In this video, senior software developer Alejandro Lujan explores the elements of Scala's language that allow you to write clean and powerful code in a more brief manner.
Here is the JavaScript code to solve the problem:
<script>
// Get the grades from the prompt
var grade1 = parseInt(prompt("Enter grade 1"));
var grade2 = parseInt(prompt("Enter grade 2"));
var grade3 = parseInt(prompt("Enter grade 3"));
var grade4 = parseInt(prompt("Enter grade 4"));
// Add all the grades
var total = grade1 + grade2 + grade3 + grade4;
// Calculate the final grade
var finalGrade = total/4;
// Display the final grade
alert("The final grade is " + finalGrade);
</script>
The document presents an example of Dijkstra's shortest path algorithm on a weighted graph to find the shortest path between vertices. It shows the steps of initializing distances from the source, selecting the vertex with the minimum distance, updating distances of neighboring vertices, and repeating until all vertices are processed. The example graph is a directed graph with vertices A to H and weighted edges, and the algorithm finds the shortest path from vertex A to all other vertices through successive updates.
This document discusses various graph algorithms including depth-first search (DFS), breadth-first search (BFS), union find, Kruskal's algorithm, Floyd-Warshall's algorithm, Dijkstra's algorithm, and bipartite graphs. It provides definitions, pseudocode, sample code, and sample problems for implementing each algorithm.
The document discusses the algorithms of breadth-first search (BFS) and depth-first search (DFS) on graphs. It provides pseudocode for BFS and DFS, and examples of running the algorithms on sample graphs. Key points include:
- BFS uses a queue to explore all neighbors of a vertex before moving to the next level. It finds the shortest paths from the source.
- DFS uses recursion to explore as deep as possible before backtracking. It identifies tree edges, back edges, and forward edges.
- Both BFS and DFS run in O(V+E) time on a graph with V vertices and E edges.
Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking
The document defines and explains several key graph concepts and algorithms, including:
- Graph representations like adjacency matrix and adjacency list.
- Graph traversal algorithms like breadth-first search (BFS) and depth-first search (DFS).
- Minimum spanning tree algorithms like Prim's algorithm.
- Single-source shortest path algorithms like Dijkstra's algorithm and Floyd's algorithm.
Pseudocode and examples are provided to illustrate how BFS, DFS, Prim's, Dijkstra's and Floyd's algorithms work on graphs. Key properties of minimum spanning trees and shortest path problems are also defined.
Depth-first search (DFS) is an algorithm that explores all the vertices reachable from a starting vertex by traversing edges in a depth-first manner. DFS uses a stack data structure to keep track of vertices to visit. It colors vertices white, gray, and black to indicate their status. DFS runs in O(V+E) time and can be used for applications like topological sorting and finding strongly connected components. The edges discovered during DFS can be classified as tree, back, forward, or cross edges based on the order in which vertices are discovered.
The document discusses graph algorithms breadth-first search (BFS) and depth-first search (DFS). It provides pseudocode for BFS and DFS algorithms and explains key aspects of each. BFS uses a queue to explore all neighbors of a node before moving to the next level, building a breadth-first tree. DFS uses recursion to explore as deep as possible along each branch before backtracking, resulting in a depth-first tree structure. Both algorithms run in O(V+E) time on a graph with V vertices and E edges.
The document introduces breadth-first search (BFS) and depth-first search (DFS), two algorithms for traversing graphs. It provides explanations of how BFS and DFS work, including pseudocode algorithms. Examples of simulations of BFS and DFS on sample graphs are shown step-by-step with diagrams. Potential applications of BFS and DFS in areas like cycle detection, finding connected components, and topological sorting are also discussed.
The document provides information on graph traversals including depth-first search (DFS) and breadth-first search (BFS). It explains that DFS uses a stack and visits nodes in a recursive, preorder manner. BFS uses a queue and visits nodes level-by-level starting from the root. Examples of DFS and BFS on sample graphs are given to illustrate the traversal order.
The document discusses Depth First Search (DFS) graph traversal algorithms. It explains that DFS traverses the graph by going as deep as possible along each branch before backtracking. It provides examples of DFS on graphs, showing the order of vertex visits and maintaining discovery/finish times. Key aspects of DFS include using recursion to visit nodes, marking nodes gray while discovering and black when finished, and distinguishing tree, back, forward and cross edges. The time complexity of DFS is O(V+E) where V is vertices and E is edges. Applications mentioned are topological sorting and finding strongly connected components.
The document discusses graph traversal algorithms depth-first search (DFS) and breadth-first search (BFS). DFS uses a stack and visits nodes by traversing as deep as possible before backtracking. BFS uses a queue and visits all nodes at each level from the starting node before moving to the next level. Examples are given applying DFS and BFS to a sample graph. Applications of DFS and BFS are also listed such as computing distances, checking for cycles/bipartiteness, and topological sorting.
This document discusses several graph algorithms:
1) Topological sort is an ordering of the vertices of a directed acyclic graph (DAG) such that for every edge from vertex u to v, u comes before v in the ordering. It can be used to find a valid schedule respecting dependencies.
2) Strongly connected components are maximal subsets of vertices in a directed graph such that there is a path between every pair of vertices. An algorithm uses depth-first search to find SCCs in linear time.
3) Minimum spanning trees find a subset of edges that connects all vertices at minimum total cost. Prim's and Kruskal's algorithms find minimum spanning trees using greedy strategies in O(E
Topological sort is an algorithm that finds an ordering of tasks that respects their dependencies. It works by performing a depth-first search (DFS) on a directed graph where vertices represent tasks and edges represent dependencies. During DFS, each vertex is assigned a finishing time. The topological sort orders the tasks according to decreasing finishing time, so that all dependencies are satisfied. The algorithm runs in O(V+E) time, where V is the number of vertices and E is the number of edges.
The document defines key concepts relating to the velocity, acceleration, and concavity of functions that describe an object's position over time. It states that velocity is the first derivative of position, acceleration is the second derivative of position, and includes examples of finding the velocity and acceleration of a marble rolling up an inclined plane. It also defines how the signs of the first and second derivatives relate to whether a function is increasing/decreasing and concave up/down. An assignment is given relating to these concepts.
This document discusses how to find articulation points in a graph using depth-first search (DFS). It begins by reviewing the DFS algorithm and concepts like discovery time. It then makes two key observations: (1) roots with two or more children and (2) internal vertices where a subtree has no back edge climbing higher are articulation points. It introduces the concept of low(v) to track the lowest discovery time reachable from v to determine if a subtree can climb higher. The algorithm runs DFS to compute low(v) values and identifies articulation points based on the two observations. The running time is O(V+E).
1. The document discusses depth-first search (DFS), including its recursive implementation which eliminates the need for an explicit stack. 2. DFS classifies the edges of a graph as tree edges, back edges, forward edges, or cross edges. Tree edges and back edges are the only possible edge types in an undirected graph searched with DFS. 3. Applications of DFS include finding cycles, articulation vertices, topological sorting of directed acyclic graphs, and finding strongly connected components.
This document contains a presentation on Breadth-First Search (BFS) given to students. The presentation includes:
- An introduction to BFS and its inventor Konrad Zuse.
- Definitions of key terms like graph, tree, vertex, level-order traversal.
- An example visualization of BFS on a graph with 14 steps.
- Pseudocode and a Java program implementing BFS.
- Applications of BFS like shortest paths, social networks, web crawlers.
- The time and space complexity of BFS is O(V+E) and O(V).
- A conclusion that BFS is an important algorithm that traverses
BFS is the most commonly used approach. BFS is a traversing algorithm where you should start traversing from a selected node (source or starting node) and traverse the graph layerwise thus exploring the neighbor nodes (nodes which are directly connected to the source node.
1) Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph) and explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level.
2) BFS uses a queue to keep track of nodes to visit at each level, processing nodes level-by-level from the queue in a FIFO order. It marks visited nodes to avoid processing them again.
3) The time complexity of BFS is O(V+E) where V is the number of vertices and E is the number of edges. BFS can be used to
This document describes graph search algorithms like breadth-first search (BFS) and their applications. It provides details on how BFS works, including that it maintains queues to search levels outwards from the starting vertex, and outputs the distance and predecessor of each vertex. BFS runs in O(V+E) time by visiting each vertex and edge once. The document also discusses how BFS can be used to find connected components in a graph and determine if a graph is bipartite.
- A topological sort is an ordering of the vertices in a directed acyclic graph. The ordering is such that for every directed edge from vertex vi to vj, vi comes before vj in the ordering. A topological sort is not possible if the graph contains a cycle.
- The topological sort algorithm finds vertices with no incoming edges and adds them to the ordering. It then removes these vertices and repeats until the graph is empty or a cycle is detected.
- The example graph shows the in-degrees of vertices in a graph. Vertex A has an in-degree of 0 and would be first in the topological sort ordering.
Breadth-first search (BFS) and depth-first search (DFS) are two graph traversal algorithms that systematically visit all vertices and edges of a graph. BFS discovers vertices in order of distance from the source vertex, allowing it to compute shortest paths. DFS recursively explores as far as possible along each branch before backtracking, imposing a tree structure on the graph. Both algorithms run in O(V+E) time, where V is the number of vertices and E is the number of edges. DFS classifies edges as tree, forward, back, or cross edges and can detect cycles based on the presence of back edges.
This document discusses depth-first search (DFS), a graph search algorithm that explores edges deeper whenever possible. It uses a LIFO queue and colors vertices as white, gray, or black to track the search process. DFS recursively visits unexplored neighbor vertices, coloring them gray before exploring their edges. The running time is O(V+E) as DFS calls itself once per vertex and explores each edge once.
A simple algorithm I devised to enumerate the disjoint paths between a pair of vertices in a given graph. This algorithm was devised as a part of my course-work for Masters [Tech.] at IIT-Banaras Hindu University.
The curriculum document discusses social studies and focuses on the environment both before and after children wash a car. It examines the environment of the car wash location initially and then analyzes changes to the environment after the children complete the car washing activity. The document appears to use a hands-on car washing project to teach social studies lessons about environments and environmental changes.
Alejandro Lujan introduces us to String Interpolation, a feature of Scala that allows us to have placeholders inside of string definitions, and explains why you would want to use them. Video included!
The document introduces breadth-first search (BFS) and depth-first search (DFS), two algorithms for traversing graphs. It provides explanations of how BFS and DFS work, including pseudocode algorithms. Examples of simulations of BFS and DFS on sample graphs are shown step-by-step with diagrams. Potential applications of BFS and DFS in areas like cycle detection, finding connected components, and topological sorting are also discussed.
The document provides information on graph traversals including depth-first search (DFS) and breadth-first search (BFS). It explains that DFS uses a stack and visits nodes in a recursive, preorder manner. BFS uses a queue and visits nodes level-by-level starting from the root. Examples of DFS and BFS on sample graphs are given to illustrate the traversal order.
The document discusses Depth First Search (DFS) graph traversal algorithms. It explains that DFS traverses the graph by going as deep as possible along each branch before backtracking. It provides examples of DFS on graphs, showing the order of vertex visits and maintaining discovery/finish times. Key aspects of DFS include using recursion to visit nodes, marking nodes gray while discovering and black when finished, and distinguishing tree, back, forward and cross edges. The time complexity of DFS is O(V+E) where V is vertices and E is edges. Applications mentioned are topological sorting and finding strongly connected components.
The document discusses graph traversal algorithms depth-first search (DFS) and breadth-first search (BFS). DFS uses a stack and visits nodes by traversing as deep as possible before backtracking. BFS uses a queue and visits all nodes at each level from the starting node before moving to the next level. Examples are given applying DFS and BFS to a sample graph. Applications of DFS and BFS are also listed such as computing distances, checking for cycles/bipartiteness, and topological sorting.
This document discusses several graph algorithms:
1) Topological sort is an ordering of the vertices of a directed acyclic graph (DAG) such that for every edge from vertex u to v, u comes before v in the ordering. It can be used to find a valid schedule respecting dependencies.
2) Strongly connected components are maximal subsets of vertices in a directed graph such that there is a path between every pair of vertices. An algorithm uses depth-first search to find SCCs in linear time.
3) Minimum spanning trees find a subset of edges that connects all vertices at minimum total cost. Prim's and Kruskal's algorithms find minimum spanning trees using greedy strategies in O(E
Topological sort is an algorithm that finds an ordering of tasks that respects their dependencies. It works by performing a depth-first search (DFS) on a directed graph where vertices represent tasks and edges represent dependencies. During DFS, each vertex is assigned a finishing time. The topological sort orders the tasks according to decreasing finishing time, so that all dependencies are satisfied. The algorithm runs in O(V+E) time, where V is the number of vertices and E is the number of edges.
The document defines key concepts relating to the velocity, acceleration, and concavity of functions that describe an object's position over time. It states that velocity is the first derivative of position, acceleration is the second derivative of position, and includes examples of finding the velocity and acceleration of a marble rolling up an inclined plane. It also defines how the signs of the first and second derivatives relate to whether a function is increasing/decreasing and concave up/down. An assignment is given relating to these concepts.
This document discusses how to find articulation points in a graph using depth-first search (DFS). It begins by reviewing the DFS algorithm and concepts like discovery time. It then makes two key observations: (1) roots with two or more children and (2) internal vertices where a subtree has no back edge climbing higher are articulation points. It introduces the concept of low(v) to track the lowest discovery time reachable from v to determine if a subtree can climb higher. The algorithm runs DFS to compute low(v) values and identifies articulation points based on the two observations. The running time is O(V+E).
1. The document discusses depth-first search (DFS), including its recursive implementation which eliminates the need for an explicit stack. 2. DFS classifies the edges of a graph as tree edges, back edges, forward edges, or cross edges. Tree edges and back edges are the only possible edge types in an undirected graph searched with DFS. 3. Applications of DFS include finding cycles, articulation vertices, topological sorting of directed acyclic graphs, and finding strongly connected components.
This document contains a presentation on Breadth-First Search (BFS) given to students. The presentation includes:
- An introduction to BFS and its inventor Konrad Zuse.
- Definitions of key terms like graph, tree, vertex, level-order traversal.
- An example visualization of BFS on a graph with 14 steps.
- Pseudocode and a Java program implementing BFS.
- Applications of BFS like shortest paths, social networks, web crawlers.
- The time and space complexity of BFS is O(V+E) and O(V).
- A conclusion that BFS is an important algorithm that traverses
BFS is the most commonly used approach. BFS is a traversing algorithm where you should start traversing from a selected node (source or starting node) and traverse the graph layerwise thus exploring the neighbor nodes (nodes which are directly connected to the source node.
1) Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph) and explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level.
2) BFS uses a queue to keep track of nodes to visit at each level, processing nodes level-by-level from the queue in a FIFO order. It marks visited nodes to avoid processing them again.
3) The time complexity of BFS is O(V+E) where V is the number of vertices and E is the number of edges. BFS can be used to
This document describes graph search algorithms like breadth-first search (BFS) and their applications. It provides details on how BFS works, including that it maintains queues to search levels outwards from the starting vertex, and outputs the distance and predecessor of each vertex. BFS runs in O(V+E) time by visiting each vertex and edge once. The document also discusses how BFS can be used to find connected components in a graph and determine if a graph is bipartite.
- A topological sort is an ordering of the vertices in a directed acyclic graph. The ordering is such that for every directed edge from vertex vi to vj, vi comes before vj in the ordering. A topological sort is not possible if the graph contains a cycle.
- The topological sort algorithm finds vertices with no incoming edges and adds them to the ordering. It then removes these vertices and repeats until the graph is empty or a cycle is detected.
- The example graph shows the in-degrees of vertices in a graph. Vertex A has an in-degree of 0 and would be first in the topological sort ordering.
Breadth-first search (BFS) and depth-first search (DFS) are two graph traversal algorithms that systematically visit all vertices and edges of a graph. BFS discovers vertices in order of distance from the source vertex, allowing it to compute shortest paths. DFS recursively explores as far as possible along each branch before backtracking, imposing a tree structure on the graph. Both algorithms run in O(V+E) time, where V is the number of vertices and E is the number of edges. DFS classifies edges as tree, forward, back, or cross edges and can detect cycles based on the presence of back edges.
This document discusses depth-first search (DFS), a graph search algorithm that explores edges deeper whenever possible. It uses a LIFO queue and colors vertices as white, gray, or black to track the search process. DFS recursively visits unexplored neighbor vertices, coloring them gray before exploring their edges. The running time is O(V+E) as DFS calls itself once per vertex and explores each edge once.
A simple algorithm I devised to enumerate the disjoint paths between a pair of vertices in a given graph. This algorithm was devised as a part of my course-work for Masters [Tech.] at IIT-Banaras Hindu University.
The curriculum document discusses social studies and focuses on the environment both before and after children wash a car. It examines the environment of the car wash location initially and then analyzes changes to the environment after the children complete the car washing activity. The document appears to use a hands-on car washing project to teach social studies lessons about environments and environmental changes.
Alejandro Lujan introduces us to String Interpolation, a feature of Scala that allows us to have placeholders inside of string definitions, and explains why you would want to use them. Video included!
This document contains a portfolio and contact information for Paul Partlow. It includes summaries of over 30 of his illustration and graphic design projects from 2012 to 2014 for schools including Rhode Island School of Design and Suffolk County Community College. The projects cover a wide range of mediums from graphite, ink and digital to found objects. They include personal works as well as assignments covering topics like visual storytelling, conveying personality, and combining cityscapes.
This document provides a summary of Korean cuisine and traditional Korean dishes. It discusses how Korean food often involves fermentation, which adds beneficial bacteria and nutrients. It also notes that Korean diet may help prevent obesity due to its emphasis on vegetables, soups and stews. Some key Korean dishes summarized are bibimbap (mixed rice bowl), doenjang jjigae (soybean paste stew), yukgaejang (spicy beef soup), and maeuntang (hot spicy fish soup). The document promotes Korean food as a healthy and balanced way of eating.
This presentation explores the benefits of functional programming, especially with respect to reliability. It presents a sample of types that allow many program invariants to be enforced by compilers. We also discuss the industrial adoption of functional programming, and conclude with a live coding demo in Scala.
This presentation provides an overview on Value Classes in Scala, which is explained in the video on the last slide by Alejandro Lujan. He explains why you would want to use them, outlines the restrictions that are associated with them, and shows examples of how you would use them. Value classes are a mechanism that Scala provides to create a certain type of wrapper classes that provide memory and performance optimizations. In this video, we show a use case for Tiny Types with Value classes.
Senior Software Developer and Lead Trainer Alejandro Lujan explains pattern matching, a very powerful and elegant feature of Scala, using a series of examples.
Learn more about this topic and find more presentation on Scala at:
Senior Software Developer Alejandro Lujan discusses the collections API in Scala, and provides some insight into what it can do with with some examples.
In his latest Typesafe tutorial video, Alejandro Lujan explains for expressions in Scala, and provides an example of them in action.
For expressions are a very useful construct that can simplify manipulation of collections and several other data structures. They can be used in place of nested for loops, or to replace calls to map and flatMap in non-collection structures.
Learn more
This document provides information about the sun, earth, and moon through a presentation by Dr. Marjorie Anne Wallace. It asks and answers several questions about these celestial bodies, including what causes day and night, their relative sizes, what percentage of the atmosphere is various gases, tidal patterns, and other details. It explains concepts such as rotation, revolution, phases of the moon, and seasons in 3-5 sentences per topic.
Did you miss Scala Days 2015 in San Francisco? Have no fear! BoldRadius was there and we've compiled the best of the best! Here are the highlights of a great conference.
As a full-time Scala developer, I often find myself talking about Scala and functional programming in different kinds of situations, ranging from meeting a friend working in J2EE, Ruby or C++, to dedicated Scala Meetups aiming to promote deeper understanding of the language. However, something occurred to me lately. By hanging out with people who have some Scala knowledge or experience, I am somewhat holding on to a safe place. By presenting only to people who are curious about Scala, I'm preaching to the converted.
To make a long story short, I recently made an attempt at getting out of my comfort zone by presenting about how making the transition from Java to Scala makes total sense (from Java developer point of view). The presentation went through proof-hearing of approximately 60 experienced Java programmers (with almost no prior Scala knowledge) gathered in one room for a Lunch & Learn. Here are my slides.
Punishment Driven Development #agileinthecityLouise Elliott
What is the first thing we do when a major issue occurs in a live system? Sort it out of course. Then we start the hunt for the person to blame so that they can suffer the appropriate punishment. What do we do if a person is being awkward in the team and won’t agree to our ways of doing things? Ostracise them of course, and see how long it is until they hand in their notice – problem solved.
This highly interactive talk delves into why humans have this tendency to blame and punish. It looks at real examples of punishment within the software world and the results which were achieved. These stories not only cover managers punishing team members but also punishment within teams and self-punishment. We are all guilty of some of the behaviours discussed.
This is aimed at everyone involved in software development. It covers:
• Why we tend to blame and punish others.
• The impact of self-blame.
• The unintended (but predictable) results from punishment.
• The alternatives to punishment, which get real results.
This document discusses immutability in Scala. It recommends immutability to avoid unexpected values, concurrent state issues, and most container types are immutable by default. It provides examples of using vals for immutable variables, creating new instances instead of modifying existing ones, using the copy method for case classes, and hiding vars to prevent external modification of mutable state. It cautions being mindful of modifying immutable nested structures, fields initialized each instance, and closing over mutable state.
This document provides an overview of Scala for Java developers. It covers Scala basics like running on the JVM and supporting functional programming. It also summarizes key Scala syntax like type declarations, expressions, loops, functions, classes, traits, objects, and collections. The document compares Java and Scala concepts and provides resources to learn more about Scala.
From java to kotlin beyond alt+shift+cmd+k - Droidcon italyFabio Collini
Kotlin is a first-class language for Android development since Google I/O 2017. And it’s here to stay!
Thanks to Android Studio it’s really easy to introduce Kotlin in an existing project, the configuration is trivial and then we can convert Java classes to Kotlin using a Alt+Shift+Cmd+K. But the new syntax is the just beginning, using Kotlin we can improve our code making it more readable and simpler to write.
In this talk we’ll see how to use some Kotlin features (for example data classes, collections, coroutines and delegates) to simplify Android development comparing the code with the equivalent “modern” Java code. It’s not fair to compare Kotlin code with plain Java 6 code so the Java examples will use lambdas and some external libraries like RxJava and AutoValue.
1- Create a class called Point that has two instance variables, defi.pdfjeeteshmalani1
1- Create a class called Point that has two instance variables, defined as private, as follows: An x
coordinate and a y coordinate of type integer.
a) Write two constructors for the Point class as follows: A default constructor that sets the class
instance variables to zero and a constructor that receives two integer values and sets the instance
variables to them.
b) Write the set and get methods for the Point class to set and return the values of its instance
variables.
c) Write a toString() method for the Point class that prints the class name (Point) and the value of
its instance variables.
2- Create three classes called Circle, Rectangle and Square with the following properties:
- The Circle class has two instance variables – a position of type Point (the class that you just
created above) and a radius of type double. The instance variables position and radius specify the
position of the center and radius of the circle, respectively.
- The Rectangle class has three instance variables – a position of type Point (the class that you
just created above), a length and a width of type double. The instance variables position, length
and width specify the position of the top left corner, length and width of the rectangle,
respectively.
- The Square class has two instance variables – a position of type Point (the class that you just
created above) and a lengthof type double. The instance variables position andlength specify the
position of the top left corner and length of the square, respectively.
For each of the Circle, Rectangle andSquare classes do the following:
a) Define the instance variables as private.
b) Write two constructors for each class as follows: A default constructor that sets the instance
variables to zero and a constructor that receives values for the instance variables and sets them.
c) Write the set and get methods for each class to set and return the values of their instance
variables. For example, one of the get methods would return a Point.
d) Write a toString() method for each class that prints the class name (Circle, Rectangle or
Square) and the value of its instance variables.
e) Write two methods called getPerimeter() and getArea() for each class, which calculate and
return the perimeter and area of the class, respectively. For example, the getArea() method of the
Square class returns the area of the Square object.
3- Test the above classes by writing a class called GeometricTest.java that does the following:
- Creates instances of the Circle, Rectangle and Square classes as follows:
- Circle: positioned at x = 7, y = 3 and the radius = 4.5.
- Rectangle: positioned at x = 3, y = -1 and the length = 4.0 and width = 6.0.
- Square: positioned at x = 5, y = 8 and the length = 2.0.
- Prints each of the above objects.
- Changes the length of the Square object to 5.0.
- Prints the perimeter and area of each of each of the above objects.
- Compares the x coordinates of the Square and Rectangle classes and prints a message
spec.
Ti1220 Lecture 2: Names, Bindings, and ScopesEelco Visser
The document provides an outline and messages from a lecture on names, bindings, and scopes in programming languages. It discusses key concepts such as:
- Names (identifiers) in programming languages and their forms
- Variables, their attributes like name, address, value, type, lifetime, and scope
- Binding and binding time, including static and dynamic binding
- Scope rules for variables in different languages like block-scoped languages like Java versus function-scoped languages like JavaScript
- Special cases like nested functions, recursion, and hoisting behavior in JavaScript
The document discusses creating a Rectangle class with length and width attributes that default to 1. It includes member functions to calculate the perimeter and area. Set and get functions validate that length and width are between 0 and 20. The class is defined with private length and width variables and public setter, getter, and calculation functions. Main tests the class by creating Rectangle objects and outputting their attributes.
Quark: A Purely-Functional Scala DSL for Data Processing & AnalyticsJohn De Goes
Quark is a new Scala DSL for data processing and analytics that runs on top of the Quasar Analytics compiler. Quark is adept at processing semi-structured data and compiles query plans to operations that run entirely inside a target data source. In this presentation, John A. De Goes provides an overview of the open source library, showing several use cases in data processing and analytics. John also demonstrates a powerful technique that every developer can use to create their own purely-functional, type-safe DSLs in the Scala programming language.
This document provides an overview of basic TypeScript types including Boolean, Number, String, Array, Enum, Any, Void, and interfaces. It discusses TypeScript's structural subtyping approach to type checking interfaces based on their shape. The document also covers classes in TypeScript including inheritance, private/public modifiers, accessors, static properties, and how TypeScript code compiles to JavaScript.
TypeScript provides basic types like Boolean, Number, String, Array, Enum, Any, and Void to define the type of a variable. It allows defining interfaces to describe the shape of an object and ensure variables match the expected structure. Classes can be created that implement interfaces, support inheritance, and use public/private modifiers. Accessors can be used to control read/write access to class properties. Static properties exist on the class level rather than instance level. Classes can also be defined using constructor functions for additional flexibility.
The document discusses TypeScript modules. It begins by showing how to organize validators into a single file module. It then demonstrates splitting the module across multiple files by creating LettersOnlyValidator.ts and ZipCodeValidator.ts files that both reference a Validation.ts file and export classes that implement the module's interface. This allows validators to be modularized while splitting code across separate files.
All Aboard The Scala-to-PureScript Express!John De Goes
Many Scala programmers have embraced functional programming, but the syntax and semantics of programming languages in the Haskell family remains a mystery. In this talk, Scala developers (and to some extent, Java developers) will see how the types, data structures, traits / interfaces, packages, and so forth translate into their PureScript counterparts.
The document discusses Scala programming concepts including object-oriented programming, functional programming, collections, pattern matching, and more. It provides code examples of defining objects and classes, functions, for expressions, match expressions, case classes, traits, generics, and collections like List and Map.
1. A function is a section of code that performs a specific task and makes programming simpler by splitting problems into sub-problems.
2. There are different types of functions including void functions without arguments, void functions with input arguments, and functions that return a single result.
3. Functions allow code to be reused by calling the function from the main program or from other functions. Functions can take input arguments and return values to provide modularity and simplify programming.
1. A function is a section of code that performs a specific task and makes programming simpler by splitting problems into sub-problems.
2. There are different types of functions including void functions without arguments, void functions with input arguments, and functions that return a single result.
3. Functions allow code to be reused by calling the function from the main program or from other functions. Functions can take input arguments and return values to provide modularity and simplify programming.
The document discusses RxSwift, which is a library for reactive programming with Swift. It combines ReactiveX with Swift by providing Observables and Observers. Observables allow data streams to be observed and manipulated through operators like map, filter, etc. The document provides examples of using RxSwift to validate a password field by observing text changes and mapping valid/invalid states to display feedback. It also shows an example of observing a nickname field to call an API on valid input. Overall, the document introduces the key concepts of RxSwift like Observables, Observers, operators, and provides examples of validating user input fields reactively.
An array is a collection of data that holds a fixed number of values of the same type. Arrays can be one-dimensional or multidimensional. Array elements are accessed using indices starting from 0. Arrays can be passed to functions by passing the array name, which passes the base address. Strings in C are implemented as character arrays terminated by a null character. Common string functions like strcpy(), strcat(), strlen() etc. are used to manipulate strings.
The document discusses various concepts related to objects and classes in C++ including copy constructors, shallow vs deep copy, customizing copy constructors, immutable objects, static vs instance members, friend functions and classes, const member functions and objects, and the difference between structs and classes. Specifically, it provides examples to illustrate deep copying of objects containing dynamic memory, implementing copy constructors for deep copy, using static member functions and data, allowing access to private members using friend functions and classes, and ensuring objects are immutable using const.
This document discusses C++ classes and object-oriented programming. It defines classes as user-defined types that encapsulate data and functions. Classes allow programmers to model real-world entities as objects with attributes and behaviors. The document provides examples of class definitions, member access specifiers, constructors, member functions, and accessing class members. It demonstrates how classes are used to define objects and encapsulate data in C++.
Class extension should be used deliberately, not just to add fields and methods incrementally. When defining classes through extension, it is important to consider type compatibility. In the example of LatLong subclasses, type compatibility is maintained by defining a common interface (LatLong) that the subclasses extend while adding their own fields. This allows methods like bearing calculation to work across any object of a LatLong subclass type.
In this webinar, Michael Nash of BoldRadius explores the Typesafe Reactive Platform.
The Typesafe Reactive Platform is a suite of technologies and tools that support the creation of reactive applications, that is, applications that handle the kind of responsiveness requirements, data volume, and user load that was out of practical reach only a few years ago.
From analysis of the human genome to wearable technology to communications at a massive scale, BoldRadius has the premier team of experts with decades of collective experience in designing and building these types of applications, and in helping teams adopt these tools.
Patrick Premont of BoldRadius presented this talk at Scala By The Bay 2015.
Why do data structure lookups often return Options? Could we safely eliminate all the recovery code that we hope is never called? We will see how Scala’s type system lets us express referential integrity constraints to achieve unparalleled reliability. We apply the technique to in-memory data structures using the Total-Map library and consider how to extend the benefits to persisted data.
How You Convince Your Manager To Adopt Scala.js in ProductionBoldRadius Solutions
Dave Sugden and Katrin Shechtman of BoldRadius presented this talk at Scala By The Bay 2015.
The talk will present fully functional sample application developed with Scala.js, scalatags, scalacss and other Scala and Typesafe technologies. We aim to show all the pros and cons for having Scala coast-to-coast approach to web-application development and encourage people not to shy away from asking difficult questions challenging this approach. Participants can expect to gain a clear view on the current state of the Scala based client side technologies and take away an activator template with application code that could be used as a base for technical discussions with their peers and managers.
Domain Driven Design with Onion Architecture is a powerful combination of Architecture Patterns that can dramatically improve code quality and can help you learn a great deal about writing "clean" code.
Senior Software Developer and Trainer Alejandro Lujan explains sealed classes, why they are needed, and how to implement them in Scala. Read more on the BoldRadius blog: http://boldradius.com/blog-post/VBB3uzIAADYAiiSy/sealed-classes-in-scala
BoldRadius' Senior Software Developer Alejandro Lujan explains how to use higher order functions in Scala and illustrates them with some examples.
See the accompanying video at www.boldradius.com/blog
Mike Kelland and the BoldRadius team lead an interactive discussion at Scala Days 2014 in Berlin on adopting the Typesafe Reactive Platform and creating change in your organization.
We explored approaches to solving the pain points that may arise, presenting tools, strategies and resources designed to help you adopt the Typesafe Reactive Platform today.
This document discusses modeling products and categories in Scala using case classes. It describes the attributes of products (name, category, optional description) and categories (name, optional parent category). It shows how to define regular classes in Java and Scala to represent these, but notes that Scala case classes provide additional benefits out of the box, such as a companion object with factory methods, automatic fields from class parameters, nicer toString output, and attribute-based equality. It cautions that case classes have bytecode overhead and generated code that could clutter interfaces.
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex
Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
Procurement Insights Cost To Value Guide.pptxJon Hansen
Procurement Insights integrated Historic Procurement Industry Archives, serves as a powerful complement — not a competitor — to other procurement industry firms. It fills critical gaps in depth, agility, and contextual insight that most traditional analyst and association models overlook.
Learn more about this value- driven proprietary service offering here.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma
My talk for the Indian School of Business (ISB) Emerging Leaders Program Cohort 9. In this talk, I discussed key issues around adoption of GenAI in business - benefits, opportunities and limitations. I also discussed how my research on Theory of Cognitive Chasms helps address some of these issues
Increasing Retail Store Efficiency How can Planograms Save Time and Money.pptxAnoop Ashok
In today's fast-paced retail environment, efficiency is key. Every minute counts, and every penny matters. One tool that can significantly boost your store's efficiency is a well-executed planogram. These visual merchandising blueprints not only enhance store layouts but also save time and money in the process.
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.