This series of slides describes how to develop a twitter application.
This slide describes how to search tweets using Twitter Search RESTful Open API and how to implement it using Twitter4J.
Analysis of StackOverflow posts/user data trend analysis. Predicting time to answer (classification) using Weka. CSCI599 final project on Social media data analytics
Analysis of StackOverflow posts/user data trend analysis. Predicting time to answer (classification) using Weka. CSCI599 final project on Social media data analytics
May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.
A Learning to Rank Project on a Daily Song Ranking ProblemSease
Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situation; understanding how to adapt a specific dataset and to design the best approach to solve a ranking problem in a real-world scenario is thus crucial.This talk aims to illustrate how to set up and build a Learning to Rank (LTR) project starting from the available data, in our case a Spotify Dataset (available on Kaggle) on the Worldwide Daily Song Ranking, and ending with the implementation of a ranking model. A step by step (phased) approach to cope with this task using open source libraries will be presented.We will examine in depth the most important part of the pipeline that is the data preprocessing and in particular how to model and manipulate the features in order to create the proper input dataset, tailored to the machine learning algorithm requirements.
In the last few years, Artificial Intelligence applications have become more and more sophisticated and often operate like algorithmic “black boxes” for decision-making. Due to this fact, some questions naturally arise when working with these models: why should we trust a certain decision taken by these algorithms? Why and how was this prediction made? Which variables mostly influenced the prediction? The most crucial challenge with complex machine learning models is therefore their interpretability and explainability. This talk aims to illustrate an overview of the most popular explainability techniques and their application in Learning to Rank. In particular, we will examine in depth a powerful library called SHAP with both theoretical and practical insights; we will talk about its amazing tools to give an explanation of the model behaviour, especially how each feature impacts the model’s output, and we will explain to you how to interpret the results in a Learning to Rank scenario.
Being your core domain involving real world entities ( such as hotels, restaurant, cars ...) or text documents, searching for similar entities, given one in input, is a very common use case for most of the systems that involve information retrieval. This presentation will start describing how much this problem is present across a variety of different scenarios and how you can use the More Like This feature in the Apache Lucene library to solve it. Building on the introduction the focus will be on how the More Like This module internally works, all the components involved end to end, BM25 text similarity metric and how this has been included through a cospicuos refactor and testing process. The presentation will include real world usage examples and future developments such as improved query building through positional phrase queries and term relevancy scoring pluggability.
Introduction into Search Engines and Information RetrievalA. LE
Gives a brief introduction into search engines and information retrieval. Covers basics about Google and Yahoo, fundamental terms in the area of information retrieval and an introduction into the famous page rank algorithm
From Academic Papers To Production : A Learning To Rank StoryAlessandro Benedetti
This talk is about the journey to bring Learning To Rank (LTR from now on) to the e-commerce domain in a real world scenario, including all the pitfalls and disillutions involved.
LTR is a fantastic approach to solve complex ranking problems but industry domains are far from being the ideal world where those technologies were designed and experimented : open source software implementations are not working perfectly out of the box and require advanced tuning; industry training data is dirty, noisy and incomplete.
This talk will guide you through the different phases and technologies involved in a LTR project with a pragmatic approach.
Feature Engineering, Domain Modelling, Training Set Building, Model Training, Search Integration and Online Evaluation : each of them presents different challenges in the real world and must be carefully approached.
The Crossref/ORCID Auto-Update: all you need to knowCrossref
This webinar describes the practical details of the ORCID auto update functionality that authors and publishers need to know.
It covers:
- Purpose and benefits
- What researchers need to know
- What Crossref publishers need to know
This presentation took place on October 29, 2015.
The link to the recording of this webinar is: https://bitly.com/orcidautoupdatewebinar.
Data scraping, Web crawlers are programs that extract information out of world wide web. This presentation aims to cover all relevant topics that one should know before building a crawler.
Development of a system that automatically generates (kind of) storylines out of social media aggregated around hashtags, following links being shared.
May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.
A Learning to Rank Project on a Daily Song Ranking ProblemSease
Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situation; understanding how to adapt a specific dataset and to design the best approach to solve a ranking problem in a real-world scenario is thus crucial.This talk aims to illustrate how to set up and build a Learning to Rank (LTR) project starting from the available data, in our case a Spotify Dataset (available on Kaggle) on the Worldwide Daily Song Ranking, and ending with the implementation of a ranking model. A step by step (phased) approach to cope with this task using open source libraries will be presented.We will examine in depth the most important part of the pipeline that is the data preprocessing and in particular how to model and manipulate the features in order to create the proper input dataset, tailored to the machine learning algorithm requirements.
In the last few years, Artificial Intelligence applications have become more and more sophisticated and often operate like algorithmic “black boxes” for decision-making. Due to this fact, some questions naturally arise when working with these models: why should we trust a certain decision taken by these algorithms? Why and how was this prediction made? Which variables mostly influenced the prediction? The most crucial challenge with complex machine learning models is therefore their interpretability and explainability. This talk aims to illustrate an overview of the most popular explainability techniques and their application in Learning to Rank. In particular, we will examine in depth a powerful library called SHAP with both theoretical and practical insights; we will talk about its amazing tools to give an explanation of the model behaviour, especially how each feature impacts the model’s output, and we will explain to you how to interpret the results in a Learning to Rank scenario.
Being your core domain involving real world entities ( such as hotels, restaurant, cars ...) or text documents, searching for similar entities, given one in input, is a very common use case for most of the systems that involve information retrieval. This presentation will start describing how much this problem is present across a variety of different scenarios and how you can use the More Like This feature in the Apache Lucene library to solve it. Building on the introduction the focus will be on how the More Like This module internally works, all the components involved end to end, BM25 text similarity metric and how this has been included through a cospicuos refactor and testing process. The presentation will include real world usage examples and future developments such as improved query building through positional phrase queries and term relevancy scoring pluggability.
Introduction into Search Engines and Information RetrievalA. LE
Gives a brief introduction into search engines and information retrieval. Covers basics about Google and Yahoo, fundamental terms in the area of information retrieval and an introduction into the famous page rank algorithm
From Academic Papers To Production : A Learning To Rank StoryAlessandro Benedetti
This talk is about the journey to bring Learning To Rank (LTR from now on) to the e-commerce domain in a real world scenario, including all the pitfalls and disillutions involved.
LTR is a fantastic approach to solve complex ranking problems but industry domains are far from being the ideal world where those technologies were designed and experimented : open source software implementations are not working perfectly out of the box and require advanced tuning; industry training data is dirty, noisy and incomplete.
This talk will guide you through the different phases and technologies involved in a LTR project with a pragmatic approach.
Feature Engineering, Domain Modelling, Training Set Building, Model Training, Search Integration and Online Evaluation : each of them presents different challenges in the real world and must be carefully approached.
The Crossref/ORCID Auto-Update: all you need to knowCrossref
This webinar describes the practical details of the ORCID auto update functionality that authors and publishers need to know.
It covers:
- Purpose and benefits
- What researchers need to know
- What Crossref publishers need to know
This presentation took place on October 29, 2015.
The link to the recording of this webinar is: https://bitly.com/orcidautoupdatewebinar.
Data scraping, Web crawlers are programs that extract information out of world wide web. This presentation aims to cover all relevant topics that one should know before building a crawler.
Development of a system that automatically generates (kind of) storylines out of social media aggregated around hashtags, following links being shared.
Mining Social media for Product Development & Marketing Insightsjuliannacole
Highlights of final project for Big Data & NoSQL Programming to analyze Twitter streams for select hashtags in effort to determine product development and marketing insights.
Social Semantic Web on Facebook Open Graph protocol and Twitter AnnotationsMyungjin Lee
This Presentation show what the Social Semantic Web is and how Facebook Open Graph protocol and Twitter Annotations colligate with the Social Semantic Web.
Development of Twitter Application #8 - Streaming APIMyungjin Lee
This series of slides describes how to develop a twitter application.
This slide shows how to search tweets using Twitter Search RESTful Open API and how to implement it using Twitter4J.
Development of Twitter Application #4 - Timeline and TweetMyungjin Lee
This series of slides describes how to develop a twitter application.
This slide describes how to implement Twitter applications related to collecting a set of tweets using Twitter4J.
Social Media Data Collection & AnalysisScott Sanders
A non-technical primer on how to collect and analyze social media data. This was an invited lecture by Biostatistics and Bioinformatics Department in the School of Public Health at the University of Louisville.
The web has changed! Users spend more time on mobile than on desktops and expect to have an amazing user experience on both. APIs are the heart of the new web as the central point of access data, encapsulating logic and providing the same data and same features for desktops and mobiles. In this workshop, Antonio will show you how to create complex APIs in an easy and quick way using API Platform built on Symfony.
Sumo Logic exposes the Search Job API for access to resources and log data from third-party scripts and applications.
Targeting experienced Sumo Administrators, this webinar shows you how to leverage the Search Job API to interact with the Sumo Logic service. Everyone attending should be familiar with the concepts of RESTful web services and JSON. Through theory and demo, this webinar covers:
Creating a Search Job
Checking Status of a Search Job
Paging through messages and records
JSP 프로그래밍 #02 서블릿과 JSP 시작하기
2.1 톰캣 애플리케이션 만들기 (온라인 강의: https://youtu.be/04LIGWKCFjY)
2.2 간단한 서블릿 만들기 (온라인 강의: https://youtu.be/4ajw5EsxYE8)
2.3 간단한 JSP 만들기 (온라인 강의: https://youtu.be/6h-qH8pGdT8)
2.4 간단한 자바빈즈 만들기 (온라인 강의: https://youtu.be/TlgXkAWi1sc)
JSP 프로그래밍 #01 웹 프로그래밍
1.1 웹 (온라인 강의: https://youtu.be/qDZXXHhMr4A)
1.2 서블릿 (온라인 강의: https://youtu.be/a8hHeUhbz2k)
1.3 JSP(Java Server Page) (온라인 강의: https://youtu.be/Q4ezLP6KLwM)
1.4 프로그래밍을 위한 환경 설정 (온라인 강의: https://youtu.be/k2eR6gLULA8)
2018년 7월 5일에 있었던 한국인터넷거버넌스포럼(KrIGF)에서 발표한 "오픈 데이터와 인공지능" 발표자료입니다.
다음과 같은 내용을 담고 있습니다.
* 오픈데이터의 정의
* 오픈데이터의 중요성
* 인공지능
* 인공지능에서 데이터의 중요성
* 제한된 데이터 환경에서의 문제점
* 인공지능을 위한 오픈데이터의 중요성
* 더 나은 인공지능 시대를 위한 제언
2017년 4월에 진행된 도서관최신동향 과정에 있었던 발표자료입니다.
서지 분야에서의 Linked Data의 개념과 활용에 대한 내용을 담고 있습니다.
구체적으로는 아래와 같은 내용을 포함합니다.
- Linked Data란 무엇인가?
- 왜 도서관에서 Linked Data를 이야기하는가?
- Linked Data를 누가 쓰고 있나?
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
Development of Twitter Application #7 - Search
1. Linked Data &
Semantic Web
Technology
Development of
Twitter Applications
Part 7. Search API
Dr. Myungjin Lee
2. Linked Data & Semantic Web Technology
Search API
• Search REST API
– find and return a collection of relevant Tweets based
on matching a specified query
• Limitation
– not complete index of all Tweets, but instead an index
of recent 6-9 days of Tweets
– cannot find Tweets older than about a week.
– limited due to complexity
– not support authentication meaning all queries are
made anonymously.
– focused in relevance and not completeness
– limited to 1,000 characters in query length, including
any operators.
2
3. Linked Data & Semantic Web Technology
Search Operator
3
Example Finds tweets...
twitter search containing both "twitter" and "search". This is the default operator
"happy hour" containing the exact phrase "happy hour"
love OR hate containing either "love" or "hate" (or both)
beer -root containing "beer" but not "root"
#haiku containing the hashtag "haiku"
from:twitterapi sent from the user @twitterapi
to:twitterapi sent to the user @twitterapi
place:opentable:2 about the place with OpenTable ID 2
place:247f43d441defc03 about the place with Twitter ID 247f43d441defc03
@twitterapi mentioning @twitterapi
superhero since:2011-05-09 containing "superhero" and sent since date "2011-05-09"
twitterapi until:2011-05-09 containing "twitterapi" and sent before the date "2011-05-09".
movie -scary :) containing "movie", but not "scary", and with a positive attitude.
flight :( containing "flight" and with a negative attitude.
traffic ? containing "traffic" and asking a question.
hilarious filter:links containing "hilarious" and with a URL.
news source:tweet_button containing "news" and entered via the Tweet Button
4. Linked Data & Semantic Web Technology
GET search/tweets
• Resource URL
– https://api.twitter.com/1.1/search/tweets.json
• Parameters
• Other Information
– Requests per rate limit window: 180/user, 450/app
– Authentication: Required
– Response Object: Tweets
q
required
A UTF-8, URL-encoded search query of 1,000 characters maximum, including
operators. Queries may additionally be limited by complexity.
geocode
optional
Returns tweets by users located within a given radius of the given latitude/longitude.
lang
optional
Restricts tweets to the given language, given by an ISO 639-1 code. Language
detection is best-effort.
result_type
optional
Optional. Specifies what type of search results you would prefer to receive. The
current default is "mixed." Valid values include:
* mixed: Include both popular and real time results in the response.
* recent: return only the most recent results in the response
* popular: return only the most popular results in the response.
count
optional
The number of tweets to return per page, up to a maximum of 100. Defaults to 15.
until
optional
Returns tweets generated before the given date. Date should be formatted as
YYYY-MM-DD.
since_id
optional
Returns results with an ID greater than (that is, more recent than) the specified ID.
max_id
optional
Returns results with an ID less than (that is, older than) or equal to the specified ID.
include_entities
optional
The entities node will be disincluded when set to false.
callback
optional
If supplied, the response will use the JSONP format with a callback of the given
name.
4