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By analysing the explanation of the classical heapsort algorithm via the method of levels of abstraction mainly due to Floridi, we give a concrete and precise example of how to deal with algorithmic knowledge. To do so, we introduce a concept already implicit in the method, the ‘gradient of explanations’. Analogously to the gradient of abstractions, a gradient of explanations is a sequence of discrete levels of explanation each one refining the previous, varying formalisation, and thus providing progressive evidence for hidden information. Because of this sequential and coherent uncovering of the information that explains a level of abstraction—the heapsort algorithm in our guiding example—the notion of gradient of explanations allows to precisely classify purposes in writing software according to the informal criterion of ‘depth’, and to give a precise meaning to the notion of ‘concreteness’.

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C faqs interview questions placement paper 2013

c language interview questions www.latestoffcampus.com latest placement papers c faqs mnc placement papers for freshers

Rejuvenate Pointcut: A Tool for Pointcut Expression Recovery in Evolving Aspe...

Rejuvenate Pointcut: A Tool for Pointcut Expression Recovery in Evolving Aspe...Raffi Khatchadourian

Invited tool demonstration at the 8th International Conference on Aspect-Oriented Software Development (AOSD '09), Charlottesville, VA, USA, March 2-6, 2009.Function

This document discusses functions in programming. It begins by explaining the importance of dividing programs into smaller, more manageable parts called functions. Functions take inputs, process them, and may produce outputs. The document then discusses features of functions like modular programming, reusability of code, and manageability. It defines key terms like function name, arguments, and return values. It provides examples of defining and calling functions, and different types of functions based on parameters and return types.

1 puc programming using c++

This document provides an overview of object-oriented concepts and C++ programming. It discusses procedural programming, structured programming, and object-oriented programming approaches. It describes the key characteristics of OOP including modularity, abstraction, encapsulation, inheritance, polymorphism, and dynamic binding. The document also discusses the history and characteristics of the C++ programming language, and covers C++ tokens, identifiers, keywords, and constants.

Looping

The document discusses loops and iterations in Python programming. It introduces while and for loops. While loops repeat a block of code until a test condition is false. For loops are generally used to iterate over lists, tuples, strings, etc. The document provides examples of using while loops to calculate factorial, exponentiation, and the terms of arithmetic and geometric progressions. It also discusses using loops to generate patterns by nesting for loops, with the outer loop iterating over rows and the inner loop producing a variable number of stars in each row based on the row number.

Python Objects

The document discusses Python objects and data types. It covers strings, numbers, lists, tuples, and other basic data types in Python. Strings can be indexed and sliced, and operations like concatenation and length calculation can be performed on strings. Lists and tuples are sequence data types that allow ordered collections of elements. Numbers support arithmetic operators and can include integers, floats, complexes, decimals, and rationals.

A bypass of cohen's impossibility result

This document discusses Cohen's impossibility result on virus detection and introduces an alternative approach called "risk assessment of security hazards". It summarizes the following:
1) Cohen's impossibility result showed that a uniform tool cannot exist to detect viruses in all programs. This is demonstrated using a "contradictory virus" program.
2) The document defines a notion of "security hazard risk assessment" to determine if a program exhibits security hazards, avoiding the issues in Cohen's result. However, decidability is lost if divergence is considered a security hazard.
3) Several modifications are proposed to the idea of forecasting security hazards to bypass Cohen's result, such as allowing an additional "meaningless"

A New Approach to Optimal Code Formatting

This document describes a new algorithm for optimally formatting source code. It uses dynamic programming to choose between alternative layouts of code specified by combinators. The layouts are scored based on a cost function that accounts for line breaks and characters beyond a right margin. By considering the interaction of layout choices across a whole program, the algorithm can pick the globally optimal formatting.

C faqs interview questions placement paper 2013

c language interview questions www.latestoffcampus.com latest placement papers c faqs mnc placement papers for freshers

Rejuvenate Pointcut: A Tool for Pointcut Expression Recovery in Evolving Aspe...

Rejuvenate Pointcut: A Tool for Pointcut Expression Recovery in Evolving Aspe...Raffi Khatchadourian

Invited tool demonstration at the 8th International Conference on Aspect-Oriented Software Development (AOSD '09), Charlottesville, VA, USA, March 2-6, 2009.Function

This document discusses functions in programming. It begins by explaining the importance of dividing programs into smaller, more manageable parts called functions. Functions take inputs, process them, and may produce outputs. The document then discusses features of functions like modular programming, reusability of code, and manageability. It defines key terms like function name, arguments, and return values. It provides examples of defining and calling functions, and different types of functions based on parameters and return types.

1 puc programming using c++

This document provides an overview of object-oriented concepts and C++ programming. It discusses procedural programming, structured programming, and object-oriented programming approaches. It describes the key characteristics of OOP including modularity, abstraction, encapsulation, inheritance, polymorphism, and dynamic binding. The document also discusses the history and characteristics of the C++ programming language, and covers C++ tokens, identifiers, keywords, and constants.

Looping

The document discusses loops and iterations in Python programming. It introduces while and for loops. While loops repeat a block of code until a test condition is false. For loops are generally used to iterate over lists, tuples, strings, etc. The document provides examples of using while loops to calculate factorial, exponentiation, and the terms of arithmetic and geometric progressions. It also discusses using loops to generate patterns by nesting for loops, with the outer loop iterating over rows and the inner loop producing a variable number of stars in each row based on the row number.

Python Objects

The document discusses Python objects and data types. It covers strings, numbers, lists, tuples, and other basic data types in Python. Strings can be indexed and sliced, and operations like concatenation and length calculation can be performed on strings. Lists and tuples are sequence data types that allow ordered collections of elements. Numbers support arithmetic operators and can include integers, floats, complexes, decimals, and rationals.

A bypass of cohen's impossibility result

This document discusses Cohen's impossibility result on virus detection and introduces an alternative approach called "risk assessment of security hazards". It summarizes the following:
1) Cohen's impossibility result showed that a uniform tool cannot exist to detect viruses in all programs. This is demonstrated using a "contradictory virus" program.
2) The document defines a notion of "security hazard risk assessment" to determine if a program exhibits security hazards, avoiding the issues in Cohen's result. However, decidability is lost if divergence is considered a security hazard.
3) Several modifications are proposed to the idea of forecasting security hazards to bypass Cohen's result, such as allowing an additional "meaningless"

A New Approach to Optimal Code Formatting

This document describes a new algorithm for optimally formatting source code. It uses dynamic programming to choose between alternative layouts of code specified by combinators. The layouts are scored based on a cost function that accounts for line breaks and characters beyond a right margin. By considering the interaction of layout choices across a whole program, the algorithm can pick the globally optimal formatting.

OOP in C++

This document discusses object-oriented programming in C++. It covers several topics related to OOP in C++ including classes, constructors, destructors, inheritance, polymorphism, and templates. The document consists of lecture slides that define key concepts and provide examples to illustrate how various OOP features work in C++.

SYNTAX AND SEMANTICS FOR CINNAMON PROGRAMMING

This document introduces and defines the syntax and semantics of "cinnamons", a new computation model intended to form the theoretical foundation for Control Network Programming (CNP). CNP is a visual, declarative programming approach that combines graphs and primitives. Cinnamons are the simplest possible version of CNP programs, defined rigorously with minimal features for formal study. The syntax defines cinnamons as a set of graphs called subnets, with arrows labeled as primitives or subnet calls. Semantics specify how cinnamons compute by traversing graphs and executing primitives. Cinnamons are shown to be Turing complete by equivalence to WHILE programs.

Towards Practical Homomorphic Encryption with Efficient Public key Generation

With the advent of cloud computing several security
and privacy challenges are put forth. To deal with many of
these privacy issues, ‘processing the encrypted data’ has been
identified as a potential solution, which requires a Fully
Homomorphic Encryption (FHE) scheme. After the
breakthrough work of Craig Gentry in devising an FHE,
several new homomorphic encryption schemes and variants
have been proposed. However, all those theoretically feasible
schemes are not viable for practical deployment due to their
high computational complexities. In this work, a variant of
the DGHV’s integer based Somewhat Homomorphic
Encryption (SHE) scheme with an efficient public key
generation method is presented. The complexities of various
algorithms involved in the scheme are significantly low. The
semantic security of the variant is based on the two-element
Partial Approximate Greatest Common Divisors (PAGCD)
problem. Experimental results prove that the proposed scheme
is very much efficient than any other integer based SHE
scheme existing today and hence practical.

C programming notes

C programming is a general-purpose language developed in the 1970s to write operating systems like UNIX. It is one of the most widely used languages, particularly for systems programming. Some key facts: C was created to develop UNIX and is still widely used for operating systems, compilers, databases and other modern programs. It has various data types like integers, floats, characters, arrays and structures. Variables are defined with a data type and can be initialized. C code is written in files with a .c extension and preprocessed before compilation.

C programming | Class 8 | III Term

The C programming language was developed in the 1970s and remains widely used today. It is an efficient, flexible language suitable for system software like operating systems as well as application programs. C has basic elements like keywords, constants, variables, and operators. It defines fundamental data types like integers, floating-point numbers, and characters. C supports a variety of operators for arithmetic, relational, logical, and assignment operations. Variables and constants are declared and defined using rules for naming identifiers and representing numeric, character, string, and escape sequence values.

UNIT III POINTERS IN C

This power point is used for B.Sc. Second year Students of Computer Science .This covers idea about how to pointers used in Advance C programming

Introduction Of C++

This PPT covers a small introduction of C++.
1. OOPS Feature.
2. Pointers
3. References
4. Class, Constructor(s)

Pointers and call by value, reference, address in C

The document discusses various C programming concepts including:
1. It provides an example program to calculate the area of a circle using #define and shows how to conditionally compile code blocks using #if, #elif, #else and #endif.
2. It explains that the C preprocessor transforms code before compilation by allowing users to define macros and includes several built-in preprocessor directives like #include, #define, and #if.
3. It discusses static and dynamic memory allocation in C, listing functions like malloc(), calloc(), free(), and realloc() for dynamic allocation and provides examples of their use.

Getting started with c++

This document provides an introduction to C++ programming, covering key concepts like characters, tokens, keywords, identifiers, literals, operators, I/O streams, variables, comments, and common errors. It explains that Bjarne Stroustrup extended C to create C++, adding object-oriented features from Simula. The main components discussed are the building blocks of any C++ program - characters, tokens, data types, and basic input/output operations.

2010 bristol q1_formal-property-checkers

This document presents a methodology that uses a formal property checker to analyze uncovered branches from module-level simulation in order to more efficiently and effectively achieve coverage closure. The methodology generates simple formal properties for each uncovered branch based on temporal induction principles. If the properties pass, the branch can be proven unreachable and filtered from the coverage report. This allows automation of the analysis process and was found to identify a greater percentage of unreachable code compared to built-in formal tools. The methodology was applied successfully to coverage closure for the Infineon TriCore 1.6 microcontroller.

C Programming

The document provides an overview of the C programming language. It discusses the history and creation of C, key features of C like portability and efficiency, C program structure, data types in C including integer, floating point, and character types, and variable naming conventions. It also covers basic C programs and examples to illustrate the use of data types, operators, and input/output functions in C.

Report on c and c++

The document provides an overview of the C standard library. It includes a table listing common C standard library header files and briefly describing their purpose, such as <stdio.h> for input/output functions and <stdlib.h> for memory allocation and process control. The C standard library contains functions for tasks like string manipulation, mathematics, random numbers, memory management and more. It provides a standard library that is common across C implementations.

26 Jo P Feb 09

This document discusses an integer overflow bug that can occur when calculating the midpoint or average of two integers in binary search and merge sort algorithms in Java. Specifically, the line of code "int mid = (low + high) / 2" can overflow for large values of low and high. The document proposes two solutions to avoid this overflow: 1) Using the unsigned right shift operator ">>" to calculate the midpoint as "int mid = (low + high) >> 1", and 2) Using the bitwise operators "AND" and "XOR" to calculate the average as "(i & j) + (i ^ j) / 2". It also briefly mentions another solution seen in classic C programming books of using subtraction instead

Problem solving

This document discusses algorithms and problem solving. It covers exploring the algorithmic approach to problem solving, learning about algorithm development, and becoming aware of the problem solving process. Key points include:
- An algorithm is a step-by-step process for solving a problem in a finite number of steps.
- The problem solving process involves analyzing the problem, designing an algorithm to solve it, implementing the algorithm in code, and maintaining the program if needed.
- Object-oriented design methodologies like brainstorming, filtering, scenarios, and responsibility algorithms can help discover algorithms to solve problems.

C language (Collected By Dushmanta)

This document provides an overview of the C programming language. It discusses the origins and development of C from the B programming language. Some key features and benefits of C discussed include its low-level capabilities as a second-generation language, structured programming approach, use of functions and libraries, and support for pointers, memory allocation, recursion, and bit manipulation. The document also covers C's fundamental and derived data types, defining variables and strings, and the structure of C functions including single-level functions.

Lecture 11 semantic analysis 2

This document discusses inherited and synthesized attributes in semantic analysis using syntax-directed translation (SDT). It covers:
- Synthesized attributes are defined by semantic rules associated with productions and rely only on child nodes, while inherited attributes rely on parent/sibling nodes.
- Terminals can have synthesized attributes from lexing but not inherited attributes. Nonterminals can have both.
- Annotated parse trees show attribute values, while dependency graphs determine evaluation order.
- S-attributed definitions rely only on synthesized attributes and evaluate bottom-up. L-attributed definitions restrict inherited attributes to avoid cycles.
- SDTs can construct syntax trees during parsing to decouple parsing from translation

BREAKING MIGNOTTE’S SEQUENCE BASED SECRET SHARING SCHEME USING SMT SOLVER

This document summarizes a research paper that proposes a new method for reconstructing secrets from secret sharing schemes using an SMT solver with one less than the threshold number of shares. It introduces Mignotte's sequence-based secret sharing, describes how shares are generated and distributed, and how an SMT solver is used to check the satisfiability of logical formulas representing constraints on the shares to reconstruct the secret with one less than the threshold. The method is demonstrated with an example and results showing the secret is successfully reconstructed from two shares out of a threshold of three.

Frequent english words

This document defines and provides examples of 50 common English words:
- It defines words like synonym, antonym, and provides example words like queued, thrashed, and turmoil.
- It also defines words like franchise, constituent, exhorted, and negotiable that are used in business and politics.
- Finally, it defines and provides examples of words used in different contexts like personal, haunts, pout, and cleavage.

Compututer Graphics - Color Modeling And Rendering

This document discusses various color models and rendering techniques. It describes additive and subtractive color models, including the RGB, CMY, and HSV color models. It also discusses illumination models, including ambient light, diffuse reflection, and specular reflection. Common rendering techniques like Gouraud shading and Phong shading are summarized, which interpolate lighting across triangle surfaces. Ray tracing is also briefly explained as a technique for simulating light paths.

10CSL67 CG LAB PROGRAM 5

The document describes the Cohen-Suderland line clipping algorithm. It divides a 2D space into 9 regions using a 4-bit code to identify each region. The algorithm clips a line segment against the clipping window by computing the intersection points with boundary lines if the line extends outside the window, repeatedly clipping portions until the line is fully within the window. Pseudocode and an example are provided to demonstrate the step-by-step clipping of a sample line.

Region filling and object removal by exemplar based image inpainting

To get rid of (an) object(s) at a picture or to restore a picture from scratches or holes, Criminisi at el. suggested an algorithm which is combied "texture synthesis" and "inpainting". I made the slide to present at a class to introduce this algorithm. I refered a slide http://bit.ly/1Ng7DNt. I wish this slide may help you to understand the algorithm. Thank you.

Area filling algo

The above ppt discuss about :-
Area fill algorithm :-
Scan line polygon fill algorithm
Boundary fill algorithm
Flood fill algorithm
2-D Viewing

OOP in C++

This document discusses object-oriented programming in C++. It covers several topics related to OOP in C++ including classes, constructors, destructors, inheritance, polymorphism, and templates. The document consists of lecture slides that define key concepts and provide examples to illustrate how various OOP features work in C++.

SYNTAX AND SEMANTICS FOR CINNAMON PROGRAMMING

This document introduces and defines the syntax and semantics of "cinnamons", a new computation model intended to form the theoretical foundation for Control Network Programming (CNP). CNP is a visual, declarative programming approach that combines graphs and primitives. Cinnamons are the simplest possible version of CNP programs, defined rigorously with minimal features for formal study. The syntax defines cinnamons as a set of graphs called subnets, with arrows labeled as primitives or subnet calls. Semantics specify how cinnamons compute by traversing graphs and executing primitives. Cinnamons are shown to be Turing complete by equivalence to WHILE programs.

Towards Practical Homomorphic Encryption with Efficient Public key Generation

With the advent of cloud computing several security
and privacy challenges are put forth. To deal with many of
these privacy issues, ‘processing the encrypted data’ has been
identified as a potential solution, which requires a Fully
Homomorphic Encryption (FHE) scheme. After the
breakthrough work of Craig Gentry in devising an FHE,
several new homomorphic encryption schemes and variants
have been proposed. However, all those theoretically feasible
schemes are not viable for practical deployment due to their
high computational complexities. In this work, a variant of
the DGHV’s integer based Somewhat Homomorphic
Encryption (SHE) scheme with an efficient public key
generation method is presented. The complexities of various
algorithms involved in the scheme are significantly low. The
semantic security of the variant is based on the two-element
Partial Approximate Greatest Common Divisors (PAGCD)
problem. Experimental results prove that the proposed scheme
is very much efficient than any other integer based SHE
scheme existing today and hence practical.

C programming notes

C programming is a general-purpose language developed in the 1970s to write operating systems like UNIX. It is one of the most widely used languages, particularly for systems programming. Some key facts: C was created to develop UNIX and is still widely used for operating systems, compilers, databases and other modern programs. It has various data types like integers, floats, characters, arrays and structures. Variables are defined with a data type and can be initialized. C code is written in files with a .c extension and preprocessed before compilation.

C programming | Class 8 | III Term

The C programming language was developed in the 1970s and remains widely used today. It is an efficient, flexible language suitable for system software like operating systems as well as application programs. C has basic elements like keywords, constants, variables, and operators. It defines fundamental data types like integers, floating-point numbers, and characters. C supports a variety of operators for arithmetic, relational, logical, and assignment operations. Variables and constants are declared and defined using rules for naming identifiers and representing numeric, character, string, and escape sequence values.

UNIT III POINTERS IN C

This power point is used for B.Sc. Second year Students of Computer Science .This covers idea about how to pointers used in Advance C programming

Introduction Of C++

This PPT covers a small introduction of C++.
1. OOPS Feature.
2. Pointers
3. References
4. Class, Constructor(s)

Pointers and call by value, reference, address in C

The document discusses various C programming concepts including:
1. It provides an example program to calculate the area of a circle using #define and shows how to conditionally compile code blocks using #if, #elif, #else and #endif.
2. It explains that the C preprocessor transforms code before compilation by allowing users to define macros and includes several built-in preprocessor directives like #include, #define, and #if.
3. It discusses static and dynamic memory allocation in C, listing functions like malloc(), calloc(), free(), and realloc() for dynamic allocation and provides examples of their use.

Getting started with c++

This document provides an introduction to C++ programming, covering key concepts like characters, tokens, keywords, identifiers, literals, operators, I/O streams, variables, comments, and common errors. It explains that Bjarne Stroustrup extended C to create C++, adding object-oriented features from Simula. The main components discussed are the building blocks of any C++ program - characters, tokens, data types, and basic input/output operations.

2010 bristol q1_formal-property-checkers

This document presents a methodology that uses a formal property checker to analyze uncovered branches from module-level simulation in order to more efficiently and effectively achieve coverage closure. The methodology generates simple formal properties for each uncovered branch based on temporal induction principles. If the properties pass, the branch can be proven unreachable and filtered from the coverage report. This allows automation of the analysis process and was found to identify a greater percentage of unreachable code compared to built-in formal tools. The methodology was applied successfully to coverage closure for the Infineon TriCore 1.6 microcontroller.

C Programming

The document provides an overview of the C programming language. It discusses the history and creation of C, key features of C like portability and efficiency, C program structure, data types in C including integer, floating point, and character types, and variable naming conventions. It also covers basic C programs and examples to illustrate the use of data types, operators, and input/output functions in C.

Report on c and c++

The document provides an overview of the C standard library. It includes a table listing common C standard library header files and briefly describing their purpose, such as <stdio.h> for input/output functions and <stdlib.h> for memory allocation and process control. The C standard library contains functions for tasks like string manipulation, mathematics, random numbers, memory management and more. It provides a standard library that is common across C implementations.

26 Jo P Feb 09

This document discusses an integer overflow bug that can occur when calculating the midpoint or average of two integers in binary search and merge sort algorithms in Java. Specifically, the line of code "int mid = (low + high) / 2" can overflow for large values of low and high. The document proposes two solutions to avoid this overflow: 1) Using the unsigned right shift operator ">>" to calculate the midpoint as "int mid = (low + high) >> 1", and 2) Using the bitwise operators "AND" and "XOR" to calculate the average as "(i & j) + (i ^ j) / 2". It also briefly mentions another solution seen in classic C programming books of using subtraction instead

Problem solving

This document discusses algorithms and problem solving. It covers exploring the algorithmic approach to problem solving, learning about algorithm development, and becoming aware of the problem solving process. Key points include:
- An algorithm is a step-by-step process for solving a problem in a finite number of steps.
- The problem solving process involves analyzing the problem, designing an algorithm to solve it, implementing the algorithm in code, and maintaining the program if needed.
- Object-oriented design methodologies like brainstorming, filtering, scenarios, and responsibility algorithms can help discover algorithms to solve problems.

C language (Collected By Dushmanta)

This document provides an overview of the C programming language. It discusses the origins and development of C from the B programming language. Some key features and benefits of C discussed include its low-level capabilities as a second-generation language, structured programming approach, use of functions and libraries, and support for pointers, memory allocation, recursion, and bit manipulation. The document also covers C's fundamental and derived data types, defining variables and strings, and the structure of C functions including single-level functions.

Lecture 11 semantic analysis 2

This document discusses inherited and synthesized attributes in semantic analysis using syntax-directed translation (SDT). It covers:
- Synthesized attributes are defined by semantic rules associated with productions and rely only on child nodes, while inherited attributes rely on parent/sibling nodes.
- Terminals can have synthesized attributes from lexing but not inherited attributes. Nonterminals can have both.
- Annotated parse trees show attribute values, while dependency graphs determine evaluation order.
- S-attributed definitions rely only on synthesized attributes and evaluate bottom-up. L-attributed definitions restrict inherited attributes to avoid cycles.
- SDTs can construct syntax trees during parsing to decouple parsing from translation

BREAKING MIGNOTTE’S SEQUENCE BASED SECRET SHARING SCHEME USING SMT SOLVER

This document summarizes a research paper that proposes a new method for reconstructing secrets from secret sharing schemes using an SMT solver with one less than the threshold number of shares. It introduces Mignotte's sequence-based secret sharing, describes how shares are generated and distributed, and how an SMT solver is used to check the satisfiability of logical formulas representing constraints on the shares to reconstruct the secret with one less than the threshold. The method is demonstrated with an example and results showing the secret is successfully reconstructed from two shares out of a threshold of three.

OOP in C++

OOP in C++

SYNTAX AND SEMANTICS FOR CINNAMON PROGRAMMING

SYNTAX AND SEMANTICS FOR CINNAMON PROGRAMMING

Towards Practical Homomorphic Encryption with Efficient Public key Generation

Towards Practical Homomorphic Encryption with Efficient Public key Generation

C programming notes

C programming notes

C programming | Class 8 | III Term

C programming | Class 8 | III Term

UNIT III POINTERS IN C

UNIT III POINTERS IN C

Introduction Of C++

Introduction Of C++

Pointers and call by value, reference, address in C

Pointers and call by value, reference, address in C

Getting started with c++

Getting started with c++

2010 bristol q1_formal-property-checkers

2010 bristol q1_formal-property-checkers

C Programming

C Programming

Report on c and c++

Report on c and c++

26 Jo P Feb 09

26 Jo P Feb 09

Problem solving

Problem solving

C language (Collected By Dushmanta)

C language (Collected By Dushmanta)

Lecture 11 semantic analysis 2

Lecture 11 semantic analysis 2

BREAKING MIGNOTTE’S SEQUENCE BASED SECRET SHARING SCHEME USING SMT SOLVER

BREAKING MIGNOTTE’S SEQUENCE BASED SECRET SHARING SCHEME USING SMT SOLVER

Frequent english words

This document defines and provides examples of 50 common English words:
- It defines words like synonym, antonym, and provides example words like queued, thrashed, and turmoil.
- It also defines words like franchise, constituent, exhorted, and negotiable that are used in business and politics.
- Finally, it defines and provides examples of words used in different contexts like personal, haunts, pout, and cleavage.

Compututer Graphics - Color Modeling And Rendering

This document discusses various color models and rendering techniques. It describes additive and subtractive color models, including the RGB, CMY, and HSV color models. It also discusses illumination models, including ambient light, diffuse reflection, and specular reflection. Common rendering techniques like Gouraud shading and Phong shading are summarized, which interpolate lighting across triangle surfaces. Ray tracing is also briefly explained as a technique for simulating light paths.

10CSL67 CG LAB PROGRAM 5

The document describes the Cohen-Suderland line clipping algorithm. It divides a 2D space into 9 regions using a 4-bit code to identify each region. The algorithm clips a line segment against the clipping window by computing the intersection points with boundary lines if the line extends outside the window, repeatedly clipping portions until the line is fully within the window. Pseudocode and an example are provided to demonstrate the step-by-step clipping of a sample line.

Region filling and object removal by exemplar based image inpainting

To get rid of (an) object(s) at a picture or to restore a picture from scratches or holes, Criminisi at el. suggested an algorithm which is combied "texture synthesis" and "inpainting". I made the slide to present at a class to introduce this algorithm. I refered a slide http://bit.ly/1Ng7DNt. I wish this slide may help you to understand the algorithm. Thank you.

Area filling algo

The above ppt discuss about :-
Area fill algorithm :-
Scan line polygon fill algorithm
Boundary fill algorithm
Flood fill algorithm
2-D Viewing

Polygon filling

The document discusses different techniques for filling polygons, including boundary fill, flood fill, and scan-line fill methods. It provides details on how each technique works, such as using a seed point and filling neighboring pixels for boundary fill, replacing all pixels of a selected color for flood fill, and drawing pixels between edge intersections for each scan line for scan-line fill. Examples are given to illustrate the filling process for each method.

Fill area algorithms

The document describes different algorithms for filling polygon and area shapes, including scanline fill, boundary fill, and flood fill algorithms. The scanline fill algorithm works by determining intersections of boundaries with scanlines and filling color between intersections. Boundary fill works by starting from an interior point and recursively "painting" neighboring points until the boundary is reached. Flood fill replaces a specified interior color. Both can be 4-connected or 8-connected. The document also discusses problems that can occur and more efficient span-based approaches.

Computer graphics

The document discusses algorithms for drawing circles and filling polygons on a computer screen. It covers the mid-point circle algorithm for determining pixel positions on a circle, as well as boundary filling and flood filling algorithms for coloring the interior of polygon shapes. The mid-point circle algorithm uses a decision parameter to iteratively calculate pixel coordinates on the circle path. Filling algorithms like boundary fill use recursion to color neighboring pixels of the same color as the initially selected point.

Lecture filling algorithms

A polygon is a closed two-dimensional shape with straight or curved sides. It can be defined by an ordered sequence of vertices and edges connecting consecutive vertices. The scan line polygon fill algorithm uses an odd-even rule to determine if a point is inside or outside the polygon by counting edge crossings along a scan line from that point to infinity. Boundary fill and flood fill are two area filling algorithms that color the interior of a polygon or region by recursively filling neighboring pixels of the same color.

COMPUTER GRAPHICS LAB MANUAL

This document contains a computer graphics lab manual with instructions and sample code for programming graphics experiments. It includes:
1. An introduction and list of experiments covering topics like drawing lines, circles, ellipses, implementing transformations and clipping.
2. Samples of experiment documents with aims, descriptions of algorithms, code samples and questions. The experiments cover drawing pixels, lines using DDA and Bresenham's algorithms, circles using Bresenham's algorithm, and ellipses.
3. The code samples demonstrate how to use graphics functions in C like initgraph, putpixel, getpixel to implement various computer graphics algorithms.

Output primitives computer graphics c version

This document describes various algorithms for drawing lines in computer graphics, including the Digital Differential Analyzer (DDA) algorithm and Bresenham's line algorithm. The DDA algorithm samples a line at discrete positions by calculating changes in one coordinate by a fixed amount and determining the corresponding value of the other coordinate. Bresenham's algorithm uses only incremental integer calculations to determine which of two possible pixel positions is closer to the true line at each sample step.

Clipping Algorithm In Computer Graphics

This document discusses window clipping techniques for computer graphics. It introduces point and line clipping, describing a brute force approach and the more efficient Cohen-Sutherland clipping algorithm. It then explains the Sutherland-Hodgman area clipping algorithm. Key concepts covered include using region codes to efficiently determine which lines and portions of lines need to be clipped to a window.

Computer Graphics

The document discusses various topics related to computer graphics and video display devices. It begins with definitions of key terms like scan conversion and rasterization. It then discusses properties of video display devices like persistence and resolution. Various input and output devices are mentioned along with color display techniques. Concepts related to CRTs like beam retrace and frame buffers are explained. The document also covers graphics transformations, projections, animation, and algorithms like Bresenham's line drawing and Cohen-Sutherland line clipping.

region-filling

This document discusses techniques for filling 2D shapes and regions in raster graphics. It covers seed fill algorithms that start with an interior seed point and grow outward, filling neighboring pixels. Boundary fill and flood fill are described as variations. The document also discusses raster-based filling that processes shapes one scanline at a time. Methods for filling polygons are presented, including using the even-odd rule or winding number rule to determine if a point is inside the polygon boundary.

Frequent english words

Frequent english words

Compututer Graphics - Color Modeling And Rendering

Compututer Graphics - Color Modeling And Rendering

10CSL67 CG LAB PROGRAM 5

10CSL67 CG LAB PROGRAM 5

Region filling and object removal by exemplar based image inpainting

Region filling and object removal by exemplar based image inpainting

Area filling algo

Area filling algo

Polygon filling

Polygon filling

Fill area algorithms

Fill area algorithms

Computer graphics

Computer graphics

Lecture filling algorithms

Lecture filling algorithms

COMPUTER GRAPHICS LAB MANUAL

COMPUTER GRAPHICS LAB MANUAL

Output primitives computer graphics c version

Output primitives computer graphics c version

Clipping Algorithm In Computer Graphics

Clipping Algorithm In Computer Graphics

Computer Graphics

Computer Graphics

region-filling

region-filling

Aad introduction

This document provides an introduction to algorithms and algorithm analysis. It defines an algorithm as a set of unambiguous instructions to solve a problem in a finite amount of time. The most famous early algorithm is Euclid's algorithm for calculating greatest common divisors. Algorithm analysis involves proving an algorithm's correctness and analyzing its running time and space complexity. Common notations for analyzing complexity include Big-O, which provides upper bounds, Big-Omega, which provides lower bounds, and Big-Theta, which provides tight bounds. The goal of analysis is to determine the most efficient algorithm by evaluating performance as problem size increases.

Design & Analysis Of Algorithm

All Units Notes Question & Answer
created By Vijay kumar Sharma
MCA STUDENT (JAIPUR NATIONAL UNIVERSITY)

3 algorithm-and-flowchart

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.

Daa chapter 1

The document discusses algorithms and their analysis. It begins by defining an algorithm and listing requirements like being unambiguous and finite. It describes writing algorithms using pseudocode or flowcharts and proving their correctness. The document then discusses analyzing algorithms by measuring their time and space efficiency using orders of growth. It explains analyzing best, worst, and average cases and counting basic operations. Finally, it provides examples of analyzing simple algorithms involving if statements and loops.

Algoritmos

Exposure of Javier Solano, PhD Professor in Computational Physics and System Engineering, National University of Engineering Lima - Peru. Concept, definition and representation of algorithms. Tree constructs, Unified Modeling Languaje (UML).

DATA STRUCTURE.pdf

The document discusses stacks and queues as linear data structures. A stack follows LIFO (last in first out) where the last element inserted is the first removed. Common stack operations are push to insert and pop to remove elements. Stacks can be implemented using arrays or linked lists. A queue follows FIFO (first in first out) where the first element inserted is the first removed. Common queue operations are enqueue to insert and dequeue to remove elements. Queues can also be implemented using arrays or linked lists. Circular queues and priority queues are also introduced.

DATA STRUCTURE

The document discusses stacks and queues as linear data structures. A stack follows LIFO (last in first out) where the last element inserted is the first to be removed. Common stack operations are push to add an element and pop to remove an element. Stacks can be implemented using arrays or linked lists. A queue follows FIFO (first in first out) where the first element inserted is the first to be removed. Common queue operations are enqueue to add an element and dequeue to remove an element. Queues can also be implemented using arrays or linked lists. Circular queues and priority queues are also discussed briefly.

Modeling Aspects with AP&P Components

This document outlines how AP&P (Aspect-oriented Programming with Aspectual Programming Components) components can be used to model aspects. It describes the key elements of AP&P components including participants, expected operations, replacements, and new operations/fields. It provides examples of how aspects can be defined using AP&P components to instrument access operations. It also discusses how aspects can be deployed with applications using connectors, and how aspects can be composed by deploying them simultaneously with the same application.

Types of Algorithms.ppt

This document provides a summary of an algorithms course taught by Ali Zaib Khan. It includes the course code, title, instructor details, term, duration, and course contents which cover various algorithm design techniques. It also lists the required textbooks and discusses advance algorithm analysis. Finally, it categorizes different types of algorithms such as recursive, backtracking, divide-and-conquer, dynamic programming, greedy, branch-and-bound, brute force, and randomized algorithms.

How to Analyze the Results of LinearPrograms—Part 1 Prelimi.docx

How to Analyze the Results of Linear
Programs—Part 1: Preliminaries
HARVEY J. GREENBERG Mathematics Department
University of Colorado at Denver
PO Box 173364
Denver. Colorado 80217-3364
In a four part series, I describe ways to analyze the results of
linear programs beyond what is commonly described in text-
books. My intent is to capture the thought process in analysis
with two objectives. First, I want to provide a guide to those
getting started in applications of linear programming by sug-
gesting useful ways of looking at the results. Second, I want to
help create an artificially intelligent environment for the analy-
sis of results by presenting a protocol that a knowledge engi-
neer can use. The former has been in the folklore for decades;
the latter is part of a project to develop an intelligent mathe-
matical programming system. This first part of the series con-
tains basic terms and concepts used in the other three parts:
price interpretation, infeasibility diagnosis, and forcing
substructures.
A great deal of research and develop-ment activity in large-scale linear
programming (LP) has been devoted to
solving problems faster. A medium-size
problem by today's standards contains
about 5,000 equations and 20,000 vari-
ables. Even microcomputer versions can
handle thousands of equations and vari-
ables, and supercomputers have been used
for problems with millions of variables!
How can we understand the results? At
one level, in the interests of model man-
agement, we must verify that the solution
obtained makes sense with respect to the
Cupyrighr S) 1993, The Inslilute of Management Sciencos
OO91-21U2/93/23O4/OO56S0I.25
This paptr was refereed.
PROCRAMMENG—LINEAR
INTERFACES 23: 4 July-August 1993 (pp. 56-67)
LINEAR PROGRAMS
problem represented by tho linear pro-
gram.
Once we think we have a good run, we
must delve into the meaning of a solution.
Questions of sensitivity play a direct role,
such as What if . . .? and Why . . .? For
example, we may ask the following. What
if the demand for a commodity increases?
What if capacity is expanded? What if
some resource is made available? Why did
this plant not operate? Why is total pro-
duction so low? Why is the price of some
commodity so large? Why does a certain
flow pattern occur? Is it preferred to others
because of the economic trade-off, or are
the flows forced by the constraints?
Textbook wisdom does not go far
enough in answering these questions in
practical terms (see Gal [1979] for an excel-
lent mathematical treatment). Also, once
an answer is obtained in some mathemati-
cal way, how can we present the answer to
problem owners who might not know lin-
ear programming? We must be able to look
at different views of linear programs and
their pieces, for example, using graphic tech-
niques to present information about flows.
Before we can venture into this world of
analysis, we must understand how linear
programs are constructed. In this overview,
I describe and illustra ...

Program concep sequential statements

The document discusses the concepts of programs and algorithms. It defines a program as a group of sequential statements that are executed line by line to achieve a goal. An algorithm is described as a step-by-step process that precisely defines a sequence of operations to solve a computational problem. The document also outlines the key components of a programming language, characteristics of good programs, stages of the program development process, and provides examples of algorithms.

Introduction to Algorithms & flow charts

A short guide to algorithms, pseudocodes & flow charts for the better understanding of Programming Skills.

VCE Unit 01 (2).pptx

The document discusses algorithms, data abstraction, asymptotic analysis, arrays, polynomials, and sparse matrices. It defines algorithms and discusses their advantages and disadvantages. It explains how to design an algorithm and describes iterative and recursive algorithms. It defines data abstraction and gives an example using smartphones. It discusses time and space complexity analysis and different asymptotic notations like Big O, Omega, and Theta. It describes what arrays are, different types of arrays, and applications of arrays. It explains how to represent and add polynomials using linked lists. Finally, it defines sparse matrices and two methods to represent them using arrays and linked lists.

My Research Poster

This document discusses knowledge representation and reasoning in artificial intelligence. It focuses on intelligent agents, which are software systems that can exhibit intelligent behavior by creating mathematical models of their environment and applying rules to solve problems. One way to design intelligent agents is through Answer Set Prolog, a declarative programming language. Answer Set Prolog allows agents to be given a description of their goals and environment and find solutions without being explicitly programmed. Intelligent agents can be used to create more efficient search algorithms, smarter robots, and aid in understanding human cognition.

Turbo prolog 2.0 basics

In this PDF you will find the basics of Turbo Prolog 2.0 with some good program and it's output. Also it's second part is coming in next month or week.
For any query ------------------> sohupatel8828@gmail.com
For programs ----------------> https://github.com/UltraHopeful/Turbo-Prolog-2.0

Lecture Note-2: Performance analysis of Algorithms

The execution of an algorithm requires various resources of the computer system to complete the task. The
performance of algorithms depends on the use of these resources. The important resources which contribute
to the efficiency of the algorithms are the memory space and the time required for successful execution of
algorithm. The efficiency of an algorithm is measured in terms of the time and the space required for its
execution therefore analysis of algorithms is divided into two categories

Lecture Note-1: Algorithm and Its Properties

An algorithm is a tool for solving any computational problem. It may be defined as a sequence of finite,
precise and unambiguous instructions which are applied either to perform a computation or to solve a
computational problem. These instructions are applied on some raw data called the input, and the
solution of the problem produced is called the output.

User_42751212015Module1and2pagestocompetework.pdf.docx

User_42751212015Module1and2pagestocompetework.pdf
User_42751212015Module1and2pagestocompetework_1.pdf
User_42751212015Module2Homework(CIS330).docx
[INSERT TITLE HERE] 1
Running head: [INSERT TITLE HERE]
[INSERT TITLE HERE]
Student Name
Allied American University
Author Note
This paper was prepared for [INSERT COURSE NAME], [INSERT COURSE ASSIGNMENT] taught by [INSERT INSTRUCTOR’S NAME].
Directions: Please complete each of the following exercises. Please read the instructions carefully.
For all “short programming assignments,” include source code files in your submission.
1. Short programming assignment. Combine the malloc2D function of program 3.16 with the adjacency matrix code of program 3.18 to write a program that allows the user to first enter the count of vertices, and then enter the graph edges. The program should then output the graph with lines of the form:
There is an edge between 0 and 3.
2. Short programming assignment. Modify your program for question 2.1 so that after the adjacency matrix is created, it is then converted to an adjacency list, and the output is generated from the list.
3. Short programming assignment. Modify program 4.7 from the text, overloading the == operator to work for this ADT using a friend function.
4. Is the ADT given in program 4.7 a first-class ADT? Explain your answer.
5. Suppose you are given the source code for a C++ class, and asked if the class shown is an ADT. On what factors would your decision be based?
6. How does using strings instead of simple types like integers alter the O-notation of operations?
User_42751212015Module1Homework(CIS330)Corrected (1).docx
[INSERT TITLE HERE] 1
Running head: [INSERT TITLE HERE]
[INSERT TITLE HERE]
Student Name
Allied American University
Author Note
This paper was prepared for [INSERT COURSE NAME], [INSERT COURSE ASSIGNMENT] taught by [INSERT INSTRUCTOR’S NAME].
Directions: Please refer to your textbook to complete the following exercises.1. Refer to page 12 of your text to respond to the following:Show the contents of the id array after each union operation when you use the quick find algorithm (Program I.I) to solve the connectivity problem for the sequence 0-2, 1-4, 2-5, 3-6, 0-4, 6-0, and 1-3. Also give the number of times the program accesses the id array for each input pair.2. Refer to page 12 of your text to respond to the following:Show the contents of the id array after each union operation when you use the quick union algorithm (Program I.I) to solve the connectivity problem for the sequence 0-2, 1-4, 2-5, 3-6, 0-4, 6-0, and 1-3. Also give the number of times the program accesses the id array for each input pair.3. Refer to figures 1.7 and 1.8 on pages 16 and 17 of the text. Give the contents of the id array after each union operation for the weighted quick union algorithm running on the examples corresponding to figures 1.7 and 1.84. For what value is N is 10N lg N>2N2 ...

TIME EXECUTION OF DIFFERENT SORTED ALGORITHMS

what is Algorithm and classification and its complexity
Time Complexity
Time Space trade-off
Asymptotic time complexity of algorithm and its notation
Why do we need to classify running time of algorithm into growth rates?
Big O-h notation and example
Big omega notation and example
Big theta notation and its example
best among the 3 notation
finding complexity f(n) for certain cases
1. Average case
2.Best case
3.Worst case
Searching
Sorting
complexity of Sorting
Conclusion

Create and analyse programs

This document discusses how to analyze programs for efficiency. It explains that programs should be analyzed both before and after implementation to estimate things like computation time and storage requirements. Key aspects to consider include the number of times loops and statements will be executed, how algorithms and data structures are implemented, and how the program's performance compares to alternatives. An example program for computing the nth Fibonacci number is provided to illustrate some of these concepts.

Aad introduction

Aad introduction

Design & Analysis Of Algorithm

Design & Analysis Of Algorithm

3 algorithm-and-flowchart

3 algorithm-and-flowchart

Daa chapter 1

Daa chapter 1

Algoritmos

Algoritmos

DATA STRUCTURE.pdf

DATA STRUCTURE.pdf

DATA STRUCTURE

DATA STRUCTURE

Modeling Aspects with AP&P Components

Modeling Aspects with AP&P Components

Types of Algorithms.ppt

Types of Algorithms.ppt

How to Analyze the Results of LinearPrograms—Part 1 Prelimi.docx

How to Analyze the Results of LinearPrograms—Part 1 Prelimi.docx

Program concep sequential statements

Program concep sequential statements

Introduction to Algorithms & flow charts

Introduction to Algorithms & flow charts

VCE Unit 01 (2).pptx

VCE Unit 01 (2).pptx

My Research Poster

My Research Poster

Turbo prolog 2.0 basics

Turbo prolog 2.0 basics

Lecture Note-2: Performance analysis of Algorithms

Lecture Note-2: Performance analysis of Algorithms

Lecture Note-1: Algorithm and Its Properties

Lecture Note-1: Algorithm and Its Properties

User_42751212015Module1and2pagestocompetework.pdf.docx

User_42751212015Module1and2pagestocompetework.pdf.docx

TIME EXECUTION OF DIFFERENT SORTED ALGORITHMS

TIME EXECUTION OF DIFFERENT SORTED ALGORITHMS

Create and analyse programs

Create and analyse programs

Point-free semantics of dependent type theories

The developments we did in our stay at the University of Canterbury, kindly hosted by Douglas Bridges and Hannes Diener.

The Graph Minor Theorem: a walk on the wild side of graphs

The document summarizes the Graph Minor Theorem, which states that the set of all finite graphs forms a well-quasi ordering under the graph minor relation. It discusses Robertson and Seymour's 500-page proof of this theorem. It then outlines Nash-Williams' technique for proving that a quasi-order is a well-quasi ordering and describes an attempted proof of the Graph Minor Theorem using this technique. However, the attempt fails due to issues with the "coherence" of embeddings between decomposed components of graphs. Maintaining coherence of embeddings under the graph minor relation is identified as the key challenge in finding a simpler proof of the Graph Minor Theorem.

Explaining the Kruskal Tree Theore

The famous Kruskal's tree theorem states that the collection of finite trees labelled over a well quasi order and ordered by homeomorphic embedding, forms a well quasi order. Its intended mathematical meaning is that the collection of finite, connected and acyclic graphs labelled over a well quasi order is a well quasi order when it is ordered by the graph minor relation.
Oppositely, the standard proof(s) shows the property to hold for trees in the Computer Science's sense together with an ad-hoc, inductive notion of embedding. The mathematical result follows as a consequence in a somewhat unsatisfactory way.
In this talk, a variant of the standard proof will be illustrated explaining how the Computer Science and the graph-theoretical statements are strictly coupled, thus explaining why the double statement is justified and necessary.

The Graph Minor Theorem: a walk on the wild side of graphs

The Graph Minor Theorem says that the collection of finite graphs
ordered by the minor relation is a well quasi order. This apparently
innocent statement hides a monstrous proof: the original result by
Robertson and Seymour is about 500 pages and twenty articles, in which a
new and deep branch of Graph Theory has been developed.
The theorem is famous and full of consequences both on the theoretical side
of Mathematics and in applications, e.g., to Computer Science. But there
is no concise proof available, although many attempts have been made.
In this talk, arising from one such failed attempts, an analysis of the
Graph Minor Theorem is presented. Why is it so hard?
Assuming to use the by-now standard Nash-Williams's approach to prove it,we will
illustrate a number of methods which allow to solve or circumvent some
of the difficulties. Finally, we will show that the core of this line of
thought lies in a coherence question which is common to many parts of
Mathematics: elsewhere it has been solved, although we were unable to
adapt those solutions to the present framework. So, there is hope for a
short proof of the Graph Minor Theorem but it will not be elementary.

Dealing with negative results

This document discusses how to present negative results in research in a positive manner. It notes that while the goal of research is to find positive answers, negative results are also useful for understanding what does not work and setting limits. The document provides examples of reframing negative results positively, such as precisely defining where a procedure fails instead of just saying it failed, or stating positive theorems about the limitations of methods. It also addresses how to present inherent negative results like impossibility theorems and how to address "dead ends" in research.

Variations on the Higman's Lemma

The document discusses variations on Higman's Lemma, which states that a set of finite sequences ordered by embedding forms a well quasi-order if and only if the underlying set ordered does. The document defines well quasi-orders and related concepts. It then examines properties of products, coproducts, equalizers, coequalizers and exponentiation in categories of well quasi-orders and related categories. It proves Higman's Lemma using categorical concepts like equalizers and coequalizers. It also examines properties of categories of descending chains of a quasi-order and how these relate to properties of the original quasi-order.

Dealing with negative results

Invited lecture in the course "Research Methods and Proposal Writing", Dublin Institute of Technology, kindly hosted by Dr. Luca Longo

Well Quasi Orders in a Categorical Setting

Talk given in the symposium "Well-quasiorderings: From Theory to Applications", DMV, Hamburg, 25 September 2015

Proof-Theoretic Semantics: Point-free meaninig of first-order systems

This document summarizes a talk on providing a semantics for first-order logical theories using logical categories. The semantics interprets formulae as objects in a category and proofs as morphisms, without assuming elements exist. Quantifiers are interpreted using stars and costars. A logical category is a prelogical category where stars and costars exist to interpret all formulae. This semantics is sound and complete - a formula is true if a proof morphism exists. The semantics can interpret many other approaches and inconsistent theories have "trivial" models.

Point-free foundation of Mathematics

A talk I gave at the Yonsei University, Seoul in July 21st, 2015.
The aim was to show my background contribution to the CORCON (Correctness by Construction) research project.
I have to thank Prof. Byunghan Kim and Dr Gyesik Lee for their kind hospitality.

Fondazione point-free della matematica

Intervento alla giornata dipartimentale del Dipartimento di Scienza e Alta Tecnologia, Università degli Studi dell'Insubria, 24 giugno 2015

Numerical Analysis and Epistemology of Information

The slides of my presentation at the workshop "Philosophical Aspects of Computer Science", European Centre for Living Technology, University “Ca’ Foscari”, Venice, March 2015.

L'occhio del biologo: elementi di fotografia

The slides of the course "L'occhio del biologo", Alta Formazione, Università degli Studi dell'Insubria.
It is a small course on the fundamentals of photography oriented towards the scientific photography in a biological laboratory.

Constructive Adpositional Grammars, Formally

Slides of the talk I gave in Amsterdam on 13th Feb 2015 as an invited lecturer at the DIP colloquium, ILLC, UvA.

Marie Skłodowska Curie Intra-European Fellowship

A brief report of my experience as a Marie Curie Research Fellow in Leeds to illustrate to my colleagues what means to participate in such a program.
I have to acknowledge the kind invitation of the Research Office of the Università degli Studi dell'Insubria and the Rector delegate to research, Prof. Umberto Piarulli.

Programming modulo representations

This document discusses representing data types and programming in an abstract way that hides the concrete representation of data. It presents an approach where programs operate on abstract representations of data rather than concrete implementations, allowing computations to be performed without inspecting the output. As an example, it shows an abstract implementation of list concatenation that computes correctly without knowing the concrete list representation. This approach ensures correctness while preventing inspection of results.

Programming modulo representations

This talk aims at introducing, through a very simple example, a way to represent data types in the λ-calculus, and thus, in functional programming languages, so that the structure of the data types itself becomes a parameter.
This very simple technical trick allows to reconsider programming as a way to express morphisms between models of a logical theory. As an application, it allows to realise a way to perform anonymous computations.
From a philosophical point of view, the presented approach shows how it is possible to conceive a real programming system where properties like correctness of programs can be proved, but data cannot be inspected, not even in principle.

June 22nd 2014: Seminar at JAIST

In this talk, logically distributive categories are introduced to provide a sound and complete semantics to multi-sorted, first-order, intuitionistic-based logical theories. The peculiar aspect is that no universe is required to interpret terms, making the semantics really point-free.

CORCON2014: Does programming really need data structures?

This talk tries to suggest how computer programming can be conceptually simplified by using abstract mathematics, in particular categorical semantics, so to achieve the 'correctness by construction' paradigm paying no price in term of efficiency.
Also, it introduces an alternative point of view on what is a program and how to conceive data structures, namely as computable morphisms between models of a logical theory.

Fondazione point-free della matematica

Seminario tenuto al Dipartimento di Filosofia, Università degli Studi di Firenze, 19 aprile 2013.

Point-free semantics of dependent type theories

Point-free semantics of dependent type theories

The Graph Minor Theorem: a walk on the wild side of graphs

The Graph Minor Theorem: a walk on the wild side of graphs

Explaining the Kruskal Tree Theore

Explaining the Kruskal Tree Theore

The Graph Minor Theorem: a walk on the wild side of graphs

The Graph Minor Theorem: a walk on the wild side of graphs

Dealing with negative results

Dealing with negative results

Variations on the Higman's Lemma

Variations on the Higman's Lemma

Dealing with negative results

Dealing with negative results

Well Quasi Orders in a Categorical Setting

Well Quasi Orders in a Categorical Setting

Proof-Theoretic Semantics: Point-free meaninig of first-order systems

Proof-Theoretic Semantics: Point-free meaninig of first-order systems

Point-free foundation of Mathematics

Point-free foundation of Mathematics

Fondazione point-free della matematica

Fondazione point-free della matematica

Numerical Analysis and Epistemology of Information

Numerical Analysis and Epistemology of Information

L'occhio del biologo: elementi di fotografia

L'occhio del biologo: elementi di fotografia

Constructive Adpositional Grammars, Formally

Constructive Adpositional Grammars, Formally

Marie Skłodowska Curie Intra-European Fellowship

Marie Skłodowska Curie Intra-European Fellowship

Programming modulo representations

Programming modulo representations

Programming modulo representations

Programming modulo representations

June 22nd 2014: Seminar at JAIST

June 22nd 2014: Seminar at JAIST

CORCON2014: Does programming really need data structures?

CORCON2014: Does programming really need data structures?

Fondazione point-free della matematica

Fondazione point-free della matematica

Immersive Learning That Works: Research Grounding and Paths Forward

We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.

Sciences of Europe journal No 142 (2024)

Sciences of Europe journal No 142 (2024)

Applied Science: Thermodynamics, Laws & Methodology.pdf

When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.

AJAY KUMAR NIET GreNo Guava Project File.pdf

AJAY KUMAR NIET GreNo Guava Project PDF File

Microbiology of Central Nervous System INFECTIONS.pdf

Microbiology of CNS infection

Gadgets for management of stored product pests_Dr.UPR.pdf

Insectsplayamajorroleinthedeteriorationoffoodgrainscausingbothquantitativeandqualitativelosses
Wellprovedthatnogranariescanbefilledwithgrainswithoutinsectsastheharvestedproducecontainegg(or)larvae(or)pupae(or)adultinsectinthembecauseoffieldcarryoverinfestationwhichcannotbeavoidedindevelopingcountrieslikeIndia
Simpletechnologiesfortimelydetectionofinsectsinthestoredproduceandtherebyplantimelycontrolmeasures

Anti-Universe And Emergent Gravity and the Dark Universe

Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.

The binding of cosmological structures by massless topological defects

Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.

CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)

Description:
Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics.
From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise.
Key topics covered include:
Crystal Structures: Unravel the mysteries of crystalline arrangements and their significance in determining material properties.
Band Theory: Explore the electronic band structure of solids and understand how it influences their conductive properties.
Semiconductor Physics: Delve into the behavior of semiconductors, including doping, carrier transport, and device applications.
Magnetic Properties: Investigate the magnetic behavior of solids, including ferromagnetism, antiferromagnetism, and ferrimagnetism.
Optical Properties: Examine the interaction of light with solids, including absorption, reflection, and transmission phenomena.
With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.

Summary Of transcription and Translation.pdf

Hello Everyone Here We are Sharing You with The process of protien Synthesis in very short points you will be Able to understand It Very well

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team

Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.Authoring a personal GPT for your research and practice: How we created the Q...

Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.

Pests of Storage_Identification_Dr.UPR.pdf

InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
Graininfestation
Directdamage
Indirectly
•theexuviae,skin,deadinsects
•theirexcretawhichmakefoodunfitforhumanconsumption
About600speciesofinsectshavebeenassociatedwithstoredgrainproducts
100speciesofinsectpestsofstoredproductscauseeconomiclosses

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

Chemistry of Proteins

8.Isolation of pure cultures and preservation of cultures.pdf

Isolation of pure culture, its various method.

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...

(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation

Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...

Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.

HOW DO ORGANISMS REPRODUCE?reproduction part 1

reproduction part 1
class 10 ncert

Immersive Learning That Works: Research Grounding and Paths Forward

Immersive Learning That Works: Research Grounding and Paths Forward

Sciences of Europe journal No 142 (2024)

Sciences of Europe journal No 142 (2024)

Applied Science: Thermodynamics, Laws & Methodology.pdf

Applied Science: Thermodynamics, Laws & Methodology.pdf

AJAY KUMAR NIET GreNo Guava Project File.pdf

AJAY KUMAR NIET GreNo Guava Project File.pdf

Microbiology of Central Nervous System INFECTIONS.pdf

Microbiology of Central Nervous System INFECTIONS.pdf

Gadgets for management of stored product pests_Dr.UPR.pdf

Gadgets for management of stored product pests_Dr.UPR.pdf

Anti-Universe And Emergent Gravity and the Dark Universe

Anti-Universe And Emergent Gravity and the Dark Universe

The binding of cosmological structures by massless topological defects

The binding of cosmological structures by massless topological defects

CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)

CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)

Summary Of transcription and Translation.pdf

Summary Of transcription and Translation.pdf

Tissue fluids_etiology_volume regulation_pressure.pptx

Tissue fluids_etiology_volume regulation_pressure.pptx

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...

Authoring a personal GPT for your research and practice: How we created the Q...

Authoring a personal GPT for your research and practice: How we created the Q...

Pests of Storage_Identification_Dr.UPR.pdf

Pests of Storage_Identification_Dr.UPR.pdf

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

8.Isolation of pure cultures and preservation of cultures.pdf

8.Isolation of pure cultures and preservation of cultures.pdf

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...

(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...

EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...

EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...

HOW DO ORGANISMS REPRODUCE?reproduction part 1

HOW DO ORGANISMS REPRODUCE?reproduction part 1

- 1. Algorithms and Their Explanations CiE 2014 — History and Philosophy of Computing 24th June 2014 M Benini1 F Gobbo2 1Università degli Studi dell’Insubria marco.benini@uninsubria.it 2Universiteit van Amsterdam F.Gobbo@uva.nl
- 2. Introduction The purpose of this talk is to speak about explanations of algorithms. The term explanation is understood in the following sense: the verb to explain has the paradigmatic form “A explains B to C”; the noun explanation denote the act of explaining where the subject A is hidden, i.e., “explanation of B to C”; the explanation is proposed on a given level of abstraction M set by A, living in a given context. Therefore, an explanation lives in a context where the explanandum, i.e., the explained object B, as well as the beneﬁciary C are clearly deﬁned. On the contrary, the explaining agent A remains implicit, and also the level of abstraction M. (2 of 18)
- 3. The Method of the Levels of Abstraction In Floridi’s approach to the Philosophy of Information, an explanation is always understood on one or more Levels of Abstraction (LoAs), clearly deﬁned. In other words, the explanation of B to be grasped: has to be implemented by the agent C; over another LoA N; N is, again, part of the context, like M. The subject A provides the purpose of the implementation to B; in turn, the agent C has to “understand” the purpose, and he has also to use an amount of knowledge and information to build the new LoA N. Collectively, the purpose and the used information are called Level of Explanation (LoE). Evidently, the LoE is (at least in part) implicit and so it should be elicited by B in order to construct the LoA N. (3 of 18)
- 4. Minimal information So, ﬁxed a context, the question we want to address is: What amount of information the agent needs in order to construct the required level of abstraction N over the given one M? This is best understood looking at a concrete example: the Heapsort algorithm. It has been chosen because it is simple although not trivial, and it illustrates in a nutshell all the fundamental aspects of the above question. Also, since it is classical, there is no need to explain it in the ﬁrst place: we can focus just on the analysis of explanations. (4 of 18)
- 5. The scenario A software house has to produce a program to solve some given problem. Its employees are the programmer who has to convert a given speciﬁcation to a working code in some programming language; the software designer who has to analyse the problem and to describe an algorithmic solution which becomes the set of speciﬁcations; the algorithm designer who takes the general speciﬁcations from the software designer and convert them into precise descriptions of what the programmer must implement. Each of these agents are part of an informational organism (inforg) which is given by the agent and the computer where the program has to be implemented. In turn, each of these inforgs has an appropriate LoA the agent acts upon. (5 of 18)
- 6. The programmer’s view The programmer receives a speciﬁcation, in the form of an algorithm. His purpose is to code this into an executable piece of code. In our example, the speciﬁcation is the Heapsort algorithm. The programmer’s LoA is given by the programming language, in a broad sense, and by the view of the machine he has through the language. Our question becomes: What “explains” the Heapsort algorithm to the programmer, enabling him to successfully achieve his purpose? (6 of 18)
- 7. The Heapsort algorithm Heapsort(A : array) ≡ BuildMaxHeap(A) for i ←len(A) downto 2 do exchange(A[1],A[i]) heapsize(A)←heapsize(A)−1 MaxHeapify(A,1) BuildMaxHeap(A : array) ≡ heapsize(A)←len(A) for i ← len(A)/2 downto 1 do MaxHeapify(A,i) left(i : N) ≡ 2i right(i : N) ≡ 2i +1 MaxHeapify(A: array,i : N) ≡ m←i l ←left(i) r ←right(i) if l ≤ heapsize(A) & A[l] A[m] then m←l if r ≤ heapsize(A) & A[r] A[m] then m←r if m = i then exchange(A[i],A[m]) MaxHeapify(A,m) exchange(a: element,b: element) ≡ x ←a; a←b; b ←x (7 of 18)
- 8. The programmer’s explanation The programmer needs to know how to read the speciﬁcation and to correctly convert it into an executable piece of code in the programming language. In other words, he has to build a new LoA, which extends the programming language with a new function, Heapsort. Thus, the minimal explanation the programmer needs of the above pseudo-code is the one which allows him to fulﬁl his task. This explanation reduces to know how to correctly interpret the pseudo-code into the programming language construction. E.g., he needs to know that ← stands for assignment. This amount of explanation suﬃces to show that the implementation exactly matches the pseudo-code, and so, it is correct with respect to the speciﬁcation the programmer is given. (8 of 18)
- 9. The software designer’s view The purpose of the software designer is to construct a set of speciﬁcations that collectively allows to write a program that solves the problem. The software designer, in our example, has reduced a part of the solution to the need of sorting an array. So, he has to understand what are the features this sorting algorithm must have. For example, he has to decide that stability does not matter, while eﬃciency is important. For a software designer, an algorithm is not a procedure, but a collection of properties. He has to decide what properties are relevant to solve the problem, and to pass them to the algorithm designer, who converts them into pieces of pseudo-code, and then, to the programmer, who convert the pseudo-code into an executable program. (9 of 18)
- 10. The software designer’s explanation After identifying sorting as a (sub-)problem to be solved, the software designer has to decide what the sorting procedure should actually do. For him, the “right” sorting procedure must exhibit some features, while it may not have others because irrelevant to the solution he designs. Thus, the explanation of HeapSort is, for him, a collection of properties: it sorts an array; it rearranges the input, instead of generating a sorted copy; it is not stable; it is eﬃcient among comparison-based sorting algorithms. This explanation allows the software designer to show that the solution he provides is correct, i.e., it solves the original problem within the given constraints. Note how the software designer does not need to know how Heapsort operates! (10 of 18)
- 11. The algorithm designer’s view As the software designer is interested in “what” an algorithm does, the algorithm designer focuses on “how” an algorithm achieves its result. In our example, the algorithm designer may think that sorting data in a tree is more eﬃcient than sorting data in a random-access sequence. Of course, this is true when the tree satisﬁes some conditions, like that each node’s key is less than the parent’s. Understanding that an array can be interpreted as a tree, allows to write an algorithm that does not use an additional data structure. In this way, eventually, the algorithm designer will be able to prove that the designed pseudo-code, which is the previously shown Heapsort procedure, is correct, that is, it sorts an array. Also, destructive manipulation of the input and lack of stability immediately follow from the correctness proof. Showing that Heapsort is eﬃcient requires to reduce the execution time, measured as number of computational steps, to a set of mutually recursive equations. And the solution of these equations clearly shows that Heapsort operates in O(nlogn) with n the length of the array. (11 of 18)
- 12. The algorithm designer’s explanation So, the algorithm designer is able to show that his pseudo-code, the LoA he constructed, satisﬁes the speciﬁcation he received. To provide this evidence, he has to show that the pseudo-code sorts an array and do it within the constraints put by the software designer, which amounts to explain how the computational process he designed eventually produces the output; to calculate the complexity of the algorithm, which amounts to deduce from the algorithm and to solve a set of recurrence equations. The knowledge and information to obtain these two goals are essential to justify his work, so this is the minimal explanation which suits the algorithm designer’s purpose. (12 of 18)
- 13. The Minimal Levels of Abstraction When the purpose has been accomplished, the resulting machine shows (at least) two levels of abstraction: 1. the LoA which has been used to implement Heapsort over it; 2. the LoA which has Heapsort as a primitive. Each of these LoAs can be described within Floridi’s framework, thus being in strict interaction with a corresponding level of observation, which enables us to measure information. Although very simple, these two LoAs provide a gradient of abstractions: the description of the system used by the software house of our example is, really, the gradient, i.e., the sum of the two LoAs together with their mutual interactions. (13 of 18)
- 14. The Levels of Explanation of an Algorithm The Heapsort procedure as implemented can be explained in, at least, four diﬀerent ways: 1. to the machine, it is explained by the operational semantics, encoded in the computer machinery, which allows to execute the code. 2. to the programmer, it is explained as the adherence to the pseudo-code; 3. to the system designer, it is explained as a set of properties that allows the procedure to take its role inside the program; 4. to the algorithm designer, it is explained as an algorithm which solves a useful problem, and which enjoys a number of “good” properties; Each Level of Explanation (LoE) allows to fulﬁl a speciﬁc purpose, the one of the explaining agent. In the last three cases this has been already illustrated, while in the ﬁrst, the purpose is simply to execute the procedure on a given input. (14 of 18)
- 15. A gradient of explanation In Floridi’s terms, each complex, structured information, is understood on one of more Levels of Abstractions (LoAs), where each one presumes a Level of Explanation (LoE) that is used to construct what we informally called explanation. And, the four explanations are strictly linked one to the others, together providing an explanation for (part of) the purpose of the software house: to provide a program that solves a speciﬁc problem. (15 of 18)
- 16. Minimal explanations and measures A gradient of explanations allows for a very simple way to measure distance between explanations: we say that the machine “understands” the code less than the software designer because the explanation of the machine is at level 1 in the gradient, while the software designer’s is at level 3. It is important to remark that this measure is relative to a gradient and a LoA: it does not make sense to use it outside this context. Nevertheless, this measure can be made more “objective” when we consider minimal explanations — as we tried to do — because any other gradient of explanations on the same LoA and inforgs would become an extension of the minimal one. (16 of 18)
- 17. Conclusion The concept of the Gradient of Explanations (GoE) presented here was naturally derived within the Method of the LoAs. It is important to note, that the GoE has a diﬀerent epistemological status compared to the Gradient of Abstractions (GoA). The consequences of the introduction of the GoE are not yet fully explored, and its formalisation is still preliminary. However, the Heapsort example will act as a concrete guideline so to apply the GoE in other cases. (17 of 18)
- 18. Thank you for your attention! (18 of 18)