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Register a new uservINUS VOICE ASSISTANT FOR IoT PLATFORM THINGER.IO
المساعد الصوتي VINUS لمنصة إنترنت اﻷشياء THINGER.IO
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Tishreen University مشروع تخرج
Publication date
2019
fields
Communication Engineering
and research's language is
العربية
Created by
Ali Ibrahim
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يلعب إنترنت الأشياء دوراً أساسياً في حياتنا اليوم من إدارة تدفق الركاب في المطارات إلى المنازل والمدن الذكيّة إلى الاعتناء بالمسنين، فهو يهدف إلى تحسين الحياة في جميع المجالات، وقدم ساهم التطور التكنولوجي الذي نشهده إلى انتشاره بشكل واسع في العديد من القطاعات. المنصّات (Platforms) هي الجزء البرمجي الداعم الذي يربط كل شيء ضمن نظام إنترنت الأشياء. حيث تسهل المنصّة الاتصالات، تدفق البيانات، إدارة الأجهزة، ووظيفة التطبيقات. تعتبر منصة Thinger.io من منصات إنترنت الأشياء السهلة الاستخدام والتي تقدم خدمات متنوعة للمستخدمين، حيث تتيح المنصة اتصالية مختلف أنواع الأجهزة والشرائح. فكانت الفكرة إنشاء مساعد شخصي يعمل بالأوامر الصوتية للتحكم بالأجهزة المتصلة بمنصة Thinger.io عن بعد عبر الإنترنت في الزمن الحقيقي، والهدف من إضافة هذه الإمكانية إلى المنصة جعل التعامل معها أكثر بساطة لإتاحة الفرصة لأي شخص أياً كان عمره أو خبرته استخدامها لتسهيل حياته بالطريقة التي يختارها، حيث أنّ المساعد Vinus -كما أطلقنا عليه- يتمتع بالمرونة والموثوقية وقابلية العمل مع أي تطبيق كان.
Internet of Things plays a key role in our lives today from managing airport passenger traffic, smart houses and cities to taking care of the elderly, it aims to improve life in all areas, and the technological development we are seeing has contributed to a wide spread in many domains. Platforms are the supporting software that connects everything within the Internet of things system. The platform facilitates communication, data flow, device management, and application functionality. The Thinger.io platform is an easy-to-use platform that provides a variety of services to users. The platform enables communication of various types of devices and chipsets. The idea was to create a personal assistant that works via voice commands to control devices connected to the Thinger.io platform remotely over the Internet in real time, The aim of adding this possibility to the platform is to make it simpler to allow anyone of any age or experience to use it to facilitate their life the way they choose, whereas The Vinus Assistant - as we called it - has the flexibility, reliability and functionality to deal with any application.
References used
Gubbi, J., Buyya, R., Marusic, S., M., P.: Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems 29(7), 1645–1660 (2013)
Borgia, E.: The Internet of Things vision: Key features, applications and open issues. Computer Communications 54, 1–31 (2014)
[3] Thinger.io official documentation. 10Feb. 2019. http://docs.thinger.io/api/ http://docs.thinger.io/console/
[4] Digital ocean tutorials. 12Mar. 2019. https://www.digitalocean.com/community/tutorials/how-to-create-a-self-signed-ssl-certificate-for-apache-in-ubuntu-16-04
[5] Mozilla developers’ community. 5Apr. 2019. https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API/Using_the_Web_Speech_API#Speech_recognition
[6] Mozilla developers’ community. 14Apr. 2019. https://developer.mozilla.org/en-US/docs/Web/API/SpeechSynthesisUtterance
[7] NodeMcu official documentation. 27Apr. 2019. https://nodemcu.readthedocs.io/en/master/
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إنترنت الأشياء أضحى أكثر من شبكات سيارات أو ماكينات صنع القهو بل أصبح شبكات كبير
مترابطة مع بعضها البعض وإن أمن هذه الشبكات يؤثر بشكل مباشر أو غير مباشر على أدائها،
فهناك كثير من الناس والشركات التي تتعامل مع إنترنت الأشياء لا تعلم ماذا يحدث على
الشبكة
من عمليات تنصت وسرقة بيانات وغيرها. مع تأمين شبكات إنترنت الأشياء أصبحت هذه
القضايا تحدياا كبيراا، وبما أننا غير قادرين على إيقاف شبكات إنترنت الأشياء من النمو فإن
موضوع الأمان أصبح من الضروري جداا البحث فيه واقتراح استراتيجيات أمنية لحماية الشبكات
من الثغرات الأمنية
Software Defined Radios (SDR) are highly configurable hardware and
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example: channel estimation, equalisation, forward error correction,
adaptive antennas, rake receiving, modulation, advanced compression
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One of the first building blocks to create a voice assistant relates to the task of tagging entities or attributes in user queries. This can be particularly challenging when entities are in the tenth of millions, as is the case of e.g. music catalogs
. Training slot tagging models at an industrial scale requires large quantities of accurately labeled user queries, which are often hard and costly to gather. On the other hand, voice assistants typically collect plenty of unlabeled queries that often remain unexploited. This paper presents a weakly-supervised methodology to label large amounts of voice query logs, enhanced with a manual filtering step. Our experimental evaluations show that slot tagging models trained on weakly-supervised data outperform models trained on hand-annotated or synthetic data, at a lower cost. Further, manual filtering of weakly-supervised data leads to a very significant reduction in Sentence Error Rate, while allowing us to drastically reduce human curation efforts from weeks to hours, with respect to hand-annotation of queries. The method is applied to successfully bootstrap a slot tagging system for a major music streaming service that currently serves several tens of thousands of daily voice queries.
Reliable tagging of Temporal Expressions (TEs, e.g., Book a table at L'Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs). However, there is a dearth of resources and systems for the VA domain, since publicly-available te
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Information extraction is a core technology of natural language processing, which extracts some meaningful phrases/clauses from unstructured or semistructured content to a particular topic. It can be said to be the core technology of many language te
chnologies and applications. This paper introduces AI Clerk Platform, which aims to accelerate and improve the entire process and convenience of the development of information extraction tools. AI Clerk Platform provides a friendly and intuitive visualized manual labeling interface, sets suitable semantic label in need, and implements, distributes and controls manual labeling tasks, so that users can complete customized information extraction models without programming and view the automatically predict results of models by three method. AI Clerk Platform further assists in the development of other natural language processing technologies and the derivation of application services.
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