Information retrieval 7 boolean model
Download as PPTX, PDF0 likes1,269 views
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.
1 of 11
Download to read offline
Ad
Recommended
Information Retrieval Models
Information Retrieval ModelsNisha Arankandath The document discusses various information retrieval models, including:
1) Classic models like Boolean and vector space models that use index terms to represent documents and queries.
2) Probabilistic models that view IR as estimating the probability of relevance between documents and queries.
3) Structured models that incorporate document structure, including models based on non-overlapping text regions and hierarchical document structure.
4) Browsing models like flat, structure-guided, and hypertext models for navigating document collections.
Probabilistic information retrieval models & systems
Probabilistic information retrieval models & systemsSelman Bozkır The document discusses probabilistic information retrieval and Bayesian approaches. It introduces concepts like conditional probability, Bayes' theorem, and the probability ranking principle. It explains how probabilistic models estimate the probability of relevance between a document and query by representing them as term sets and making probabilistic assumptions. The goal is to rank documents by the probability of relevance to present the most likely relevant documents first.
Model of information retrieval (3)
Model of information retrieval (3)9866825059 The document discusses information retrieval models. It describes the Boolean retrieval model, which represents documents and queries as sets of terms combined with Boolean operators. Documents are retrieved if they satisfy the Boolean query, but there is no ranking of results. The Boolean model has limitations including difficulty expressing complex queries, controlling result size, and ranking results. It works best for simple, precise queries when users know exactly what they are searching for.
Information retrieval introduction
Information retrieval introductionnimmyjans4 This document provides an overview of information retrieval models. It begins with definitions of information retrieval and how it differs from data retrieval. It then discusses the retrieval process and logical representations of documents. A taxonomy of IR models is presented including classic, structured, and browsing models. Boolean, vector, and probabilistic models are explained as examples of classic models. The document concludes with descriptions of ad-hoc retrieval and filtering tasks and formal characteristics of IR models.
Probabilistic retrieval model
Probabilistic retrieval modelbaradhimarch81 The document discusses probabilistic retrieval models in information retrieval. It introduces three influential probabilistic models: (1) Maron and Kuhns' 1960 model which calculates the probability of relevance based on historical user data; (2) Salton's model which estimates the probability of term occurrence in relevant documents; (3) A model that ranks documents by the probability of relevance and considers retrieval as a decision between costs of retrieving non-relevant vs. not retrieving relevant documents. The document provides background on the development of probabilistic IR models and challenges of estimating probabilities for evaluation.
Information retrieval s
Information retrieval ssilambu111 The document discusses information retrieval, which involves obtaining information resources relevant to an information need from a collection. The information retrieval process begins when a user submits a query. The system matches queries to database information, ranks objects based on relevance, and returns top results to the user. The process involves document acquisition and representation, user problem representation as queries, and searching/retrieval through matching and result retrieval.
information retrieval --> dictionary.ppt
information retrieval --> dictionary.pptssusere3b1a2 This document provides an introduction to information retrieval concepts including document ingestion, tokenization, terms, normalization, stemming, and lemmatization. It discusses how documents are parsed and tokenized, dealing with complications from different languages and formats. Terms are the normalized words that are indexed, and the document examines issues with stop words, numbers, case folding, and dealing with other languages. Stemming and lemmatization are introduced as ways to reduce inflectional variants of words.
Ppt evaluation of information retrieval system
Ppt evaluation of information retrieval systemsilambu111 The document discusses the evaluation of information retrieval systems. Evaluation is defined as systematically determining a subject's merit using a set of standards. The main purposes of evaluation are to compare the performance of different systems, assess how well systems meet their goals, and identify ways to improve effectiveness. Evaluation can consider managerial or user viewpoints. Common criteria include recall, precision, fallout, generality, effectiveness, efficiency, usability, satisfaction, and cost. Recall measures the proportion of relevant documents retrieved while precision measures the proportion of retrieved documents that are relevant. Evaluation helps identify ways to improve information retrieval system performance.
Information retrieval system
Information retrieval systemLeslie Vargas 1. The document defines key terms related to information retrieval systems such as information, retrieval, system, and discusses the basic components and functions of IRS.
2. It explains that the role of users is to formulate queries, and the role of librarians is to assist users in meeting their information needs.
3. The document contrasts older IRS that retrieved entire documents with modern IRS that allow storage, organization, and access to text and multimedia information through techniques like keyword searching and hyperlinks.
Introduction to Information Retrieval
Introduction to Information RetrievalRoi Blanco 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.
Information Retrieval
Information RetrievalWeb Science Research Group at Institute of Business Administration, Karachi, Pakistan 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.
The vector space model
The vector space modelpkgosh The document discusses the vector space model for representing text documents and queries in information retrieval systems. It describes how documents and queries are represented as vectors of term weights, with each term being assigned a weight based on its frequency in the document or query. The vector space model allows documents and queries to be compared by calculating the similarity between their vector representations. Terms that are more frequent in a document and less frequent overall are given higher weights through techniques like TF-IDF weighting. This vector representation enables efficient retrieval of documents ranked by similarity to the query.
Automatic indexing
Automatic indexingdhatchayaninandu Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
Information retrieval concept, practice and challenge
Information retrieval concept, practice and challengeGan Keng Hoon This short lecture gives a brief overview of basic concept of information retrieval, a practical case and some challenges ahead.
Term weighting
Term weightingPrimya Tamil The document discusses various aspects of term weighting which is important for text retrieval systems, including term frequency, document frequency, inverse document frequency, and how they are used to calculate TF-IDF weights for terms. It also covers stoplists, stemming, and the bag-of-words model which represents text as vectors of word occurrences without considering word order. Term weighting schemes play a major role in the similarity measures used by information retrieval systems to determine document relevance.
Functions of information retrival system(1)
Functions of information retrival system(1)silambu111 The document discusses information retrieval systems. It defines information retrieval as the process of searching collections of documents to identify those dealing with a particular subject. Information retrieval systems aim to facilitate literature searching. They involve representing, storing, organizing, and providing access to information items so that users can easily find information of interest. Information retrieval draws from multiple disciplines and involves subsystems for documents, users, and searching/matching.
Chapter 1 Introduction to Information Storage and Retrieval.pdf
Chapter 1 Introduction to Information Storage and Retrieval.pdfHabtamu100 This course outline provides information about an Information Storage and Retrieval course for third year Information Technology students. The course will cover introductory concepts of information storage and retrieval over 5 ECTS credits across one semester. Topics will include automatic text operations, indexing structures, retrieval models, evaluation, query languages, and current issues. Assessment will include assignments, tests, a project, midterm, and final exam.
Inverted index
Inverted indexKrishna Gehlot An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.
Boolean,vector space retrieval Models 
Boolean,vector space retrieval Models Primya Tamil The document discusses various information retrieval models including Boolean, vector space, and probabilistic models. It provides details on how documents and queries are represented and compared in the vector space model. Specifically, it explains that in this model, documents and queries are represented as vectors of term weights in a multi-dimensional space. The similarity between a document and query vector is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
Distributed DBMS - Unit 6 - Query Processing
Distributed DBMS - Unit 6 - Query ProcessingGyanmanjari Institute Of Technology Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
Informatio retrival evaluation
Informatio retrival evaluationNidhirBiswas The document discusses the evaluation of information retrieval systems. It describes several evaluation criteria used such as recall, precision, fallout and generality. It also outlines major information retrieval evaluation experiments including Cranfield tests, MEDLARS, SMART and TREC. Evaluation is important to assess how well a system satisfies its objectives, how efficiently it works, and whether it is worthwhile. Different methods are used to evaluate systems from the viewpoints of users and managers.
6&7-Query Languages & Operations.ppt
6&7-Query Languages & Operations.pptBereketAraya The document discusses various types of query languages used in information retrieval systems. It describes keyword-based queries, single-word queries, phrase queries, multiple-word queries, Boolean queries, weighted queries, pattern queries, and natural language queries. Different query types allow users to express their information needs in organized ways to retrieve relevant results through combining words and assigning weights or using wildcards.
CS6007 information retrieval - 5 units notes
CS6007 information retrieval - 5 units notesAnandh Arumugakan This document provides a full syllabus with questions and answers related to the course "Information Retrieval" including definitions of key concepts, the historical development of the field, comparisons between information retrieval and web search, applications of IR, components of an IR system, and issues in IR systems. It also lists examples of open source search frameworks and performance measures for search engines.
Information retrieval-systems notes
Information retrieval-systems notesBAIRAVI T This document provides an overview of an information retrieval system. It defines an information retrieval system as a system capable of storing, retrieving, and maintaining information such as text, images, audio, and video. The objectives of an information retrieval system are to minimize the overhead for a user to locate needed information. The document discusses functions like search, browse, indexing, cataloging, and various capabilities to facilitate querying and retrieving relevant information from the system.
Ontology and Ontology Libraries: a Critical Study
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Information retrieval (introduction) 
Information retrieval (introduction) Primya Tamil The document discusses key concepts related to information retrieval including data, information, knowledge, and wisdom. It defines information retrieval as the tracing and recovery of specific information from stored data through searching. The main aspects of the information retrieval process are described as querying a collection to retrieve relevant objects that may partially match the query. Precision and recall are discussed as important measures for information retrieval systems.
Vector space model in information retrieval
Vector space model in information retrievalTharuka Vishwajith Sarathchandra 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
Indexing Techniques: Their Usage in Search Engines for Information Retrieval
Indexing Techniques: Their Usage in Search Engines for Information RetrievalVikas Bhushan 1. The document discusses indexing techniques and their usage in modern search engines. It covers the transition from manual to automated indexing and different indexing methods.
2. Current trends in indexing and information retrieval are discussed such as XML indexing and its components. Future applications for indexers are also mentioned.
3. The conclusion emphasizes enhancements to indexing procedures like weighted indexing and linking of terms to improve retrieval of accurate information.
presnetion of Ir topic is how to retirve information.pdf
presnetion of Ir topic is how to retirve information.pdfJawadAbid8 Information Retrieval (IR) is a critical field of computer science and information technology that focuses on the efficient storage, organization, and retrieval of data from large collections, typically in response to specific user queries. It has evolved significantly since its inception, playing a pivotal role in various applications ranging from search engines and e-commerce platforms to digital libraries and recommendation systems. IR systems rely on a combination of sophisticated algorithms and data structures, such as inverted indexes, to enable rapid access to relevant information. Central to IR are the concepts of relevance, precision, and recall. Relevance determines the degree to which retrieved information meets the user's needs, while precision and recall evaluate the effectiveness of the retrieval process, with precision focusing on the proportion of retrieved results that are relevant and recall emphasizing the proportion of relevant results successfully retrieved from the entire dataset. Striking a balance between these metrics is a fundamental challenge in designing IR systems. Techniques employed in IR range from Boolean retrieval, which utilizes logical operators like AND, OR, and NOT to filter results, to more advanced methods such as vector space models and probabilistic approaches. Vector space models represent documents and queries as vectors in a multi-dimensional space, enabling the calculation of similarity scores using metrics like cosine similarity. Probabilistic models, such as BM25, leverage statistical principles to estimate the likelihood of a document's relevance to a given query, considering factors like term frequency and document length. In recent years, the integration of Natural Language Processing (NLP) has revolutionized IR, allowing systems to better understand the semantics and context of queries. NLP techniques, including tokenization, stemming, lemmatization, and sentiment analysis, preprocess textual data to enhance retrieval accuracy. Furthermore, the advent of machine learning and deep learning has introduced new paradigms in IR. Neural networks and transformer models, such as BERT and GPT, have redefined the landscape by enabling context-aware retrieval that goes beyond mere keyword matching, capturing the nuances of human language to deliver highly relevant results. Despite these advancements, IR faces numerous challenges, including the ambiguity of user queries, which can obscure intent, and the sheer scale of data generated daily, which demands robust and scalable infrastructure for indexing and retrieval. Multilingual and multimodal retrieval adds another layer of complexity, requiring systems to process and understand data in diverse languages and formats, including text, images, audio, and video. Applications of IR are diverse and impactful. Search engines, the most recognizable manifestation of IR, have become indispensable tools for accessing information on the web, handling
Information retrieval 6 ir models
Information retrieval 6 ir modelsVaibhav Khanna 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.
Ad
More Related Content
What's hot (20)
Information retrieval system
Information retrieval systemLeslie Vargas 1. The document defines key terms related to information retrieval systems such as information, retrieval, system, and discusses the basic components and functions of IRS.
2. It explains that the role of users is to formulate queries, and the role of librarians is to assist users in meeting their information needs.
3. The document contrasts older IRS that retrieved entire documents with modern IRS that allow storage, organization, and access to text and multimedia information through techniques like keyword searching and hyperlinks.
Introduction to Information Retrieval
Introduction to Information RetrievalRoi Blanco 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.
Information Retrieval
Information RetrievalWeb Science Research Group at Institute of Business Administration, Karachi, Pakistan 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.
The vector space model
The vector space modelpkgosh The document discusses the vector space model for representing text documents and queries in information retrieval systems. It describes how documents and queries are represented as vectors of term weights, with each term being assigned a weight based on its frequency in the document or query. The vector space model allows documents and queries to be compared by calculating the similarity between their vector representations. Terms that are more frequent in a document and less frequent overall are given higher weights through techniques like TF-IDF weighting. This vector representation enables efficient retrieval of documents ranked by similarity to the query.
Automatic indexing
Automatic indexingdhatchayaninandu Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
Information retrieval concept, practice and challenge
Information retrieval concept, practice and challengeGan Keng Hoon This short lecture gives a brief overview of basic concept of information retrieval, a practical case and some challenges ahead.
Term weighting
Term weightingPrimya Tamil The document discusses various aspects of term weighting which is important for text retrieval systems, including term frequency, document frequency, inverse document frequency, and how they are used to calculate TF-IDF weights for terms. It also covers stoplists, stemming, and the bag-of-words model which represents text as vectors of word occurrences without considering word order. Term weighting schemes play a major role in the similarity measures used by information retrieval systems to determine document relevance.
Functions of information retrival system(1)
Functions of information retrival system(1)silambu111 The document discusses information retrieval systems. It defines information retrieval as the process of searching collections of documents to identify those dealing with a particular subject. Information retrieval systems aim to facilitate literature searching. They involve representing, storing, organizing, and providing access to information items so that users can easily find information of interest. Information retrieval draws from multiple disciplines and involves subsystems for documents, users, and searching/matching.
Chapter 1 Introduction to Information Storage and Retrieval.pdf
Chapter 1 Introduction to Information Storage and Retrieval.pdfHabtamu100 This course outline provides information about an Information Storage and Retrieval course for third year Information Technology students. The course will cover introductory concepts of information storage and retrieval over 5 ECTS credits across one semester. Topics will include automatic text operations, indexing structures, retrieval models, evaluation, query languages, and current issues. Assessment will include assignments, tests, a project, midterm, and final exam.
Inverted index
Inverted indexKrishna Gehlot An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.
Boolean,vector space retrieval Models
Boolean,vector space retrieval Models Primya Tamil The document discusses various information retrieval models including Boolean, vector space, and probabilistic models. It provides details on how documents and queries are represented and compared in the vector space model. Specifically, it explains that in this model, documents and queries are represented as vectors of term weights in a multi-dimensional space. The similarity between a document and query vector is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
Distributed DBMS - Unit 6 - Query Processing
Distributed DBMS - Unit 6 - Query ProcessingGyanmanjari Institute Of Technology Query Processing : Query Processing Problem, Layers of Query Processing Query Processing in Centralized Systems – Parsing & Translation, Optimization, Code generation, Example Query Processing in Distributed Systems – Mapping global query to local, Optimization,
Informatio retrival evaluation
Informatio retrival evaluationNidhirBiswas The document discusses the evaluation of information retrieval systems. It describes several evaluation criteria used such as recall, precision, fallout and generality. It also outlines major information retrieval evaluation experiments including Cranfield tests, MEDLARS, SMART and TREC. Evaluation is important to assess how well a system satisfies its objectives, how efficiently it works, and whether it is worthwhile. Different methods are used to evaluate systems from the viewpoints of users and managers.
6&7-Query Languages & Operations.ppt
6&7-Query Languages & Operations.pptBereketAraya The document discusses various types of query languages used in information retrieval systems. It describes keyword-based queries, single-word queries, phrase queries, multiple-word queries, Boolean queries, weighted queries, pattern queries, and natural language queries. Different query types allow users to express their information needs in organized ways to retrieve relevant results through combining words and assigning weights or using wildcards.
CS6007 information retrieval - 5 units notes
CS6007 information retrieval - 5 units notesAnandh Arumugakan This document provides a full syllabus with questions and answers related to the course "Information Retrieval" including definitions of key concepts, the historical development of the field, comparisons between information retrieval and web search, applications of IR, components of an IR system, and issues in IR systems. It also lists examples of open source search frameworks and performance measures for search engines.
Information retrieval-systems notes
Information retrieval-systems notesBAIRAVI T This document provides an overview of an information retrieval system. It defines an information retrieval system as a system capable of storing, retrieving, and maintaining information such as text, images, audio, and video. The objectives of an information retrieval system are to minimize the overhead for a user to locate needed information. The document discusses functions like search, browse, indexing, cataloging, and various capabilities to facilitate querying and retrieving relevant information from the system.
Ontology and Ontology Libraries: a Critical Study
Ontology and Ontology Libraries: a Critical StudyDebashisnaskar The concept of digital library revolutionized its popularity with the development of networking technology. Digital library stores various kind of documents in digitized format that enables user smooth access to these documents at subsidized costs. In the recent past, a similar concept i.e., ontology library has gained popularity among the communities like semantic web, artificial intelligence, information science, philosophy, linguistics, and so forth.
Information retrieval (introduction)
Information retrieval (introduction) Primya Tamil The document discusses key concepts related to information retrieval including data, information, knowledge, and wisdom. It defines information retrieval as the tracing and recovery of specific information from stored data through searching. The main aspects of the information retrieval process are described as querying a collection to retrieve relevant objects that may partially match the query. Precision and recall are discussed as important measures for information retrieval systems.
Vector space model in information retrieval
Vector space model in information retrievalTharuka Vishwajith Sarathchandra 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
Indexing Techniques: Their Usage in Search Engines for Information Retrieval
Indexing Techniques: Their Usage in Search Engines for Information RetrievalVikas Bhushan 1. The document discusses indexing techniques and their usage in modern search engines. It covers the transition from manual to automated indexing and different indexing methods.
2. Current trends in indexing and information retrieval are discussed such as XML indexing and its components. Future applications for indexers are also mentioned.
3. The conclusion emphasizes enhancements to indexing procedures like weighted indexing and linking of terms to improve retrieval of accurate information.
Similar to Information retrieval 7 boolean model (20)
presnetion of Ir topic is how to retirve information.pdf
presnetion of Ir topic is how to retirve information.pdfJawadAbid8 Information Retrieval (IR) is a critical field of computer science and information technology that focuses on the efficient storage, organization, and retrieval of data from large collections, typically in response to specific user queries. It has evolved significantly since its inception, playing a pivotal role in various applications ranging from search engines and e-commerce platforms to digital libraries and recommendation systems. IR systems rely on a combination of sophisticated algorithms and data structures, such as inverted indexes, to enable rapid access to relevant information. Central to IR are the concepts of relevance, precision, and recall. Relevance determines the degree to which retrieved information meets the user's needs, while precision and recall evaluate the effectiveness of the retrieval process, with precision focusing on the proportion of retrieved results that are relevant and recall emphasizing the proportion of relevant results successfully retrieved from the entire dataset. Striking a balance between these metrics is a fundamental challenge in designing IR systems. Techniques employed in IR range from Boolean retrieval, which utilizes logical operators like AND, OR, and NOT to filter results, to more advanced methods such as vector space models and probabilistic approaches. Vector space models represent documents and queries as vectors in a multi-dimensional space, enabling the calculation of similarity scores using metrics like cosine similarity. Probabilistic models, such as BM25, leverage statistical principles to estimate the likelihood of a document's relevance to a given query, considering factors like term frequency and document length. In recent years, the integration of Natural Language Processing (NLP) has revolutionized IR, allowing systems to better understand the semantics and context of queries. NLP techniques, including tokenization, stemming, lemmatization, and sentiment analysis, preprocess textual data to enhance retrieval accuracy. Furthermore, the advent of machine learning and deep learning has introduced new paradigms in IR. Neural networks and transformer models, such as BERT and GPT, have redefined the landscape by enabling context-aware retrieval that goes beyond mere keyword matching, capturing the nuances of human language to deliver highly relevant results. Despite these advancements, IR faces numerous challenges, including the ambiguity of user queries, which can obscure intent, and the sheer scale of data generated daily, which demands robust and scalable infrastructure for indexing and retrieval. Multilingual and multimodal retrieval adds another layer of complexity, requiring systems to process and understand data in diverse languages and formats, including text, images, audio, and video. Applications of IR are diverse and impactful. Search engines, the most recognizable manifestation of IR, have become indispensable tools for accessing information on the web, handling
Information retrieval 6 ir models
Information retrieval 6 ir modelsVaibhav Khanna 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 Learning
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.
Enriching Solr with Deep Learning for a Question Answering System - Sanket Sh...
Enriching Solr with Deep Learning for a Question Answering System - Sanket Sh...Lucidworks The document discusses research into using deep learning to improve question answering systems. It describes using Solr to retrieve documents and then using machine learning models to rerank the results. The research compared various supervised and unsupervised models for question similarity and answer selection tasks. For question similarity, ensemble models using TFIDF and sentence embeddings performed best. For answer selection, deep learning models outperformed traditional models when sufficient training data was available.
Information retrival system and PageRank algorithm
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...
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
Deductive databases
Deductive databasesJohn Popoola 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 Conversations
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.
The research process steps
The research process stepsRoger Watson The document outlines the key steps in the research process, including developing the research project, reviewing literature, creating a research proposal, conducting a pilot study, and project management. It discusses developing a research question and aims, reviewing background literature and gaps, designing methodology, and plans for analysis, reporting, and writing up results. Methods of searching literature systematically using databases and evaluating sources are also covered. The importance of a pilot study and a clear timetable for the research proposal and project are emphasized.
Information Retrieval
Information Retrievalssbd6985 Information retrieval systems aim to find documents relevant to a user's information need. Search engines are a common example, allowing users to enter queries and receiving a list of relevant web pages. Effective systems represent documents and queries statistically based on word frequencies and use scoring functions to rank documents by estimated relevance to the query. Evaluation involves measuring a system's precision, the proportion of returned documents that are relevant, and recall, the proportion of all relevant documents that are returned.
information retrieval
information retrievalssbd6985 Information retrieval systems aim to find documents relevant to a user's information need. Search engines are a common example, allowing users to enter queries and receiving a list of relevant web pages. Effective systems represent documents and queries statistically based on word frequencies and use scoring functions to rank documents by estimated relevance to the query. Evaluation involves measuring a system's precision, the proportion of returned documents that are relevant, and recall, the proportion of all relevant documents that are returned.
Information retrieval 13 alternative set theoretic models
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.
Text mining
Text miningKoshy Geoji This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
Credible workshop
Credible workshopKhalid Belhajjame This document discusses research objects and scientific workflows. It introduces research objects as a way to aggregate all elements needed to understand a research investigation, including datasets, results, experiments, and provenance. Scientific workflows are presented as tools for automating data-intensive scientific activities, with prospective and retrospective provenance capturing the intended and actual methods. The document outlines an approach to summarizing complex workflows using semantic annotations of workflow motifs and reduction primitives like collapse and eliminate. This distills provenance traces for improved understanding and querying.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...
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.
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...Joaquin Delgado PhD. 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.
Query Dependent Pseudo-Relevance Feedback based on Wikipedia
Query Dependent Pseudo-Relevance Feedback based on WikipediaYI-JHEN LIN This document summarizes a presentation on using Wikipedia for query expansion in pseudo-relevance feedback (PRF) for information retrieval. Three query expansion methods are proposed: 1) a baseline relevance model, 2) selecting terms from the Wikipedia entity page for entity/ambiguous queries, and 3) two field-based methods (unsupervised and supervised) that consider term distributions and structures. Experiments on TREC collections show the query-dependent approach, which selects the expansion method based on query type, outperforms the baseline relevance model. Future work could include exploring term selection for broader queries and evaluating on additional datasets.
Chapter 4 IR Models.pdf
Chapter 4 IR Models.pdfHabtamu100 The document discusses information retrieval (IR) models, including the Boolean, vector space, and probabilistic models. The Boolean model represents documents and queries as sets of index terms and determines relevance through binary term presence, while the vector space model represents documents and queries as weighted vectors in a multidimensional space and ranks documents by calculating similarity between document and query vectors. The probabilistic model determines relevance probabilities based on the likelihood of terms appearing in relevant vs. non-relevant documents.
Where Search Meets Machine Learning: Presented by Diana Hu & Joaquin Delgado,...
Where Search Meets Machine Learning: Presented by Diana Hu & Joaquin Delgado,...Lucidworks This document discusses how machine learning problems can be framed as search-based systems and how search technologies can be leveraged to build and serve machine learning models at scale. It begins with an introduction to search and information retrieval systems. It then discusses how recommender systems and other machine learning problems can be viewed as search problems involving relevance, ranking, and retrieval. The document explores options for integrating machine learning models into search systems like Solr and Lucene using techniques like custom scoring plugins and the Predictive Model Markup Language (PMML). It provides examples of training models and exporting them to PMML for use in search systems.
A task-based scientific paper recommender system for literature review and ma...
A task-based scientific paper recommender system for literature review and ma...Aravind Sesagiri Raamkumar
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...
RecSys 2015 Tutorial - Scalable Recommender Systems: Where Machine Learning m...Joaquin Delgado PhD.
Ad
More from Vaibhav Khanna (20)
Information and network security 47 authentication applications
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 algorithm
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 standard
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 signatures
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 signatures
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 code
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 code
Information and network security 41 message authentication codeVaibhav Khanna Message authentication aims to protect integrity, validate originator identity, and provide non-repudiation. It addresses threats like masquerading, content or sequence modification, and source/destination repudiation. A Message Authentication Code (MAC) provides assurance that a message is unaltered and from the sender by appending a cryptographic checksum to the message dependent on the key and content. The receiver can validate the MAC to verify integrity and authenticity.
Information and network security 40 sha3 secure hash algorithm
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 algorithm
Information and network security 39 secure hash algorithmVaibhav Khanna The Secure Hash Algorithm (SHA) is a cryptographic hash function developed by the US National Security Agency. SHA-512 is the latest version that produces a 512-bit hash value. It processes message blocks of 1024 bits using an 80-step compression function that updates a 512-bit buffer. Each step uses a 64-bit value derived from the message and a round constant. SHA-512 supports messages up to 2^128 bits in length and adds between 1 and 1023 padding bits as needed.
Information and network security 38 birthday attacks and security of hash fun...
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 37 hash functions and message authentication
Information and network security 37 hash functions and message authenticationVaibhav Khanna Message Authentication Code ... A message authentication code (MAC) is similar to a cryptographic hash, except that it is based on a secret key
Information and network security 35 the chinese remainder theorem
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 primality
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 algorithm
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 cryptosystems
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 cryptography
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 numbers
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 algorithm
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 blowfish
Information and network security 28 blowfishVaibhav Khanna Blowfish is a symmetric block cipher designed as a replacement for DES. It encrypts data in 64-bit blocks using a variable-length key. The algorithm uses substitution boxes and a complex key schedule to encrypt the data in multiple rounds. It is very fast, uses little memory, and is resistant to cryptanalysis due to its complex key schedule and substitution boxes.
Information and network security 27 triple des
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
Ad
Recently uploaded (20)
Landscape of Requirements Engineering for/by AI through Literature Review
Landscape of Requirements Engineering for/by AI through Literature ReviewHironori Washizaki Hironori Washizaki, "Landscape of Requirements Engineering for/by AI through Literature Review," RAISE 2025: Workshop on Requirements engineering for AI-powered SoftwarE, 2025.
Who Watches the Watchmen (SciFiDevCon 2025)
Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe.
We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved?
Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security.
Talk from SciFiDevCon 2025
https://www.scifidevcon.com/courses/2025-scifidevcon/contents/680efa43ae4f5
Adobe After Effects Crack FREE FRESH version 2025
Adobe After Effects Crack FREE FRESH version 2025kashifyounis067 🌍📱👉COPY LINK & PASTE ON GOOGLE http://drfiles.net/ 👈🌍
Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited.
Here's a more detailed breakdown:
Motion Graphics:
.
After Effects is powerful for creating animated titles, transitions, and other visual elements to enhance the look of videos and presentations.
Visual Effects:
.
It's used extensively in film and television for creating special effects like green screen compositing, object manipulation, and other visual enhancements.
Video Compositing:
.
After Effects allows users to combine multiple video clips, images, and graphics to create a final, cohesive visual.
Animation:
.
It uses keyframes to create smooth, animated sequences, allowing for precise control over the movement and appearance of objects.
Integration with Adobe Creative Cloud:
.
After Effects is part of the Adobe Creative Cloud, a suite of software that includes other popular applications like Photoshop and Premiere Pro.
Post-Production Tool:
.
After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.
What Do Contribution Guidelines Say About Software Testing? (MSR 2025)
What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.
Download YouTube By Click 2025 Free Full Activated
Download YouTube By Click 2025 Free Full Activatedsaniamalik72555 Copy & Past Link 👉👉
https://dr-up-community.info/
"YouTube by Click" likely refers to the ByClick Downloader software, a video downloading and conversion tool, specifically designed to download content from YouTube and other video platforms. It allows users to download YouTube videos for offline viewing and to convert them to different formats.
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.
Adobe Marketo Engage Champion Deep Dive - SFDC CRM Synch V2 & Usage Dashboards
Adobe Marketo Engage Champion Deep Dive - SFDC CRM Synch V2 & Usage DashboardsBradBedford3 Join Ajay Sarpal and Miray Vu to learn about key Marketo Engage enhancements. Discover improved in-app Salesforce CRM connector statistics for easy monitoring of sync health and throughput. Explore new Salesforce CRM Synch Dashboards providing up-to-date insights into weekly activity usage, thresholds, and limits with drill-down capabilities. Learn about proactive notifications for both Salesforce CRM sync and product usage overages. Get an update on improved Salesforce CRM synch scale and reliability coming in Q2 2025.
Key Takeaways:
Improved Salesforce CRM User Experience: Learn how self-service visibility enhances satisfaction.
Utilize Salesforce CRM Synch Dashboards: Explore real-time weekly activity data.
Monitor Performance Against Limits: See threshold limits for each product level.
Get Usage Over-Limit Alerts: Receive notifications for exceeding thresholds.
Learn About Improved Salesforce CRM Scale: Understand upcoming cloud-based incremental sync.
Download Wondershare Filmora Crack [2025] With Latest![Download Wondershare Filmora Crack [2025] With Latest](https://cdn.slidesharecdn.com/ss_thumbnails/neo4j-howkgsareshapingthefutureofgenerativeaiatawssummitlondonapril2024-240426125209-2d9db05d-250419-250428115407-a04afffa-thumbnail.jpg?width=560&fit=bounds)
Download Wondershare Filmora Crack [2025] With Latesttahirabibi60507 Copy & Past Link 👉👉
http://drfiles.net/
Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Revolutionizing Residential Wi-Fi PPT.pptx
Revolutionizing Residential Wi-Fi PPT.pptxnidhisingh691197 Discover why Wi-Fi 7 is set to transform wireless networking and how Router Architects is leading the way with next-gen router designs built for speed, reliability, and innovation.
Avast Premium Security Crack FREE Latest Version 2025
Avast Premium Security Crack FREE Latest Version 2025mu394968 🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍
Avast Premium Security is a paid subscription service that provides comprehensive online security and privacy protection for multiple devices. It includes features like antivirus, firewall, ransomware protection, and website scanning, all designed to safeguard against a wide range of online threats, according to Avast.
Key features of Avast Premium Security:
Antivirus: Protects against viruses, malware, and other malicious software, according to Avast.
Firewall: Controls network traffic and blocks unauthorized access to your devices, as noted by All About Cookies.
Ransomware protection: Helps prevent ransomware attacks, which can encrypt your files and hold them hostage.
Website scanning: Checks websites for malicious content before you visit them, according to Avast.
Email Guardian: Scans your emails for suspicious attachments and phishing attempts.
Multi-device protection: Covers up to 10 devices, including Windows, Mac, Android, and iOS, as stated by 2GO Software.
Privacy features: Helps protect your personal data and online privacy.
In essence, Avast Premium Security provides a robust suite of tools to keep your devices and online activity safe and secure, according to Avast.
Adobe Illustrator Crack FREE Download 2025 Latest Version
Adobe Illustrator Crack FREE Download 2025 Latest Versionkashifyounis067 🌍📱👉COPY LINK & PASTE ON GOOGLE http://drfiles.net/ 👈🌍
Adobe Illustrator is a powerful, professional-grade vector graphics software used for creating a wide range of designs, including logos, icons, illustrations, and more. Unlike raster graphics (like photos), which are made of pixels, vector graphics in Illustrator are defined by mathematical equations, allowing them to be scaled up or down infinitely without losing quality.
Here's a more detailed explanation:
Key Features and Capabilities:
Vector-Based Design:
Illustrator's foundation is its use of vector graphics, meaning designs are created using paths, lines, shapes, and curves defined mathematically.
Scalability:
This vector-based approach allows for designs to be resized without any loss of resolution or quality, making it suitable for various print and digital applications.
Design Creation:
Illustrator is used for a wide variety of design purposes, including:
Logos and Brand Identity: Creating logos, icons, and other brand assets.
Illustrations: Designing detailed illustrations for books, magazines, web pages, and more.
Marketing Materials: Creating posters, flyers, banners, and other marketing visuals.
Web Design: Designing web graphics, including icons, buttons, and layouts.
Text Handling:
Illustrator offers sophisticated typography tools for manipulating and designing text within your graphics.
Brushes and Effects:
It provides a range of brushes and effects for adding artistic touches and visual styles to your designs.
Integration with Other Adobe Software:
Illustrator integrates seamlessly with other Adobe Creative Cloud apps like Photoshop, InDesign, and Dreamweaver, facilitating a smooth workflow.
Why Use Illustrator?
Professional-Grade Features:
Illustrator offers a comprehensive set of tools and features for professional design work.
Versatility:
It can be used for a wide range of design tasks and applications, making it a versatile tool for designers.
Industry Standard:
Illustrator is a widely used and recognized software in the graphic design industry.
Creative Freedom:
It empowers designers to create detailed, high-quality graphics with a high degree of control and precision.
Meet the Agents: How AI Is Learning to Think, Plan, and Collaborate
Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov Imagine if apps could think, plan, and team up like humans. Welcome to the world of AI agents and agentic user interfaces (UI)! In this session, we'll explore how AI agents make decisions, collaborate with each other, and create more natural and powerful experiences for users.
Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...
Mastering Fluent Bit: Ultimate Guide to Integrating Telemetry Pipelines with ...Eric D. Schabell It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).
Exploring Wayland: A Modern Display Server for the Future
Exploring Wayland: A Modern Display Server for the FutureICS Wayland is revolutionizing the way we interact with graphical interfaces, offering a modern alternative to the X Window System. In this webinar, we’ll delve into the architecture and benefits of Wayland, including its streamlined design, enhanced performance, and improved security features.
Agentic AI Use Cases using GenAI LLM models
Agentic AI Use Cases using GenAI LLM modelsManish Chopra This document presents specific use cases for Agentic AI (Artificial Intelligence), featuring Large Language Models (LLMs), Generative AI, and snippets of Python code alongside each use case.
PDF Reader Pro Crack Latest Version FREE Download 2025
PDF Reader Pro Crack Latest Version FREE Download 2025mu394968 🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍
PDF Reader Pro is a software application, often referred to as an AI-powered PDF editor and converter, designed for viewing, editing, annotating, and managing PDF files. It supports various PDF functionalities like merging, splitting, converting, and protecting PDFs. Additionally, it can handle tasks such as creating fillable forms, adding digital signatures, and performing optical character recognition (OCR).
Salesforce Aged Complex Org Revitalization Process .pdf
Salesforce Aged Complex Org Revitalization Process .pdfSRINIVASARAO PUSULURI Revitalizing a high-volume, underperforming Salesforce environment requires a structured, phased plan. The objective for company is to stabilize, scale, and future-proof the platform.
Here presenting various improvement techniques that i learned over a decade of experience
Adobe Illustrator Crack | Free Download & Install Illustrator
Adobe Illustrator Crack | Free Download & Install Illustratorusmanhidray Copy & Link Here 👉👉
http://drfiles.net/
Adobe Illustrator is a vector graphics editor and design software, developed and marketed by Adobe, used for creating logos, icons, illustrations, and other graphics that can be scaled without loss of quality. It's a powerful tool for graphic designers, web designers, and artists who need to create crisp, scalable artwork for various applications like print, web, and mobile.
Get & Download Wondershare Filmora Crack Latest [2025]![Get & Download Wondershare Filmora Crack Latest [2025]](https://cdn.slidesharecdn.com/ss_thumbnails/revolutionizingresidentialwi-fi-250422112639-60fb726f-250429170801-59e1b240-thumbnail.jpg?width=560&fit=bounds)
Get & Download Wondershare Filmora Crack Latest [2025]saniaaftab72555 Copy & Past Link 👉👉
https://dr-up-community.info/
Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Top 10 Client Portal Software Solutions for 2025.docx
Top 10 Client Portal Software Solutions for 2025.docxPortli Discover the top 10 client portal software solutions for 2025. Streamline communication, ensure security, and enhance client experience.
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
- 5. The Boolean Model • The three conjunctive components for the query
- 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
- 11. Assignment • Explain the Boolean Model of Information Retrieval.