Topological methods are techniques for software component retrieval from repositories based on similarity between query specifications and component properties. They rely on defining a distance measure between queries and components. PageRank is used to calculate importance scores for components based on their relationships to other components defined by shared keywords. It is an iterative process where initial scores are calculated and used to recalculate new scores until they converge. PageRank allows for ranking of components in a repository based on their relevance to queries.
This presentation describes in simple terms how the PageRank algorithm by Google founders works. It displays the actual algorithm as well as tried to explain how the calculations are done and how ranks are assigned to any webpage.
This presentation won me the best presentation award at my University Tech fest "Allegretto" in 2008.
I have also presented this seminar as a part of B.Tech curriculum in 7th Semester.
For a long time, PageRank has been widely used for authority computation and has been adopted as a solid
baseline for evaluating social influence related applications. However, when measuring the authority of network
nodes, the traditional PageRank method does not take the nodes’ prior knowledge into consideration.
Also, the connection between PageRank and social influence modeling methods is not clearly established.
To that end, this article provides a focused study on understanding PageRank as well as the relationship
between PageRank and social influence analysis. Along this line, we first propose a linear social influence
model and reveal that this model generalizes the PageRank-based authority computation by introducing
some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting
more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of
linear model with general constraints, we provide an upper bound for identifying nodes with top authorities.
Moreover, we extend the proposed linear model for better measuring the authority of the given node sets,
and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental
evaluations on four real-world networks validate the effectiveness of the proposed linear model with
respect to different constraint settings. The results show that the methods with more reasonable constraints
can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by
PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.
Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
This presentation describes in simple terms how the PageRank algorithm by Google founders works. It displays the actual algorithm as well as tried to explain how the calculations are done and how ranks are assigned to any webpage.
This presentation won me the best presentation award at my University Tech fest "Allegretto" in 2008.
I have also presented this seminar as a part of B.Tech curriculum in 7th Semester.
For a long time, PageRank has been widely used for authority computation and has been adopted as a solid
baseline for evaluating social influence related applications. However, when measuring the authority of network
nodes, the traditional PageRank method does not take the nodes’ prior knowledge into consideration.
Also, the connection between PageRank and social influence modeling methods is not clearly established.
To that end, this article provides a focused study on understanding PageRank as well as the relationship
between PageRank and social influence analysis. Along this line, we first propose a linear social influence
model and reveal that this model generalizes the PageRank-based authority computation by introducing
some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting
more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of
linear model with general constraints, we provide an upper bound for identifying nodes with top authorities.
Moreover, we extend the proposed linear model for better measuring the authority of the given node sets,
and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental
evaluations on four real-world networks validate the effectiveness of the proposed linear model with
respect to different constraint settings. The results show that the methods with more reasonable constraints
can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by
PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.
Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
Data Structure Assignment help , Data Structure Online tutorsjohn mayer
Get the 24/7 tutors for Data Structure Assignment help & Data Structure homework help. Data Structure tutors are available 24/7 in order to provide the complete academic assistance for the Data Structure assignments.
http://www.globalwebtutors.com/data-structure-assignment-help
Contextual Information Elicitation in Travel Recommender SystemsMatthias Braunhofer
Context-Aware Recommender Systems are advisory applications that exploit users’ preference knowledge contained in datasets of context-dependent user ratings, i.e., ratings augmented with the description of the contextual situation detected when the user experienced the item and rated it. Since the space of context-dependent ratings increases exponentially in size with the number of contextual factors, and because certain contextual information is still hard to acquire automatically (e.g., the user’s mood or the travellers’ group composition), it is fundamental to identify and acquire only those factors that truly influence the user preferences and consequently the ratings and the recommendations. In this paper, we propose a novel method that estimates the impact of a contextual factor on rating predictions and adaptively elicits from the users only the relevant ones. Our experimental evaluation, on two travel-related datasets, shows that our method compares favorably to other state-of-the-art context selection methods.
Conference Proceedings of the National Level Technical Symposium on Emerging Trends in Technology, TECHNOVISION ’10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April, 2010
This article describes how integrate Java with Microsoft Technology. Sometimes there may be need an application where integrate both technologies. This article describes how to call some Java methods from .NET code, and pass some values to Java or .NET and vice versa. This is a simple ASP.NET application, which interacts with Java Applets while performing another operation. The application is very simple to do, but the main thing behind the scene is the idea and implementation logic.
Presented in the National Level Technical Symposium on Emerging Trends in Technology [TECHNOVISION ’10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April, 2010]
Workshop on Basics of Software Engineering (DFD, UML and Project Culture)Dr Sukhpal Singh Gill
Three days workshop on Basics of Software Engineering at Thapar University, Patiala on 7th-9th, 2013. Workshop on Basics of Software Engineering (DFD, UML and Project Culture)
Software Requirements Specification (SRS) for Online Tower Plotting System (O...Dr Sukhpal Singh Gill
Software Requirements Specification (SRS) for Online Tower Plotting System (OTPS) created during Master of Engineering in Software Engineering at Thapar University, Patiala, Punjab, India in Software Project Management (SPM) in 2011.
SRS of Case Study Based Software Engineering Project Development: State of Art
Download Link:
http://www.slideshare.net/sukhpalsinghgill/case-study-based-software-engineering-project-development-state-of-art
Case Study Based Software Engineering Project Development: State of ArtDr Sukhpal Singh Gill
Publised in International Journal of Scientific Research in Computer Science Applications and Management Studies (IJSRCSAMS), Volume 2, Issue 3 (May 2013).
Step by Step Development of Software Project
An approach to learn Software Project Management Practically.
SDLC phases of Software Engineering
Project Completed at Thapar University, Patiala, Punjab, India.
Download Link:
http://arxiv.org/ftp/arxiv/papers/1306/1306.2502.pdf
http://www.ijsrcsams.com/images/stories/Past_Issue_Docs/ijsrcsamsv2i3p31.pdf
SRS of this Project can be downloaded from :
http://www.slideshare.net/sukhpalsinghgill/software-requirements-specification-srs-for-online-tower-plotting-system-otps
Constructors, Destructors, call in parameterized Constructor, Multiple constructor in a class, Explicit/implicit call, Copy constructor, Dynamic Constructors and call in parameterized Constructor
Data Structure Assignment help , Data Structure Online tutorsjohn mayer
Get the 24/7 tutors for Data Structure Assignment help & Data Structure homework help. Data Structure tutors are available 24/7 in order to provide the complete academic assistance for the Data Structure assignments.
http://www.globalwebtutors.com/data-structure-assignment-help
Contextual Information Elicitation in Travel Recommender SystemsMatthias Braunhofer
Context-Aware Recommender Systems are advisory applications that exploit users’ preference knowledge contained in datasets of context-dependent user ratings, i.e., ratings augmented with the description of the contextual situation detected when the user experienced the item and rated it. Since the space of context-dependent ratings increases exponentially in size with the number of contextual factors, and because certain contextual information is still hard to acquire automatically (e.g., the user’s mood or the travellers’ group composition), it is fundamental to identify and acquire only those factors that truly influence the user preferences and consequently the ratings and the recommendations. In this paper, we propose a novel method that estimates the impact of a contextual factor on rating predictions and adaptively elicits from the users only the relevant ones. Our experimental evaluation, on two travel-related datasets, shows that our method compares favorably to other state-of-the-art context selection methods.
Conference Proceedings of the National Level Technical Symposium on Emerging Trends in Technology, TECHNOVISION ’10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April, 2010
This article describes how integrate Java with Microsoft Technology. Sometimes there may be need an application where integrate both technologies. This article describes how to call some Java methods from .NET code, and pass some values to Java or .NET and vice versa. This is a simple ASP.NET application, which interacts with Java Applets while performing another operation. The application is very simple to do, but the main thing behind the scene is the idea and implementation logic.
Presented in the National Level Technical Symposium on Emerging Trends in Technology [TECHNOVISION ’10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April, 2010]
Workshop on Basics of Software Engineering (DFD, UML and Project Culture)Dr Sukhpal Singh Gill
Three days workshop on Basics of Software Engineering at Thapar University, Patiala on 7th-9th, 2013. Workshop on Basics of Software Engineering (DFD, UML and Project Culture)
Software Requirements Specification (SRS) for Online Tower Plotting System (O...Dr Sukhpal Singh Gill
Software Requirements Specification (SRS) for Online Tower Plotting System (OTPS) created during Master of Engineering in Software Engineering at Thapar University, Patiala, Punjab, India in Software Project Management (SPM) in 2011.
SRS of Case Study Based Software Engineering Project Development: State of Art
Download Link:
http://www.slideshare.net/sukhpalsinghgill/case-study-based-software-engineering-project-development-state-of-art
Case Study Based Software Engineering Project Development: State of ArtDr Sukhpal Singh Gill
Publised in International Journal of Scientific Research in Computer Science Applications and Management Studies (IJSRCSAMS), Volume 2, Issue 3 (May 2013).
Step by Step Development of Software Project
An approach to learn Software Project Management Practically.
SDLC phases of Software Engineering
Project Completed at Thapar University, Patiala, Punjab, India.
Download Link:
http://arxiv.org/ftp/arxiv/papers/1306/1306.2502.pdf
http://www.ijsrcsams.com/images/stories/Past_Issue_Docs/ijsrcsamsv2i3p31.pdf
SRS of this Project can be downloaded from :
http://www.slideshare.net/sukhpalsinghgill/software-requirements-specification-srs-for-online-tower-plotting-system-otps
Constructors, Destructors, call in parameterized Constructor, Multiple constructor in a class, Explicit/implicit call, Copy constructor, Dynamic Constructors and call in parameterized Constructor
PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of "measuring" its relative importance within the set. The algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is referred to as the PageRank of E and denoted by {\displaystyle PR(E).} PR(E). Other factors like Author Rank can contribute to the importance of an entity.
A PageRank results from a mathematical algorithm based on the webgraph, created by all World Wide Web pages as nodes and hyperlinks as edges, taking into consideration authority hubs such as cnn.com or usa.gov. The rank value indicates an importance of a particular page. A hyperlink to a page counts as a vote of support. The PageRank of a page is defined recursively and depends on the number and PageRank metric of all pages that link to it ("incoming links"). A page that is linked to by many pages with high PageRank receives a high rank itself.
Numerous academic papers concerning PageRank have been published since Page and Brin's original paper.[5] In practice, the PageRank concept may be vulnerable to manipulation. Research has been conducted into identifying falsely influenced PageRank rankings. The goal is to find an effective means of ignoring links from documents with falsely influenced PageRank.
Other link-based ranking algorithms for Web pages include the HITS algorithm invented by Jon Kleinberg (used by Teoma and now Ask.com),the IBM CLEVER project, the TrustRank algorithm and the hummingbird algorithm.
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.
Optimizing Search Engines(A Mathematical Point view)
Following things covered here
- A basic introduction to Search Engine Optimizing.
Introduction to Google and Bing Webmaster.
- Use of Google Toolbar to see Page Rank of each page(Calculating importance of each page for Google Search Engines.)
- PageRank Algorithm(I will the focus on this point mostly).
- How it is useful to real SEO and practical implementation of SEO.
- Google Bomb.
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.
"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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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
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.
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
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/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
2. Topological methods
Topological methods are based on the simple
premise that, given a query that describes
some required features, we are interested in
identifying library assets that come closest to
providing these features. Such methods are
critically dependent on what it means to come
closest, which in turn depends on some
definition of distance between the query and
candidate assets [1].
3. Categories of Topological methods
• Exclusive approximate retrieval: Methods that
fall into this category make a distinction
between two retrieval goals: exact retrieval and
approximate retrieval, whereby we seek to
identify library assets that completely satisfy all
the requirements of the query.
• Inclusive approximate retrieval: Methods that
fall into this category make no distinction
between exact retrieval and approximate
retrieval. Rather, they focus on identifying
library assets that minimize some measure of
distance to the query.
4. Measures of distance can be divided into two
broad classes
• Measures of functional (semantic) distance,
which reflect the extent of similarity between
the functional properties of the query and those
of candidate components.
• Measures of structural (syntactic) distance,
which reflect the extent of similarity between
the structure of (solutions to) the query and
the structure of candidate components.
6. The Google PageRank Algorithm is
used in Topological methods to
retrieve a software assets from
software repository.
7. What is PageRank?
• In short PageRank is a “vote”, by all the other
pages on the Web, about how important a
page is [3].
• A link to a page counts as a vote of support
• PR(A) = (1-d) + d(PR(T1)/C(T1)
+…+PR(Tn)/C(Tn))
8. Breaking Down the Equation
• PR(Tn) - Each page has a notion of its own self-importance. That’s “PR(T1)”
for the first page in the web all the way up to “PR(Tn)” for the last page
• C(Tn) - Each page spreads its vote out evenly amongst all of it’s outgoing
links. The count, or number, of outgoing links for page 1 is “C(T1)”, “C(Tn)”
for page n, and so on for all pages.
• PR(Tn)/C(Tn) - so if our page (page A) has a backlink from page “n” the
share of the vote page A will get is “PR(Tn)/C(Tn)”
• d(… - All these fractions of votes are added together but, to stop the other
pages having too much influence, this total vote is “damped down” by
multiplying it by 0.85 (the factor “d”)
• (1 - d) - The (1 – d) bit at the beginning is a bit of probability math magic so
the “sum of all web pages’ PageRank's will be one”: it adds in the bit lost
by the d(…. It also means that if a page has no links to it (no backlinks) even
then it will still get a small PR of 0.15 (i.e. 1 – 0.85).
9. How is it Calculated?
• The PR of each page depends on the PR of the
pages pointing to it.
• But we won’t know what PR those pages have
until the pages pointing to them have their PR
calculated and so on.
• So what we do is make a guess.
10. Simple Example
• Each page has one outgoing link (backlink). So that
means [2] :
• C(T1) = 1 for A
and
• C(T2) = 1 for B
11. We don’t know what their PR should be to begin with, so we
will just guess 1 as a safe random number.
• d (damping factor) = 0.85
• PR(A)= (1 – d) + d(PR(T1)/C(T1))= (1 – d) + d(1/1)
i.e.
• PR(A)= 0.15 + 0.85 * 1
=1
• PR(B)= 0.15 + 0.85 * 1
=1
12. Let’s Do It Again with Another Number. Let’s try 0 and re-
calculate…
• PR(A)= 0.15 + 0.85 * 0
= 0.15
= 0.15 + 0.85 *
• PR(B) 0.15
= 0.2775
• Now we have calculated a “next best guess” so we just plug it in the
equation again…
• PR(A)= 0.15 + 0.85 * 0.2775
= 0.385875
• PR(B)= 0.15 + 0.85 * 0.385875
= 0.47799375
And again…
• PR(A)= 0.15 + 0.85 * 0.47799375
= 0.5562946875
• PR(B)= 0.15 + 0.85 * 0.5562946875
= 0.622850484375
13. Principle
• It doesn’t matter where you start your guess,
once the PageRank calculations have settled
down, the “normalized probability
distribution” (the average PageRank for all
pages) will be 1.0
• In software repository we are using software
assets instead of pages and also using
relationships among software assets based on
their keywords instead of links.
15. References:
[1] A survey of software reuse libraries A. Mili a,_, R. Mili
b and R.T. Mittermeir Annals of Software
Engineering 5 (1998) 349–414 349
[2] http://wwwdb.stanford.edu/~backrub/google.html
http://www-db.stanford.edu/~backrub/google.html
[3] Semantic Component Retrieval in Software
Engineering Inaugural dissertation zur Erlangung des
akademischen Grades eines Doktors der
Naturwissenschaften der, Universitat Mannheim,
Mannheim, 2008