The Boolean model is a classical information retrieval model based on set theory and Boolean logic. Queries are specified as Boolean expressions to retrieve documents that either contain or do not contain the query terms. All term frequencies are binary and documents are retrieved based on an exact match to the query terms. However, this model has limitations as it does not rank documents and queries are difficult for users to translate into Boolean expressions, often returning too few or too many results.
Vector space model or term vector model is an algebraic model for representing text documents as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System
This talk features the basics behind the science of Information Retrieval with a story-mode on information and its various aspects. It then takes you through a quick journey into the process behind building of the search engine.
Vector space model or term vector model is an algebraic model for representing text documents as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System
This talk features the basics behind the science of Information Retrieval with a story-mode on information and its various aspects. It then takes you through a quick journey into the process behind building of the search engine.
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
INTRODUCTION TO INFORMATION RETRIEVAL
This lecture will introduce the information retrieval problem, introduce the terminology related to IR, and provide a history of IR. In particular, the history of the web and its impact on IR will be discussed. Special attention and emphasis will be given to the concept of relevance in IR and the critical role it has played in the development of the subject. The lecture will end with a conceptual explanation of the IR process, and its relationships with other domains as well as current research developments.
INFORMATION RETRIEVAL MODELS
This lecture will present the models that have been used to rank documents according to their estimated relevance to user given queries, where the most relevant documents are shown ahead to those less relevant. Many of these models form the basis for many of the ranking algorithms used in many of past and today’s search applications. The lecture will describe models of IR such as Boolean retrieval, vector space, probabilistic retrieval, language models, and logical models. Relevance feedback, a technique that either implicitly or explicitly modifies user queries in light of their interaction with retrieval results, will also be discussed, as this is particularly relevant to web search and personalization.
Information retrieval 10 vector and probabilistic modelsVaibhav Khanna
Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings.
Information retrieval 14 fuzzy set models of irVaibhav Khanna
Fuzzy Model is a set theoretic model of document retrieval based on fuzzy theory. An opposite to this is the Exact match mechanism by which only the objects satisfying some well specified criteria, against object attributes, are returned to the user as a query answer.
The goal of information retrieval (IR) is to provide users with those documents that will satisfy their information need. The information need can be understood as forming a pyramid, where only its peak is made visible by users in the form of a conceptual query.
UMAP16: A Framework for Dynamic Knowledge Modeling in Textbook-Based LearningYun Huang
Various e-learning systems that provide electronic textbooks
are gathering data on large numbers of student reading interactions. This data can potentially be used to model
student knowledge acquisition. However, reading activity
is often overlooked in canonical student modeling. Prior
studies modeling learning from reading either estimate student
knowledge at the end of all reading activities, or use quiz performance data with expert-crafted knowledge components (KCs). In this work, we demonstrate that the dynamic modeling of student knowledge is feasible and that automatic text analysis can be applied to save expert effort. We propose a data-driven approach for dynamic student modeling in textbook-based learning. We formulate the problem of modeling learning from reading as a reading time prediction problem, reconstruct existing popular student models (such as Knowledge Tracing) and explore two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. We evaluate the proposed framework using a dataset collected from a Human-Computer Interaction course. Results show that our approach for reading modeling is plausible; the proposed Knowledge Tracing-based student model reliably outperforms baselines and the latent semantic-based approach
can be a promising way to construct a KC model. Serving
as the first step to model dynamic knowledge in textbook-based
learning, our framework can be applied to a broader context of open-corpus personalized learning.
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
INTRODUCTION TO INFORMATION RETRIEVAL
This lecture will introduce the information retrieval problem, introduce the terminology related to IR, and provide a history of IR. In particular, the history of the web and its impact on IR will be discussed. Special attention and emphasis will be given to the concept of relevance in IR and the critical role it has played in the development of the subject. The lecture will end with a conceptual explanation of the IR process, and its relationships with other domains as well as current research developments.
INFORMATION RETRIEVAL MODELS
This lecture will present the models that have been used to rank documents according to their estimated relevance to user given queries, where the most relevant documents are shown ahead to those less relevant. Many of these models form the basis for many of the ranking algorithms used in many of past and today’s search applications. The lecture will describe models of IR such as Boolean retrieval, vector space, probabilistic retrieval, language models, and logical models. Relevance feedback, a technique that either implicitly or explicitly modifies user queries in light of their interaction with retrieval results, will also be discussed, as this is particularly relevant to web search and personalization.
Information retrieval 10 vector and probabilistic modelsVaibhav Khanna
Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings.
Information retrieval 14 fuzzy set models of irVaibhav Khanna
Fuzzy Model is a set theoretic model of document retrieval based on fuzzy theory. An opposite to this is the Exact match mechanism by which only the objects satisfying some well specified criteria, against object attributes, are returned to the user as a query answer.
The goal of information retrieval (IR) is to provide users with those documents that will satisfy their information need. The information need can be understood as forming a pyramid, where only its peak is made visible by users in the form of a conceptual query.
UMAP16: A Framework for Dynamic Knowledge Modeling in Textbook-Based LearningYun Huang
Various e-learning systems that provide electronic textbooks
are gathering data on large numbers of student reading interactions. This data can potentially be used to model
student knowledge acquisition. However, reading activity
is often overlooked in canonical student modeling. Prior
studies modeling learning from reading either estimate student
knowledge at the end of all reading activities, or use quiz performance data with expert-crafted knowledge components (KCs). In this work, we demonstrate that the dynamic modeling of student knowledge is feasible and that automatic text analysis can be applied to save expert effort. We propose a data-driven approach for dynamic student modeling in textbook-based learning. We formulate the problem of modeling learning from reading as a reading time prediction problem, reconstruct existing popular student models (such as Knowledge Tracing) and explore two automatic text analysis approaches (bag-of-words-based and latent semantic-based) to build the KC model. We evaluate the proposed framework using a dataset collected from a Human-Computer Interaction course. Results show that our approach for reading modeling is plausible; the proposed Knowledge Tracing-based student model reliably outperforms baselines and the latent semantic-based approach
can be a promising way to construct a KC model. Serving
as the first step to model dynamic knowledge in textbook-based
learning, our framework can be applied to a broader context of open-corpus personalized learning.
Information retrival system and PageRank algorithmRupali Bhatnagar
We discuss the various models for Information retrieval system present in literature and discuss them mathematically. We also study the PageRank Algorithm which is used for relevant search.
A task-based scientific paper recommender system for literature review and ma...Aravind Sesagiri Raamkumar
My PhD oral defense presentation (as of Oct 3rd 2017)
The dissertation can be requested at this link https://www.researchgate.net/publication/323308750_A_task-based_scientific_paper_recommender_system_for_literature_review_and_manuscript_preparation
A presentation on a special category of databases called Deductive Databases. It is an attempt to merge logic programming with relational database. Other types include Object-oriented databases, Graph databases, XML databases, Multi-model databases, etc.
Asking Clarifying Questions in Open-Domain Information-Seeking ConversationsMohammad Aliannejadi
Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience.
Alternatively, systems can improve user satisfaction by proactively asking questions of the users to clarify their information needs. Asking clarifying questions is especially important in conversational systems since they can only return a limited number of (often only one) result(s).
In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems. To this end, we propose an offline evaluation methodology for the task and collect a dataset, called Qulac, through crowdsourcing. Our dataset is built on top of the TREC Web Track 2009-2012 data and consists of over 10K question-answer pairs for 198 TREC topics with 762 facets.
Our experiments on an oracle model demonstrate that asking only one good question leads to over 170% retrieval performance improvement in terms of P@1, which clearly demonstrates the potential impact of the task. We further propose a retrieval framework consisting of three components: question retrieval, question selection, and document retrieval. In particular, our question selection model takes into account the original query and previous question-answer interactions while selecting the next question. Our model significantly outperforms competitive baselines. To foster research in this area, we have made Qulac publicly available.
Information retrieval 13 alternative set theoretic modelsVaibhav Khanna
Alternative Set Theoretic Models
Fuzzy Set Model :a set theoretic model of document retrieval based on fuzzy theory.
Extended Boolean Model:a set theoretic model of document retrieval based on an extension of the classic Boolean model. The idea is to interpret partial matches as Euclidean distances represented in a vectorial space of index terms.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Lucene/Solr Revolution 2015: Where Search Meets Machine LearningJoaquin Delgado PhD.
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Lucene/Solr Revolution 2015: Where Search Meets Machine LearningS. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Information and network security 47 authentication applicationsVaibhav Khanna
Kerberos provides a centralized authentication server whose function is to authenticate users to servers and servers to users. In Kerberos Authentication server and database is used for client authentication. Kerberos runs as a third-party trusted server known as the Key Distribution Center (KDC).
Information and network security 46 digital signature algorithmVaibhav Khanna
The Digital Signature Algorithm (DSA) is a Federal Information Processing Standard for digital signatures, based on the mathematical concept of modular exponentiation and the discrete logarithm problem. DSA is a variant of the Schnorr and ElGamal signature schemes
Information and network security 45 digital signature standardVaibhav Khanna
The Digital Signature Standard is a Federal Information Processing Standard specifying a suite of algorithms that can be used to generate digital signatures established by the U.S. National Institute of Standards and Technology in 1994
Information and network security 44 direct digital signaturesVaibhav Khanna
The Direct Digital Signature is only include two parties one to send message and other one to receive it. According to direct digital signature both parties trust each other and knows there public key. The message are prone to get corrupted and the sender can declines about the message sent by him any time
Information and network security 43 digital signaturesVaibhav Khanna
Digital signatures are the public-key primitives of message authentication. In the physical world, it is common to use handwritten signatures on handwritten or typed messages. ... Digital signature is a cryptographic value that is calculated from the data and a secret key known only by the signer
Information and network security 42 security of message authentication codeVaibhav Khanna
Message Authentication Requirements
Disclosure: Release of message contents to any person or process not possess- ing the appropriate cryptographic key.
Traffic analysis: Discovery of the pattern of traffic between parties. ...
Masquerade: Insertion of messages into the network from a fraudulent source
Information and network security 41 message authentication codeVaibhav Khanna
In cryptography, a message authentication code, sometimes known as a tag, is a short piece of information used to authenticate a message—in other words, to confirm that the message came from the stated sender and has not been changed.
Information and network security 40 sha3 secure hash algorithmVaibhav Khanna
SHA-3 is the latest member of the Secure Hash Algorithm family of standards, released by NIST on August 5, 2015. Although part of the same series of standards, SHA-3 is internally different from the MD5-like structure of SHA-1 and SHA-2
Information and network security 39 secure hash algorithmVaibhav Khanna
The Secure Hash Algorithms are a family of cryptographic hash functions published by the National Institute of Standards and Technology as a U.S. Federal Information Processing Standard, including: SHA-0: A retronym applied to the original version of the 160-bit hash function published in 1993 under the name "SHA"
Information and network security 38 birthday attacks and security of hash fun...Vaibhav Khanna
Birthday attack can be used in communication abusage between two or more parties. ... The mathematics behind this problem led to a well-known cryptographic attack called the birthday attack, which uses this probabilistic model to reduce the complexity of cracking a hash function
Information and network security 35 the chinese remainder theoremVaibhav Khanna
In number theory, the Chinese remainder theorem states that if one knows the remainders of the Euclidean division of an integer n by several integers, then one can determine uniquely the remainder of the division of n by the product of these integers, under the condition that the divisors are pairwise coprime.
Information and network security 34 primalityVaibhav Khanna
A primality test is an algorithm for determining whether an input number is prime. Among other fields of mathematics, it is used for cryptography. Unlike integer factorization, primality tests do not generally give prime factors, only stating whether the input number is prime or not
Information and network security 33 rsa algorithmVaibhav Khanna
RSA algorithm is asymmetric cryptography algorithm. Asymmetric actually means that it works on two different keys i.e. Public Key and Private Key. As the name describes that the Public Key is given to everyone and Private key is kept private
Information and network security 32 principles of public key cryptosystemsVaibhav Khanna
Public-key cryptography, or asymmetric cryptography, is an encryption scheme that uses two mathematically related, but not identical, keys - a public key and a private key. Unlike symmetric key algorithms that rely on one key to both encrypt and decrypt, each key performs a unique function.
Information and network security 31 public key cryptographyVaibhav Khanna
Public-key cryptography, or asymmetric cryptography, is a cryptographic system that uses pairs of keys: public keys, and private keys. The generation of such key pairs depends on cryptographic algorithms which are based on mathematical problems termed one-way function
Information and network security 30 random numbersVaibhav Khanna
Random numbers are fundamental building blocks of cryptographic systems and as such, play a key role in each of these elements. Random numbers are used to inject unpredictable or non-deterministic data into cryptographic algorithms and protocols to make the resulting data streams unrepeatable and virtually unguessable
Information and network security 29 international data encryption algorithmVaibhav Khanna
International Data Encryption Algorithm (IDEA) is a once-proprietary free and open block cipher that was once intended to replace Data Encryption Standard (DES). IDEA has been and is optionally available for use with Pretty Good Privacy (PGP). IDEA has been succeeded by the IDEA NXT algorithm
Information and network security 28 blowfishVaibhav Khanna
Blowfish is a symmetric-key block cipher, designed in 1993 by Bruce Schneier and included in many cipher suites and encryption products. Blowfish provides a good encryption rate in software and no effective cryptanalysis of it has been found to date
Information and network security 27 triple desVaibhav Khanna
Part of what Triple DES does is to protect against brute force attacks. The original DES symmetric encryption algorithm specified the use of 56-bit keys -- not enough, by 1999, to protect against practical brute force attacks. Triple DES specifies the use of three distinct DES keys, for a total key length of 168 bits
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
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✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Using IESVE for Room Loads Analysis - Australia & New Zealand
Information retrieval 7 boolean model
1. Information Retrieval : 7
Boolean Model
Prof Neeraj Bhargava
Vaibhav Khanna
Department of Computer Science
School of Engineering and Systems Sciences
Maharshi Dayanand Saraswati University Ajmer
2. The Boolean Model
• Simple model based on set theory and Boolean algebra
• Queries specified as boolean expressions
– quite intuitive and precise semantics
– neat formalism
– example of query
• Term-document frequencies in the term-document matrix are
all binary
3. • The (standard) Boolean model of information retrieval (BIR)
is a classical information retrieval (IR) model and, at the same
time, the first and most-adopted one. ...
• The BIR is based on Boolean logic and classical set theory in
that both the documents to be searched and the user's query
are conceived as sets of terms
• Retrieval is based on whether the documents contain the
query terms or not .
4. The Boolean Model
• A term conjunctive component that satisfies a query q is
called a query conjunctive component c(q)
• A query q rewritten as a disjunction of those components is
called the disjunct normal form qDNF
• To illustrate, consider
6. The Boolean Model
• This approach works even if the vocabulary of the collection
includes terms not in the query
• Consider that the vocabulary is given by
• Then, a document dj that contains only terms ka, kb, and kc is
represented by c(dj) = (1, 1, 1, 0)
7. The Boolean Model
• The similarity of the document dj to the query
q is defined as
• The Boolean model predicts that each
document is either relevant or non-relevant
8. Advantages of Boolean Model
• Clean formalism
• Easy to implement
• Intuitive concept
9. Disadvantages of Boolean Model
• Exact matching may retrieve too few or too
many documents
• Hard to translate a query into a Boolean
expression
• All terms are equally weighted
• More like data retrieval than information
retrieval
10. Drawbacks of the Boolean Model
• Retrieval based on binary decision criteria with no
notion of partial matching
• No ranking of the documents is provided (absence of
a grading scale)
• Information need has to be translated into a Boolean
expression, which most users find awkward
• The Boolean queries formulated by the users are
most often too simplistic
• The model frequently returns either too few or too
many documents in response to a user query
