Created by
Jihad Abdullah
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. تعد إدارة وفهم بيانات المواقع المجمعة قضيتين مهمتين لهذه التطبيقات. تقدم هذه الورقة طرقًا لاستخراج مواقع مثيرة للاهتمام من البيانات المكانية والزمانية. الهدف من هذه الورقة هو تجميع آثار GPS الفردية للحصول على رؤى عن الأماكن المثيرة للاهتمام. يمكن الحصول على هذه المواقع المهمة من خلال معالجة البيانات من الأجهزة التي تعمل بنظام GPS للمستخدمين الذين يعيشون في منطقة جغرافية معينة.
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References used
https://www.researchgate.net/publication/254004719_Mining_GPS_data_to_determine_interesting_locations
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The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been propos
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في هذا البحث تٌستخدم تقنيات استكشاف الصور كالتجميع و قواعد الاستكشاف لاستكشاف المعرفة من الصورة و أيضاً يستخدم دمج الميزات متعددة الوسائط مثل البصرية و النصية.
Large pretrained language models using the transformer neural network architecture are becoming a dominant methodology for many natural language processing tasks, such as question answering, text classification, word sense disambiguation, text comple
tion and machine translation. Commonly comprising hundreds of millions of parameters, these models offer state-of-the-art performance, but at the expense of interpretability. The attention mechanism is the main component of transformer networks. We present AttViz, a method for exploration of self-attention in transformer networks, which can help in explanation and debugging of the trained models by showing associations between text tokens in an input sequence. We show that existing deep learning pipelines can be explored with AttViz, which offers novel visualizations of the attention heads and their aggregations. We implemented the proposed methods in an online toolkit and an offline library. Using examples from news analysis, we demonstrate how AttViz can be used to inspect and potentially better understand what a model has learned.
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