The document contains R code for analyzing and visualizing banking data from Indonesia from 2000-2009. It includes code to create data frames, plot line charts comparing total assets, deposits and credits over time, and create other graphs like histograms, boxplots, pie charts and dot charts to explore and visualize the banking indicators data.
1. Complex numbers can be represented as ordered pairs of real numbers (a,b) and have definitions for addition, multiplication, and multiplication by scalars.
2. Common notation for complex numbers includes the zero (0,0), unity (1,0), and the complex conjugate (a,-b). Functions like the real part, imaginary part, absolute value, and argument are also introduced.
3. Complex numbers are connected to trigonometry through Euler's formula eiθ = cosθ + i sinθ and relations involving exponentiation, differentiation, and roots of unity.
This document discusses different types of computations in functional programming such as Option, List, Future, State, and IO. It explains how to create computations of these types by wrapping values, and how to use computations by mapping and applying functions to the wrapped values. It also covers applicative functors and how they allow applying functions to multiple arguments wrapped in computations. Some examples of applicative operations on Option, List, Future, State and IO are given. Finally, it briefly discusses traversals, which allow transforming data structures of type F[A] to F[B] by applying a function A => F[B] to each element.
The document summarizes key concepts from a chapter on vectors and geometry in 3D space. [1] It introduces three-dimensional coordinate systems using ordered triples (x,y,z) and defines the distance formula between two points in 3D space. [2] It also defines concepts like the sphere equation and vectors, including their representation, magnitude, addition/subtraction, and dot and cross products. [3] It concludes by covering lines, planes, and their equations, as well as cylindrical coordinates.
The document discusses the use of Data Definition Language (DDL) to define and modify database tables through commands like CREATE TABLE, DROP TABLE, and ALTER TABLE. It describes different data types that can be used as table columns like VARCHAR, INTEGER, DATE, and constraints like PRIMARY KEY, FOREIGN KEY, and NOT NULL. DDL is used to specify the structure and schema of database tables and relationships between tables.
Using R in financial modeling provides an introduction to using R for financial applications. It discusses importing stock price data from various sources and visualizing it using basic graphs and technical indicators. It also covers topics like calculating returns, estimating distributions of returns, correlations, volatility modeling, and value at risk calculations. The document provides examples of commands and functions in R to perform these financial analytics tasks on sample stock price data.
The document discusses a joint work between Georg Gottlob from the Computing Laboratory at the University of Oxford's Department of Computer Science and G. Orsi and A. Pieris. It presents formal logic rules and models, including intensional databases, Datalog rules, and least Herbrand models. It also contains questions regarding these logical expressions.
1. Complex numbers can be represented as ordered pairs of real numbers (a,b) and have definitions for addition, multiplication, and multiplication by scalars.
2. Common notation for complex numbers includes the zero (0,0), unity (1,0), and the complex conjugate (a,-b). Functions like the real part, imaginary part, absolute value, and argument are also introduced.
3. Complex numbers are connected to trigonometry through Euler's formula eiθ = cosθ + i sinθ and relations involving exponentiation, differentiation, and roots of unity.
This document discusses different types of computations in functional programming such as Option, List, Future, State, and IO. It explains how to create computations of these types by wrapping values, and how to use computations by mapping and applying functions to the wrapped values. It also covers applicative functors and how they allow applying functions to multiple arguments wrapped in computations. Some examples of applicative operations on Option, List, Future, State and IO are given. Finally, it briefly discusses traversals, which allow transforming data structures of type F[A] to F[B] by applying a function A => F[B] to each element.
The document summarizes key concepts from a chapter on vectors and geometry in 3D space. [1] It introduces three-dimensional coordinate systems using ordered triples (x,y,z) and defines the distance formula between two points in 3D space. [2] It also defines concepts like the sphere equation and vectors, including their representation, magnitude, addition/subtraction, and dot and cross products. [3] It concludes by covering lines, planes, and their equations, as well as cylindrical coordinates.
The document discusses the use of Data Definition Language (DDL) to define and modify database tables through commands like CREATE TABLE, DROP TABLE, and ALTER TABLE. It describes different data types that can be used as table columns like VARCHAR, INTEGER, DATE, and constraints like PRIMARY KEY, FOREIGN KEY, and NOT NULL. DDL is used to specify the structure and schema of database tables and relationships between tables.
Using R in financial modeling provides an introduction to using R for financial applications. It discusses importing stock price data from various sources and visualizing it using basic graphs and technical indicators. It also covers topics like calculating returns, estimating distributions of returns, correlations, volatility modeling, and value at risk calculations. The document provides examples of commands and functions in R to perform these financial analytics tasks on sample stock price data.
The document discusses a joint work between Georg Gottlob from the Computing Laboratory at the University of Oxford's Department of Computer Science and G. Orsi and A. Pieris. It presents formal logic rules and models, including intensional databases, Datalog rules, and least Herbrand models. It also contains questions regarding these logical expressions.
DDS is becoming a key integration technology for the Internet of Things. A wide variety of industries are using DDS to connect real-world systems. These include healthcare, industrial automation, automotive, energy, transportation, and manufacturing. In these real-world, real-time systems, the right answer delivered too late is wrong. DDS provides a scalable, high-performance software data bus to handle the demanding volume, variety, and speed of data.
DDS is a powerful technology that can be difficult to implement quickly. Now, RTI is introducing a new tool that accelerates development by orders of magnitude! This webinar will show you how to quickly go from an initial concept to a working and fully functional implementation in hours instead of weeks. The new tool, Prototyper, leverages simple scripting to build distributed modules for RTI Connext™. It quickly turns concepts into implementation. We will illustrate it using a real-world example – starting from an initial system concept, we will walk through the 5 critical steps to build a complete working system.
This webinar is for you, if you have ever wondered:
I have an idea! How can I quickly show a working proof of concept?
I have a very short timeline and/or a very limited staff. Can DDS help me get it done faster than other technology options?
I am new to DDS. How can I quickly get something working without having to become a DDS expert?
I am defining a data model. Is is better to model the data this way or that?
I have a data model. Do these QoS policies make sense?
I have a working system. Are the data flows working correctly? How can I test and validate?
This document summarizes research on using deformable models for object recognition. It discusses using deformable part models to detect objects by optimizing part locations. Efficient algorithms like dynamic programming and min-convolutions are used for matching. Non-rigid objects are modeled using triangulated polygons that can deform individual triangles. Hierarchical shape models capture shape variations. The document applies these techniques to the PASCAL visual object recognition challenge, achieving state-of-the-art results on 10 of 20 object categories through discriminatively trained, multiscale deformable part models.
This document summarizes algorithms for large-scale data mining using MapReduce, including:
1) Information retrieval algorithms like distributed grep, calculating URL access frequency, and constructing the reverse web link graph.
2) Graph algorithms like PageRank, which is computed through an iterative process of message passing between nodes.
3) Clustering algorithms like canopy clustering, which uses two distance thresholds to create overlapping clusters in a single pass over the data.
Object Detection with Discrmininatively Trained Part based Modelszukun
The document describes an object detection method using deformable part-based models that are discriminatively trained. The models consist of root filters and deformable part filters at multiple resolutions. Latent SVM training is used to learn the filters and deformation costs from weakly labeled images. The method achieved state-of-the-art results on the PASCAL object detection challenge, outperforming other methods in accuracy and speed.
This document provides an introduction to financial modeling in R. It begins with basic R commands for calculations, vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs, distributions, correlations, and linear regression. More advanced topics include non-linear regression, graphics packages, downloading stock data, and estimating volatility and value at risk. Practical exercises are provided to work with financial data, estimate distributions, correlations, and models.
The document provides a summary of mathematics formulae for Form 4 students. It includes:
1) Common functions and their derivatives such as absolute value, inverse, quadratic, and fractional functions.
2) Key concepts in algebra including the quadratic formula, nature of roots, and forming quadratic equations from roots.
3) Essential statistics measures like mean, median, variance, and standard deviation.
4) Formulas for coordinate geometry topics like distance, gradient, parallel and perpendicular lines, and locus equations.
5) Rules for differentiation including algebraic, fractional, and chain rule.
This document provides an overview and introduction to using the statistical programming language R. It begins with basic commands for performing calculations and creating vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs and statistical distributions, correlations, linear and nonlinear regression, advanced graphics, and accessing financial data packages. The document concludes with proposing practical tasks for workshop participants to work with financial data in R.
The document discusses models of web graphs and their properties. It describes experimental observations of real-world web graphs that found they are sparse, small-world, and have power-law degree distributions. The Barabási-Albert preferential attachment model is introduced that generates graphs with these properties by adding new vertices that attach to existing vertices with probability proportional to their degree. The Bollobás-Riordan model provides an alternative construction of growing graphs with preferential attachment that has useful mathematical properties. The degree distributions produced by this model are also discussed.
This document provides an overview of an introductory course on using R for statistical analysis. It covers topics such as the R environment and language, working with objects and data types, importing and manipulating data, and performing basic analyses and visualizations. The course materials are divided into sections covering the R workspace, reading and writing data, data manipulation, plotting, and more advanced techniques. Examples are provided throughout to demonstrate key R functions and capabilities.
Esoft Metro Campus - Diploma in Web Engineering - (Module IX) Using Extensions and Image Manipulation
(Template - Virtusa Corporate)
Contents:
Image Manipulation with PHP
GD Library
ImageCreate()
ImageColorAllocate()
Drawing shapes and lines
imageellipse()
imagearc()
imagepolygon()
imagerectangle()
imageline()
Creating a new image
Using a Color Fill
imagefilledellipse()
imagefilledarc()
imagefilledpolygon()
imagefilledrectangle()
Basic Pie Chart
3D Pie Chart
Modifying Existing Images
imagecreatefrompng()
imagecolortransparent()
imagecopymerge()
Creating a new image…
Stacking images…
Imagestring()
Draw a string
This document provides notes and formulae on additional mathematics for Form 5. It covers topics such as progressions, integration, vectors, trigonometric functions, and probability. For progressions, it defines arithmetic and geometric progressions and gives the formulas for calculating the nth term and sum of terms. For integration, it provides rules and formulas for integrating polynomials, trigonometric functions, and expressions with ax+b. It also defines vectors and their operations including vector addition and subtraction. Other sections cover trigonometric functions, their definitions, relationships and graphs, as well as probability topics such as calculating probabilities of events and distributions like the binomial.
The document contains 10 problems involving index numbers and linear programming. Problem 1 provides a table with prices and weightages of items in 2008 and 2009. It asks to calculate index numbers and values based on the given information. Problem 2 similarly provides a table with price indices, percentages, and constraints to calculate unknown values. The remaining problems follow a similar pattern of providing tables of data on price indices, weights, constraints, and requiring calculations of index numbers and values based on the information given.
This document provides an overview of the Behavior Tracker Pro mobile application for collecting and analyzing behavior data on Android devices. It allows users to discreetly record frequency, duration, and ABC data on behaviors. The app has four main views for collecting data, analyzing graphs, managing client profiles, and managing behavioral definitions. It provides flexible options to record different behavior types and supports adding new clients, observers, behaviors, and other response definitions as needed by the user. Data can be analyzed through graphs showing daily trends, phase changes, and behavior rates that are generated from the collection sessions.
- R can be used as a calculator to perform basic math operations like addition, subtraction, multiplication, division, exponents, logarithms, and trigonometric functions. It handles complex numbers and vectors.
- Matrices can be created using cbind() and rbind() functions. Elements are extracted using row and column indices. Common operations include addition, subtraction, scalar and element-wise multiplication on matrices.
- Eigenvalues and eigenvectors of a matrix can be computed using the eigen() function. The uniroot() function finds the root of a univariate function by calling a user-defined function.
The document discusses a Turkish business that provides customer support services. It has over 8,500 employees across 15 locations serving 120 million contacts per year. The business focuses on maintaining up-to-date information through periodic research and development and addressing challenges through human resources training programs and technological solutions like remote assistance tools. The goal is to successfully resolve customer issues and connect with customers of all backgrounds.
Palma is the capital city of the Balearic Islands located on the south coast of Mallorca. The population of Palma proper is over 401,000 people, making it the largest city in the Balearics. Palma has been populated by different civilizations over hundreds of years including Romans, Byzantines, Muslims, and Christians. Some of Palma's most prominent landmarks include the Gothic cathedral, Almudaina Castle, and La Rambla street. The city also has many shopping centers, plazas, and villages outside the city that can be accessed by train or bus from Plaza España station.
The document outlines 10 life lessons learned by Duygu Sevinç Sevin over the course of her career. Early on, she was ambitious and studied international relations, but later realized she preferred business over diplomacy. She struggled in exams despite social success and realized she needed to study more. She learned the importance of having a global mindset rather than just focusing on her home country. Later, she realized she was more interested in business than legislation and found her passion in brand management. Networking provided new opportunities and challenges for her career.
I am Manish Kumar, currently working in L&T Construction, as a Senior Planning Engineer (Electrical) having 3.7 years of experience in Project Planning & Control.
Completed 2 years of regular PG Program from NICMAR, Pune in Project Engineering Management.
1) The document discusses various plotting techniques in R using sample data on diamonds, including scatter plots, adjusting plot attributes like colors, symbols and sizes, adding lines and legends, fitting linear models, and 3D scatter plots.
2) Methods covered include basic scatter plots, adjusting colors of titles, axes and backgrounds, adding points with different symbols to represent years of data, customizing line types and widths, using xyplot for grouped data, and adding linear regression lines and R^2 values to plots.
3) Advanced techniques demonstrated include 3D scatter plots using the scatterplot3D package, adding density rug plots, and smoothScatter for smoothed color density representations.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
The document discusses production functions in the long run. It defines production functions as tools that express the relationship between production inputs like capital and labor, and the resulting output. In the long run, production functions assume that both capital and labor are variable inputs that firms can adjust. Isoquant curves illustrate combinations of capital and labor that produce the same output level. The slopes of isoquant curves indicate the marginal rate of technical substitution between inputs. Returns to scale refer to how output changes proportionally with changes in all inputs, and can exhibit increasing, constant, or diminishing patterns.
This document provides an overview of plotting and image processing capabilities in Matlab. It discusses how to generate basic scatter plots and customize axis properties. It also explains how digital images are constructed as arrays and can be displayed, rotated, and converted to grayscale using commands like plot, surf, image, and imagesc. The document demonstrates plotting multiple lines and images on the same figure. It describes how image processing techniques like Sobel filtering can be used to detect edges in an image.
DDS is becoming a key integration technology for the Internet of Things. A wide variety of industries are using DDS to connect real-world systems. These include healthcare, industrial automation, automotive, energy, transportation, and manufacturing. In these real-world, real-time systems, the right answer delivered too late is wrong. DDS provides a scalable, high-performance software data bus to handle the demanding volume, variety, and speed of data.
DDS is a powerful technology that can be difficult to implement quickly. Now, RTI is introducing a new tool that accelerates development by orders of magnitude! This webinar will show you how to quickly go from an initial concept to a working and fully functional implementation in hours instead of weeks. The new tool, Prototyper, leverages simple scripting to build distributed modules for RTI Connext™. It quickly turns concepts into implementation. We will illustrate it using a real-world example – starting from an initial system concept, we will walk through the 5 critical steps to build a complete working system.
This webinar is for you, if you have ever wondered:
I have an idea! How can I quickly show a working proof of concept?
I have a very short timeline and/or a very limited staff. Can DDS help me get it done faster than other technology options?
I am new to DDS. How can I quickly get something working without having to become a DDS expert?
I am defining a data model. Is is better to model the data this way or that?
I have a data model. Do these QoS policies make sense?
I have a working system. Are the data flows working correctly? How can I test and validate?
This document summarizes research on using deformable models for object recognition. It discusses using deformable part models to detect objects by optimizing part locations. Efficient algorithms like dynamic programming and min-convolutions are used for matching. Non-rigid objects are modeled using triangulated polygons that can deform individual triangles. Hierarchical shape models capture shape variations. The document applies these techniques to the PASCAL visual object recognition challenge, achieving state-of-the-art results on 10 of 20 object categories through discriminatively trained, multiscale deformable part models.
This document summarizes algorithms for large-scale data mining using MapReduce, including:
1) Information retrieval algorithms like distributed grep, calculating URL access frequency, and constructing the reverse web link graph.
2) Graph algorithms like PageRank, which is computed through an iterative process of message passing between nodes.
3) Clustering algorithms like canopy clustering, which uses two distance thresholds to create overlapping clusters in a single pass over the data.
Object Detection with Discrmininatively Trained Part based Modelszukun
The document describes an object detection method using deformable part-based models that are discriminatively trained. The models consist of root filters and deformable part filters at multiple resolutions. Latent SVM training is used to learn the filters and deformation costs from weakly labeled images. The method achieved state-of-the-art results on the PASCAL object detection challenge, outperforming other methods in accuracy and speed.
This document provides an introduction to financial modeling in R. It begins with basic R commands for calculations, vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs, distributions, correlations, and linear regression. More advanced topics include non-linear regression, graphics packages, downloading stock data, and estimating volatility and value at risk. Practical exercises are provided to work with financial data, estimate distributions, correlations, and models.
The document provides a summary of mathematics formulae for Form 4 students. It includes:
1) Common functions and their derivatives such as absolute value, inverse, quadratic, and fractional functions.
2) Key concepts in algebra including the quadratic formula, nature of roots, and forming quadratic equations from roots.
3) Essential statistics measures like mean, median, variance, and standard deviation.
4) Formulas for coordinate geometry topics like distance, gradient, parallel and perpendicular lines, and locus equations.
5) Rules for differentiation including algebraic, fractional, and chain rule.
This document provides an overview and introduction to using the statistical programming language R. It begins with basic commands for performing calculations and creating vectors, matrices, and data frames. It then covers importing and exporting data, basic graphs and statistical distributions, correlations, linear and nonlinear regression, advanced graphics, and accessing financial data packages. The document concludes with proposing practical tasks for workshop participants to work with financial data in R.
The document discusses models of web graphs and their properties. It describes experimental observations of real-world web graphs that found they are sparse, small-world, and have power-law degree distributions. The Barabási-Albert preferential attachment model is introduced that generates graphs with these properties by adding new vertices that attach to existing vertices with probability proportional to their degree. The Bollobás-Riordan model provides an alternative construction of growing graphs with preferential attachment that has useful mathematical properties. The degree distributions produced by this model are also discussed.
This document provides an overview of an introductory course on using R for statistical analysis. It covers topics such as the R environment and language, working with objects and data types, importing and manipulating data, and performing basic analyses and visualizations. The course materials are divided into sections covering the R workspace, reading and writing data, data manipulation, plotting, and more advanced techniques. Examples are provided throughout to demonstrate key R functions and capabilities.
Esoft Metro Campus - Diploma in Web Engineering - (Module IX) Using Extensions and Image Manipulation
(Template - Virtusa Corporate)
Contents:
Image Manipulation with PHP
GD Library
ImageCreate()
ImageColorAllocate()
Drawing shapes and lines
imageellipse()
imagearc()
imagepolygon()
imagerectangle()
imageline()
Creating a new image
Using a Color Fill
imagefilledellipse()
imagefilledarc()
imagefilledpolygon()
imagefilledrectangle()
Basic Pie Chart
3D Pie Chart
Modifying Existing Images
imagecreatefrompng()
imagecolortransparent()
imagecopymerge()
Creating a new image…
Stacking images…
Imagestring()
Draw a string
This document provides notes and formulae on additional mathematics for Form 5. It covers topics such as progressions, integration, vectors, trigonometric functions, and probability. For progressions, it defines arithmetic and geometric progressions and gives the formulas for calculating the nth term and sum of terms. For integration, it provides rules and formulas for integrating polynomials, trigonometric functions, and expressions with ax+b. It also defines vectors and their operations including vector addition and subtraction. Other sections cover trigonometric functions, their definitions, relationships and graphs, as well as probability topics such as calculating probabilities of events and distributions like the binomial.
The document contains 10 problems involving index numbers and linear programming. Problem 1 provides a table with prices and weightages of items in 2008 and 2009. It asks to calculate index numbers and values based on the given information. Problem 2 similarly provides a table with price indices, percentages, and constraints to calculate unknown values. The remaining problems follow a similar pattern of providing tables of data on price indices, weights, constraints, and requiring calculations of index numbers and values based on the information given.
This document provides an overview of the Behavior Tracker Pro mobile application for collecting and analyzing behavior data on Android devices. It allows users to discreetly record frequency, duration, and ABC data on behaviors. The app has four main views for collecting data, analyzing graphs, managing client profiles, and managing behavioral definitions. It provides flexible options to record different behavior types and supports adding new clients, observers, behaviors, and other response definitions as needed by the user. Data can be analyzed through graphs showing daily trends, phase changes, and behavior rates that are generated from the collection sessions.
- R can be used as a calculator to perform basic math operations like addition, subtraction, multiplication, division, exponents, logarithms, and trigonometric functions. It handles complex numbers and vectors.
- Matrices can be created using cbind() and rbind() functions. Elements are extracted using row and column indices. Common operations include addition, subtraction, scalar and element-wise multiplication on matrices.
- Eigenvalues and eigenvectors of a matrix can be computed using the eigen() function. The uniroot() function finds the root of a univariate function by calling a user-defined function.
The document discusses a Turkish business that provides customer support services. It has over 8,500 employees across 15 locations serving 120 million contacts per year. The business focuses on maintaining up-to-date information through periodic research and development and addressing challenges through human resources training programs and technological solutions like remote assistance tools. The goal is to successfully resolve customer issues and connect with customers of all backgrounds.
Palma is the capital city of the Balearic Islands located on the south coast of Mallorca. The population of Palma proper is over 401,000 people, making it the largest city in the Balearics. Palma has been populated by different civilizations over hundreds of years including Romans, Byzantines, Muslims, and Christians. Some of Palma's most prominent landmarks include the Gothic cathedral, Almudaina Castle, and La Rambla street. The city also has many shopping centers, plazas, and villages outside the city that can be accessed by train or bus from Plaza España station.
The document outlines 10 life lessons learned by Duygu Sevinç Sevin over the course of her career. Early on, she was ambitious and studied international relations, but later realized she preferred business over diplomacy. She struggled in exams despite social success and realized she needed to study more. She learned the importance of having a global mindset rather than just focusing on her home country. Later, she realized she was more interested in business than legislation and found her passion in brand management. Networking provided new opportunities and challenges for her career.
I am Manish Kumar, currently working in L&T Construction, as a Senior Planning Engineer (Electrical) having 3.7 years of experience in Project Planning & Control.
Completed 2 years of regular PG Program from NICMAR, Pune in Project Engineering Management.
1) The document discusses various plotting techniques in R using sample data on diamonds, including scatter plots, adjusting plot attributes like colors, symbols and sizes, adding lines and legends, fitting linear models, and 3D scatter plots.
2) Methods covered include basic scatter plots, adjusting colors of titles, axes and backgrounds, adding points with different symbols to represent years of data, customizing line types and widths, using xyplot for grouped data, and adding linear regression lines and R^2 values to plots.
3) Advanced techniques demonstrated include 3D scatter plots using the scatterplot3D package, adding density rug plots, and smoothScatter for smoothed color density representations.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
The document discusses production functions in the long run. It defines production functions as tools that express the relationship between production inputs like capital and labor, and the resulting output. In the long run, production functions assume that both capital and labor are variable inputs that firms can adjust. Isoquant curves illustrate combinations of capital and labor that produce the same output level. The slopes of isoquant curves indicate the marginal rate of technical substitution between inputs. Returns to scale refer to how output changes proportionally with changes in all inputs, and can exhibit increasing, constant, or diminishing patterns.
This document provides an overview of plotting and image processing capabilities in Matlab. It discusses how to generate basic scatter plots and customize axis properties. It also explains how digital images are constructed as arrays and can be displayed, rotated, and converted to grayscale using commands like plot, surf, image, and imagesc. The document demonstrates plotting multiple lines and images on the same figure. It describes how image processing techniques like Sobel filtering can be used to detect edges in an image.
Formal Verification of Programming LanguagesJason Reich
The document discusses the history of formally verifying programming language compiler implementations from the 1960s to the 2000s. It summarizes early work in the 1960s and 1970s proving correctness of compilers for simple arithmetic languages. Later work in the 1980s and 1990s proved correctness for compilers targeting more complex languages and using mechanized theorem proving. Recent work in the 2000s has formally verified compilers for realistic languages to low-level targets using proof assistants like Coq.
R is a free software environment for statistical computing and graphics that provides a wide variety of statistical techniques and graphical methods. It includes base functions and packages, and is used through interfaces like RStudio. R represents data using objects like vectors, matrices, and data frames. Common operations include calculations, generating random variables, and visualizing data. R can be used to analyze a glass fragment dataset to visualize compositions and potentially classify an unknown fragment.
This document discusses using ClojureScript and React for frontend engineering with simplicity and functionality. It introduces Om, a library that provides an interface between ClojureScript and React. Datascript is presented as an immutable in-memory database and Datalog query engine for ClojureScript. Examples show how to create a database from JSON data retrieved from an API and perform queries on the in-memory data. The conclusion is that simplicity wins for frontend engineering.
R can be used to summarize and visualize data in various ways. Descriptive statistics like mean, median, range can summarize a single variable. Correlation and regression can show relationships between two variables. Frequency tables and cross tabs show counts and proportions of variables. Graphs like bar plots, histograms, boxplots and more can visualize one or more variables. Pie charts, scatter plots and heat maps are other options. R has functions for each of these techniques to explore and communicate patterns in data.
The document provides an overview of using the R programming language for data munging, modeling, visualization and simulation. It discusses R's capabilities for data manipulation, statistical modeling, visualization and as a programming language. Specific functions and code examples are provided for basic math operations, probability distributions, data structures and simulation.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
This document provides an introduction to the basics of MATLAB. It discusses where to find help in MATLAB, how to work with matrices and perform basic operations on them. It also covers logical conditions, different types of loops (for, while, if/else), how to create scripts and functions. Finally, it provides an overview of visualization and graphics in MATLAB as well as an introduction to the image processing toolbox.
R is a software package for data analysis and graphical representation. It provides functions, results of analysis as objects, and a flexible environment for model building. This document provides tutorials on basic R operations including computation, vectors, matrices, and graphics. Key functions introduced are cbind(), rbind(), seq(), rep(), and matrix() for creating and manipulating objects, and plot() for data visualization.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
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তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
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at Integral University, Lucknow, 06.06.2024
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Your Skill Boost Masterclass: Strategies for Effective Upskilling
R graphics by Novi Reandy Sasmita
1. R Graphics
Practice and Theory
Monday, March 26, 2012
Novi Reandy Sasmita
novireandysasmita@gmail.com
http://www.researchgate.net/profile/Novi_Sasmita
4. R Graphics: Plot
# Make data to be matrix and remove the first coloumb of bank
bank_1=as.matrix(bank[,-1])
# Graph the first row of bank_1 vector with 10 value
2500
plot(bank_1[1,])
2000
bank_1[1, ]
1500
1000
2 4 6 8 10
Index
5. R Graphics: Plot
#Define bank_1[1,] with name total asset
total_asset= bank_1[1,]
Total Assets (trillions of Rp)
2500
# Graph total_asset using blue points overlayed by a line
plot(total_asset, type=“o”, col=“blue”)
#Create a title with a red, blod/italic font
2000
title(main="Total Assets (trillions of Rp)", col.main="red",
total_asset
font.main=4)
1500
1000
2 4 6 8 10
Index
6. R Graphics: Line Chart
# Compute the largest y value used in the data (or we could just
use range again)
Indicators of the Condition of Comercial Banks in Indonesia,2000-2009
> max_y=max(bank_1)
2500
# Define colors to be used for tree data 2250
Total Asset
Deposites
Credits
> plot_warna=c("blue","red","green") 2000
1750
# Graph bank_1 using y axis that ranges from 0 to max_y
1500
# Turn off axes and annotations (axis labels) so we can
Trillions
1250
# specify them ourself
> plot(bank_1[1,], 1000
type="o",col=plot_warna[1],ylim=c(0,max_y),axes=FALSE, 750
ann=FALSE)
500
250
# Make x axis using 2000-2009 labels
0
> axis(1, at=1:10, lab=c("2000", "2001", "2002", "2003", "2004",
"2005", "2006", "2007", "2008", "2009")) 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Years
# Make y axis with horizontal labels that display ticks at
# every 250 marks. 250*0:max_y is equivalent to c(250,500,750,…).
> axis(2,las=1,at=250*0:max_y)
7. R Graphics: Line Chart
# Create box around plot
> box() Indicators of the Condition of Comercial Banks in Indonesia,2000-2009
2500
# Graph Deposites with red dashed line and square points 2250
Total Asset
Deposites
Credits
> lines(bank_1[2,], type="o", pch=22, lty=2, col=plot_warna[2]) 2000
1750
# Graph Credits with green dotted line and diamond points 1500
Trillions
> lines(bank_1[3,], type="o", pch=23, lty=3, col=plot_warna[3]) 1250
1000
# Create a title with a red, bold/italic font 750
500
> title(main="Indicators of the Condition of Comercial Banks in
Indonesia,2000-2009", col.main="red", font.main=4) 250
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
# Label the x and y axes with dark green text
Years
> title(xlab="Years", col.lab=rgb(0,0.5,0))
> title(ylab="Trillions", col.lab=rgb(0,0.5,0))
8. R Graphics: Line Chart
# Create a legend at (2400)
# (cex) and uses the same line colors and points used by Indicators of the Condition of Comercial Banks in Indonesia,2000-2009
# the actual plots 2500
> legend(2400,c("Total Asset","Deposites","Credits"), cex=0.8, 2250
Total Asset
Deposites
Credits
col=plot_warna, pch=21:23, lty=1:3) 2000
1750
1500
Trillions
1250
1000
750
500
250
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Years
9. R Graphics: Boxplot
# make graph with bank_1
2500
> boxplot(bank_1)
2000
1500
1000
500
0
data2000 data2001 data2002 data2003 data2004 data2005 data2006 data2007 data2008 data2009
10. Data 2000
R Graphics: Histogram
4
# Graph autos with adjacent bars using rainbow colors
> hist(bank_1[,1], col="green", main="Data 2000",
xlab="Trillions")
3
Frequency
2
1
0
0 200 400 600 800 1000 1200
Trillions
11. R Graphics: Barplot
# Graph autos with adjacent bars using rainbow colors
Indicators of the Condition of Comercial Banks in Indonesia,2000-2009
> barplot(bank_1, main="Indicators of the Condition of
2500
Comercial Banks in Indonesia,2000-2009",ylab="Trillions", Total Assets (trillions of Rp)
Deposits(trillions of Rp)
beside=TRUE, col=rainbow(7)) Credit (trillions of Rp)
loan to Deposit Ratio-LDR (%)
Return on Assets-ROA (%)
2000
Non Performing Loans-NPL (%)
Capital Adequacy Ratio-CAR (%)
# Place the legend at the top-left corner with no frame # using
1500
rainbow colors
Trillions
> legend("topleft",c("Total Assets (trillions of
1000
Rp)","Deposits(trillions of Rp)","Credit (trillions of Rp)", "loan
to Deposit Ratio-LDR (%)","Return on Assets-ROA (%)", "Non
Performing Loans-NPL (%)","Capital Adequacy Ratio-CAR
500
(%)"),cex=0.8,bty="n", fill=rainbow(7))
0
data2000 data2002 data2004 data2006 data2008
12.
13. R Graphics: Piechart Persentasi Total Suara
# Define total suara vector with 4 values
> total_suara=c(46442,101325,189858,128230) 1
21.8%
2
# Define some colors ideal for total suara 3
> warna=rainbow(length(total_suara)) 4
10%
# Calculate the percentage for candidate, rounded to one
# decimal place
> persentasi=round(total_suara/sum(total_suara)*100,1) 40.8%
# Concatenate a '%' char after each value
> persentasi=paste(persentasi, "%", sep="")
27.5%
# Create a pie chart with defined heading and custom colors #
and labels
> pie(total_suara, main="Persentasi Total Suara", col=warna,
labels=persentasi, cex=0.8)
# Create a legend at the right
> legend("topright", c("1","2","3","4"),cex=1.7, fill=warna)
14. R Graphics: Dotchart 1
data2009
data2008
data2007
data2006
Indicators of the Condition of Comercial Banks in Indonesia,2000-2009
data2005
# Create a colored dotchart for bank_1 data2004
data2003
data2002
data2001
data2000
with smaller labels 2
data2009
data2008
data2007
data2006
> dotchart(t(bank_1),
data2005
data2004
data2003
data2002
data2001
data2000
color=rainbow(length(bank_1)), 3
data2009
data2008
data2007
main="Indicators of the Condition of data2006
data2005
data2004
data2003
data2002
Comercial Banks in Indonesia,2000-
data2001
data2000
4
data2009
data2008
2009",cex=0.8) data2007
data2006
data2005
data2004
data2003
data2002
data2001
data2000
5
data2009
data2008
data2007
data2006
data2005
data2004
data2003
data2002
data2001
data2000
6
data2009
data2008
data2007
data2006
data2005
data2004
data2003
data2002
data2001
data2000
7
data2009
data2008
data2007
data2006
data2005
data2004
data2003
data2002
data2001
data2000
0 500 1000 1500 2000 2500