This document discusses competitive programming (CP). It defines CP as a "mind sport" involving problem solving under constraints, not a computer engineering field. The goal of CP is to increase logical ability and write code for challenging situations. Reasons to do CP include it being trendy now and to improve problem solving and data structures and algorithms (DSA) skills. The roadmap involves learning time/space complexity, selecting a language, learning DSA, and practicing problems. Benefits include helping with coding interviews, improving problem solving, adding skills to one's resume, and demonstrating abilities publicly. To be a CP master requires practice.
The document discusses a seminar presentation on mind reading computers. It begins with an introduction on how people express mental states through facial expressions and gestures. It then discusses what mind reading is, how it works using sensors to measure blood oxygen levels in the brain, and the process which involves facial detection and emotional classification techniques. Applications are discussed including using it to help paralyzed people communicate and potential issues around privacy breaches. It concludes that research is underway to allow computers to respond to brain activity.
The document discusses operating systems, including their functions, components, and types. It provides details on single-user single-tasking operating systems, single-user multi-tasking operating systems, and multi-user multi-tasking operating systems. Examples of commonly used operating systems like Windows and MacOS are described. Key aspects of operating systems that are covered include process management, memory management, I/O device management, and providing a user interface.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
The document discusses the 8-puzzle problem and the A* algorithm. The 8-puzzle problem involves a 3x3 grid with 8 numbered tiles and 1 blank space that can be moved. The A* algorithm maintains a tree of paths from the initial to final state, extending the paths one step at a time until the final state is reached. It is complete and optimal but depends on the accuracy of the heuristic used to estimate costs.
The document is a presentation about TensorFlow. It begins with an introduction that defines machine learning and deep learning. It then discusses what TensorFlow is, including that it is an open-source library for deep learning and ML, was developed by Google Brain, and uses data flow graphs to represent computations. The presentation explains benefits of TensorFlow like parallelism, distributed execution, and portability. It provides examples of companies using TensorFlow and demonstrates cool projects that can be built with it, like image classification, object detection, and speech recognition. Finally, it concludes that TensorFlow is helping achieve amazing advancements in machine learning.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
This document outlines a proposed tourism management system for India. It introduces India as a popular tourist destination with diverse culture, art, traditions and history. It discusses popular regions in northern and southern India that attract tourists. The problem statement notes a lack of relationship between travel agencies and customers. The objectives are to match travel services to customer priorities, build strong customer relationships, and provide accurate travel information. The plan of action includes developing the frontend and backend, connecting forms to a database, and launching the website. Data flow diagrams, use case diagrams and ER diagrams will model the system. The next steps are to code the project, add graphics and database tables, and launch the website online.
This document discusses competitive programming and provides tips for getting started. It defines competitive programming as solving well-defined problems by writing computer programs within time and memory limits. It outlines some merits like fun and practice, and demrits like potential addiction. Tips are provided such as learning a programming language, practicing on online judges, and learning data structures and algorithms. Finally, it lists some online and onsite programming contests as well as university courses relevant to competitive programming.
Competitive programming involves writing programs to solve problems within a limited time period, typically ranging from hours to days, either individually or in teams. These contests test algorithm skills, basic math skills, programming skills, speed, creativity, and debugging abilities. Some prestigious contests include the ACM ICPC, Google Code Jam, TopCoder Open, and Facebook Hacker Cup. Competitive programming improves logical and analytical skills, looks good on a CV, and is fun, while also preparing participants for coding rounds during job interviews at many companies. Sites like Topcoder, SPOJ, Codeforces, Codechef, and Hackerrank allow programmers to practice for competitions.
The document provides an introduction to competitive programming, which involves solving algorithm and data structure problems quickly under time and memory constraints. It discusses what competitive programming tests, how to get started, problem properties, examples, where to practice, tips for practicing, reasons for doing competitive programming, drawbacks, prestigious contests, regular contests, and how KIIT students are performing. The high-level goal of competitive programming is improving programming and problem-solving skills through regular practice and competition. It is recognized by major tech companies and helps build useful everyday skills.
This document introduces competitive programming competitions like ACM ICPC and Google Code Jam. It discusses training platforms such as Codeforces and Kattis that contain practice problems. It categorizes common problem types like ad hoc, complete search, divide and conquer, greedy, and dynamic programming to help determine a problem's difficulty and appropriate solving algorithm. Key problem types are defined, with ad hoc being the easiest and complete search involving brute force searching the entire search space.
Competitive programming involves writing computer programs to solve problems according to provided specifications in a competition format. It tests algorithmic skills, programming skills, speed, creativity, debugging skills, and more. To get started, participants should understand complexity, choose a programming language, learn data structures, and brush up on basics like number theory. Problem properties include absolute correctness checks, background stories to hide the actual problem, and examples of input/output formats. Practice is important and can be done on various online judge systems like CodeChef and Hackerrank.
This document provides tips and strategies for practicing competitive programming to achieve goals like learning new algorithms and programming languages. It recommends practicing regularly both individually and in small teams on online judges. When practicing, choosing problems that provide feedback like explanations is important for learning. The document also provides sample practice schedules, lists competitive programming contests and websites to learn theory, and includes links to additional practice materials.
This document outlines an introduction to competitive programming and problem solving algorithms. It discusses that competitive programming involves writing programs to solve well-known computer science problems quickly. To be successful requires coding quickly, identifying problem types, analyzing time complexity, and extensive practice. The document then covers four basic problem solving paradigms - complete search, divide and conquer, greedy algorithms, and dynamic programming. It provides details on complete search, including that it involves searching the entire solution space and is useful when no clever algorithm exists or the input size is small.
The document discusses the steps of problem solving which include identifying the problem, understanding it, identifying alternative solutions, selecting the best solution, listing instructions to implement it, and evaluating the solution. It also describes types of problems that can be solved algorithmically or heuristically and discusses problem solving with computers including defining solutions, results, programs, and artificial intelligence. Finally, it outlines some difficulties in problem solving like eliminating good alternatives or not following logical steps.
Key Features
•Covers a broad range of algorithms in depth
•Each chapter
–focuses on an algorithm
–discusses its design techniques and areas of application
•Algorithms are written in Pseudocode
Learn More: www.phindia.com
This document provides an overview of algorithms and algorithm analysis. It discusses key concepts like what an algorithm is, different types of algorithms, and the algorithm design and analysis process. Some important problem types covered include sorting, searching, string processing, graph problems, combinatorial problems, geometric problems, and numerical problems. Examples of specific algorithms are given for some of these problem types, like various sorting algorithms, search algorithms, graph traversal algorithms, and algorithms for solving the closest pair and convex hull problems.
What it teaches you?
It is a field where every coder showcases their problem-solving skills under various constraints that forces them to think creatively and efficiently.
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
This document is a 14-page exam for the Cambridge International Examinations Computer Science 2210/12 exam from May/June 2015. The exam covers topics such as cookies, security issues, mobile phone touch screen technologies, logic gates, sensors, and computing terms. Candidates are required to answer multiple choice and written response questions testing their knowledge of these topics in computer science.
The brute force algorithm finds the maximum subarray of a given array by calculating the sum of all possible contiguous subarrays. It has a time complexity of O(n^2) as it calculates the sum in two nested loops from index 1 to n. While simple to implement, the brute force approach is only efficient for small problem sizes due to its quadratic time complexity. More optimized algorithms are needed for large arrays.
Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic of how the program will work before developing the actual code for a program.
The document discusses symbolic mathematics, which involves manipulating mathematical expressions using algorithms and software. It provides examples of symbolic mathematics software like Mathematica. Symbolic AI was previously dominant but neural networks can now solve more abstract problems by translating symbols into tree structures. The document also discusses the early natural language program STUDENT, which could solve algebra word problems by translating the input into equations and using algebraic techniques to solve for the unknown values.
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
Complexity analysis - The Big O NotationJawad Khan
Complexity analysis determines how resource requirements like time and memory scale with problem size. Computation time depends on hardware, while complexity analyzes algorithm scaling. Big O notation describes asymptotic function growth. Common complexities are O(1) constant, O(log n) logarithmic, O(n) linear, O(n^2) quadratic. Statements are O(1), if/else max branch, loops run n times, nested loops run n*m times, functions match calling structure, and when statements have undefined time.
The document provides an introduction to web technology. It discusses the following key points in 3 sentences:
The World Wide Web was created by Tim Berners-Lee in 1989 and allows for hyperlinked documents accessed over the Internet using protocols like HTTP. Common web technologies include HTML for content, HTTP for transmission, and protocols like TCP/IP, FTP, SMTP, and Telnet. Developing a website requires planning such as identifying target users, determining objectives and scope, and assembling a team with the necessary skills across areas like design, programming, and quality assurance.
C is a general purpose, high-level programming language that was developed in the 1970s. It is widely used to create operating systems and is the precursor to many popular languages today. C code is compiled into machine-readable code that can be run on different computer systems. C has features like control structures, looping statements, arrays, and macros that make it well-suited for both business and scientific applications. It is commonly used to write operating systems like UNIX, Windows, Mac OS, and Linux.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
This document discusses competitive programming and provides tips for getting started. It defines competitive programming as solving well-defined problems by writing computer programs within time and memory limits. It outlines some merits like fun and practice, and demrits like potential addiction. Tips are provided such as learning a programming language, practicing on online judges, and learning data structures and algorithms. Finally, it lists some online and onsite programming contests as well as university courses relevant to competitive programming.
Competitive programming involves writing programs to solve problems within a limited time period, typically ranging from hours to days, either individually or in teams. These contests test algorithm skills, basic math skills, programming skills, speed, creativity, and debugging abilities. Some prestigious contests include the ACM ICPC, Google Code Jam, TopCoder Open, and Facebook Hacker Cup. Competitive programming improves logical and analytical skills, looks good on a CV, and is fun, while also preparing participants for coding rounds during job interviews at many companies. Sites like Topcoder, SPOJ, Codeforces, Codechef, and Hackerrank allow programmers to practice for competitions.
The document provides an introduction to competitive programming, which involves solving algorithm and data structure problems quickly under time and memory constraints. It discusses what competitive programming tests, how to get started, problem properties, examples, where to practice, tips for practicing, reasons for doing competitive programming, drawbacks, prestigious contests, regular contests, and how KIIT students are performing. The high-level goal of competitive programming is improving programming and problem-solving skills through regular practice and competition. It is recognized by major tech companies and helps build useful everyday skills.
This document introduces competitive programming competitions like ACM ICPC and Google Code Jam. It discusses training platforms such as Codeforces and Kattis that contain practice problems. It categorizes common problem types like ad hoc, complete search, divide and conquer, greedy, and dynamic programming to help determine a problem's difficulty and appropriate solving algorithm. Key problem types are defined, with ad hoc being the easiest and complete search involving brute force searching the entire search space.
Competitive programming involves writing computer programs to solve problems according to provided specifications in a competition format. It tests algorithmic skills, programming skills, speed, creativity, debugging skills, and more. To get started, participants should understand complexity, choose a programming language, learn data structures, and brush up on basics like number theory. Problem properties include absolute correctness checks, background stories to hide the actual problem, and examples of input/output formats. Practice is important and can be done on various online judge systems like CodeChef and Hackerrank.
This document provides tips and strategies for practicing competitive programming to achieve goals like learning new algorithms and programming languages. It recommends practicing regularly both individually and in small teams on online judges. When practicing, choosing problems that provide feedback like explanations is important for learning. The document also provides sample practice schedules, lists competitive programming contests and websites to learn theory, and includes links to additional practice materials.
This document outlines an introduction to competitive programming and problem solving algorithms. It discusses that competitive programming involves writing programs to solve well-known computer science problems quickly. To be successful requires coding quickly, identifying problem types, analyzing time complexity, and extensive practice. The document then covers four basic problem solving paradigms - complete search, divide and conquer, greedy algorithms, and dynamic programming. It provides details on complete search, including that it involves searching the entire solution space and is useful when no clever algorithm exists or the input size is small.
The document discusses the steps of problem solving which include identifying the problem, understanding it, identifying alternative solutions, selecting the best solution, listing instructions to implement it, and evaluating the solution. It also describes types of problems that can be solved algorithmically or heuristically and discusses problem solving with computers including defining solutions, results, programs, and artificial intelligence. Finally, it outlines some difficulties in problem solving like eliminating good alternatives or not following logical steps.
Key Features
•Covers a broad range of algorithms in depth
•Each chapter
–focuses on an algorithm
–discusses its design techniques and areas of application
•Algorithms are written in Pseudocode
Learn More: www.phindia.com
This document provides an overview of algorithms and algorithm analysis. It discusses key concepts like what an algorithm is, different types of algorithms, and the algorithm design and analysis process. Some important problem types covered include sorting, searching, string processing, graph problems, combinatorial problems, geometric problems, and numerical problems. Examples of specific algorithms are given for some of these problem types, like various sorting algorithms, search algorithms, graph traversal algorithms, and algorithms for solving the closest pair and convex hull problems.
What it teaches you?
It is a field where every coder showcases their problem-solving skills under various constraints that forces them to think creatively and efficiently.
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
This document is a 14-page exam for the Cambridge International Examinations Computer Science 2210/12 exam from May/June 2015. The exam covers topics such as cookies, security issues, mobile phone touch screen technologies, logic gates, sensors, and computing terms. Candidates are required to answer multiple choice and written response questions testing their knowledge of these topics in computer science.
The brute force algorithm finds the maximum subarray of a given array by calculating the sum of all possible contiguous subarrays. It has a time complexity of O(n^2) as it calculates the sum in two nested loops from index 1 to n. While simple to implement, the brute force approach is only efficient for small problem sizes due to its quadratic time complexity. More optimized algorithms are needed for large arrays.
Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic of how the program will work before developing the actual code for a program.
The document discusses symbolic mathematics, which involves manipulating mathematical expressions using algorithms and software. It provides examples of symbolic mathematics software like Mathematica. Symbolic AI was previously dominant but neural networks can now solve more abstract problems by translating symbols into tree structures. The document also discusses the early natural language program STUDENT, which could solve algebra word problems by translating the input into equations and using algebraic techniques to solve for the unknown values.
BackTracking Algorithm: Technique and ExamplesFahim Ferdous
This slides gives a strong overview of backtracking algorithm. How it came and general approaches of the techniques. Also some well-known problem and solution of backtracking algorithm.
Complexity analysis - The Big O NotationJawad Khan
Complexity analysis determines how resource requirements like time and memory scale with problem size. Computation time depends on hardware, while complexity analyzes algorithm scaling. Big O notation describes asymptotic function growth. Common complexities are O(1) constant, O(log n) logarithmic, O(n) linear, O(n^2) quadratic. Statements are O(1), if/else max branch, loops run n times, nested loops run n*m times, functions match calling structure, and when statements have undefined time.
The document provides an introduction to web technology. It discusses the following key points in 3 sentences:
The World Wide Web was created by Tim Berners-Lee in 1989 and allows for hyperlinked documents accessed over the Internet using protocols like HTTP. Common web technologies include HTML for content, HTTP for transmission, and protocols like TCP/IP, FTP, SMTP, and Telnet. Developing a website requires planning such as identifying target users, determining objectives and scope, and assembling a team with the necessary skills across areas like design, programming, and quality assurance.
C is a general purpose, high-level programming language that was developed in the 1970s. It is widely used to create operating systems and is the precursor to many popular languages today. C code is compiled into machine-readable code that can be run on different computer systems. C has features like control structures, looping statements, arrays, and macros that make it well-suited for both business and scientific applications. It is commonly used to write operating systems like UNIX, Windows, Mac OS, and Linux.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
Jeremiah Yancy - Objectives for Software design and testingJeremiah Yancy
Mr. Jeremiah Yancy possesses extensive experience and industry knowledge in his field and has managed multiple startups from inception to Initial Public Offering.
The document discusses algorithms and flowcharts. It defines an algorithm as a finite set of steps to solve a problem and notes that algorithms can be expressed in various ways, including pseudocode and flowcharts. Pseudocode uses a language similar to programming but without specific syntax, making it readable by programmers familiar with different languages. A flowchart provides a graphical representation of an algorithm's logical flow. The document provides examples of algorithms expressed in pseudocode and represented through flowcharts, such as finding the average of two numbers and calculating the largest of several inputs. It also discusses common flowchart structures like sequence, selection, and iteration.
This document provides an introduction to computer programming skills for civil engineering students. It discusses why engineers need programming abilities and how computer modeling is important for engineering problems. The lecture covers algorithms, finite element analysis, and examples of programming applications in areas like fluid dynamics, geotechnics, and groundwater flow. Limitations of models and the importance of verification and validation are also addressed. An example algorithm for extracting annual maximum river discharge data from daily records is presented to illustrate programming concepts.
Here are the algorithms in pseudocode:
1. Find largest number of unknown set:
- Read first number and assign to largest
- Read next number
- If number is greater than largest, assign it to largest
- Repeat step 2 until no more numbers
- Output largest
2. Find average of numbers:
- Read number of elements n
- Initialize sum = 0
- Initialize i = 0
- Repeat while i < n
- Read element at array[i]
- Add element to sum
- Increment i
- Calculate average = sum / n
- Output average
Here are the algorithms in pseudocode:
1. Find largest number of unknown set:
- Read first number and assign to largest
- Read next number
- If number is greater than largest, assign it to largest
- Repeat step 2 until no more numbers
- Output largest
2. Find average of numbers:
- Read number of elements n
- Initialize sum = 0
- Initialize i = 0
- Repeat while i < n
- Read element at array[i]
- Add element to sum
- Increment i
- Calculate average = sum / n
- Output average
This document contains solutions to a midterm exam for a software engineering course. It provides answers to 17 questions about topics like version control, testing, metrics, refactoring, and object-oriented design. Code examples are included and analyzed for code smells. The document aims to test students' understanding of key software engineering principles and best practices covered in the course.
Here are some content ideas for your Panaflex sign that are both funny and professional:
Funny Options
1. "Warning: Workshop Ahead - Brain Cells at Work"
2. "Get Ready to Geek Out: [Workshop Name]"
3. "Unleash Your Inner [Topic] Guru"
Professional Options
1. "Expert Insights: [Workshop Name]"
2. "Elevate Your Skills: [Workshop Name]"
3. "Learn from the Best: [Workshop Name]"
Combining Both
1. "Serious Learning, Minimal Seriousness: [Workshop Name]"
2. "The [Topic] Extravaganza: Learn, Laugh, Repeat"
3. "Where Passion Meets Expertise: [Workshop Name]"
Feel free to pick the one that suits your workshop's tone and style!
How to complement TDD with static analysisPVS-Studio
TDD is one of the most popular software development techniques. I like this technology in general, and we employ it to some extent. The main thing is not to run to extremes when using it. One shouldn't fully rely on it alone forgetting other methods of software quality enhancement. In this article, I will show you how the static code analysis methodology can be used by programmers using TDD to additionally secure themselves against errors.
The document discusses solving programming challenges on Coursera. It introduces two simple challenges: computing the sum of two digits and finding the maximum pairwise product in an array. For each challenge, it provides the problem statement, sample inputs/outputs, and time/memory limits. It then walks through designing algorithms, implementing solutions in C++, Java and Python, testing for errors, and submitting to Coursera for grading. For the pairwise product, a faster O(n) algorithm is presented after the initial O(n^2) solution fails due to timeout. Debugging and improving the fast algorithm is also demonstrated.
The document summarizes a lecture on algorithms and algorithm design from a course on algorithms. It introduces the concept of algorithms and what will be covered in the course, which includes analyzing algorithms, sorting techniques as a case study, and various algorithmic problems and solutions. It also discusses implementation issues and keeping mathematical analysis independent of specific programming languages or machines. As an example, it analyzes the brute force algorithm for solving the 2D maxima problem in pseudo-code and calculates its running time.
The document provides information on problem solving techniques using algorithms and pseudocode. It discusses key terms, the software development method of problem solving, and basic algorithm control structures like sequence, selection and repetition. It also covers developing algorithms, using pseudocode to represent algorithms, variables, control structures like if-then-else statements and looping constructs. Examples are provided to demonstrate how to represent problems algorithmically using pseudocode and how to develop and test algorithms using a desk checking table.
The document provides an overview of algorithms and data structures. It discusses what an algorithm is, different algorithm design strategies, approaches to analyzing algorithms, and key computational problems like sorting, searching, and graph problems. It also covers fundamental data structures like lists, stacks, queues, and trees. Major sections include introductions to algorithms, analysis of algorithms, problem solving techniques, and linear and non-linear data structures.
The document introduces algorithms and their role in computing. It defines computational problems as relationships between inputs and outputs. Algorithms are tools used to solve well-specified computational problems and must be correct and efficient. Examples of algorithms are given for common problems like determining if a number is prime or sorting a list alphabetically. Key aspects of algorithms like variables, instructions, selection, repetition and documentation are outlined. Examples are provided of analyzing algorithm efficiency based on runtime. Pseudo-code conventions for writing algorithms are also presented.
CP4151 ADSA unit1 Advanced Data Structures and AlgorithmsSheba41
The document introduces algorithms and their role in computing. It defines computational problems as relationships between inputs and outputs. Algorithms are tools used to solve well-specified computational problems and must be correct and efficient. Examples of algorithms are provided, along with their components like variables, instructions, selections, and repetitions. The document also discusses algorithm efficiency and provides examples comparing the efficiencies of different sorting algorithms. Pseudo-code conventions for writing algorithms are also outlined.
This document discusses complexity analysis of algorithms. It defines an algorithm and lists properties like being correct, unambiguous, terminating, and simple. It describes common algorithm design techniques like divide and conquer, dynamic programming, greedy method, and backtracking. It compares divide and conquer with dynamic programming. It discusses algorithm analysis in terms of time and space complexity to predict resource usage and compare algorithms. It introduces asymptotic notations like Big-O notation to describe upper bounds of algorithms as input size increases.
Concept of Problem Solving, Introduction to Algorithms, Characteristics of Algorithms, Introduction to Data Structure, Data Structure Classification (Linear and Non-linear, Static and Dynamic, Persistent and Ephemeral data structures), Time complexity and Space complexity, Asymptotic Notation - The Big-O, Omega and Theta notation, Algorithmic upper bounds, lower bounds, Best, Worst and Average case analysis of an Algorithm, Abstract Data Types (ADT)
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Glad to be one of only 14 members inside Kuwait to hold this credential.
Please check the members inside kuwait from this link:
https://www.rics.org/networking/find-a-member.html?firstname=&lastname=&town=&country=Kuwait&member_grade=(AssocRICS)&expert_witness=&accrediation=&page=1
Analysis of reinforced concrete deep beam is based on simplified approximate method due to the complexity of the exact analysis. The complexity is due to a number of parameters affecting its response. To evaluate some of this parameters, finite element study of the structural behavior of the reinforced self-compacting concrete deep beam was carried out using Abaqus finite element modeling tool. The model was validated against experimental data from the literature. The parametric effects of varied concrete compressive strength, vertical web reinforcement ratio and horizontal web reinforcement ratio on the beam were tested on eight (8) different specimens under four points loads. The results of the validation work showed good agreement with the experimental studies. The parametric study revealed that the concrete compressive strength most significantly influenced the specimens’ response with the average of 41.1% and 49 % increment in the diagonal cracking and ultimate load respectively due to doubling of concrete compressive strength. Although the increase in horizontal web reinforcement ratio from 0.31 % to 0.63 % lead to average of 6.24 % increment on the diagonal cracking load, it does not influence the ultimate strength and the load-deflection response of the beams. Similar variation in vertical web reinforcement ratio leads to an average of 2.4 % and 15 % increment in cracking and ultimate load respectively with no appreciable effect on the load-deflection response.
Fluid mechanics is the branch of physics concerned with the mechanics of fluids (liquids, gases, and plasmas) and the forces on them. Originally applied to water (hydromechanics), it found applications in a wide range of disciplines, including mechanical, aerospace, civil, chemical, and biomedical engineering, as well as geophysics, oceanography, meteorology, astrophysics, and biology.
It can be divided into fluid statics, the study of various fluids at rest, and fluid dynamics.
Fluid statics, also known as hydrostatics, is the study of fluids at rest, specifically when there's no relative motion between fluid particles. It focuses on the conditions under which fluids are in stable equilibrium and doesn't involve fluid motion.
Fluid kinematics is the branch of fluid mechanics that focuses on describing and analyzing the motion of fluids, such as liquids and gases, without considering the forces that cause the motion. It deals with the geometrical and temporal aspects of fluid flow, including velocity and acceleration. Fluid dynamics, on the other hand, considers the forces acting on the fluid.
Fluid dynamics is the study of the effect of forces on fluid motion. It is a branch of continuum mechanics, a subject which models matter without using the information that it is made out of atoms; that is, it models matter from a macroscopic viewpoint rather than from microscopic.
Fluid mechanics, especially fluid dynamics, is an active field of research, typically mathematically complex. Many problems are partly or wholly unsolved and are best addressed by numerical methods, typically using computers. A modern discipline, called computational fluid dynamics (CFD), is devoted to this approach. Particle image velocimetry, an experimental method for visualizing and analyzing fluid flow, also takes advantage of the highly visual nature of fluid flow.
Fundamentally, every fluid mechanical system is assumed to obey the basic laws :
Conservation of mass
Conservation of energy
Conservation of momentum
The continuum assumption
For example, the assumption that mass is conserved means that for any fixed control volume (for example, a spherical volume)—enclosed by a control surface—the rate of change of the mass contained in that volume is equal to the rate at which mass is passing through the surface from outside to inside, minus the rate at which mass is passing from inside to outside. This can be expressed as an equation in integral form over the control volume.
The continuum assumption is an idealization of continuum mechanics under which fluids can be treated as continuous, even though, on a microscopic scale, they are composed of molecules. Under the continuum assumption, macroscopic (observed/measurable) properties such as density, pressure, temperature, and bulk velocity are taken to be well-defined at "infinitesimal" volume elements—small in comparison to the characteristic length scale of the system, but large in comparison to molecular length scale
Lidar for Autonomous Driving, LiDAR Mapping for Driverless Cars.pptxRishavKumar530754
LiDAR-Based System for Autonomous Cars
Autonomous Driving with LiDAR Tech
LiDAR Integration in Self-Driving Cars
Self-Driving Vehicles Using LiDAR
LiDAR Mapping for Driverless Cars
π0.5: a Vision-Language-Action Model with Open-World GeneralizationNABLAS株式会社
今回の資料「Transfusion / π0 / π0.5」は、画像・言語・アクションを統合するロボット基盤モデルについて紹介しています。
拡散×自己回帰を融合したTransformerをベースに、π0.5ではオープンワールドでの推論・計画も可能に。
This presentation introduces robot foundation models that integrate vision, language, and action.
Built on a Transformer combining diffusion and autoregression, π0.5 enables reasoning and planning in open-world settings.
Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.
International Journal of Distributed and Parallel systems (IJDPS)samueljackson3773
The growth of Internet and other web technologies requires the development of new
algorithms and architectures for parallel and distributed computing. International journal of
Distributed and parallel systems is a bimonthly open access peer-reviewed journal aims to
publish high quality scientific papers arising from original research and development from
the international community in the areas of parallel and distributed systems. IJDPS serves
as a platform for engineers and researchers to present new ideas and system technology,
with an interactive and friendly, but strongly professional atmosphere.
ADVXAI IN MALWARE ANALYSIS FRAMEWORK: BALANCING EXPLAINABILITY WITH SECURITYijscai
With the increased use of Artificial Intelligence (AI) in malware analysis there is also an increased need to
understand the decisions models make when identifying malicious artifacts. Explainable AI (XAI) becomes
the answer to interpreting the decision-making process that AI malware analysis models use to determine
malicious benign samples to gain trust that in a production environment, the system is able to catch
malware. With any cyber innovation brings a new set of challenges and literature soon came out about XAI
as a new attack vector. Adversarial XAI (AdvXAI) is a relatively new concept but with AI applications in
many sectors, it is crucial to quickly respond to the attack surface that it creates. This paper seeks to
conceptualize a theoretical framework focused on addressing AdvXAI in malware analysis in an effort to
balance explainability with security. Following this framework, designing a machine with an AI malware
detection and analysis model will ensure that it can effectively analyze malware, explain how it came to its
decision, and be built securely to avoid adversarial attacks and manipulations. The framework focuses on
choosing malware datasets to train the model, choosing the AI model, choosing an XAI technique,
implementing AdvXAI defensive measures, and continually evaluating the model. This framework will
significantly contribute to automated malware detection and XAI efforts allowing for secure systems that
are resilient to adversarial attacks.
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2. What is it?
Writing programs to solve problems in a
contest environment.
One has a limited time to solve the
problems. Ranging from a few hours to
a few days.
The solution should be efficient in terms
of execution time, memory used or in
some cases even the length of code.
3. What’s new?
The code will be executed on an Online Judge (OJ).
Which means you should write code which conforms
to the specific format of input and gives the output in
the required format.
With tough constraints on execution time, it is crucial
that you use faster algorithms to avoid TLE(s) on
your test cases.
This requires a good knowledge of time complexities.
4. For example:
Say you want to write a function to
calculate x^4.
We can simply solve this with:
z=x*x*x*x;
return z; //required 3 multiplication instructions.
but a better solution will be:
z=x*x;
z=z*z;
return z; //required 2 multiplication instructions.
5. Contd…
In the previous example, one instruction did not seem to make a
difference.
But if you had to solve the problem for a few million test cases
(not uncommon in competitive coding), then you saved yourself
from a few million instructions and thereby reduced your
execution time correspondingly.
Using such clever techniques is just a small part of competitive
programming
Thus devising new and better algorithms is crucial for
competitive programming.
6. Understanding the input format
Input on OJ’s is very straightforward. You don’t need
to prompt for it like we usually do.
The first line of input is usually the number of test
cases, followed by the test cases.
7. For example:
If the input format states something like: “The first line
of input gives the no. of test cases with each test
case having 2 numbers n and m.”, then it would look
something like this:
3
2 5
6 8
3 9
8. And its code in C would be:
int a,b,c;
scanf("%d",&a);
while(a>0)
{
a--;
scanf("%d %d",&b,&c);
}
9. Getting started with competitive
coding:
Solving the problems in a coding contest is not easy.
Many online judges have problems of similar difficulty
and similar environment. A list of such judges are:-
TopCoder
SPOJ
Codechef
HackerEarth
HackerRank
Codeforces
And many more…
10. A few tips to perform better:
Have a thorough grasp on the basic
concepts first.
Develop a good understanding of data
structures like lists, trees and graphs.
Try to learn as many new and efficient
algorithms and implement them when
required.
11. Some prestigious contests:
ACM – ICPC
Google Code Jam
Topcoder Open
Facebook Hacker Cup (Recent)
Students who perform well in these contests are
some of the finest brains in the world and
instantly recognized by top institutions
(companies, academia).
12. What’s next?
Sign up on any of the plethora of online judges and
start solving their practice problems.
You will run into a lot of errors in the beginning.
Also, participate in other online contests arranged by
sites like codechef and SPOJ.
Many companies use these contests to hire students
for jobs and internships as well.
Welcome to the world of Competitive
programming!