This document discusses PageRank and HITS algorithms for ranking web pages. It provides an overview of how PageRank calculates prestige scores for pages based on link analysis and describes its strengths in being difficult to spam but also its weakness in not considering topic relevance. It also explains how HITS calculates authority and hub scores for pages based on their inlinks and outlinks, and how authorities and hubs mutually reinforce each other. However, HITS is more susceptible to spam and topic drift than PageRank.
Trend detection and analysis on TwitterLukas Masuch
By Henning Muszynski, Benjamin Räthlein & Lukas Masuch
The popularity of social media services has increased exponentially in the last few years. The combination of big social data and powerful analytical technologies makes it possible to gain highly valuable insights that otherwise might not be accessible. The Twitter Analyzer comprises several components to collect, analyze and visualize Twitter data. Therefore, we explored various related technologies to implement this tool. We collected about 38 million english tweets related to various and analyzed those data with machine learning techniques to compute the respective sentiment and detect common topics. Furthermore, we visualized the results using varying visualization techniques to emphasize different aspects such as a wordcloud, several chart-types and geospatial visualizations. Used technologies: MongoDB, Python, Twython, Python NLTK, wordcloud2.js, wordfreq, amCharts, Google BigQuery, Google Cloud Storage, CartoDB, EtcML.
Trend detection and analysis on TwitterLukas Masuch
By Henning Muszynski, Benjamin Räthlein & Lukas Masuch
The popularity of social media services has increased exponentially in the last few years. The combination of big social data and powerful analytical technologies makes it possible to gain highly valuable insights that otherwise might not be accessible. The Twitter Analyzer comprises several components to collect, analyze and visualize Twitter data. Therefore, we explored various related technologies to implement this tool. We collected about 38 million english tweets related to various and analyzed those data with machine learning techniques to compute the respective sentiment and detect common topics. Furthermore, we visualized the results using varying visualization techniques to emphasize different aspects such as a wordcloud, several chart-types and geospatial visualizations. Used technologies: MongoDB, Python, Twython, Python NLTK, wordcloud2.js, wordfreq, amCharts, Google BigQuery, Google Cloud Storage, CartoDB, EtcML.
Presented for TTI Vanguard "Shift Happens" conference (http://bit.ly/TTIVshifthappens) visit to PARC, this is an overview of technologies for making sense of diverse information -- and making decisions on it.
Microblogging today has gotten an acclaimed specific instrument among Internet clients. Endless clients share assessments on various bits of life dependably. Accordingly, microblogging districts are rich wellsprings of information for assessment mining and tendency assessment. Since microblogging has shown up by and large lately, there several investigation works that were given to this point. In our paper, we base on using Twitter, the most notable microblogging stage, for the task of feeling examination. We advise the most ideal approach to thus accumulate a corpus for assessment and evaluation mining purposes. We play out a semantic assessment of the amassed corpus and clarify found wonders. Utilizing the corpus, we build up an end classifier, that can pick positive, negative, and honest evaluations for an annual. Test assessments show that our proposed strategies are convincing and act in a way that is better than actually proposed procedures. In our appraisal, we worked with English, in any case, the proposed procedure can be utilized with some other language. Krunal Dhardev | Dr. Kamalraj R "Twitter Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42385.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42385/twitter-sentiment-analysis/krunal-dhardev
Evaluation of Web Search Engines Based on Ranking of Results and FeaturesWaqas Tariq
Search engines help the user to surf the web. Due to the vast number of web pages it is highly impossible for the user to retrieve the appropriate web page he needs. Thus, Web search ranking algorithms play an important role in ranking web pages so that the user could retrieve the page which is most relevant to the user's query. This paper presents a study of the applicability of two user-effort-sensitive evaluation measures on five Web search engines (Google, Ask, Yahoo, AOL and Bing). Twenty queries were collected from the list of most hit queries in the last year from various search engines and based upon that search engines are evaluated.
Presented for TTI Vanguard "Shift Happens" conference (http://bit.ly/TTIVshifthappens) visit to PARC, this is an overview of technologies for making sense of diverse information -- and making decisions on it.
Microblogging today has gotten an acclaimed specific instrument among Internet clients. Endless clients share assessments on various bits of life dependably. Accordingly, microblogging districts are rich wellsprings of information for assessment mining and tendency assessment. Since microblogging has shown up by and large lately, there several investigation works that were given to this point. In our paper, we base on using Twitter, the most notable microblogging stage, for the task of feeling examination. We advise the most ideal approach to thus accumulate a corpus for assessment and evaluation mining purposes. We play out a semantic assessment of the amassed corpus and clarify found wonders. Utilizing the corpus, we build up an end classifier, that can pick positive, negative, and honest evaluations for an annual. Test assessments show that our proposed strategies are convincing and act in a way that is better than actually proposed procedures. In our appraisal, we worked with English, in any case, the proposed procedure can be utilized with some other language. Krunal Dhardev | Dr. Kamalraj R "Twitter Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42385.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42385/twitter-sentiment-analysis/krunal-dhardev
Evaluation of Web Search Engines Based on Ranking of Results and FeaturesWaqas Tariq
Search engines help the user to surf the web. Due to the vast number of web pages it is highly impossible for the user to retrieve the appropriate web page he needs. Thus, Web search ranking algorithms play an important role in ranking web pages so that the user could retrieve the page which is most relevant to the user's query. This paper presents a study of the applicability of two user-effort-sensitive evaluation measures on five Web search engines (Google, Ask, Yahoo, AOL and Bing). Twenty queries were collected from the list of most hit queries in the last year from various search engines and based upon that search engines are evaluated.
Ambiguity Resolution in Information Retrievalkevig
With the advancement of the web it is very difficult to keep up with the amplifying requirements of learning
on web, to satisfy user’s expectation. Users demand with the updated and accurate results. To solve the
queries Search Engines use different techniques. Google the most famous search engine uses Page Ranking
Algorithm. Ranking Algorithms arrange the results according to the user’s needs. This paper deals with
“Page Rank Algorithm”. Our proposed algorithm is an extension of page rank algorithm which refines the
results so that user gets what he/she expects. We have used a measure Average Precision to compare Page
Rank algorithm and the proposed algorithm, and proved that our algorithm provides better results.
The PageRank algorithm is an important algorithm which is implemented to determine the quality of a page on the web. With search engines attaining a high position in guiding the traffic on the internet, PageRank is an important factor to determine its flow. Since link analysis is used in search engine's ranking systems, link based spam structure known as link farms are created by spammers to generate a high PageRank for their and in turn a target page. In this paper, we suggest a method through which these structures can be detected and thus the overall ranking results can be improved.
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
Hi All,
This Presentation will feature more about the working of search engine how do the inner functionality takes place. In the later half of the Presentation the Page Rank will be explained in depth. how do they calculate it, How it differing from the actual PR, Google PR. How frequently they do update the PR value in the google. and lots more with calculation and few examples.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
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.
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.
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UI automation Sample
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Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
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
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.
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.
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
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3. PageRank 7.3 Introduction
HITS was presented by Jon Kleinberg in January, 1998 at
the Ninth Annual ACM-SIAM Symposium on Discrete
Algorithms..
PageRank was presented by Sergey Brin and Larry Page
at the Seventh International World Wide Web Conference
(WWW7) in April, 1998.
-Based on the algorithm, they built the search engine
Google
4. PageRank 7.3.1 PageRank Algorithm
PageRank (PR)is a static ranking of Web pages.
PageRank is based on the measure of prestige in social
networks, the PageRank value of each page can be
regarded as its prestige.
5. PageRank 7.3.1 PageRank Algorithm
Concepts:
In-links of page i: These are the hyperlinks that point to
page i from other pages. Usually, hyperlinks from the
same site are not considered.
Out-links of page i: These are the hyperlinks that point
out to other pages from page i. Usually, links to pages of
the same site are not considered.
In-links Out-links
6. PageRank 7.3.1 PageRank Algorithm
uses G=(V, E) [G=graph, V=pages, E=links]
PageRank Score:
※ Oj is the number of
out-links of page j
7. PageRank 7.3.1 PageRank Algorithm
doesn’t not quite suffice.
(隨機性下的發生)
Based on the Markov chain:
※ Aij(1) is the probability of going
from i to j in 1 transition
10. PageRank 7.3.1 PageRank Algorithm
The random surfer has two options:
1. With probability d, he randomly chooses an out-link to follow.
2. With probability 1-d, he jumps to a random page without a link.
Ex3:
12. PageRank 7.3.2 Strengths and Weaknesses
1.The advantage of PageRank is its ability to fight spam.
Since it is not easy for Web page owner to add in-links into
his/her page from other important pages, it is thus not easy
to influence PageRank.
Nevertheless, there are reported ways to influence PageRank.
Recognizing and fighting spam is an important issue in
Web search.
13. PageRank 7.3.2 Strengths and Weaknesses
2. Another major advantage of PageRank is that it is a global
measure and is query independent.
At the query time, only a lookup is needed to find the value
to be integrated with other strategies to rank the pages.
It is thus very efficient at the query time.
14. PageRank 7.3.2 Strengths and Weaknesses
1. The main criticism is also the query-independence nature of
PageRank. It could not distinguish between pages that are
authoritative in general and pages that are authoritative on
the query topic.
15. PageRank 7.3.3 Timed PageRank and Recency Search
The Web is a dynamic environment. It changes constantly.
Quality pages in the past may not be quality pages now or
in the future.
Many outdated pages and links are not deleted. This causes
problems for Web search because such outdated pages
may still be ranked high. - Thus, search has a temporal
dimension.
16. PageRank 7.3.3 Timed PageRank and Recency Search
Time-Sensitive ranking algorithm called TS-Rank.
the surfer can take one of the two actions:
1. With probability f(ti), he randomly chooses an out-going
link to follow.
2. With probability 1-f(ti), he jumps to a random page
without a link.
17. PageRank 7.3.3 Timed PageRank and Recency Search
Time-Sensitive ranking algorithm called TS-Rank.
18. HITS 7.4
Introduction
HITS Algorithm
Finding Other Eigenvectors
Relationships with Co-Citation and
Bibliographic Coupling
Strengths and Weaknesses of HITS
19. HITS 7.4 Introduction
HITS stands for Hypertext Induced Topic Search
Statement :
expands the list of relevant pages returned by a search
engine and then produces two rankings of the expanded
set of pages, authority ranking and hub ranking.
Authority :
a page with many in-links.
A good authority is a page pointed to by many good hubs.
Hub :
a page with many out-links.
A good hub is a page that points to many good authorities.
20. HITS 7.4 Introduction
Authority :
a page with many in-links.
A good authority is a page pointed to by many good hubs.
Hub1
http1
http2
http3….
HubN
http1
http2
http3….
Hub2
http1
http2
http3….
Authority
21. HITS 7.4 Introduction
Hub :
a page with many out-links.
A good hub is a page that points to many good authorities.
Hub
http1
http2
http3….
Authority
1 Authority
2
Authority
N
authorities and hubs have a mutual reinforcement relationship
22. HITS 7.4.1 HITS Algorithm
uses G=(V, E) [G=graph, V=pages, E=links]
計算page i 的authority 分數a(i), hub 分數h(i).
The mutual reinforcing relationship of the two scores is
represented as follows:
23. HITS 7.4.1 HITS Algorithm
Writing them in the matrix form,
a scores = (a(1), a(2), …, a(n))T
h scores = (h(1), h(2), …, h(n))T
a = LT La
h = L LTa
26. HITS 7.4.2 Finding Other Eigenvectors
Each of such collections could potentially be relevant to the
query topic, but they could be well separated from one
another in the graph G for a variety of reasons.
For example,
1. The query string may represent a topic that may arise as
a term in the multiple communities, e.g. “classification”.
2. The query string may refer to a highly polarized issue,
involving groups that are not likely to link to one another,
e.g. “abortion”.
27. HITS 7.4.3 Relationships with Co-Citation and
Bibliographic Coupling
An authority page is like an influential research paper
(publication) which is cited by many subsequent papers.
A hub page is like a survey paper which cites many other
papers (including those influential papers).
28. HITS 7.4.4 Strengths and Weaknesses of HITS
The main strength of HITS is its ability to rank pages
according to the query topic, which may be able to
provide more relevant authority and hub pages.
However, HITS has several disadvantages:
1. HITS does not have the anti-spam capability of PageRank.
2. HITS is topic drift. because people put hyperlinks
for all kinds of reasons, including favor, spamming…
3. The query time evaluation is also a major drawback.
Performing eigenvector computation are all time
consuming operations.
outdated 過時、未更新的
temporal 時間的
For a complete new page in a Web site, which has few or no in-links, we can use the average TS-Rank value of the past pages of the site, which
represents the reputation of the site.