This document summarizes a framework for extracting blog posts and comments from web pages. It discusses two main stages: 1) Locating the main text using DOM tree structure and identifying noises like advertisements. The main text usually contains the most words and largest visual space. 2) Finding the separator between posts and comments using an information quantity algorithm based on HTML tag frequencies. An experiment on 25910 blog pages from the top 100 domains achieved over 90% precision on locating the main text and separator.
Modified version of Chapter 18 of the book Fundamentals_of_Database_Systems,_6th_Edition with review questions
as part of database management system course
Modified version of Chapter 18 of the book Fundamentals_of_Database_Systems,_6th_Edition with review questions
as part of database management system course
Dynamic multi level indexing Using B-Trees And B+ TreesPooja Dixit
B-TREE, Properties of B-Tree, B-Tree of minimum degree 3, Drawbacks of B-Tree, B+ tree, B+ tree, Structure of the internal nodes of a B+ tree , structure of the leaf nodes of a B+ tree , Example of B+ tree
This presentation contains a complete understanding of file system Interface, Directory Structure, How operating System access the files and their directories. So I hope you will easily understand the concept of file system interface. Access Methos are also discussed in this presentation.
Naming Entities and Locating Mobile EntitiesRajat Kumar
What is a name in Distributed Systems?
A name in a distributed system is a string of bits or characters that is used to refer to an entity.
What is an Entity?
An entity in a distributed system can be practically anything.
Examples include resources such as hosts, printers, disks, and files, processes, users, mailboxes, newsgroups, Web pages, graphical windows, messages, network connections
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
http://youtu.be/YD5Yk6cNsdE
The need to retain customers, and get more out of them, is increasingly apparent in the need for what Loyalty 360 calls expectation matching: building loyalty and engagement by using behavioral insights from customers to meet their expectations in Operations Management
Dynamic multi level indexing Using B-Trees And B+ TreesPooja Dixit
B-TREE, Properties of B-Tree, B-Tree of minimum degree 3, Drawbacks of B-Tree, B+ tree, B+ tree, Structure of the internal nodes of a B+ tree , structure of the leaf nodes of a B+ tree , Example of B+ tree
This presentation contains a complete understanding of file system Interface, Directory Structure, How operating System access the files and their directories. So I hope you will easily understand the concept of file system interface. Access Methos are also discussed in this presentation.
Naming Entities and Locating Mobile EntitiesRajat Kumar
What is a name in Distributed Systems?
A name in a distributed system is a string of bits or characters that is used to refer to an entity.
What is an Entity?
An entity in a distributed system can be practically anything.
Examples include resources such as hosts, printers, disks, and files, processes, users, mailboxes, newsgroups, Web pages, graphical windows, messages, network connections
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
http://youtu.be/YD5Yk6cNsdE
The need to retain customers, and get more out of them, is increasingly apparent in the need for what Loyalty 360 calls expectation matching: building loyalty and engagement by using behavioral insights from customers to meet their expectations in Operations Management
hi people!!! this is my first presentation hope u like it n might help u for ur further studies and good step for ur future!!!!! I'm pretty much sure u'll like it and if u find interesting plz comment or give a like. your like means a lot...!!!!!! :)
FINANCIAL MANAGEMENT- Sources of finance
Sources of finance can be classified into:
Internal sources (raised from within the organisation)
External (raised from an outside source)
Internal Sources Owner’s investment
Internal Sources Retained Profits
Internal Sources Sale of Stock
Internal Sources Sale of Fixed Assets
Internal Sources Debt Collection
External Sources Bank Loan
External Sources Share Issue
External Sources Share Issue
Boilerplate Removal and Content Extraction from Dynamic Web PagesIJCSEA Journal
Web pages not only contain main content, but also other elements such as navigation panels,
advertisements and links to related documents. To ensure the high quality of web page, a good boilerplate
removal algorithm is needed to extract only the relevant contents from web page. Main textual contents are
just included in HTML source code which makes up the files. The goal of content extraction or boilerplate
detection is to separate the main content from navigation chrome, advertising blocks, and copyright notices
in web pages. The system removes boilerplate and extracts main content. In this system, there are two
phases: Feature Extraction phase and Clustering phase. The system classifies the noise or content from HTML web page. Content Extraction algorithm describes to get high performance without parsing DOM trees. After observation the HTML tags, one line may not contain a piece of complete information and long texts are distributed in close lines, this system uses Line-Block concept to determine the distance of any two neighbor lines with text and Feature Extraction such as text-to-tag ratio (TTR), anchor text-to-text ratio (ATTR) and new content feature as Title Keywords Density (TKD) classifies noise or content. After extracting the features, the system uses these features as parameters in threshold method to classify the block are content or non- content.
Web pages not only contain main content, but also other elements such as navigation panels,
advertisements and links to related documents. To ensure the high quality of web page, a good boilerplate
removal algorithm is needed to extract only the relevant contents from web page. Main textual contents are
just included in HTML source code which makes up the files. The goal of content extraction or boilerplate
detection is to separate the main content from navigation chrome, advertising blocks, and copyright notices
in web pages. The system removes boilerplate and extracts main content. In this system, there are two
phases: Feature Extraction phase and Clustering phase. The system classifies the noise or content from
HTML web page. Content Extraction algorithm describes to get high performance without parsing DOM
trees. After observation the HTML tags, one line may not contain a piece of complete information and long
texts are distributed in close lines, this system uses Line-Block concept to determine the distance of any
two neighbor lines with text and Feature Extraction such as text-to-tag ratio (TTR), anchor text-to-text
ratio (ATTR) and new content feature as Title Keywords Density (TKD) classifies noise or content. After
extracting the features, the system uses these features as parameters in threshold method to classify the
block are content or non- content.
Survey on article extraction and comment monitoring techniquesAnunaya
The online News publisher publishes their news in the form of articles. Most of the online news websites provide the facility for their users to comment on the news article and as a result a lot of people comment on the news article. Hence news web page contains huge data in the form of article content and comments data, etc and have a good potential to be a resource for many Information Retrieval Systems and Data Mining Applications. The extraction of the main content (Article content) from a web page has always been a challenging task because a web page contains other information like advertisements and hyperlinks etc. which is not related to Article Text. In this survey, we review various techniques which are proposed by various researchers to extract the article content from a news web site. We also learn various techniques which monitor and analyze the comments for various applications like popularity prediction of articles and identification of discussions thread in the comments data.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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
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/
Designing Great Products: The Power of Design and Leadership by Chief Designe...
20081009 meeting
1. Blog Post and Comment Extraction Using Information Quantity of Web Format 4th Asia Infomation Retrieval Symposium, AIRS 2008 Reportor ： Che-Min Liao
![Framework of Blog Extraction ,[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)