This document discusses requirements for designing a framework to analyze text datasets. It identifies several key variations in importing datasets related to file sources, formats and schemas. It then proposes using high-level reader classes to handle different datasets. The document outlines the STAT domain model which includes concepts like RawCorpus to represent raw document collections, Processor to process data, Corpus to represent data for machine learning, Trainer for algorithms, Model to store learned parameters, Classifier to classify documents, Prediction for output classifications, Evaluator to evaluate predictions and Evaluation for results.
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
http://youtu.be/YD5Yk6cNsdE
FireWatir - Web Application Testing Using Ruby and Firefoxangrez
FireWatir is a tool used for functional testing a web application using Ruby and Firefox browser. You can use the scripts written using WATiR that targets IE with slight changes with FireWatir
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:
http://youtu.be/YD5Yk6cNsdE
FireWatir - Web Application Testing Using Ruby and Firefoxangrez
FireWatir is a tool used for functional testing a web application using Ruby and Firefox browser. You can use the scripts written using WATiR that targets IE with slight changes with FireWatir
The TileDB Array Data Storage Manager - Stavros Papadopoulos, Jake BolewskiPyData
TileDB is an open-source storage manager for multi-dimensional sparse and dense array data. It has a novel architecture that addresses some of the pain points in storing array data on “big-data” and “cloud” storage architectures. This talk will highlight TileDB’s design and its ability to integrate with analysis environments relevant to the PyData community such as Python, R, Julia, etc.
File Handling Presentation As well as File handling code in java Update, Delete, Search, View, Insert in file code are available in this presentation in you face some issues so contact me 03244064060 , Also in my Email: azeemaj101@gmail.com and Twitter @azeemaj101
A Standard Data Format for Computational Chemistry: CSXStuart Chalk
An overview of the Common Standard for eXchange (CSX) a new markup language for the storage of computational chemistry calculation data. CSX stores publication and molecular system metadata along with calculation data and, optionally, raw input and output files associated with a calculation. The computational chemistry community is invited to participate in the development of CSX. For more information see http://www.chemicalsemantics.com.
Determining Requirements In System Analysis And DsignAsaduzzaman Kanok
Requirements determination and requirements structuring are two core components of system analysis. Traditionally, interviewing, questionnaires, directly observing and analyzing documents are four main methods adopted by system analysts to collect information. JAD and prototyping are two modern requirements determination methodologies, which are developed and based on the previous traditional methods. A well-structured representation of system requirements can dramatically improve the communication among analysts, designers, users, and programmers. DFD, structured English, decision tables, decision trees, and E-R diagrams are traditional primary requirements structuring tools. Nowadays, RAD and OOA are emerging to help streamline and shorten the total SDLC. While RAD SDLC packs traditional analysis phase and part of design phase into one step, OOA tries to make the outcomes of analysis phase can be reused by the following developing phases
The TileDB Array Data Storage Manager - Stavros Papadopoulos, Jake BolewskiPyData
TileDB is an open-source storage manager for multi-dimensional sparse and dense array data. It has a novel architecture that addresses some of the pain points in storing array data on “big-data” and “cloud” storage architectures. This talk will highlight TileDB’s design and its ability to integrate with analysis environments relevant to the PyData community such as Python, R, Julia, etc.
File Handling Presentation As well as File handling code in java Update, Delete, Search, View, Insert in file code are available in this presentation in you face some issues so contact me 03244064060 , Also in my Email: azeemaj101@gmail.com and Twitter @azeemaj101
A Standard Data Format for Computational Chemistry: CSXStuart Chalk
An overview of the Common Standard for eXchange (CSX) a new markup language for the storage of computational chemistry calculation data. CSX stores publication and molecular system metadata along with calculation data and, optionally, raw input and output files associated with a calculation. The computational chemistry community is invited to participate in the development of CSX. For more information see http://www.chemicalsemantics.com.
Determining Requirements In System Analysis And DsignAsaduzzaman Kanok
Requirements determination and requirements structuring are two core components of system analysis. Traditionally, interviewing, questionnaires, directly observing and analyzing documents are four main methods adopted by system analysts to collect information. JAD and prototyping are two modern requirements determination methodologies, which are developed and based on the previous traditional methods. A well-structured representation of system requirements can dramatically improve the communication among analysts, designers, users, and programmers. DFD, structured English, decision tables, decision trees, and E-R diagrams are traditional primary requirements structuring tools. Nowadays, RAD and OOA are emerging to help streamline and shorten the total SDLC. While RAD SDLC packs traditional analysis phase and part of design phase into one step, OOA tries to make the outcomes of analysis phase can be reused by the following developing phases
Requirements engineering with UML [Software Modeling] [Computer Science] [Vri...Ivano Malavolta
This presentation is about a lecture I gave within the "Software Modeling" course of the Computer Science bachelor program, of the Vrije Universiteit Amsterdam.
http://www.ivanomalavolta.com
Language-agnostic data analysis workflows and reproducible researchAndrew Lowe
This was a talk that I gave at CERN at the Inter-experimental Machine Learning (IML) Working Group Meeting in April 2017 about language-agnostic (or polyglot) analysis workflows. I show how it is possible to work in multiple languages and switch between them without leaving the workflow you started. Additionally, I demonstrate how an entire workflow can be encapsulated in a markdown file that is rendered to a publishable paper with cross-references and a bibliography (and with raw LaTeX file produced as a by-product) in a simple process, making the whole analysis workflow reproducible. For experimental particle physics, ROOT is the ubiquitous data analysis tool, and has been for the last 20 years old, so I also talk about how to exchange data to and from ROOT.
Language Server Protocol - Why the Hype?mikaelbarbero
The Language Server Protocol developed by Microsoft for Visual Studio Code is a language and IDE agnostic protocol which clearly separates language semantics from UI presentation. Language developers can implement the protocol and benefit from immediate support in all IDEs, while IDE developers, who implement the protocol get automatic support for all these languages without having to write any language-specific code. This session will let you learn more about the innards of the LSP. We will also have an overview of the current implementations in Eclipse, and outside Eclipse as well.
Composable Parallel Processing in Apache Spark and WeldDatabricks
The main reason people are productive writing software is composability -- engineers can take libraries and functions written by other developers and easily combine them into a program. However, composability has taken a back seat in early parallel processing APIs. For example, composing MapReduce jobs required writing the output of every job to a file, which is both slow and error-prone. Apache Spark helped simplify cluster programming largely because it enabled efficient composition of parallel functions, leading to a large standard library and high-level APIs in various languages. In this talk, I'll explain how composability has evolved in Spark's newer APIs, and also present a new research project I'm leading at Stanford called Weld to enable much more efficient composition of software on emerging parallel hardware (multicores, GPUs, etc).
Speaker: Matei Zaharia
Standardizing on a single N-dimensional array API for PythonRalf Gommers
MXNet workshop Dec 2020 presentation on the array API standardization effort ongoing in the Consortium for Python Data API Standards - see data-apis.org
1 Project 2 Introduction - the SeaPort Project seri.docxhoney725342
1
Project 2
Introduction - the SeaPort Project series
For this set of projects for the course, we wish to simulate some of the aspects of a number of Sea Ports.
Here are the classes and their instance variables we wish to define:
SeaPortProgram extends JFrame
o variables used by the GUI interface
o world: World
Thing implement Comparable <Thing>
o index: int
o name: String
o parent: int
World extends Thing
o ports: ArrayList <SeaPort>
o time: PortTime
SeaPort extends Thing
o docks: ArrayList <Dock>
o que: ArrayList <Ship> // the list of ships waiting to dock
o ships: ArrayList <Ship> // a list of all the ships at this port
o persons: ArrayList <Person> // people with skills at this port
Dock extends Thing
o ship: Ship
Ship extends Thing
o arrivalTime, dockTime: PortTime
o draft, length, weight, width: double
o jobs: ArrayList <Job>
PassengerShip extends Ship
o numberOfOccupiedRooms: int
o numberOfPassengers: int
o numberOfRooms: int
CargoShip extends Ship
o cargoValue: double
o cargoVolume: double
o cargoWeight: double
Person extends Thing
o skill: String
Job extends Thing - optional till Projects 3 and 4
o duration: double
o requirements: ArrayList <String>
// should be some of the skills of the persons
PortTime
o time: int
Eventually, in Projects 3 and 4, you will be asked to show the progress of the jobs using JProgressBar's.
2
Here's a very quick overview of all projects:
1. Read a data file, create the internal data structure, create a GUI to display the structure, and let
the user search the structure.
2. Sort the structure, use hash maps to create the structure more efficiently.
3. Create a thread for each job, cannot run until a ship has a dock, create a GUI to show the
progress of each job.
4. Simulate competing for resources (persons with particular skills) for each job.
Project 2 General Objectives
Project 2 - Map class, Comparator, sorting
Use the JDK Map class to write more efficient code when constructing the internal data
structures from the data file.
Implement SORTING using the Comparator interface together with the JDK support for sorting
data structures, thus sorting on different fields of the classes from Project 1.
Extend the GUI from Project 1 to let the user sort the data at run-time.
Documentation Requirements:
You should start working on a documentation file before you do anything else with these projects, and
fill in items as you go along. Leaving the documentation until the project is finished is not a good idea for
any number of reasons.
The documentation should include the following (graded) elements:
Cover page (including name, date, project, your class information)
Design
o including a UML class diagram
o classes, variables and methods: what they mean and why they are there
o tied to the requirements of the project
User's Guide
o how would a user start and run your pro ...
Quantopix Analytics System (QAS) is a platform for data analysis and for developing analytics apps. QAS connects to most of Enterprise Class SQL Database Managers and provides instant capabilities to build datasets and data groups from disjointed databases to prepare it for analysis. QAS provides a comprehensive and extensible set of statistical functions to instantly profile your data. It comes with advanced yet easy to invoke charting capabilities for interactively visualizing the data as well as generating static chart images. QAS comes with a built-in PHP and JavaScript App builder to help users extend the system functions and create custom applications for specific business needs.
Rapid App Development
QAS lets you build analysis Apps within minutes using a powerful set of APIs for data manipulation including time-series and text classifications. QAS includes a comprehensive list of math, statistics, and matrix manipulation functions for numeric analysis. The APIs include Multiple Linear Regression model generation, k-means clustering model generation, and a Predict API for both models.
This presentation explain about "Apache Cassandra's concepts and architecture".
My friends and colleagues said
"This presentation should be release on public space to help many peoples work in IT"
so, I upload this file for everyone love "Technology for the people"
This presentation used for educating the employee of KT last year.
Source-to-source transformations: Supporting tools and infrastructurekaveirious
Introduction to source-to-source transformation. Concept and overview. Basics of existing tools (TXL, ROSE, Cetus, EDG, C-to-C, Memphis); pros and cons. Part of an internal evaluation for selecting a source-to-source transformation tool.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
2. To design a framework, how many variations we need to protect? How many functionalities we need to provide for supporting all these variations? QUESTIONS
13. Domain Concept: RawCorpus A collection of RawDocument , supporting collection operations: - Add new RawDocument element - Remove existing RawDocument element - Accessing elements in the collection - …
15. Domain Concept: RawDocument An object with one or more string fields, serving as a non-processed, in-memory representation of a document unit - Like Java beans with getter and setter - All fields must be string type, even for numbers
17. Domain Concept: Processor An object that processes RawCorpus and produces Corpus . - Linguistic: Tokenizer, Stemmer, StopRemover, PosTagger, … - Machine learning: Feature-specific, document-specific
18. Domain Concept: Corpus An object representing a collection of Document for use by machine learning side of framework. This object provides a notion of splits which is commonly used (e.g., train, test)
19. Domain Concept: Trainer A representation of a machine learning algorithm, which can learn from a Corpus and produce a Model .
20. Domain Concept: Model An object of what machine learning algorithm (i.e., Trainer ) creates to store parameters that are "learned" from the data (i.e., Corpus )
21. Domain Concept: Classifier An object that maps Documents to target values (label, number, probability). It takes a Corpus and a Model as inputs, and produces a Prediction associated with the Corpus according to the Model .
22. Domain Concept: Prediction A collection of target values (label, number, probability) that associate with a Corpus , i.e., a collection of Document .
23. Domain Concept: Evaluator An object used for comparing the Prediction against its associated Corpus and generating Evaluation
24. Domain Concept: Evaluation A representation of evaluation result given by a Evaluator , in a summarized manner.
26. STAT (brief) Domain Model Note : We ignore texts on connectors for brevity. Some connections are not drawn because of space limitation Corpus Reader Processor RawCorpus Trainer Model Classifier Prediction Evaluator Evaluation Writer Vocabulary
27. STAT Domain Model Note : We ignore texts above lines for brevity Corpus Reader Processor RawCorpus Trainer Model Classifier Prediction Evaluator Evaluation Writer
28. STAT Domain Model Note : We ignore texts above lines for brevity Corpus Reader Processor RawCorpus Trainer Model Classifier Prediction Evaluator Evaluation Document RawDocument