Text mining lab (summer 2017) - Word Vector Representation
•
1 like•101 views
Text Mining lab for summer 2017 course. Main focus was on word vector representation using word2vec and other word embedding implementations.
Report
Share
Report
Share
Download to read offline
Recommended
Practical Collapsed Stochastic Variational Inference
The document describes different methods for collapsed stochastic variational inference for topic models:
1) Classic collapsed LDA inference iterates between updating word topic assignments and topic distributions for each document.
2) Stochastic practical collapsed variational inference for HDP extends this by adding stochastic updates to the hyperparameters.
3) Stochastic variational inference for LDA further extends this with stochastic updates to the word-topic counts for each document.
4) An evaluation compares the predictive performance of these methods by measuring perplexity on held-out data from the Associated Press corpus.
[論文紹介] Towards Understanding Linear Word Analogies
・本資料は下記イベントにおける第三者による論文紹介用資料です。間違い等のご指摘は竹中までご連絡お願いいたします。
・イベント情報
https://nlpaper-challenge.connpass.com/event/152676/
ACL網羅的サーベイ報告会, 2019/11/02
・紹介論文
Ethayarajh et al. "Towards Understanding Linear Word Analogies", ACL2019
Tips And Tricks For Bioinformatics Software Engineering
This document provides tips and tricks for software engineering in bioinformatics. It discusses using object-oriented software design principles like encapsulation and inheritance. It also covers best practices like automating documentation, performance optimization, working with data using databases and file formats, parallel and distributed computing, hardware acceleration, and web services.
(Semantic Web Technologies and Applications track) "MIRROR: Automatic R2RML M...
MIRROR is a tool that automatically generates R2RML mappings from relational databases. It extracts tables and relationships from the database schema and generates two sets of mappings: 1) mappings that produce direct mapping triples, and 2) mappings that encode the relationships between tables as additional triples. The tool was tested on W3C direct mapping test cases and an extension of the D011 test case to include one-to-many, many-to-many relationships and subclass relationships.
DSD-INT 2018 Work with iMOD MODFLOW models in Python - Visser Bootsma
Presentation by Martijn Visser and Huite Bootsma (Deltares) at the iMOD International User Day 2018, during Delft Software Days - Edition 2018. Tuesday 13 November 2018, Delft.
Applying your Convolutional Neural Networks
Part 3 of the Deep Learning Fundamentals Series, this session starts with a quick primer on activation functions, learning rates, optimizers, and backpropagation. Then it dives deeper into convolutional neural networks discussing convolutions (including kernels, local connectivity, strides, padding, and activation functions), pooling (or subsampling to reduce the image size), and fully connected layer. The session also provides a high-level overview of some CNN architectures. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
PGQL: A Language for Graphs
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: transmart-data
tranSMART Community Meeting 5-7 Nov 13 - Session 3: transmart-data
Management of tranSMART's Environment
Gustavo Lopes
The Hyve B.V.
Recommended
Practical Collapsed Stochastic Variational Inference
The document describes different methods for collapsed stochastic variational inference for topic models:
1) Classic collapsed LDA inference iterates between updating word topic assignments and topic distributions for each document.
2) Stochastic practical collapsed variational inference for HDP extends this by adding stochastic updates to the hyperparameters.
3) Stochastic variational inference for LDA further extends this with stochastic updates to the word-topic counts for each document.
4) An evaluation compares the predictive performance of these methods by measuring perplexity on held-out data from the Associated Press corpus.
[論文紹介] Towards Understanding Linear Word Analogies
・本資料は下記イベントにおける第三者による論文紹介用資料です。間違い等のご指摘は竹中までご連絡お願いいたします。
・イベント情報
https://nlpaper-challenge.connpass.com/event/152676/
ACL網羅的サーベイ報告会, 2019/11/02
・紹介論文
Ethayarajh et al. "Towards Understanding Linear Word Analogies", ACL2019
Tips And Tricks For Bioinformatics Software Engineering
This document provides tips and tricks for software engineering in bioinformatics. It discusses using object-oriented software design principles like encapsulation and inheritance. It also covers best practices like automating documentation, performance optimization, working with data using databases and file formats, parallel and distributed computing, hardware acceleration, and web services.
(Semantic Web Technologies and Applications track) "MIRROR: Automatic R2RML M...
MIRROR is a tool that automatically generates R2RML mappings from relational databases. It extracts tables and relationships from the database schema and generates two sets of mappings: 1) mappings that produce direct mapping triples, and 2) mappings that encode the relationships between tables as additional triples. The tool was tested on W3C direct mapping test cases and an extension of the D011 test case to include one-to-many, many-to-many relationships and subclass relationships.
DSD-INT 2018 Work with iMOD MODFLOW models in Python - Visser Bootsma
Presentation by Martijn Visser and Huite Bootsma (Deltares) at the iMOD International User Day 2018, during Delft Software Days - Edition 2018. Tuesday 13 November 2018, Delft.
Applying your Convolutional Neural Networks
Part 3 of the Deep Learning Fundamentals Series, this session starts with a quick primer on activation functions, learning rates, optimizers, and backpropagation. Then it dives deeper into convolutional neural networks discussing convolutions (including kernels, local connectivity, strides, padding, and activation functions), pooling (or subsampling to reduce the image size), and fully connected layer. The session also provides a high-level overview of some CNN architectures. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
PGQL: A Language for Graphs
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: transmart-data
tranSMART Community Meeting 5-7 Nov 13 - Session 3: transmart-data
Management of tranSMART's Environment
Gustavo Lopes
The Hyve B.V.
IA3_presentation.pptx
The document discusses optimizing the performance of Word2Vec on multicore systems through a technique called Context Combining. Some key points:
- Context Combining improves Word2Vec training efficiency by combining related windows that share samples, improving floating point throughput and reducing overhead.
- Experiments on Intel and Intel Knights Landing processors show Context Combining (pSGNScc) achieves up to 1.28x speedup over prior work (pWord2Vec) and maintains comparable accuracy to state-of-the-art implementations.
- Parallel scaling tests show pSGNScc achieves near linear speedup up to 68 cores, utilizing more of the available computational resources than previous techniques.
- Future
Introduction to Graph Databases @ SAI
This document discusses big data and graph databases. It begins by introducing the author and defining big data in terms of volume, variety and velocity. It then discusses how graph databases can help tackle problems related to big data by being schema-less, allowing for index-free queries, and enabling traversal-based queries. Several use cases for graph databases are provided like recommendations, access control and social networks. The document concludes by discussing potential applications of graph databases for longitudinal patient data, innovation networking and various domains.
Enabling semantic integration
This document describes Jean-Paul Calbimonte's doctoral research on enabling semantic integration of streaming data sources. The research aims to provide semantic query interfaces for streaming data, expose streaming data for the semantic web, and integrate streaming sources through ontology mappings. The approach involves ontology-based data access to streams, a semantic streaming query language, and semantic integration of distributed streams. Work done so far includes defining a language (SPARQLSTR) for querying RDF streams and enabling an engine to support streaming data sources through ontology mappings. Future work involves query optimization and quantitative evaluation.
Querying your database in natural language by Daniel Moisset PyData SV 2014
Most end users can't write a database query, and yet, they often have the need to access information that keyword-based searches can't retrieve precisely. Lately, there's been an explosion of proprietary Natural Language Interfaces to knowledge databases, like Siri, Google Now and Wolfram Alpha. On the open side, huge knowledge bases like DBpedia and Freebase exists, but access to them is typically limited to using formal database query languages. We implemented Quepy as an approach to provide a solution for this problem. Quepy is an open source framework to transform Natural Language questions into semantic database queries that can be used with popular knowledge databases like, for example, DBPedia and Freebase. So instead of requiring end users to learn to write some query language, a Quepy Application can fills the gap, allowing end users to make their queries in "plain English". In this talk we would discuss the techniques used in Quepy, what additional work can be done, and its limitations.
Probabilistic Data Structures and Approximate Solutions Oleksandr Pryymak
Probabilistic Data Structures and Approximate Solutions by Oleksandr Pryymak. http://nbviewer.ipython.org/gist/235/d3ee622926b5f77f03df
How to easily find the optimal solution without exhaustive search using Genet...
Genetic algorithms are a type of stochastic optimization technique inspired by biological evolution. They can be used to find optimal or suboptimal solutions to problems that are difficult to solve directly. The document discusses using genetic algorithms to solve optimization problems in software projects, including minimizing nutrition plan differences and maximizing intersection of traffic data. It provides an example of using genetic algorithms to solve a Diophantine equation and summaries that genetic algorithms are easy to start but computationally expensive and only find local optima or suboptimal solutions.
sos4R @ OGC TC
Presentation of the sos4R SOS client for R at the Open Geospatial Consortium Technical Committee meeting in Bonn.
[Gary entsminger] turbo_pascal_for_windows_bible(book_fi.org)
This document describes the Turbo Pascal for Windows integrated development environment (IDE) and command-line compiler. It covers the IDE menus such as File, Edit, Run, Compile, Options, and Window. It also describes the editor commands and the syntax of the command-line compiler. The document provides an overview of creating Windows applications using Turbo Pascal.
Introduction to Julia
This document introduces Julia and provides an overview of its key features. It begins with introductions and background on why Julia was created. It then covers basic Julia concepts like variables, arithmetic operators, control flow, arrays, functions, and parallelization capabilities. The document also discusses Julia's built-in package ecosystem and provides examples of packages like DataFrames. It aims to provide attendees with foundational knowledge of the Julia programming language.
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance (WWW...
A deck of slides for "N-gram IDF: A Global Term Weighting Scheme Based on Information Distance" (Shirakawa et al.) that was presented at 24th International World Wide Web Conference (WWW 2015).
India software developers conference 2013 Bangalore
This document discusses using the R programming language for data science and data analysis. It provides an overview of R basics like data structures and simple operations. It also presents a case study on activity recognition using accelerometer data from smartphones. Various data analysis steps are demonstrated like feature extraction, visualization, and using classifiers like decision trees and random forests. The document concludes by emphasizing the importance of combining data science and domain knowledge to gain insights from data.
MMDS 2014: Myria (and Scalable Graph Clustering with RelaxMap)
A talk I gave at the MMDS workshop June 2014 on the Myria system as well as some of Seung-Hee Bae's work on scalable graph clustering.
https://mmds-data.org/
Let's build a parser!
The document introduces building a parser in PHP by explaining reasons for common fears around parsing, showing examples of language grammars like BNF and EBNF, and demonstrating how to generate a parser in PHP using PEG parsing expressions to parse a sample query language across multiple versions, with the potential to optimize parsed queries.
CS571: Distributional semantics
The document discusses several topics in natural language processing including distributional semantics, language models, word embeddings, and neural network models like word2vec. It introduces techniques for distributional semantics using distributional properties of words from large datasets. Language models are discussed including n-gram models and language class models that incorporate word classes. Word embedding techniques like word2vec are introduced for generating word vectors using neural networks.
VoxxedDays Luxembourg 2019
This document discusses different API options for databases: REST, gRPC, and GraphQL. It begins with an overview of Apache Cassandra and its key features as a distributed database. It then covers an API design methodology, including conceptual and logical data modeling, mapping queries to tables, and creating the physical schema. The document presents criteria for evaluating API choices and provides pros and cons of REST, gRPC, and GraphQL. It concludes that REST is best for CRUD operations, gRPC for high performance services, and GraphQL for discoverability and flexible payloads.
Concepts of JetBrains MPS
The document discusses domain-specific languages (DSLs) and model-driven development using the Meta Programming System (MPS). It provides examples of DSL concepts like abstract syntax trees, language modularization, projectional editing, and static semantics. It also outlines features for defining a language in MPS, including structure, editor, code generation, and integration with Java. MPS allows focusing languages on specific domains and processes models instead of text.
Deep Dive on Deep Learning (June 2018)
This document provides a summary of a presentation on deep learning concepts, common architectures, Apache MXNet, and infrastructure for deep learning. The agenda includes an overview of deep learning concepts like neural networks and training, common architectures like convolutional neural networks and LSTMs, a demonstration of Apache MXNet's symbolic and imperative APIs, and a discussion of infrastructure for deep learning on AWS like optimized EC2 instances and Amazon SageMaker.
Dagstuhl seminar talk on querying big graphs
The document describes techniques for querying large graph datasets. It discusses how graphs arise in many domains like social networks, biological networks, and knowledge graphs. It outlines some challenges in querying big graphs like heterogeneity, uncertainty, and massive scale. It then presents the author's work on approximate subgraph matching to enable flexible querying of heterogeneous graphs. The key ideas are converting graph structures to vectors to compute similarity, defining a cost function for subgraph matching, and using loopy belief propagation for inference. Experimental results on real datasets demonstrate the effectiveness of the proposed techniques.
High-Performance Graph Analysis and Modeling
This document discusses knowledge discovery and machine learning on graph data. It makes three main observations:
1) Graphs are typically constructed from input data rather than given directly, as relationships must be inferred.
2) Graph data management is challenging due to issues like large size, dynamic nature, heterogeneity and attribution.
3) Useful insights and accurate modeling depend on the representation of the data as a graph, such as through decomposition, feature learning or other techniques.
The Future of Brain-Powered Learning
The document discusses the potential future of brain-machine interfaces (BMI) in education in the year 2050. It describes a proposed BMI learning network called BLiNK that would allow for direct brain-to-brain communication and knowledge sharing between learners and instructors. BLiNK is suggested to offer personalized learning pathways, virtual classrooms, access to knowledge databases, and real-time analytics on learning progress. The pros of BLiNK include learner-centered and on-demand education, while the cons could include issues regarding privacy, health, and potential for brain hacking or mind control if not properly regulated.
Introduction to Fundamentals of RNNs
The document provides an overview of recurrent neural networks (RNNs). RNNs can model sequential data by incorporating information about previous elements in the sequence through their hidden state. RNNs share parameters across time steps and can capture long-term dependencies. They are well-suited for applications involving time series data and natural language. The document reviews the basic components of RNNs including recurrent neurons, unfolding RNNs over time, and training RNNs using backpropagation through time.
More Related Content
Similar to Text mining lab (summer 2017) - Word Vector Representation
IA3_presentation.pptx
The document discusses optimizing the performance of Word2Vec on multicore systems through a technique called Context Combining. Some key points:
- Context Combining improves Word2Vec training efficiency by combining related windows that share samples, improving floating point throughput and reducing overhead.
- Experiments on Intel and Intel Knights Landing processors show Context Combining (pSGNScc) achieves up to 1.28x speedup over prior work (pWord2Vec) and maintains comparable accuracy to state-of-the-art implementations.
- Parallel scaling tests show pSGNScc achieves near linear speedup up to 68 cores, utilizing more of the available computational resources than previous techniques.
- Future
Introduction to Graph Databases @ SAI
This document discusses big data and graph databases. It begins by introducing the author and defining big data in terms of volume, variety and velocity. It then discusses how graph databases can help tackle problems related to big data by being schema-less, allowing for index-free queries, and enabling traversal-based queries. Several use cases for graph databases are provided like recommendations, access control and social networks. The document concludes by discussing potential applications of graph databases for longitudinal patient data, innovation networking and various domains.
Enabling semantic integration
This document describes Jean-Paul Calbimonte's doctoral research on enabling semantic integration of streaming data sources. The research aims to provide semantic query interfaces for streaming data, expose streaming data for the semantic web, and integrate streaming sources through ontology mappings. The approach involves ontology-based data access to streams, a semantic streaming query language, and semantic integration of distributed streams. Work done so far includes defining a language (SPARQLSTR) for querying RDF streams and enabling an engine to support streaming data sources through ontology mappings. Future work involves query optimization and quantitative evaluation.
Querying your database in natural language by Daniel Moisset PyData SV 2014
Most end users can't write a database query, and yet, they often have the need to access information that keyword-based searches can't retrieve precisely. Lately, there's been an explosion of proprietary Natural Language Interfaces to knowledge databases, like Siri, Google Now and Wolfram Alpha. On the open side, huge knowledge bases like DBpedia and Freebase exists, but access to them is typically limited to using formal database query languages. We implemented Quepy as an approach to provide a solution for this problem. Quepy is an open source framework to transform Natural Language questions into semantic database queries that can be used with popular knowledge databases like, for example, DBPedia and Freebase. So instead of requiring end users to learn to write some query language, a Quepy Application can fills the gap, allowing end users to make their queries in "plain English". In this talk we would discuss the techniques used in Quepy, what additional work can be done, and its limitations.
Probabilistic Data Structures and Approximate Solutions Oleksandr Pryymak
Probabilistic Data Structures and Approximate Solutions by Oleksandr Pryymak. http://nbviewer.ipython.org/gist/235/d3ee622926b5f77f03df
How to easily find the optimal solution without exhaustive search using Genet...
Genetic algorithms are a type of stochastic optimization technique inspired by biological evolution. They can be used to find optimal or suboptimal solutions to problems that are difficult to solve directly. The document discusses using genetic algorithms to solve optimization problems in software projects, including minimizing nutrition plan differences and maximizing intersection of traffic data. It provides an example of using genetic algorithms to solve a Diophantine equation and summaries that genetic algorithms are easy to start but computationally expensive and only find local optima or suboptimal solutions.
sos4R @ OGC TC
Presentation of the sos4R SOS client for R at the Open Geospatial Consortium Technical Committee meeting in Bonn.
[Gary entsminger] turbo_pascal_for_windows_bible(book_fi.org)
This document describes the Turbo Pascal for Windows integrated development environment (IDE) and command-line compiler. It covers the IDE menus such as File, Edit, Run, Compile, Options, and Window. It also describes the editor commands and the syntax of the command-line compiler. The document provides an overview of creating Windows applications using Turbo Pascal.
Introduction to Julia
This document introduces Julia and provides an overview of its key features. It begins with introductions and background on why Julia was created. It then covers basic Julia concepts like variables, arithmetic operators, control flow, arrays, functions, and parallelization capabilities. The document also discusses Julia's built-in package ecosystem and provides examples of packages like DataFrames. It aims to provide attendees with foundational knowledge of the Julia programming language.
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance (WWW...
A deck of slides for "N-gram IDF: A Global Term Weighting Scheme Based on Information Distance" (Shirakawa et al.) that was presented at 24th International World Wide Web Conference (WWW 2015).
India software developers conference 2013 Bangalore
This document discusses using the R programming language for data science and data analysis. It provides an overview of R basics like data structures and simple operations. It also presents a case study on activity recognition using accelerometer data from smartphones. Various data analysis steps are demonstrated like feature extraction, visualization, and using classifiers like decision trees and random forests. The document concludes by emphasizing the importance of combining data science and domain knowledge to gain insights from data.
MMDS 2014: Myria (and Scalable Graph Clustering with RelaxMap)
A talk I gave at the MMDS workshop June 2014 on the Myria system as well as some of Seung-Hee Bae's work on scalable graph clustering.
https://mmds-data.org/
Let's build a parser!
The document introduces building a parser in PHP by explaining reasons for common fears around parsing, showing examples of language grammars like BNF and EBNF, and demonstrating how to generate a parser in PHP using PEG parsing expressions to parse a sample query language across multiple versions, with the potential to optimize parsed queries.
CS571: Distributional semantics
The document discusses several topics in natural language processing including distributional semantics, language models, word embeddings, and neural network models like word2vec. It introduces techniques for distributional semantics using distributional properties of words from large datasets. Language models are discussed including n-gram models and language class models that incorporate word classes. Word embedding techniques like word2vec are introduced for generating word vectors using neural networks.
VoxxedDays Luxembourg 2019
This document discusses different API options for databases: REST, gRPC, and GraphQL. It begins with an overview of Apache Cassandra and its key features as a distributed database. It then covers an API design methodology, including conceptual and logical data modeling, mapping queries to tables, and creating the physical schema. The document presents criteria for evaluating API choices and provides pros and cons of REST, gRPC, and GraphQL. It concludes that REST is best for CRUD operations, gRPC for high performance services, and GraphQL for discoverability and flexible payloads.
Concepts of JetBrains MPS
The document discusses domain-specific languages (DSLs) and model-driven development using the Meta Programming System (MPS). It provides examples of DSL concepts like abstract syntax trees, language modularization, projectional editing, and static semantics. It also outlines features for defining a language in MPS, including structure, editor, code generation, and integration with Java. MPS allows focusing languages on specific domains and processes models instead of text.
Deep Dive on Deep Learning (June 2018)
This document provides a summary of a presentation on deep learning concepts, common architectures, Apache MXNet, and infrastructure for deep learning. The agenda includes an overview of deep learning concepts like neural networks and training, common architectures like convolutional neural networks and LSTMs, a demonstration of Apache MXNet's symbolic and imperative APIs, and a discussion of infrastructure for deep learning on AWS like optimized EC2 instances and Amazon SageMaker.
Dagstuhl seminar talk on querying big graphs
The document describes techniques for querying large graph datasets. It discusses how graphs arise in many domains like social networks, biological networks, and knowledge graphs. It outlines some challenges in querying big graphs like heterogeneity, uncertainty, and massive scale. It then presents the author's work on approximate subgraph matching to enable flexible querying of heterogeneous graphs. The key ideas are converting graph structures to vectors to compute similarity, defining a cost function for subgraph matching, and using loopy belief propagation for inference. Experimental results on real datasets demonstrate the effectiveness of the proposed techniques.
High-Performance Graph Analysis and Modeling
This document discusses knowledge discovery and machine learning on graph data. It makes three main observations:
1) Graphs are typically constructed from input data rather than given directly, as relationships must be inferred.
2) Graph data management is challenging due to issues like large size, dynamic nature, heterogeneity and attribution.
3) Useful insights and accurate modeling depend on the representation of the data as a graph, such as through decomposition, feature learning or other techniques.
Similar to Text mining lab (summer 2017) - Word Vector Representation (20)
Querying your database in natural language by Daniel Moisset PyData SV 2014
Querying your database in natural language by Daniel Moisset PyData SV 2014
Probabilistic Data Structures and Approximate Solutions Oleksandr Pryymak
Probabilistic Data Structures and Approximate Solutions Oleksandr Pryymak
How to easily find the optimal solution without exhaustive search using Genet...
How to easily find the optimal solution without exhaustive search using Genet...
[Gary entsminger] turbo_pascal_for_windows_bible(book_fi.org)
[Gary entsminger] turbo_pascal_for_windows_bible(book_fi.org)
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance (WWW...
N-gram IDF: A Global Term Weighting Scheme Based on Information Distance (WWW...
India software developers conference 2013 Bangalore
India software developers conference 2013 Bangalore
MMDS 2014: Myria (and Scalable Graph Clustering with RelaxMap)
MMDS 2014: Myria (and Scalable Graph Clustering with RelaxMap)
More from Elvis Saravia
The Future of Brain-Powered Learning
The document discusses the potential future of brain-machine interfaces (BMI) in education in the year 2050. It describes a proposed BMI learning network called BLiNK that would allow for direct brain-to-brain communication and knowledge sharing between learners and instructors. BLiNK is suggested to offer personalized learning pathways, virtual classrooms, access to knowledge databases, and real-time analytics on learning progress. The pros of BLiNK include learner-centered and on-demand education, while the cons could include issues regarding privacy, health, and potential for brain hacking or mind control if not properly regulated.
Introduction to Fundamentals of RNNs
The document provides an overview of recurrent neural networks (RNNs). RNNs can model sequential data by incorporating information about previous elements in the sequence through their hidden state. RNNs share parameters across time steps and can capture long-term dependencies. They are well-suited for applications involving time series data and natural language. The document reviews the basic components of RNNs including recurrent neurons, unfolding RNNs over time, and training RNNs using backpropagation through time.
Thesis oral defense 2015 elvis saravia
This document summarizes a student's master's thesis project on inferring user interests from microblog data through opinion mining. The project developed a framework that uses contextual and emotion analysis of Twitter data to identify users' interests. It extracts interest candidates using POS tagging, keyword extraction, and rule-based concept extraction. It then classifies emotions and filters interests based on positive emotions. The identified interests are ranked and categorized to infer users' top interests. Experiments showed the approach achieved over 80% precision on average at identifying top interests. Future work could analyze emotion patterns over time and consider negative emotions.
An Introduction to Apache Spark
Spark is a big data processing framework built in Scala that runs on the JVM. It provides speed, generality, ease of use, and accessibility for processing large datasets. Spark features include working directly on memory for speed, supporting MapReduce, lazy evaluation of queries for optimization, and APIs for Scala, R and Python. It includes Spark Streaming for real-time data, Spark SQL for SQL queries, and MLlib for machine learning. Resilient Distributed Datasets (RDDs) are Spark's fundamental data structure, and MapReduce is a programming model used for processing large amounts of data in parallel.
The Neurochemistry of Music
Student paper presentation based on the work of J.Levitin and Mona Lisa Chanda - "The Neurochemistry of Music"
NewSQL - The Future of Databases?
NewSQL databases seek to provide the same scalable performance as NoSQL databases for online transaction processing workloads, while still maintaining the ACID guarantees of a traditional SQL database. NewSQL databases use new architectures like multi-version concurrency control and partition-level locking to allow for horizontal scaling and high availability without sacrificing consistency. They also provide highly optimized SQL engines to query data in a distributed environment.
Crowdsource Delivery System - Improving traditional delivery systems
This document proposes a crowdsourced delivery system to improve traditional delivery methods. It outlines problems with current systems like delays and inefficiency. The proposed solution uses humans as delivery runners. Sellers would post products for sale online and specify a delivery fee. The system would notify runners to deliver products fast and efficiently. Key features include tracking runner locations, recommendation systems based on location, and a reputation system to build trust. The goal is a reliable, efficient and automated delivery process without limitations.
Relational Databases - Benefits and Challenges
This document discusses relational databases and some of their key properties and tradeoffs. It notes that consistency, availability, and partition tolerance in shared data systems can only achieve two of the three properties at once, according to Brewer's CAP theorem. The document then examines different requirements like querying, transactions, consistency and scaling in the context of choosing a database for a startup's application. It provides an example use case of using PostgreSQL, Redis, and MongoDB together to address these requirements for a listings application called Kuai List.
Subconscious Crowdsourcing: A Feasible Data Collection Mechanism for Mental D...
1) The document proposes a method called "subconscious crowdsourcing" to collect social media data from users with mental disorders in order to build predictive models for mental disorder detection.
2) It extracts linguistic and behavioral features from Twitter data like emotion transitions and social interactions to train models for bipolar disorder and borderline personality disorder classification.
3) Experimental results show the TF-IDF model achieves the best performance in 10-fold cross validation tests and addresses the selection bias problem of prior work by detecting actual disorder sufferers rather than just those discussing mental health topics.
More from Elvis Saravia (9)
Crowdsource Delivery System - Improving traditional delivery systems
Crowdsource Delivery System - Improving traditional delivery systems
Subconscious Crowdsourcing: A Feasible Data Collection Mechanism for Mental D...
Subconscious Crowdsourcing: A Feasible Data Collection Mechanism for Mental D...
Recently uploaded
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
毕业原版【微信:41543339】【(Glasgow毕业证书)格拉斯哥大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
Recently uploaded (20)
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Text mining lab (summer 2017) - Word Vector Representation
- 1. Summer 2017 Elvis Saravia PhD, Information Systems and Applications ellfae@gmail.com Github username: omarsar Questions: sli.do (#Z217)
- 2. 2
- 3. ● ● ● ● ● ● ● Knowledge Discovery (KDD) Process 3
- 4. 4
- 5. 5
- 6. ConceptNet 6
- 7. ● ● ● 7
- 8. Motel = [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0] Hotel = [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0] ● ● One-hot representation 8
- 9. hotel = [0.728 0.234 -0.23 0.223] Distributed representation (low-dimension vector) 9
- 11. 11 Paper source: https://arxiv.org/pdf/1301.3781.pdf Feedforward Neural Net Language Model (NNLM) variables to optimize denotes window range
- 12. 12
- 13. 13 P(the|over) P(fox|over) P(jumped|over) P(the|over) P(lazy|over) P(dog|over) P(VOUT | VIN ) How to define this prob. distribution? Determines similarity in [-1,1] Get a probability in [0,1] out of a similarity in [-1,1]
- 14. 14
- 16. 16
- 17. ● https://goo.gl/ppHX65 ● ○ Gensim guide for word2vec: https://goo.gl/i2UrdH ● https://goo.gl/7b72S9 ● ● https://goo.gl/uNJDrs ● 17
- 18. 18
- 19. 19
- 20. 20
- 21. 21
- 22. 22
- 23. 23
- 25. a. Build API: (Flask/Django recommended) b. Pretrained models: (Guide: https://goo.gl/5qt2Ki) c. Visualization: d3js / plotly / tensorboard a. LSTM - (Guide: http://colah.github.io/posts/2015-08-Understanding-LSTMs/) b. CNN - (Guide: https://goo.gl/PgLUs7) c. RNN - (Guide: https://goo.gl/5L9kci a. Starting point:https://rare-technologies.com/word2vec-tutorial#app 25