Ranking systems
•Download as PPT, PDF•
0 likes•310 views
The document discusses PageRank and ranking systems. It presents a set of axioms that any valid ranking system should satisfy, including being independent of vertex names (isomorphism), handling self-edges, handling vote by committees, collapsing nodes, proxies, deletions and duplications. It then proves that PageRank is the only ranking system that satisfies all the axioms, making it a unique representation.
Report
Share
Report
Share
![[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Ranking systems
1. The Page Rank Axioms present a set of properties (axioms) for ranking systems on graphs that are satisfied by the PageRank algorithm.
2. It is shown that any ranking system that satisfies the axioms must be identical to PageRank.
3. This establishes PageRank as the unique ranking system that satisfies the presented axioms, known as the representation theorem for PageRank.
Photography and Visuals
This document discusses visual literacy skills and photographic composition. It defines visual literacy as the ability to observe, interpret, and analyze visual elements in photographs. It also defines photographic composition as the arrangement of visual elements within an image frame. The document provides tips for strong composition, including following the rule of thirds, balancing elements, using diagonal lines, and considering perspective and framing. It discusses different types of compositions like landscapes, portraits, panoramic views and macro photography.
Westera Civil And Environmental
Westera Partners is an Australian engineering consulting firm with over 30 staff across three offices. The document describes Westera Partner's extensive experience in various types of structural engineering projects including residential, commercial, and institutional buildings. It provides biographies of the directors and partners of the firm and summarizes several of Westera Partner's civil engineering projects involving residential and aged care developments.
Node Philly
Appium is a cross-platform solution for automating native and hybrid mobile apps. It allows testing an app on multiple platforms like Android and iOS with a single test. Appium uses the WebDriver protocol so any WebDriver client can be used to write automated tests. It supports automating apps without modifying them (no hooks needed) and has an active community and ecosystem around it. A demo shows how quickly one can get started automating a sample app using Appium.
Westera Corp Booklet
Westera Partners Pty Ltd is an Australian engineering consulting firm with over 30 staff across three offices. They have extensive experience in structural, civil and environmental engineering for various project types including residential, commercial, and industrial. Some of their representative project experiences include medium-density residential developments, aged care facilities, industrial buildings, and mixed-use developments. They emphasize a client-focused approach and involvement of senior engineers in all projects.
Westera Residential
Westera Partners is an Australian engineering consulting firm with over 30 staff across three offices. It has extensive experience in structural engineering for various building types including residential housing, commercial and industrial buildings, and community facilities. The document provides details about several residential projects completed by Westera Partners, highlighting how they worked with architects and builders to develop structural solutions that met design goals and site constraints.
Westera Commercial Industrial Multi Res
Westera Partners Pty Ltd is an Australian consulting engineering firm with over 30 staff across three offices. They have extensive experience in structural engineering for residential, commercial, and industrial projects including townhouses, apartment buildings, factories, aged care facilities, schools, and more. The document provides details about the company's history, personnel, directors, and several example projects they have completed.
Linkanalysis handout
This document summarizes Link Analysis and PageRank. It discusses how PageRank works by assigning importance scores to web pages based on the link structure of the web viewed as a graph. It provides formal definitions of PageRank and explains how it is computed using an iterative algorithm. It also discusses factors that influence PageRank scores like the damping factor alpha and how PageRank behaves for different values of alpha.
Recommended
Ranking systems
1. The Page Rank Axioms present a set of properties (axioms) for ranking systems on graphs that are satisfied by the PageRank algorithm.
2. It is shown that any ranking system that satisfies the axioms must be identical to PageRank.
3. This establishes PageRank as the unique ranking system that satisfies the presented axioms, known as the representation theorem for PageRank.
Photography and Visuals
This document discusses visual literacy skills and photographic composition. It defines visual literacy as the ability to observe, interpret, and analyze visual elements in photographs. It also defines photographic composition as the arrangement of visual elements within an image frame. The document provides tips for strong composition, including following the rule of thirds, balancing elements, using diagonal lines, and considering perspective and framing. It discusses different types of compositions like landscapes, portraits, panoramic views and macro photography.
Westera Civil And Environmental
Westera Partners is an Australian engineering consulting firm with over 30 staff across three offices. The document describes Westera Partner's extensive experience in various types of structural engineering projects including residential, commercial, and institutional buildings. It provides biographies of the directors and partners of the firm and summarizes several of Westera Partner's civil engineering projects involving residential and aged care developments.
Node Philly
Appium is a cross-platform solution for automating native and hybrid mobile apps. It allows testing an app on multiple platforms like Android and iOS with a single test. Appium uses the WebDriver protocol so any WebDriver client can be used to write automated tests. It supports automating apps without modifying them (no hooks needed) and has an active community and ecosystem around it. A demo shows how quickly one can get started automating a sample app using Appium.
Westera Corp Booklet
Westera Partners Pty Ltd is an Australian engineering consulting firm with over 30 staff across three offices. They have extensive experience in structural, civil and environmental engineering for various project types including residential, commercial, and industrial. Some of their representative project experiences include medium-density residential developments, aged care facilities, industrial buildings, and mixed-use developments. They emphasize a client-focused approach and involvement of senior engineers in all projects.
Westera Residential
Westera Partners is an Australian engineering consulting firm with over 30 staff across three offices. It has extensive experience in structural engineering for various building types including residential housing, commercial and industrial buildings, and community facilities. The document provides details about several residential projects completed by Westera Partners, highlighting how they worked with architects and builders to develop structural solutions that met design goals and site constraints.
Westera Commercial Industrial Multi Res
Westera Partners Pty Ltd is an Australian consulting engineering firm with over 30 staff across three offices. They have extensive experience in structural engineering for residential, commercial, and industrial projects including townhouses, apartment buildings, factories, aged care facilities, schools, and more. The document provides details about the company's history, personnel, directors, and several example projects they have completed.
Linkanalysis handout
This document summarizes Link Analysis and PageRank. It discusses how PageRank works by assigning importance scores to web pages based on the link structure of the web viewed as a graph. It provides formal definitions of PageRank and explains how it is computed using an iterative algorithm. It also discusses factors that influence PageRank scores like the damping factor alpha and how PageRank behaves for different values of alpha.
[ICDE 2012] On Top-k Structural Similarity Search
In this talk, we talk about the following classic problem: given a node in a graph, how can we efficiently track the top-k similar nodes regarding this node, by simply checking the graph link structure? This talk is accompanying with the ICDE 2012 paper "On Top-k Structural Similarity Search", which can be found at http://www.cs.ubc.ca/~peil/research.html
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Beyond the Classifier, Inspiration from Engineering Algorithms: Many data scientists work within the realm of Machine Learning and their problems are often addressable with techniques such as classifiers and recommendation engines. At Tapad, we have often had to look outside that standard toolkit to find inspiration from more traditional engineering algorithms. This has included solving our Device Graph’s connected component problem at scale as well as maintaining our Device Graph’s time-consistency in our cluster identification week over week.
Markov chains and page rankGraphs.pdf
The document discusses applications of Markov chains, including PageRank and random walks. It provides details on:
- PageRank, which was developed by Larry Page and Sergey Brin to rank web pages based on the link structure of the web. It models the random surfing of a user on the web as a Markov chain.
- The PageRank algorithm assigns initial uniform probabilities to web pages and then iteratively updates the probabilities based on the links between pages until it converges. This stationary distribution provides the ranking of pages.
- Computing PageRank on the entire web graph is slow, so Google estimates it by running the random walk for a finite number of steps to approximate the stationary distribution.
Analysis Of Algorithm
This document discusses search engines and how they work. It defines search engines as programs that use keywords to search documents and return results in order of relevance. Google, Bing and Ask are provided as examples. It then discusses how search engines calculate PageRank, the algorithm used by Google, to determine the importance of web pages. PageRank is calculated based on the number and quality of links to a page. The document also provides examples of calculating PageRank for simple networks of web pages.
Lec5 Pagerank
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses representing graphs in MapReduce, performing breadth-first search, finding shortest paths, and calculating PageRank through an iterative process of redistributing PageRank values along edges in the graph. The PageRank algorithm is broken into phases that map nodes to PageRank fragments, reduce to calculate new PageRank values, and iterate until convergence is reached. While MapReduce has limitations for iterative algorithms, this approach allows processing graph partitions in parallel through multiple MapReduce jobs.
Lec5 Pagerank
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses graph representations like adjacency matrices and sparse matrices. It explains how breadth-first search and shortest path algorithms can be implemented in MapReduce through iterative passes. It then describes how PageRank can also be distributed by mapping graph nodes to PageRank value distributions, reducing the values, and iterating until convergence is reached.
Lec5 pagerank
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses graph representations like adjacency matrices and sparse matrices. It explains how breadth-first search and shortest path algorithms can be implemented in MapReduce through iterative passes. It then describes how PageRank can also be distributed by mapping graph nodes to PageRank value distributions, reducing the values, and iterating until convergence is reached.
Pagerank (from Google)
The document discusses graph algorithms and PageRank and how they can be implemented using MapReduce. It covers graph representations like adjacency matrices and sparse matrices that are suitable for distributed computing. It also describes how breadth-first search, shortest path finding, and PageRank calculations can be broken down into MapReduce jobs by iteratively processing portions of the graph in parallel. While not optimal for highly iterative algorithms, MapReduce can help distribute the computation across multiple machines to process large graphs.
GraphFrames: Graph Queries In Spark SQL
GraphFrames provides a unified API for graph queries and algorithms in Apache Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materialized views can greatly improve performance of graph queries by enabling efficient join elimination and reordering. An evaluation found GraphFrames outperforms Neo4j for unanchored queries and approaches performance of GraphX for graph algorithms using whole-stage code generation in Spark SQL. Future work includes automatically suggesting optimal views and exploiting data partitioning.
1 chayes
This document summarizes a talk on algorithms that use locality to solve network problems efficiently. It discusses how limitations on network visibility require local algorithms that make sequential decisions using limited information. It presents local algorithms for preferential attachment networks and general graphs that solve problems like finding high-degree nodes and computing minimum dominating sets. It also describes how locality can enable sublinear-time algorithms for estimating PageRank values and solving influence maximization problems in viral marketing models. The talk outlines techniques like multiscale analysis and sparse matrix methods that allow computing PageRank summaries and influential nodes faster than previous methods.
Optimized interleaving for online retrieval evaluation
This document summarizes an academic paper that proposes a new framework for optimizing online retrieval evaluation using interleaving. The paper inverts the problem by framing interleaving algorithm design as a constrained optimization problem. The goal is to generate interleaved result lists that lead to user clicks reflecting true preferences between retrieval systems, while being robust to biases and not altering the search experience. The paper evaluates the proposed approach compared to previous interleaving methods and finds it improves sensitivity to differences between systems while avoiding biases of other approaches.
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames provides a unified API for graph queries and algorithms in Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materializing the right views, such as the triplet view for GraphX algorithms or user-defined views for queries, can improve performance. An evaluation shows GraphFrames outperforms Neo4j for unanchored queries and approaches GraphX performance for graph algorithms using Spark SQL. Future work includes automatically suggesting optimal views and exploiting attribute-based partitioning.
Web Crawling and Reinforcement Learning
Description of a "new" way to implement a crawler to avoid the bias of payed google indexing services
PageRank Algorithm In data mining
This document provides an overview of the PageRank algorithm. It begins with background on PageRank and its development by Brin and Page. It then introduces the concepts behind PageRank, including how it uses the link structure of webpages to determine importance. The core PageRank algorithm is explained, modeling the web as a graph and calculating page importance based on both the number and quality of inbound links. Iterative methods like power iteration are described for approximating solutions. Examples are given to illustrate PageRank calculations over multiple iterations. Implementation details, applications, advantages/disadvantages are also discussed at a high level. Pseudocode is included.
random test
This document discusses program analysis using random interpretation. It introduces random interpretation as a new class of program analyses that is almost as simple and efficient as random testing, but also almost as sound as abstract interpretation. Random interpretation works by choosing random inputs, executing both branches of conditionals, combining variable values at joins, and testing assertions. Examples are provided to illustrate how random interpretation can prove properties that random testing alone cannot, while avoiding the complexity of abstract interpretation. The document outlines applications of random interpretation to problems involving linear arithmetic, uninterpreted functions, and interprocedural analysis. Experimental results suggest randomized algorithms can discover more program properties than deterministic ones, while having only a small performance overhead.
PHP
This document contains a 10 question PHP quiz with multiple choice answers and explanations. The quiz covers various PHP concepts like:
- PHP can handle forms, files, databases, and more.
- Strings, arrays, objects and resources are the main variable types in PHP.
- Functions like header(), array_sum(), and filesize() can be used for redirection, math operations on arrays, and getting file sizes.
- Magic constants, loops, arrays and string functions are also covered.
The quiz provides the answers and explanations to test the reader's knowledge of basic PHP concepts.
Link Analysis
This document discusses link analysis and PageRank, an algorithm for identifying important nodes in large network graphs. It begins with an overview of graph data structures and the goal of identifying influential nodes. It then introduces PageRank, explaining its basic assumptions and showing examples of how it calculates node importance scores. The document discusses problems with the initial PageRank approach and how it was improved with the Complete PageRank algorithm. Finally, it briefly introduces Topic-sensitive PageRank, which aims to identify important nodes related to specific topics.
Degree Sequence Bounds - ICDT 2023 - Final.pptx
The document proposes using degree sequences and the degree sequence bound to provide an upper bound on query cardinality during query optimization. It defines degree sequences as the sorted list of value frequencies within each relation. It then proves that the degree sequence bound, which uses the degree sequences to construct a worst-case tensor representation of each relation, provides a tight upper bound on query output size that dominates previous bounds. The document also proposes a more efficient functional degree sequence bound that compresses the degree sequences into piecewise constant functions.
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
The Scientific community has been inclined in the last decade to define Axioms for rating the quality of centrality measures such as the PageRank importance scoring algorithm, the HITS algorithm, the SALSA, the Betweennes or even the classic Degree metric, etc.
This is a review of a WWW conference paper authored by Paolo Blodi, Alessandro Luongo and Sebestiano Vigna.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
More Related Content
Similar to Ranking systems
[ICDE 2012] On Top-k Structural Similarity Search
In this talk, we talk about the following classic problem: given a node in a graph, how can we efficiently track the top-k similar nodes regarding this node, by simply checking the graph link structure? This talk is accompanying with the ICDE 2012 paper "On Top-k Structural Similarity Search", which can be found at http://www.cs.ubc.ca/~peil/research.html
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Beyond the Classifier, Inspiration from Engineering Algorithms: Many data scientists work within the realm of Machine Learning and their problems are often addressable with techniques such as classifiers and recommendation engines. At Tapad, we have often had to look outside that standard toolkit to find inspiration from more traditional engineering algorithms. This has included solving our Device Graph’s connected component problem at scale as well as maintaining our Device Graph’s time-consistency in our cluster identification week over week.
Markov chains and page rankGraphs.pdf
The document discusses applications of Markov chains, including PageRank and random walks. It provides details on:
- PageRank, which was developed by Larry Page and Sergey Brin to rank web pages based on the link structure of the web. It models the random surfing of a user on the web as a Markov chain.
- The PageRank algorithm assigns initial uniform probabilities to web pages and then iteratively updates the probabilities based on the links between pages until it converges. This stationary distribution provides the ranking of pages.
- Computing PageRank on the entire web graph is slow, so Google estimates it by running the random walk for a finite number of steps to approximate the stationary distribution.
Analysis Of Algorithm
This document discusses search engines and how they work. It defines search engines as programs that use keywords to search documents and return results in order of relevance. Google, Bing and Ask are provided as examples. It then discusses how search engines calculate PageRank, the algorithm used by Google, to determine the importance of web pages. PageRank is calculated based on the number and quality of links to a page. The document also provides examples of calculating PageRank for simple networks of web pages.
Lec5 Pagerank
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses representing graphs in MapReduce, performing breadth-first search, finding shortest paths, and calculating PageRank through an iterative process of redistributing PageRank values along edges in the graph. The PageRank algorithm is broken into phases that map nodes to PageRank fragments, reduce to calculate new PageRank values, and iterate until convergence is reached. While MapReduce has limitations for iterative algorithms, this approach allows processing graph partitions in parallel through multiple MapReduce jobs.
Lec5 Pagerank
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses graph representations like adjacency matrices and sparse matrices. It explains how breadth-first search and shortest path algorithms can be implemented in MapReduce through iterative passes. It then describes how PageRank can also be distributed by mapping graph nodes to PageRank value distributions, reducing the values, and iterating until convergence is reached.
Lec5 pagerank
This document summarizes a lecture on graph algorithms and PageRank using MapReduce. It discusses graph representations like adjacency matrices and sparse matrices. It explains how breadth-first search and shortest path algorithms can be implemented in MapReduce through iterative passes. It then describes how PageRank can also be distributed by mapping graph nodes to PageRank value distributions, reducing the values, and iterating until convergence is reached.
Pagerank (from Google)
The document discusses graph algorithms and PageRank and how they can be implemented using MapReduce. It covers graph representations like adjacency matrices and sparse matrices that are suitable for distributed computing. It also describes how breadth-first search, shortest path finding, and PageRank calculations can be broken down into MapReduce jobs by iteratively processing portions of the graph in parallel. While not optimal for highly iterative algorithms, MapReduce can help distribute the computation across multiple machines to process large graphs.
GraphFrames: Graph Queries In Spark SQL
GraphFrames provides a unified API for graph queries and algorithms in Apache Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materialized views can greatly improve performance of graph queries by enabling efficient join elimination and reordering. An evaluation found GraphFrames outperforms Neo4j for unanchored queries and approaches performance of GraphX for graph algorithms using whole-stage code generation in Spark SQL. Future work includes automatically suggesting optimal views and exploiting data partitioning.
1 chayes
This document summarizes a talk on algorithms that use locality to solve network problems efficiently. It discusses how limitations on network visibility require local algorithms that make sequential decisions using limited information. It presents local algorithms for preferential attachment networks and general graphs that solve problems like finding high-degree nodes and computing minimum dominating sets. It also describes how locality can enable sublinear-time algorithms for estimating PageRank values and solving influence maximization problems in viral marketing models. The talk outlines techniques like multiscale analysis and sparse matrix methods that allow computing PageRank summaries and influential nodes faster than previous methods.
Optimized interleaving for online retrieval evaluation
This document summarizes an academic paper that proposes a new framework for optimizing online retrieval evaluation using interleaving. The paper inverts the problem by framing interleaving algorithm design as a constrained optimization problem. The goal is to generate interleaved result lists that lead to user clicks reflecting true preferences between retrieval systems, while being robust to biases and not altering the search experience. The paper evaluates the proposed approach compared to previous interleaving methods and finds it improves sensitivity to differences between systems while avoiding biases of other approaches.
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames provides a unified API for graph queries and algorithms in Spark SQL. It translates graph patterns and algorithms to relational operations optimized by the Spark SQL query optimizer. Materializing the right views, such as the triplet view for GraphX algorithms or user-defined views for queries, can improve performance. An evaluation shows GraphFrames outperforms Neo4j for unanchored queries and approaches GraphX performance for graph algorithms using Spark SQL. Future work includes automatically suggesting optimal views and exploiting attribute-based partitioning.
Web Crawling and Reinforcement Learning
Description of a "new" way to implement a crawler to avoid the bias of payed google indexing services
PageRank Algorithm In data mining
This document provides an overview of the PageRank algorithm. It begins with background on PageRank and its development by Brin and Page. It then introduces the concepts behind PageRank, including how it uses the link structure of webpages to determine importance. The core PageRank algorithm is explained, modeling the web as a graph and calculating page importance based on both the number and quality of inbound links. Iterative methods like power iteration are described for approximating solutions. Examples are given to illustrate PageRank calculations over multiple iterations. Implementation details, applications, advantages/disadvantages are also discussed at a high level. Pseudocode is included.
random test
This document discusses program analysis using random interpretation. It introduces random interpretation as a new class of program analyses that is almost as simple and efficient as random testing, but also almost as sound as abstract interpretation. Random interpretation works by choosing random inputs, executing both branches of conditionals, combining variable values at joins, and testing assertions. Examples are provided to illustrate how random interpretation can prove properties that random testing alone cannot, while avoiding the complexity of abstract interpretation. The document outlines applications of random interpretation to problems involving linear arithmetic, uninterpreted functions, and interprocedural analysis. Experimental results suggest randomized algorithms can discover more program properties than deterministic ones, while having only a small performance overhead.
PHP
This document contains a 10 question PHP quiz with multiple choice answers and explanations. The quiz covers various PHP concepts like:
- PHP can handle forms, files, databases, and more.
- Strings, arrays, objects and resources are the main variable types in PHP.
- Functions like header(), array_sum(), and filesize() can be used for redirection, math operations on arrays, and getting file sizes.
- Magic constants, loops, arrays and string functions are also covered.
The quiz provides the answers and explanations to test the reader's knowledge of basic PHP concepts.
Link Analysis
This document discusses link analysis and PageRank, an algorithm for identifying important nodes in large network graphs. It begins with an overview of graph data structures and the goal of identifying influential nodes. It then introduces PageRank, explaining its basic assumptions and showing examples of how it calculates node importance scores. The document discusses problems with the initial PageRank approach and how it was improved with the Complete PageRank algorithm. Finally, it briefly introduces Topic-sensitive PageRank, which aims to identify important nodes related to specific topics.
Degree Sequence Bounds - ICDT 2023 - Final.pptx
The document proposes using degree sequences and the degree sequence bound to provide an upper bound on query cardinality during query optimization. It defines degree sequences as the sorted list of value frequencies within each relation. It then proves that the degree sequence bound, which uses the degree sequences to construct a worst-case tensor representation of each relation, provides a tight upper bound on query output size that dominates previous bounds. The document also proposes a more efficient functional degree sequence bound that compresses the degree sequences into piecewise constant functions.
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
The Scientific community has been inclined in the last decade to define Axioms for rating the quality of centrality measures such as the PageRank importance scoring algorithm, the HITS algorithm, the SALSA, the Betweennes or even the classic Degree metric, etc.
This is a review of a WWW conference paper authored by Paolo Blodi, Alessandro Luongo and Sebestiano Vigna.
Similar to Ranking systems (20)
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Yael Elmatad, Senior Data Scientist, Tapad at MLconf NYC - 4/15/16
Optimized interleaving for online retrieval evaluation
Optimized interleaving for online retrieval evaluation
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
GraphFrames: Graph Queries in Spark SQL by Ankur Dave
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
Rank Monotonicity in Centrality Measures (A report about Quality guarantees f...
Recently uploaded
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Operating System Used by Users in day-to-day life.pptx
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Azure API Management to expose backend services securely
How to use Azure API Management to expose backend service securely
Finale of the Year: Apply for Next One!
Presentation for the event called "Finale of the Year: Apply for Next One!" organized by GDSC PJATK
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Recently uploaded (20)
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
Operating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptx
Azure API Management to expose backend services securely
Azure API Management to expose backend services securely
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Ranking systems
- 1. The Page Rank Axioms Based on Ranking Systems: The PageRank Axioms , by Alon Altman and Moshe Tennenholtz. Presented by Aron Matskin
- 28. DEL: Proof a c b d c b d a VBC
- 29. DEL: Proof c b d a VBC c b d a
- 30. DEL: Proof ISO,PRO c b d a c b d a
- 31. DEL: Proof PRO c d a c b d a
- 32. DEL: Proof PRO c d a c d a
- 33. DEL: Proof VBC c d a c d a
- 34. DEL: Proof VBC c d a a c d
- 37. DELETE: Proof x = = = = COL x y
- 38. DELETE: Proof PRO x y
- 40. DUPLICATE: Proof c b d a c b d a VBC
- 41. DUPLICATE: Proof c b d a VBC c b d a
- 42. DUPLICATE: Proof c b d a COL c b d a
- 43. DUPLICATE: Proof c b d a ISO,PRO c b d a
- 44. DUPLICATE: Proof c b d a COL -1 c b d a
- 45. DUPLICATE: Proof VBC -1 c b d a c b d a
- 47. Proof by Example on b and d b c a a b c a b c G A G d d d R G a b c d 0 1 1 0 0 0 0 ⅓ ½ 0 0 ⅓ ½ 0 0 ⅓ 4 1 3 3
- 49. Step 2: Choose Node to Remove b c a d
- 50. Step 3: Remove “self-edges” b c a d
- 51. Step 4: Duplicate Predecessors b c a d
- 52. Step 5: DELETE the Node b c d
- 55. Step 5: DELETE the Node b d
- 58. Step 8: Equalize by Reverse DEL b d By ISO b=d. By DEL and SE: in G’ b<d.
- 59. Example for a and d b c a d b c a d
- 60. After Removal of c b a d
- 61. Duplicate Predecessors of b b a d
- 62. DELETE b a d
- 63. DELETE Extras a d
- 64. Before Balancing a d
- 65. After Balancing a d Conclusion: a<d.
- 66. What about a and b ? b a d
- 67. What about a and b ? b a d
- 68. What about a and b ? b a
- 69. What about a and b ? b a
- 70. What about a and b ? b a
- 71. What about a and b ? b a Conclusion: a=b.
- 73. The End c b d a ½ 0 0 ½ 0 1 0 1 0 a b c a b c
Editor's Notes
- Booby Fischer was #49 on PCA ratings list in 1994, although he had not played for 20 years