Citation Networks present us with a wide variety of problems. This project interprets a large number of Computer Science Research Papers from the DBLP archives and predicts a field in which a certain author is likely to contribute in the near future.
Survey on article extraction and comment monitoring techniquesAnunaya
The online News publisher publishes their news in the form of articles. Most of the online news websites provide the facility for their users to comment on the news article and as a result a lot of people comment on the news article. Hence news web page contains huge data in the form of article content and comments data, etc and have a good potential to be a resource for many Information Retrieval Systems and Data Mining Applications. The extraction of the main content (Article content) from a web page has always been a challenging task because a web page contains other information like advertisements and hyperlinks etc. which is not related to Article Text. In this survey, we review various techniques which are proposed by various researchers to extract the article content from a news web site. We also learn various techniques which monitor and analyze the comments for various applications like popularity prediction of articles and identification of discussions thread in the comments data.
The document provides an overview of text categorization using machine learning. It discusses feature extraction from text using bag-of-words representations and term weighting. It also covers common machine learning algorithms for text categorization like Naive Bayes, k-Nearest Neighbors, Boosting, and Support Vector Machines. The document concludes by noting that text categorization is well-suited to machine learning and discusses opportunities for future work in natural language processing with machine learning.
ExperTwin: An Alter Ego in Cyberspace for Knowledge WorkersCarlos Toxtli
ExperTwin is a Knowledge Advantage Machine (KAM) that is able to collect data from your areas of interest and present it in-time, in-context and in place to the worker workspace. This research paper describes how workers can be benefited from having a personal net of crawlers (as Google does) collecting and organizing updated data relevant to their areas of interest and delivering these to their workspace.
This document summarizes a student project on aspect/topic modeling for opinion mining from tweets. The goals of the project were to preprocess tweets, apply a modified LDA technique to extract topics from tweets, and classify tweets into categories like jokes, sports, movies, and politics. The students used a probabilistic model and SVM for classification, and were able to detect new trending topics not present in training data and categorize them as potential new topics.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
A scalable gibbs sampler for probabilistic entity linkingSunny Kr
This document summarizes a research paper that proposes a scalable Gibbs sampling approach for probabilistic entity linking. The approach formulates entity linking as probabilistic inference in a topic model where each topic corresponds to a Wikipedia article. It introduces an efficient Gibbs sampling scheme that exploits the sparsity in the Wikipedia-LDA model to allow inference over millions of topics. Experimental results show it achieves state-of-the-art performance on the Aida-CoNLL dataset.
The document discusses two neural network models for reading comprehension tasks: the Attentive Reader model proposed by Herman et al. in 2015 and the Stanford Reader model proposed by Chen et al. in 2016. The author implemented a two-layer attention model inspired by these previous models that achieves a 1.5% higher accuracy on reading comprehension tasks compared to the Stanford Reader.
Citation Networks present us with a wide variety of problems. This project interprets a large number of Computer Science Research Papers from the DBLP archives and predicts a field in which a certain author is likely to contribute in the near future.
Survey on article extraction and comment monitoring techniquesAnunaya
The online News publisher publishes their news in the form of articles. Most of the online news websites provide the facility for their users to comment on the news article and as a result a lot of people comment on the news article. Hence news web page contains huge data in the form of article content and comments data, etc and have a good potential to be a resource for many Information Retrieval Systems and Data Mining Applications. The extraction of the main content (Article content) from a web page has always been a challenging task because a web page contains other information like advertisements and hyperlinks etc. which is not related to Article Text. In this survey, we review various techniques which are proposed by various researchers to extract the article content from a news web site. We also learn various techniques which monitor and analyze the comments for various applications like popularity prediction of articles and identification of discussions thread in the comments data.
The document provides an overview of text categorization using machine learning. It discusses feature extraction from text using bag-of-words representations and term weighting. It also covers common machine learning algorithms for text categorization like Naive Bayes, k-Nearest Neighbors, Boosting, and Support Vector Machines. The document concludes by noting that text categorization is well-suited to machine learning and discusses opportunities for future work in natural language processing with machine learning.
ExperTwin: An Alter Ego in Cyberspace for Knowledge WorkersCarlos Toxtli
ExperTwin is a Knowledge Advantage Machine (KAM) that is able to collect data from your areas of interest and present it in-time, in-context and in place to the worker workspace. This research paper describes how workers can be benefited from having a personal net of crawlers (as Google does) collecting and organizing updated data relevant to their areas of interest and delivering these to their workspace.
This document summarizes a student project on aspect/topic modeling for opinion mining from tweets. The goals of the project were to preprocess tweets, apply a modified LDA technique to extract topics from tweets, and classify tweets into categories like jokes, sports, movies, and politics. The students used a probabilistic model and SVM for classification, and were able to detect new trending topics not present in training data and categorize them as potential new topics.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
A scalable gibbs sampler for probabilistic entity linkingSunny Kr
This document summarizes a research paper that proposes a scalable Gibbs sampling approach for probabilistic entity linking. The approach formulates entity linking as probabilistic inference in a topic model where each topic corresponds to a Wikipedia article. It introduces an efficient Gibbs sampling scheme that exploits the sparsity in the Wikipedia-LDA model to allow inference over millions of topics. Experimental results show it achieves state-of-the-art performance on the Aida-CoNLL dataset.
The document discusses two neural network models for reading comprehension tasks: the Attentive Reader model proposed by Herman et al. in 2015 and the Stanford Reader model proposed by Chen et al. in 2016. The author implemented a two-layer attention model inspired by these previous models that achieves a 1.5% higher accuracy on reading comprehension tasks compared to the Stanford Reader.
Document Classification Using KNN with Fuzzy Bags of Word Representationsuthi
Abstract — Text classification is used to classify the documents depending on the words, phrases and word combinations according to the declared syntaxes. There are many applications that are using text classification such as artificial intelligence, to maintain the data according to the category and in many other. Some keywords which are called topics are selected to classify the given document. Using these Topics the main idea of the document can be identified. Selecting the Topics is an important task to classify the document according to the category. In this proposed system keywords are extracted from documents using TF-IDF and Word Net. TF-IDF algorithm is mainly used to select the important words by which document can be classified. Word Net is mainly used to find similarity between these candidate words. The words which are having the maximum similarity are considered as Topics(keywords). In this experiment we used TF-IDF model to find the similar words so that to classify the document. Decision tree algorithm gives the better accuracy for text classification when compared to other algorithms fuzzy system to classify text written in natural language according to topic. It is necessary to use a fuzzy classifier for this task, due to the fact that a given text can cover several topics with different degrees. In this context, traditional classifiers are inappropriate, as they attempt to sort each text in a single class in a winner-takes-all fashion. The classifier we proposeautomatically learns its fuzzy rules from training examples. We have applied it to classify news articles, and the results we obtained are promising. The dimensionality of a vector is very important in text classification. We can decrease this dimensionality by using clustering based on fuzzy logic. Depending on the similarity we can classify the document and thus they can be formed into clusters according to their Topics. After formation of clusters one can easily access the documents and save the documents very easily. In this we can find the similarity and summarize the words called Topics which can be used to classify the Documents.
IRJET- Empower Syntactic Exploration Based on Conceptual Graph using Searchab...IRJET Journal
This document discusses a proposed system for empowering syntactic exploration based on conceptual graphs using searchable symmetric encryption. It begins with an abstract that outlines using conceptual graphs and related natural language processing techniques to perform semantic search over encrypted cloud data. It then describes the system modules, including data owners who can upload and authorize access to encrypted files, data users who can search for files, and a cloud server that stores the outsourced encrypted data and indexes. Key algorithms discussed include named entity recognition, term frequency-inverse document frequency (TF-IDF) calculation, data encryption standard (DES) encryption, and hashed message authentication codes (HMACs) to identify duplicate documents. The proposed system architecture involves data owners encrypting and outsourcing documents
The document provides an overview of a proposed text categorization system using a modified Naive Bayes algorithm. It includes sections on the problem definition, objectives, literature review, methodology, proposed system architecture consisting of modules for dataset preprocessing, text categorization using the modified algorithm, a comparative study. The system would use a 20 newsgroup dataset, perform text reduction during preprocessing, classify unknown text, and compare the performance of the existing and proposed algorithms. It lists the software and hardware requirements and provides references for related work.
Sentiment analysis using naive bayes classifier Dev Sahu
This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.
This document discusses building an inverted index to efficiently support information retrieval on large document collections. It describes tokenizing documents, building a dictionary of normalized terms, and creating postings lists that map each term to the documents it appears in. Inverted indexes allow skipping linear scanning and support flexible queries by indexing term locations. The document also covers calculating precision and recall to measure system effectiveness.
Natural Language Processing with Graph Databases and Neo4jWilliam Lyon
Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation.
IRJET- Automatic Text Summarization using Text RankIRJET Journal
This document summarizes a research paper that proposes an automatic text summarization system using the Text Rank algorithm. The system takes in data from multiple sources on a particular topic and generates a concise summary bullet points without requiring the user to visit each individual site. It first concatenates and pre-processes the text from various articles, represents each sentence as a vector, calculates similarity between sentences to create a graph, then ranks sentences using PageRank to extract the top sentences for the summary. The proposed system aims to make knowledge gathering easier by providing summarized overviews of technical topics rather than requiring users to read multiple lengthy articles.
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
Orchestrating the Intelligent Web with Apache Mahoutaneeshabakharia
Apache Mahout is an open source machine learning library for developing scalable algorithms. It includes algorithms for classification, clustering, recommendation engines, and frequent pattern mining. Mahout algorithms can be run locally or on Hadoop for distributed processing. Topic modeling using latent Dirichlet allocation is demonstrated for analyzing tweets and suggesting Twitter lists. While algorithms can provide benefits, some such as digital face manipulation can also be disturbing.
This document summarizes a presentation on using an LSTM neural network to predict bitcoin price movements based on sentiment analysis of twitter data. It describes collecting over 1 million tweets related to bitcoin, representing the words in the tweets as word vectors, training an LSTM model on the vectorized tweet data with sentiment labels, and evaluating whether the predicted sentiment correlates with bitcoin price changes. While the results did not find a relationship between sentiment and price according to this model, improvements are discussed such as using a training set more similar to the actual tweet data.
IRJET - Event Notifier on Scraped Mails using NLPIRJET Journal
This document describes a system that uses natural language processing and APIs to automatically add events from a user's emails to their Google Calendar. It scrapes a user's Gmail inbox using the Gmail API to find emails containing event details. Natural language processing techniques are used to extract information like the date, time, location and event name from the email text. The extracted details are then used to automatically create calendar reminders by integrating with the Google Calendar API. This allows users to have all their email-based events in one place on their calendar without having to manually add them. The system was implemented and tested on sample emails, successfully extracting event details and adding reminders to the calendar.
The document summarizes a graduate student's project using support vector machines (SVM) for transductive learning to classify RNA-related biological abstracts. The student collected a corpus of 400 abstracts categorized into RNA-related and non-RNA-related groups. Software was developed to preprocess the abstracts, extract features, generate training and test sets for SVM Light, and test its ability to classify abstracts into different RNA categories like mRNA, tRNA, etc. The goal was to improve on keyword searches by using a small number of training examples from a specific dataset to maximize classification precision for that set.
Developing A Big Data Search Engine - Where we have gone. Where we are going:...Lucidworks
Mark Miller discussed the history and future of search engines like Lucene and Solr. He explained that Lucene search engines currently lead the field and are widely used. While search has scaled up, it remains imperfect and can be flaky at large scales. Extensive testing, including of integration and distributions, is needed to improve reliability. Leveraging Hadoop's distributed capabilities can help push search engines to be more scalable and correct at large sizes.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
Automated Software Requirements LabelingData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Machine Learning for Requirements Engineering
Speaker: Jon Patton
This project applies a number of machine learning, deep learning, and NLP techniques to solve challenging problems in requirements engineering.
The document describes building a meta-search engine that aggregates results from multiple search engines. It discusses the infrastructure including querying different search engines simultaneously, preprocessing queries, caching results, and using multithreading. It also covers re-ranking and aggregating results using methods like alpha-majority and analyzing query logs and system performance. Evaluation shows highest mean average precision for queries related to news, trending topics, and video keywords.
Michael Manukyan and Hrayr Harutyunyan gave a talk on sentence representations in the context of deep learning during Armenian NLP Meetup. They also reviewed a recent paper on machine comprehension (Wang, Jiang, 2016)
The document discusses processing Boolean queries in an information retrieval system using an inverted index. It describes the steps to process a simple conjunctive query by locating terms in the dictionary, retrieving their postings lists, and intersecting the lists. More complex queries involving OR and NOT operators are also processed in a similar way. The document also discusses optimizing query processing by considering the order of accessing postings lists.
This webinar discusses how to perform sentiment analysis on large datasets using Apache Hive. It provides an overview of sentiment analysis and demonstrates useful Hive UDFs for preprocessing text data and extracting n-grams. The webinar also includes a tutorial analyzing sentiment around the topic of "mortgage" using the MemeTracker dataset containing 90 million records of URLs, timestamps, memes and links over 36GB of JSON data. Advanced custom sentiment analysis can be developed by extending Hive's extensibility framework.
This document outlines the course plan for an Object Oriented Concepts course. It includes information on the course, content, prerequisites, outcomes, and assessment. The course has 5 modules covering Object Oriented Programming fundamentals in Java, language features, inheritance, exceptions, packages, interfaces, threads, and GUI concepts using Swings. Students will be continuously assessed through 3 internal exams and 3 assignments. The exams will assess understanding of key concepts from each module through theory and problem-solving questions. At the end of the course, students should be able to apply OOP concepts, develop Java programs, handle exceptions and interfaces, implement multi-threading, and develop basic GUI interfaces using Swings.
CSCI6505 Project:Construct search engine using ML approachbutest
This document summarizes a student project report on developing a topic-based search engine for a website using machine learning. The project uses an instance-based learning algorithm (k-nearest neighbors) to classify HTML files into topics like artificial intelligence, programming languages, etc. It includes modules for training a classifier, crawling a website to index files into topics, and a search interface for users. The report describes implementing classes for preprocessing HTML, indexing, classification, and search functionality. Sample results show a keyword-based and topic-based search interface that returns relevant files.
Document Classification Using KNN with Fuzzy Bags of Word Representationsuthi
Abstract — Text classification is used to classify the documents depending on the words, phrases and word combinations according to the declared syntaxes. There are many applications that are using text classification such as artificial intelligence, to maintain the data according to the category and in many other. Some keywords which are called topics are selected to classify the given document. Using these Topics the main idea of the document can be identified. Selecting the Topics is an important task to classify the document according to the category. In this proposed system keywords are extracted from documents using TF-IDF and Word Net. TF-IDF algorithm is mainly used to select the important words by which document can be classified. Word Net is mainly used to find similarity between these candidate words. The words which are having the maximum similarity are considered as Topics(keywords). In this experiment we used TF-IDF model to find the similar words so that to classify the document. Decision tree algorithm gives the better accuracy for text classification when compared to other algorithms fuzzy system to classify text written in natural language according to topic. It is necessary to use a fuzzy classifier for this task, due to the fact that a given text can cover several topics with different degrees. In this context, traditional classifiers are inappropriate, as they attempt to sort each text in a single class in a winner-takes-all fashion. The classifier we proposeautomatically learns its fuzzy rules from training examples. We have applied it to classify news articles, and the results we obtained are promising. The dimensionality of a vector is very important in text classification. We can decrease this dimensionality by using clustering based on fuzzy logic. Depending on the similarity we can classify the document and thus they can be formed into clusters according to their Topics. After formation of clusters one can easily access the documents and save the documents very easily. In this we can find the similarity and summarize the words called Topics which can be used to classify the Documents.
IRJET- Empower Syntactic Exploration Based on Conceptual Graph using Searchab...IRJET Journal
This document discusses a proposed system for empowering syntactic exploration based on conceptual graphs using searchable symmetric encryption. It begins with an abstract that outlines using conceptual graphs and related natural language processing techniques to perform semantic search over encrypted cloud data. It then describes the system modules, including data owners who can upload and authorize access to encrypted files, data users who can search for files, and a cloud server that stores the outsourced encrypted data and indexes. Key algorithms discussed include named entity recognition, term frequency-inverse document frequency (TF-IDF) calculation, data encryption standard (DES) encryption, and hashed message authentication codes (HMACs) to identify duplicate documents. The proposed system architecture involves data owners encrypting and outsourcing documents
The document provides an overview of a proposed text categorization system using a modified Naive Bayes algorithm. It includes sections on the problem definition, objectives, literature review, methodology, proposed system architecture consisting of modules for dataset preprocessing, text categorization using the modified algorithm, a comparative study. The system would use a 20 newsgroup dataset, perform text reduction during preprocessing, classify unknown text, and compare the performance of the existing and proposed algorithms. It lists the software and hardware requirements and provides references for related work.
Sentiment analysis using naive bayes classifier Dev Sahu
This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.
This document discusses building an inverted index to efficiently support information retrieval on large document collections. It describes tokenizing documents, building a dictionary of normalized terms, and creating postings lists that map each term to the documents it appears in. Inverted indexes allow skipping linear scanning and support flexible queries by indexing term locations. The document also covers calculating precision and recall to measure system effectiveness.
Natural Language Processing with Graph Databases and Neo4jWilliam Lyon
Originally presented at DataDay Texas in Austin, this presentation shows how a graph database such as Neo4j can be used for common natural language processing tasks, such as building a word adjacency graph, mining word associations, summarization and keyword extraction and content recommendation.
IRJET- Automatic Text Summarization using Text RankIRJET Journal
This document summarizes a research paper that proposes an automatic text summarization system using the Text Rank algorithm. The system takes in data from multiple sources on a particular topic and generates a concise summary bullet points without requiring the user to visit each individual site. It first concatenates and pre-processes the text from various articles, represents each sentence as a vector, calculates similarity between sentences to create a graph, then ranks sentences using PageRank to extract the top sentences for the summary. The proposed system aims to make knowledge gathering easier by providing summarized overviews of technical topics rather than requiring users to read multiple lengthy articles.
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
Orchestrating the Intelligent Web with Apache Mahoutaneeshabakharia
Apache Mahout is an open source machine learning library for developing scalable algorithms. It includes algorithms for classification, clustering, recommendation engines, and frequent pattern mining. Mahout algorithms can be run locally or on Hadoop for distributed processing. Topic modeling using latent Dirichlet allocation is demonstrated for analyzing tweets and suggesting Twitter lists. While algorithms can provide benefits, some such as digital face manipulation can also be disturbing.
This document summarizes a presentation on using an LSTM neural network to predict bitcoin price movements based on sentiment analysis of twitter data. It describes collecting over 1 million tweets related to bitcoin, representing the words in the tweets as word vectors, training an LSTM model on the vectorized tweet data with sentiment labels, and evaluating whether the predicted sentiment correlates with bitcoin price changes. While the results did not find a relationship between sentiment and price according to this model, improvements are discussed such as using a training set more similar to the actual tweet data.
IRJET - Event Notifier on Scraped Mails using NLPIRJET Journal
This document describes a system that uses natural language processing and APIs to automatically add events from a user's emails to their Google Calendar. It scrapes a user's Gmail inbox using the Gmail API to find emails containing event details. Natural language processing techniques are used to extract information like the date, time, location and event name from the email text. The extracted details are then used to automatically create calendar reminders by integrating with the Google Calendar API. This allows users to have all their email-based events in one place on their calendar without having to manually add them. The system was implemented and tested on sample emails, successfully extracting event details and adding reminders to the calendar.
The document summarizes a graduate student's project using support vector machines (SVM) for transductive learning to classify RNA-related biological abstracts. The student collected a corpus of 400 abstracts categorized into RNA-related and non-RNA-related groups. Software was developed to preprocess the abstracts, extract features, generate training and test sets for SVM Light, and test its ability to classify abstracts into different RNA categories like mRNA, tRNA, etc. The goal was to improve on keyword searches by using a small number of training examples from a specific dataset to maximize classification precision for that set.
Developing A Big Data Search Engine - Where we have gone. Where we are going:...Lucidworks
Mark Miller discussed the history and future of search engines like Lucene and Solr. He explained that Lucene search engines currently lead the field and are widely used. While search has scaled up, it remains imperfect and can be flaky at large scales. Extensive testing, including of integration and distributions, is needed to improve reliability. Leveraging Hadoop's distributed capabilities can help push search engines to be more scalable and correct at large sizes.
Deep Learning Enabled Question Answering System to Automate Corporate HelpdeskSaurabh Saxena
Studied feasibility of applying state-of-the-art deep learning models like end-to-end memory networks and neural attention- based models to the problem of machine comprehension and subsequent question answering in corporate settings with huge
amount of unstructured textual data. Used pre-trained embeddings like word2vec and GLove to avoid huge training costs.
Automated Software Requirements LabelingData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Machine Learning for Requirements Engineering
Speaker: Jon Patton
This project applies a number of machine learning, deep learning, and NLP techniques to solve challenging problems in requirements engineering.
The document describes building a meta-search engine that aggregates results from multiple search engines. It discusses the infrastructure including querying different search engines simultaneously, preprocessing queries, caching results, and using multithreading. It also covers re-ranking and aggregating results using methods like alpha-majority and analyzing query logs and system performance. Evaluation shows highest mean average precision for queries related to news, trending topics, and video keywords.
Michael Manukyan and Hrayr Harutyunyan gave a talk on sentence representations in the context of deep learning during Armenian NLP Meetup. They also reviewed a recent paper on machine comprehension (Wang, Jiang, 2016)
The document discusses processing Boolean queries in an information retrieval system using an inverted index. It describes the steps to process a simple conjunctive query by locating terms in the dictionary, retrieving their postings lists, and intersecting the lists. More complex queries involving OR and NOT operators are also processed in a similar way. The document also discusses optimizing query processing by considering the order of accessing postings lists.
This webinar discusses how to perform sentiment analysis on large datasets using Apache Hive. It provides an overview of sentiment analysis and demonstrates useful Hive UDFs for preprocessing text data and extracting n-grams. The webinar also includes a tutorial analyzing sentiment around the topic of "mortgage" using the MemeTracker dataset containing 90 million records of URLs, timestamps, memes and links over 36GB of JSON data. Advanced custom sentiment analysis can be developed by extending Hive's extensibility framework.
This document outlines the course plan for an Object Oriented Concepts course. It includes information on the course, content, prerequisites, outcomes, and assessment. The course has 5 modules covering Object Oriented Programming fundamentals in Java, language features, inheritance, exceptions, packages, interfaces, threads, and GUI concepts using Swings. Students will be continuously assessed through 3 internal exams and 3 assignments. The exams will assess understanding of key concepts from each module through theory and problem-solving questions. At the end of the course, students should be able to apply OOP concepts, develop Java programs, handle exceptions and interfaces, implement multi-threading, and develop basic GUI interfaces using Swings.
CSCI6505 Project:Construct search engine using ML approachbutest
This document summarizes a student project report on developing a topic-based search engine for a website using machine learning. The project uses an instance-based learning algorithm (k-nearest neighbors) to classify HTML files into topics like artificial intelligence, programming languages, etc. It includes modules for training a classifier, crawling a website to index files into topics, and a search interface for users. The report describes implementing classes for preprocessing HTML, indexing, classification, and search functionality. Sample results show a keyword-based and topic-based search interface that returns relevant files.
This document provides an agenda and materials for a one-day workshop on qualitative data analysis. The workshop will include two exercises. The first involves selecting quotes, assigning codes, and creating memos from narrative data. The second uses grounded theory methods to map themes, quotes and codes from the data. The workshop aims to teach participants tools for analyzing text, documents and images within and across different settings.
Das patrac sandpythonwithpracticalcbse11NumraHashmi
The document is a textbook on computer science and Python programming for CBSE Class XI. It covers the theory and practical syllabus prescribed by CBSE. The textbook is divided into five parts - Computer Systems and Organisation, Computational Thinking and Programming, Data Management, Society Law and Ethics, and solutions to programming exercises. It includes chapters on topics like computer hardware, Python programming concepts, SQL, NoSQL and cyber safety. Each chapter provides learning objectives, concepts, examples, questions and programming assignments. The textbook aims to help students learn computer science concepts and develop Python and database programming skills as per the CBSE Class XI syllabus.
Recommending Semantic Nearest Neighbors Using Storm and DatoAshok Venkatesan
In this talk, we present how SU has used Dato Graphlab-Create along with Apache Storm to build a minimum viable online pipeline for computing item similarity over item attributes – a key component in contextual recommendations.
The Document Engineering Company presented a webinar on lessons learned from deploying large language models with LangSmith. They discussed challenges with using LLMs on real documents, which are more complex than flat text. Documents contain structure like headings and tables, and relationships that form a knowledge graph. They demonstrated how to represent documents as XML to preserve semantics and improve retrieval augmented generation. Complex chains in production require debugging failures from issues like syntax errors or rate limits. Their approach is to regularly analyze failures, add examples to training, and fine tune models in an end-to-end process.
Notey is an analytics engine that provides more relevant search results, curated content, and contextual targeting than Google. It processes large amounts of data daily from blogs, articles, and user logs to generate recommendations and insights. Notey uses technologies like Java, node.js, EHCache, MySQL, Solr, Google Big Query, and Amazon Web Services for scalability. It employs algorithms like hot ranking, topic classification, user personalization, social network analysis, and search engine optimization to analyze data and improve search results.
MongoDB .local London 2019: Fast Machine Learning Development with MongoDBLisa Roth, PMP
Today an increasingly large number of products use machine learning and AI to deliver a great personalized user experience, and workplace software is no exception. Spoke goes beyond traditional ticketing with their friendly, AI-powered chatbot that gives workplace teams hours of time back as it automatically responds to questions on Slack, email, SMS, and web. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and serves thousands of models, with multiple customized models per client.
MongoDB .local London 2019: Fast Machine Learning Development with MongoDBMongoDB
Today an increasingly large number of products use machine learning and AI to deliver a great personalized user experience, and workplace software is no exception. Spoke goes beyond traditional ticketing with their friendly, AI-powered chatbot that gives workplace teams hours of time back as it automatically responds to questions on Slack, email, SMS, and web. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and serves thousands of models, with multiple customized models per client.
Dice.com Bay Area Search - Beyond Learning to Rank TalkSimon Hughes
This talk describes how to implement conceptual search (semantic search) within a modern search engine using the word2vec algorithm to learn concepts. We also cover how to auto-tune the search engine parameters using black box optimization techniques, and the problems of feedback loops encountered when building machine learning systems that modify the user behavior used to train the system.
Implementing Conceptual Search in Solr using LSA and Word2Vec: Presented by S...Lucidworks
The document discusses implementing conceptual search in Solr. It describes how conceptual search aims to improve recall without reducing precision by matching documents based on concepts rather than keywords alone. It explains how Word2Vec can be used to learn related concepts from documents and represent words as vectors, which can then be embedded in Solr through synonym filters and payloads to enable conceptual search queries. This allows retrieving more relevant documents that do not contain the exact search terms but are still conceptually related.
This document discusses Flipkart's use of Solr indexes to organize product data and search. It describes how Flipkart moved from indexing all data in a single CMS to a more distributed approach using services and streams to index static vs. dynamic data separately. It also discusses challenges with partial document updates in Lucene and how Flipkart leveraged updatable docvalues and value sources to integrate real-time signals for ranking and filtering.
The document provides an overview of Lucene, an open source search library. It discusses Lucene concepts like indexing, searching, analysis and contributions. The tutorial covers the basics of indexing and searching documents, analyzing text, and popular contributed modules like highlighting, spellchecking and finding similar documents. Attendees will gain hands-on experience with Lucene through code examples and exercises.
The document summarizes key topics from a recommender systems conference, including:
1. Many major companies like Netflix, Quora, and Amazon consider recommendations to be a core part of their user experience.
2. Adaptive and interactive recommendations were discussed, including how Netflix personalizes content rows based on a user's predicted mood.
3. Text modeling algorithms like word2vec were discussed for generating recommendations from content like tweets, search queries, or product descriptions.
The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly...Angelo Salatino
Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
MongoDB World 2019: Fast Machine Learning Development with MongoDBMongoDB
Today an increasingly large number of products use machine learning to deliver a great personalized user experience, and workplace software is no exception. Learn how Spoke uses MongoDB to do dynamic model training in real time from user interaction data and automatically train and serve thousands of models, with multiple customized models per client.
Topic-oriented writing structures information around topics rather than categories. It allows for easier access, scanability, modular writing by multiple authors, and facilitation of content reuse. Topic types include concept, reference, and task. A topic has a title describing its theme, followed by mixed text and images. The process involves determining topic types, writing topical titles, and describing each topic's theme. This approach can be applied to existing material by analyzing content, determining topic types, rewriting titles, and using tables to remove repetition.
Agile Mumbai 2022 - Rohit Handa | Combining Human and Artificial Intelligence...AgileNetwork
Agile Mumbai 2022
Combining Human and Artificial Intelligence for Business Agility
Rohit Handa
Director, Digital Products & Platforms, HCL Technologies Ltd
1. The document proposes a framework to extract theory and model mentions from scientific papers using distant supervision. It automatically annotates sentences using seed theory mentions from Wikipedia to generate a labeled training dataset.
2. A benchmark corpus of 4534 annotated sentences from social and behavioral science papers is created. Neural networks including BiLSTM, Transformer, and GCN models are compared for named entity recognition, with RoBERTa-BiLSTM-CRF achieving the best performance.
3. The framework can efficiently annotate large text corpora and extracts new theory names not in the original heuristic filter, providing a method for automatic theory extraction from literature.
This talk is a quick introduction to counting sketches and HyperLogLog (HLL) in particular. HLL is a probabilistic data structure that can be used for counting the number of distinct elements (cardinality) in sub-linear space. With just 2 KB memory footprint it can approximate count for millions of distinct items with an error below 2%. This has a range of applications in batch, stream, and distributed processing, most importantly reducing the amount of data we have to store or transmit over the wire, but also several pitfalls, for example when it comes to computing an intersection between two sets. In this talk, I will explain the main idea and some of the applications, show code and benchmark examples from my previous work, and provide further references for those who want to learn more.
Large-Scale Real-Time Data Management for Engagement and MonetizationSimon Lia-Jonassen
Invited talk at the Workshop on Large-Scale and Distributed Systems for Information Retrieval 2015.
Cxense helps companies understand their audience and build great online experiences. Cxense Insight and DMP let customers annotate, filter, segment and target their users based on the consumed content and performed actions in real-time. With more than 5000 active websites, Insight alone tracks more than a billion unique users with more than 15 billions page views per month. To leverage the huge amounts of data in real-time, we have built a large distributed system relying on techniques familiar from databases, information retrieval and data mining. In this talk, we outline our solutions and give some insight into the technology we use and the challenges we face. This introduction should be interesting to undergraduate and PhD students as well as experienced researchers and engineers.
Abstract: Cxense Insight helps companies to understand their audience and build great online experiences. Our interactive UI and APIs help customers to
annotate, filter, segment and target their users based on the visited content and actions in realtime. Today we already track more than half a billion of unique user identities across more than 5000 web-sites, contributing to more than 10 billions of analytics events on a monthly basis.
To leverage these amounts of data in realtime, we built a large distributed system relying on the concepts familiar from databases, information retrieval and data mining. The first part of this talk will therefore give an insight into the challenges, the architecture and the techniques we have used. While the second part of the talk will briefly demonstrate our UI and APIs in action. We hope that both parts will be interesting for undergraduate students taking IR/DB courses as well as PhD students, experienced researchers and staff.
Spark is a framework for efficient parallel data processing. It uses resilient distributed datasets (RDDs) that can be operated on in parallel, cached in memory, and recomputed when needed. The core of Spark provides functions for data sharing and basic operations like filtering, mapping, and reducing RDDs. Additional Spark modules provide capabilities for SQL, streaming, machine learning, and graph processing.
Efficient Query Processing in Distributed Search EnginesSimon Lia-Jonassen
This document outlines Simon Jonassen's research on efficient query processing in distributed search engines. It discusses three main areas:
1) Partitioned query processing, including semi-pipelined and pipelined approaches with skipping to improve throughput and latency.
2) Skipping and pruning techniques like efficient compression and linear programming to improve pruning for disjunctive queries.
3) Caching approaches including modeling static two-level caching and prefetching query results to improve search engine performance. The research is evaluated using large test collections and clusters of up to 9 nodes.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Programming Foundation Models with DSPy - Meetup Slides
No more bad news!
1. No more bad news!
News recommendation with ML and NLP.
Samia Khalid and Simon Lia-Jonassen
NTNU Cogito
March 7th, 2019
2. Contents 00 Introduction
01 Recommender architecture
02 Natural language processing
03 Recommendation model training
04 Demo and further work
3. Understand Content of news I read.
Learn my Interests over time.
Recommend news that interest me.
Introduction to News Recommendation
4. Implements three parts:
• Frontend and backend controllers.
• Feed provider and logging.
• NLP, ML and exploration workflow.
News recommender in a nutshell
https://github.com/s-j/goodnews
8. 1. Text Processing
2. Clustering
3. Topic Extraction
Natural Language Processing and Exploration
9. 1. Text Processing using spaCy
Leading open-source library for advanced NLP
10. a. Tokenization
b. Part Of Speech Tagging
c. Lemmatization
d. Stop words
1. Text Processing using spaCy
11. 1. Recognizes a sentence and
assigns a syntactic structure to
it
• “Who is the AI research director?”
2. spaCy provides a built-in
visualizer
1. Text Processing using SpaCy
Dependency Parsing
12. 1. Locate and classify named entities in text into pre-defined categories
2. Can help to answer questions like:
• “Which people, companies and products is the user interested in?”
1. Text Processing using SpaCy
Entity Recognition
13. 1. Part-of-speech Tagging:
• assigns parts of speech to each token
such as noun, verb, adjective, etc.
2. spaCy uses a statistical model to
make a prediction of which tag or
label most likely applies in the
given context
1. Text Processing using SpaCy
Distribution of POS Tags
14. 1. Text Processing using SpaCy
Word Probabilities: finding the most improbable words (noisy data)
15. 1. Text Processing using SpaCy
Analyzing top unigrams in clicked articles vs all articles (considering only PROPN and NOUNS)
16. 1. Word Vectors as input:
• 300 dimensional vectors to represent words in
numerical form
2. K-Means needs the number of cluster as
parameter:
• Try out different values until satisfied
• Can use silhouette score and distortion as metric
3. PCA for visualizing the results in 2-D
2. K-Means Clustering
18. 1. LDA considers two things:
• Each document in a corpus is a weighted combination of several topics, e.g.,
doc1-> 0.1 finance + 0.2 science + 0.5 * technology,…
• Each topic has its collection of representative keywords, e.g.,
technology -> [‘computer’, ‘microsoft’, ‘google', ...]
3. Topic Modeling: LDA
19. 2. The two probability distributions that the algorithm tries to approximate,
starting from a random initialization until convergence:
• For a given document, what is the distribution of topics that describe it?
• For a given topic, what is the distribution of its words or what is the importance (probability) of
each word in defining the topic nature?
3. Topic Modeling: LDA
22. 1. Join requests and feedback logs.
• Alternative: use a third-party dataset.
2. Use #clicks > 0 as a positive label.
• Alternative 1: use #clicks / #views
• Alternative 2: use click order
• Alternative 3: get explicit feedback
Model training
Preprocessing
23. 1. Use title and description to get:
• A bag of named entities such as person or org
(using spaCy).
• A bag of key terms from the semantic network
(using Textacy).
• A normalized sum over key term embedding
vectors found in GoogleNews word2vec dataset.
2. Hold out 20% of items for testing.
Model training
NLP features and Train/Test split
24. 1. One-hot-encode entities to get a sparse vector.
2. Compensate popularity skew using
Inverse Document Frequency (IDF).
3. Train a classifier using Gradient Boosting
Decision Trees (GBDT).
Note that we have a small and very skewed,
noisy dataset so we are not expecting good
classification performance.
Model training
Pipeline based on entities
25. 1. Hash-merge features into 100 buckets.
2. Train a GBDT classifier.
Model training
Pipeline based on semantic key terms
26. Just use logistic regression right away.
• This gives us a more relaxed prediction with a
much higher number of true positives but
also false negatives.
Model training
Pipeline based on embedding vectors
28. 1. Get NLP features for a ranking candidate.
• Equivalent to the preprocessing step in training.
2. Get "click probability" from the loaded pipeline
and use this value for ranking.
Model application
Using a trained model
Analyzes the grammatical structure of a sentence, establishing relationships between "head" words and words which modify those heads.
Dependency Parsers can read various forms of plain text input and can output various analysis formats, including part-of-speech tagged text, phrase structure trees, and a grammatical relations (typed dependency) format.
Dependency Parsing can be used to solve various complex NLP problems like Named Entity Recognition, Relation Extraction, translation.
Locates and and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.
To discard noisy data
Say something about «chars> outlier – shows we have data to clean
To describe and summarize the documents in a corpus
To describe and summarize the documents in a corpus