This document provides an introduction to microservices, including:
- Microservices are an organizational tool for scaling services independently. Monoliths work well too but do not scale as easily.
- Common communication methods between microservices include RPC and message-driven interactions using protocols like GRPC and Protobuf.
- Logging and metrics are important for microservices. The REK stack of Rsyslog, Elasticsearch and Kibana is recommended for logging. Prometheus is given as an example for metrics.
- Containerization using Docker is ideal for deploying and managing microservices. Kubernetes is a popular orchestration platform for containers both in the cloud and on-premise.
This document provides information about an educational coding robot called CodeyBot. It discusses CodeyBot's features such as its sensors, platform, display, connectivity, memory storage, programming language, power, and system requirements. The document also contains a quote from Steve Jobs about the importance of learning to program computers as it teaches people how to think.
Text Mining with R -- an Analysis of Twitter DataYanchang Zhao
This document discusses analyzing Twitter data using text mining techniques in R. It outlines extracting tweets from Twitter and cleaning the text by removing punctuation, numbers, URLs, and stopwords. It then analyzes the cleaned text by finding frequent words, word associations, and creating a word cloud visualization. It performs text clustering on the tweets using hierarchical and k-means clustering. Finally, it models topics in the tweets using partitioning around medoids clustering. The overall goal is to demonstrate various text mining and natural language processing techniques for analyzing Twitter data in R.
This document provides an overview of the architecture and components used in an AI assistant called Otto. It describes the base software including Docker, Node.js, and MongoDB. It also outlines the natural language processing, speech recognition, text-to-speech and other modules. It details the client and messaging bot architectures and includes information on I/O drivers, actions, fulfillments and the overall workflow. Finally, it lists some example intents and provides guidance on developing new actions and the hardware used including the Raspberry Pi, Re-Speaker mic HAT and power components.
This document provides an overview of machine translation and the Moses machine translation toolkit. It defines machine translation and statistical machine translation. It describes the major components of Moses, including GIZA++ for word alignment, SRILM for language modeling, and the Moses decoder. It explains how Moses uses phrase-based translation and tuning to produce translations. It also discusses how to set up and use a Moses server for translating webpages.
Conversational AI with Rasa - PyData WorkshopTom Bocklisch
Workshop building a simple chatbot with Rasa NLU and Core. Additional resources can be found in the repository https://github.com/tmbo/rasa-demo-pydata18/edit/master/README.md
Inside view of "Clova Inside" - Natural language understanding system to supp...LINE Corporation
Toshinori Sato (overlast)
LINE / VA Development Team
AI assistant with VoiceUI that enables intuitive device operation through natural actions, such as human speech and emotion, is an example that requires multiple and simultaneous applications of AI technologies, including natural language processing, voice recognition, speech synthesis, image processing, and information retrieval. LINE Clova is a platform to add AI assistant features to various smart devices in order to repond to user commands through the use of natural language, such as voice and text. This session is a candid talk on the development, operation and other related topics of natural language understanding (NLU) technology that is integral to realize VoiceUI on Clova. Topics include, NLU system overview on the current Clova platform, issues and solutions of developing AI assistant for smart devices, co-existing with skills developed and operated by third parties, LINE’s views on consistent VoiceUI design, content development case studies, and upcoming topics that LINE must address. While still at the dawn of AI assistant and smart devices, no one is certain what the flagship skill or application would look like. With this, NLU system for smart devices must continue to support increasing device types and features, as well as growing number of contents, services, and their updates. In addition, issues that become evident by using human speech and text as queries must also be resolved. This session is intended to convey the fascination of working with AI Assistant technology through sharing the excitement of facing new challenges in the development process and the technological and business opportunities in overcoming these challenges.
This document provides an introduction to microservices, including:
- Microservices are an organizational tool for scaling services independently. Monoliths work well too but do not scale as easily.
- Common communication methods between microservices include RPC and message-driven interactions using protocols like GRPC and Protobuf.
- Logging and metrics are important for microservices. The REK stack of Rsyslog, Elasticsearch and Kibana is recommended for logging. Prometheus is given as an example for metrics.
- Containerization using Docker is ideal for deploying and managing microservices. Kubernetes is a popular orchestration platform for containers both in the cloud and on-premise.
This document provides information about an educational coding robot called CodeyBot. It discusses CodeyBot's features such as its sensors, platform, display, connectivity, memory storage, programming language, power, and system requirements. The document also contains a quote from Steve Jobs about the importance of learning to program computers as it teaches people how to think.
Text Mining with R -- an Analysis of Twitter DataYanchang Zhao
This document discusses analyzing Twitter data using text mining techniques in R. It outlines extracting tweets from Twitter and cleaning the text by removing punctuation, numbers, URLs, and stopwords. It then analyzes the cleaned text by finding frequent words, word associations, and creating a word cloud visualization. It performs text clustering on the tweets using hierarchical and k-means clustering. Finally, it models topics in the tweets using partitioning around medoids clustering. The overall goal is to demonstrate various text mining and natural language processing techniques for analyzing Twitter data in R.
This document provides an overview of the architecture and components used in an AI assistant called Otto. It describes the base software including Docker, Node.js, and MongoDB. It also outlines the natural language processing, speech recognition, text-to-speech and other modules. It details the client and messaging bot architectures and includes information on I/O drivers, actions, fulfillments and the overall workflow. Finally, it lists some example intents and provides guidance on developing new actions and the hardware used including the Raspberry Pi, Re-Speaker mic HAT and power components.
This document provides an overview of machine translation and the Moses machine translation toolkit. It defines machine translation and statistical machine translation. It describes the major components of Moses, including GIZA++ for word alignment, SRILM for language modeling, and the Moses decoder. It explains how Moses uses phrase-based translation and tuning to produce translations. It also discusses how to set up and use a Moses server for translating webpages.
Conversational AI with Rasa - PyData WorkshopTom Bocklisch
Workshop building a simple chatbot with Rasa NLU and Core. Additional resources can be found in the repository https://github.com/tmbo/rasa-demo-pydata18/edit/master/README.md
Inside view of "Clova Inside" - Natural language understanding system to supp...LINE Corporation
Toshinori Sato (overlast)
LINE / VA Development Team
AI assistant with VoiceUI that enables intuitive device operation through natural actions, such as human speech and emotion, is an example that requires multiple and simultaneous applications of AI technologies, including natural language processing, voice recognition, speech synthesis, image processing, and information retrieval. LINE Clova is a platform to add AI assistant features to various smart devices in order to repond to user commands through the use of natural language, such as voice and text. This session is a candid talk on the development, operation and other related topics of natural language understanding (NLU) technology that is integral to realize VoiceUI on Clova. Topics include, NLU system overview on the current Clova platform, issues and solutions of developing AI assistant for smart devices, co-existing with skills developed and operated by third parties, LINE’s views on consistent VoiceUI design, content development case studies, and upcoming topics that LINE must address. While still at the dawn of AI assistant and smart devices, no one is certain what the flagship skill or application would look like. With this, NLU system for smart devices must continue to support increasing device types and features, as well as growing number of contents, services, and their updates. In addition, issues that become evident by using human speech and text as queries must also be resolved. This session is intended to convey the fascination of working with AI Assistant technology through sharing the excitement of facing new challenges in the development process and the technological and business opportunities in overcoming these challenges.
Create Subtitled and Translated Videos Using AWS Services (GPSTEC319) - AWS r...Amazon Web Services
Many businesses need to quickly and reliably produce a transcript from audio- or video-based content. Up until now, businesses had to incur both the expense and the lengthy process of hiring staff or a service to transcribe this content. Moreover, to produce a multi-language version of the content required translating it into the target language and possibly over-dubbing the original content with a new audio track. In this session, we walk you through the capabilities, process, and coding approach for creating subtitled and translated videos using Amazon Transcribe, Amazon Translate, and Amazon Polly.
The document discusses adding speech capabilities to a bot using Microsoft Cognitive Services Speech API. It provides an overview of the Speech API and its features, including speech to text, text to speech, and speech translation. It also covers pricing and SDKs. The presenter argues that adding speech capabilities allows the bot to support users with visual impairments or who prefer voice interaction, and can enable hands-free use and automatic translation. Examples of bots that incorporate speech features are provided. The document demonstrates how to configure a bot framework project and code to integrate speech functionality.
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
Building speech enabled products with Amazon Polly & Amazon LexAmazon Web Services
Amazon Lex and Amazon Polly are services for building conversational interfaces and converting text to speech. Lex allows developers to build bots that understand natural language and integrate with back-end systems. It features tools for building conversations using text or speech and deploying bots to messaging platforms. Polly is a text-to-speech service that converts text into high-quality speech for 47 voices and 24 languages. It offers features like SSML and lexicons to customize output. Both services aim to make building conversational applications easier and more cost-effective for developers.
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
This document provides an overview of the OpenNLP natural language processing tool. It discusses the various NLP tasks that OpenNLP can perform, including tokenization, POS tagging, named entity recognition, chunking, parsing, and co-reference resolution. It also describes how models for these tasks are trained in OpenNLP using annotated training data. The document concludes by listing some advantages and limitations of OpenNLP.
Powering NLU Engine with Apache Spark to Communicate with the WorldRahul Kumar
Building natural language processing engines is really a complex work. It requires an architecture where we glue many algorithms, data processing, and data storage techniques together to solve a single most important problem—how effectively we can understand users query in the form of text, voice, or visual gestures fast and effectively and respond them with zero error. Identifying the best tools available in the market and how we can fit these tools and libraries in our pipelines makes a great edge to build these systems.
Dr. John Tinsley discusses the latest advances in machine translation technology for patent information. He provides an overview of machine translation approaches like statistical and rule-based translation. Tinsley explains how machine translation systems analyze large datasets of translated text to statistically determine the most likely translations. While machine translation is improving, challenges remain like ambiguity, creative language use, and linguistic differences between languages. Tinsley advocates evaluating machine translation systems based on task performance rather than just translation quality.
This paper presents the method of applying speaker-independent and bidirectional speech-to-speech translation system for spontaneous dialogs in real time calling system. This technique recognizes spoken input, analyzes and translates it, and finally utters the translation. The major part of Speech translation comes under Natural language processing. Natural language processing is a branch of Artificial Intelligence that deals with analyzing, understanding and generating the languages that humans use naturally in order to interface with computers in both written and spoken contexts using natural human languages instead of computer languages. Speech Translation involves techniques to translate the spoken sentences from one language to another. The major part of speech translation involves Speech Recognition which is the translation of spoken speech to text and identifying the context and linguistic structure of the input speech. In the current scenario, the machine does not identify whether the given word is in past tense or present tense. By using the algorithm, we search for a word to check if it is past or present by searching for the sub strings, as “ed”, ”had”, ”Done”, etc., This paper gives us an idea on working with API’s to translate the input speech to the required output speech and thus increasing the efficiency of Speech Translation in cellular devices and also a mobile application that will help us to monitor all the audios present in mobile device and translate it into required language.
This document discusses language translation and provides an overview of a language translation tool. It begins with an introduction that defines translation and its objectives. It then discusses why translation is necessary in different contexts like education, business, and media. The document outlines the hardware, software, and development tools required for the language translation tool, including using Python and Visual Studio Code. It describes the methodology used in the tool, which utilizes the Googletrans library to implement Google Translate API. The modes of the translation tool include writing text, processing, output, and listening. The document concludes with discussing the future of translation and the benefits of language translators.
The document discusses language translation. It defines translation as conveying written text from a source language to a target language clearly, completely, accurately, and appropriately. The translation process involves translation, editing, and proofreading. Training programs may be able to use translation to cost-effectively expand their offerings to other languages. The document also outlines some objectives of language translation tools, including developing a system to convert between languages, providing an easy interface, and translating most languages.
This document summarizes a workshop on the Language Grid, which is a service-oriented infrastructure for multilingual societies. It discusses how the Language Grid provides various language services, such as machine translation, dictionaries, and parallel texts. It also describes how these atomic services can be composed to create new multilingual applications and services. Finally, it outlines several research projects using the Language Grid, including analyzing machine translation-mediated communication, developing multilingual localization systems, and extending the Language Grid's capabilities.
Localizing a CakePHP application involves wrapping translatable text in __() functions, generating PO files containing translations using a console shell, and manually creating PO files for each language. The PO files contain message IDs and localized strings, and get placed in directories corresponding to language codes. Configuring the application's language based on region ensures the proper translations display.
Better Accessibility with Lex, Polly, and Alexa | AWS Public Sector Summit 2017Amazon Web Services
Most AWS services can be applied at scale. We'll provide a demo of what and how these services help modernize interactions with IT systems even those with government regulations and requirements. We will also demonstrate ways the overall solution helps meet those requirements. GeorgiaGov Interactive will share their story on how they are using Alexa to reach more disabled residents, extending its informational and transactional services onto Amazon's voice-driven platform. Learn More: https://aws.amazon.com/government-education/
Assamese to English Statistical Machine TranslationKalyanee Baruah
This document provides an overview of Assamese to English statistical machine translation using the Moses toolkit. It introduces natural language processing and machine translation, and discusses the advantages and challenges of machine translation. It describes the statistical machine translation approach used, including language modeling, translation modeling, and decoding. The implementation details training the Moses decoder on a parallel corpus of 2500 Assamese-English sentences. Transliteration was added to handle untranslated proper nouns. Evaluation showed a BLEU score of 7.02. Future work includes increasing the corpus size and implementing translation without Moses.
“Neural Machine Translation for low resource languages: Use case anglais - wolof“ by Sokhar Samb - Data scientist at @THEOLEX
Abstract : We will dive into the different steps of developing a Wolof-English machine translation using JoeyNMT using the benchmark from Masakhane NLP.
This presentation took place during a joint WiMLDS meetup between Paris & Dakar.
Rasa Developer Summit - Josh Converse, Dynamic Offset - Three Part Harmony: H...Rasa Technologies
It has been said of the Beatles that the whole (the band) is greater than the sum of the parts (the band members). The same can hold true for open source software. This talk explores combining disparate open source technologies, backed by a Rasa "brain", to yield amazing results, explored through building a phone-based voice receptionist.
WHAT YOU'LL LEARN
The open source ecosystem represents a suite of great standalone technologies. Combining them in a product can yield even more amazing results
Rasa provides the much-needed flexibility for your system to react and adapt to the real world.
Leveraging open source (Rasa included) allows you to spend more time on the most interesting parts of your product.
Josh Converse is the founder of Dynamic Offset, a boutique consulting firm specializing in mobile, web, and conversational experiences. Prior to consulting he held tech lead roles at both Google and Apple.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Create Subtitled and Translated Videos Using AWS Services (GPSTEC319) - AWS r...Amazon Web Services
Many businesses need to quickly and reliably produce a transcript from audio- or video-based content. Up until now, businesses had to incur both the expense and the lengthy process of hiring staff or a service to transcribe this content. Moreover, to produce a multi-language version of the content required translating it into the target language and possibly over-dubbing the original content with a new audio track. In this session, we walk you through the capabilities, process, and coding approach for creating subtitled and translated videos using Amazon Transcribe, Amazon Translate, and Amazon Polly.
The document discusses adding speech capabilities to a bot using Microsoft Cognitive Services Speech API. It provides an overview of the Speech API and its features, including speech to text, text to speech, and speech translation. It also covers pricing and SDKs. The presenter argues that adding speech capabilities allows the bot to support users with visual impairments or who prefer voice interaction, and can enable hands-free use and automatic translation. Examples of bots that incorporate speech features are provided. The document demonstrates how to configure a bot framework project and code to integrate speech functionality.
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
Building speech enabled products with Amazon Polly & Amazon LexAmazon Web Services
Amazon Lex and Amazon Polly are services for building conversational interfaces and converting text to speech. Lex allows developers to build bots that understand natural language and integrate with back-end systems. It features tools for building conversations using text or speech and deploying bots to messaging platforms. Polly is a text-to-speech service that converts text into high-quality speech for 47 voices and 24 languages. It offers features like SSML and lexicons to customize output. Both services aim to make building conversational applications easier and more cost-effective for developers.
Nikko Ström at AI Frontiers: Deep Learning in AlexaAI Frontiers
Alexa is the service that understands spoken language in Amazon Echo and other voice enabled devices. Alexa relies heavily on machine learning and deep neural networks for speech recognition, text-to-speech, language understanding, skill selection, and more. In this talk Nikko presents an overview of deep learning in Alexa and gives a few illustrating examples.
This document provides an overview of the OpenNLP natural language processing tool. It discusses the various NLP tasks that OpenNLP can perform, including tokenization, POS tagging, named entity recognition, chunking, parsing, and co-reference resolution. It also describes how models for these tasks are trained in OpenNLP using annotated training data. The document concludes by listing some advantages and limitations of OpenNLP.
Powering NLU Engine with Apache Spark to Communicate with the WorldRahul Kumar
Building natural language processing engines is really a complex work. It requires an architecture where we glue many algorithms, data processing, and data storage techniques together to solve a single most important problem—how effectively we can understand users query in the form of text, voice, or visual gestures fast and effectively and respond them with zero error. Identifying the best tools available in the market and how we can fit these tools and libraries in our pipelines makes a great edge to build these systems.
Dr. John Tinsley discusses the latest advances in machine translation technology for patent information. He provides an overview of machine translation approaches like statistical and rule-based translation. Tinsley explains how machine translation systems analyze large datasets of translated text to statistically determine the most likely translations. While machine translation is improving, challenges remain like ambiguity, creative language use, and linguistic differences between languages. Tinsley advocates evaluating machine translation systems based on task performance rather than just translation quality.
This paper presents the method of applying speaker-independent and bidirectional speech-to-speech translation system for spontaneous dialogs in real time calling system. This technique recognizes spoken input, analyzes and translates it, and finally utters the translation. The major part of Speech translation comes under Natural language processing. Natural language processing is a branch of Artificial Intelligence that deals with analyzing, understanding and generating the languages that humans use naturally in order to interface with computers in both written and spoken contexts using natural human languages instead of computer languages. Speech Translation involves techniques to translate the spoken sentences from one language to another. The major part of speech translation involves Speech Recognition which is the translation of spoken speech to text and identifying the context and linguistic structure of the input speech. In the current scenario, the machine does not identify whether the given word is in past tense or present tense. By using the algorithm, we search for a word to check if it is past or present by searching for the sub strings, as “ed”, ”had”, ”Done”, etc., This paper gives us an idea on working with API’s to translate the input speech to the required output speech and thus increasing the efficiency of Speech Translation in cellular devices and also a mobile application that will help us to monitor all the audios present in mobile device and translate it into required language.
This document discusses language translation and provides an overview of a language translation tool. It begins with an introduction that defines translation and its objectives. It then discusses why translation is necessary in different contexts like education, business, and media. The document outlines the hardware, software, and development tools required for the language translation tool, including using Python and Visual Studio Code. It describes the methodology used in the tool, which utilizes the Googletrans library to implement Google Translate API. The modes of the translation tool include writing text, processing, output, and listening. The document concludes with discussing the future of translation and the benefits of language translators.
The document discusses language translation. It defines translation as conveying written text from a source language to a target language clearly, completely, accurately, and appropriately. The translation process involves translation, editing, and proofreading. Training programs may be able to use translation to cost-effectively expand their offerings to other languages. The document also outlines some objectives of language translation tools, including developing a system to convert between languages, providing an easy interface, and translating most languages.
This document summarizes a workshop on the Language Grid, which is a service-oriented infrastructure for multilingual societies. It discusses how the Language Grid provides various language services, such as machine translation, dictionaries, and parallel texts. It also describes how these atomic services can be composed to create new multilingual applications and services. Finally, it outlines several research projects using the Language Grid, including analyzing machine translation-mediated communication, developing multilingual localization systems, and extending the Language Grid's capabilities.
Localizing a CakePHP application involves wrapping translatable text in __() functions, generating PO files containing translations using a console shell, and manually creating PO files for each language. The PO files contain message IDs and localized strings, and get placed in directories corresponding to language codes. Configuring the application's language based on region ensures the proper translations display.
Better Accessibility with Lex, Polly, and Alexa | AWS Public Sector Summit 2017Amazon Web Services
Most AWS services can be applied at scale. We'll provide a demo of what and how these services help modernize interactions with IT systems even those with government regulations and requirements. We will also demonstrate ways the overall solution helps meet those requirements. GeorgiaGov Interactive will share their story on how they are using Alexa to reach more disabled residents, extending its informational and transactional services onto Amazon's voice-driven platform. Learn More: https://aws.amazon.com/government-education/
Assamese to English Statistical Machine TranslationKalyanee Baruah
This document provides an overview of Assamese to English statistical machine translation using the Moses toolkit. It introduces natural language processing and machine translation, and discusses the advantages and challenges of machine translation. It describes the statistical machine translation approach used, including language modeling, translation modeling, and decoding. The implementation details training the Moses decoder on a parallel corpus of 2500 Assamese-English sentences. Transliteration was added to handle untranslated proper nouns. Evaluation showed a BLEU score of 7.02. Future work includes increasing the corpus size and implementing translation without Moses.
“Neural Machine Translation for low resource languages: Use case anglais - wolof“ by Sokhar Samb - Data scientist at @THEOLEX
Abstract : We will dive into the different steps of developing a Wolof-English machine translation using JoeyNMT using the benchmark from Masakhane NLP.
This presentation took place during a joint WiMLDS meetup between Paris & Dakar.
Rasa Developer Summit - Josh Converse, Dynamic Offset - Three Part Harmony: H...Rasa Technologies
It has been said of the Beatles that the whole (the band) is greater than the sum of the parts (the band members). The same can hold true for open source software. This talk explores combining disparate open source technologies, backed by a Rasa "brain", to yield amazing results, explored through building a phone-based voice receptionist.
WHAT YOU'LL LEARN
The open source ecosystem represents a suite of great standalone technologies. Combining them in a product can yield even more amazing results
Rasa provides the much-needed flexibility for your system to react and adapt to the real world.
Leveraging open source (Rasa included) allows you to spend more time on the most interesting parts of your product.
Josh Converse is the founder of Dynamic Offset, a boutique consulting firm specializing in mobile, web, and conversational experiences. Prior to consulting he held tech lead roles at both Google and Apple.
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
What is Master Data Management by PiLog Groupaymanquadri279
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
2. Always Learning
Always Changing
Always Listening
For the Office:
Increase Productivity!
For the Maker:
Encourage Creativity!
For the Home:
Integrated Artificial Intelligence!
5. Example :
“Do I have any meetings today?”
The speech recognition engine converts the audio to the text ‘DO I HAVE
ANY MEETINGS TODAY’ and sends it to the NLP component.
The NLP component processes the text and recognises it as a
question (‘DO I HAVE’), recognises ‘MEETINGS’ and ‘TODAY’ as
keywords associated with time and a calendar. The NLP polls the
calendar API for confirmation, which it agrees.
6. The NLP constructs a message according to the Calendar API
specification and sends it.
e.g. getEvents(type=meeting, date=today);
The NLP accepts a list in response, perhaps in a format such as JSON:
{"success": true, "events": [ {"date": "2016-09-25", "time":"10:30am",
"name":"Meeting to discuss a new Welsh language project“ ,
"location":"Bangor University, Ogwen Building, Room 234"} , {"date":
"2016-09-25", "time":"12:30pm", "name":"Lunch with Delyth“ ,
"location":"Terrace Restaurant, Bangor University"} ]}
7. From which it generates the sentence:
“Yes you do. At 10:30 this morning you have a meeting to
discuss a
new Welsh language project in Bangor University, Ogwen
Building,
Room 234. Then at 12:30 you have lunch with Delyth in the
Terrace
Restaurant, Bangor University”
“Yes you do. At half past ten this morning you have a meeting
to
discuss a new Welsh language project in Bangor University,
Ogwen
Building, Room two hundred and thirty four. Then at half past
twelve
you have lunch with Delyth in the Terrace Restaurant, Bangor
University”
The text to speech engine receives the sentence result, and
voices:
8. What is Adapt?
The Adapt Intent Parser is an open source software library for
converting natural language into machine readable data
structures.
9. Ex: Will it rain in Seattle tomorrow? (Hint: probably)
Register each weather type with the engine
Declare a collection of locations.
Register each location with the engine
10.
11. R a s p b e r r y P i
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