I sopke on the Research Directions for the Department at Department of Computer Science & Engineering, IIT, Kharagpur on their "Research Scholar\'s Day\' on 06-Feb-10.
Reality of education system after 12th engineering admission mythKalyan Ranjan
This document discusses the importance of various subjects for different engineering fields. It states that mathematics is important for every branch of engineering, aptitude and reasoning are important for computer science and information technology engineering, and physics is important for electrical, mechanical, and architecture engineering. The document also provides the number of questions from each subject that may appear on engineering entrance exams.
Using Innoslate for Model-Based Systems EngineeringElizabeth Steiner
Dr. Steve Dam will walk you through the process of using Innoslate’s modeling and simulation capabilities while applying a MBSE methodology.
At its core, Innoslate is a full model-based systems engineering tool. Within Innoslate, system models are formalized and capable of simulation to derive cost, schedule, and performance data.
Your webinar will cover:
Functional modeling
Functional modeling is at the heart of how Innoslate derives new requirements and ensures logical accuracy.
Physical modeling
We can describe synthesizing the physical model in Innoslate with eight different diagrams, including the Asset Diagram, Layer Diagram, Block Definition Diagram, and Internal Block Diagram.
Executing a model
Innoslate includes a ‘Discrete Event Simulator’ to verify functional diagram’s logic, calculate cost, compute time, and quantify performance.
Relating Requirements to Diagrams
Requirements traceability ensures that the lifecycle and origin of a requirement is fully tracked. Innoslate includes relationship matrices to represent traceability relationships between entities in tabular view.
Requirements Generation
After modeling the system, often an engineer will derive textual requirements from the models by hand. Innoslate includes an automatic facility that generates requirements documents in a standard format (as outlined in “The Engineering Design of Systems: Models and Methods“).
What Does the Webinar Cover?
You'll learn how to optimize varying parameters and disciplines throughout the lifecycle of the system within cost and schedule constraints without compromising performance. Real MBSE enables the execution of many activities in parallel, thus enabling the “faster and cheaper” part.
Many people can contribute to the design and development at the same time, because the information they create can be easily linked together to form abstractions that enable you to communicate the results at all levels. Dr. Dam uses a methodology that includes the technique, processes, and tools.
This methodology isn’t the only way to have a successful MBSE capability, but all three elements must be incorporated in any methodology you use. We offer this methodology as one that has proven successful over the past decade. It is based on methodologies used since the 1960s, but updated to the modern cloud computing, artificial intelligence age; that's now emerging toward the end of the second decade of the 21st Century.
Often people today work in a similar manner to how their grandparents worked in the 1960s, just with electronic tools instead of paper and pencil. Just creating a “model” doesn’t mean you are doing effective MBSE. This webinar will show you how to take MBSE into the 21st century.
A comprehensive introduction to machine learning and deep learning along with application in finance (provided by an example of predicting bank failure). Then, the difference of ML in tech and ML in finance is outlined. Last section is excluded from the file.
The CPE Sectoral Meeting discussed the following:
1. The proposed 2018-2019 Bachelor of Science in Computer Engineering curriculum including introducing Computational Thinking using Python, removing Computer System Organization, and increasing programming subject lab hours to 6 hours per week.
2. Graduate outcomes for Computer Engineering including technical skills, thinking skills and judgment, leadership, and attitude.
3. Elective tracks in systems administration & engineering, signal/image processing and graphics, and software engineering. However, the tracks are lecture-based only which members suggested should include laboratory hours.
4. Faculty trainings and research plan with a budget of less than 7000 from the school. CPE linkages with companies and other universities.
Take a trip into the history and future of systems engineering to better understand how we can improve the discipline.
Your host, Dr. Steve Dam, discusses where systems engineering came from and where it is going. He includes discussions on how:
- complexity has changed our methodology
- systems engineering languages have evolved
- technology improvements enable better systems engineering
Contributions to the multidisciplinarity of computer science and ISSaïd Assar
Les diapos de ma présentation HDR en informatique (CNU section 27) à l'université Paris 1 Panthéon Sorbonne le vendredi 20 janvier 2017. L'enregistrement vidéo de la soutenance est visible sur https://www.youtube.com/watch?v=1ro_iaI-roA
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Slides of my presentation for Habilitation (HDR) defense in computer science (Informatique section 27 CNU) at University Paris 1 Panthéon Sorbonne on Friday January 2017.
Video recording is visible on https://www.youtube.com/watch?v=1ro_iaI-roA
The document provides an overview of artificial intelligence including its history and key application areas. It discusses how AI research evolved from rationalist traditions in ancient Greece and developments in formal logic in the 19th century. Some of the major application areas discussed include game playing, automated reasoning, expert systems, natural language processing, robotics, and machine learning. The document also covers knowledge engineering concepts such as knowledge acquisition, analysis and representation, validation, and computer-assisted knowledge elicitation techniques.
Reality of education system after 12th engineering admission mythKalyan Ranjan
This document discusses the importance of various subjects for different engineering fields. It states that mathematics is important for every branch of engineering, aptitude and reasoning are important for computer science and information technology engineering, and physics is important for electrical, mechanical, and architecture engineering. The document also provides the number of questions from each subject that may appear on engineering entrance exams.
Using Innoslate for Model-Based Systems EngineeringElizabeth Steiner
Dr. Steve Dam will walk you through the process of using Innoslate’s modeling and simulation capabilities while applying a MBSE methodology.
At its core, Innoslate is a full model-based systems engineering tool. Within Innoslate, system models are formalized and capable of simulation to derive cost, schedule, and performance data.
Your webinar will cover:
Functional modeling
Functional modeling is at the heart of how Innoslate derives new requirements and ensures logical accuracy.
Physical modeling
We can describe synthesizing the physical model in Innoslate with eight different diagrams, including the Asset Diagram, Layer Diagram, Block Definition Diagram, and Internal Block Diagram.
Executing a model
Innoslate includes a ‘Discrete Event Simulator’ to verify functional diagram’s logic, calculate cost, compute time, and quantify performance.
Relating Requirements to Diagrams
Requirements traceability ensures that the lifecycle and origin of a requirement is fully tracked. Innoslate includes relationship matrices to represent traceability relationships between entities in tabular view.
Requirements Generation
After modeling the system, often an engineer will derive textual requirements from the models by hand. Innoslate includes an automatic facility that generates requirements documents in a standard format (as outlined in “The Engineering Design of Systems: Models and Methods“).
What Does the Webinar Cover?
You'll learn how to optimize varying parameters and disciplines throughout the lifecycle of the system within cost and schedule constraints without compromising performance. Real MBSE enables the execution of many activities in parallel, thus enabling the “faster and cheaper” part.
Many people can contribute to the design and development at the same time, because the information they create can be easily linked together to form abstractions that enable you to communicate the results at all levels. Dr. Dam uses a methodology that includes the technique, processes, and tools.
This methodology isn’t the only way to have a successful MBSE capability, but all three elements must be incorporated in any methodology you use. We offer this methodology as one that has proven successful over the past decade. It is based on methodologies used since the 1960s, but updated to the modern cloud computing, artificial intelligence age; that's now emerging toward the end of the second decade of the 21st Century.
Often people today work in a similar manner to how their grandparents worked in the 1960s, just with electronic tools instead of paper and pencil. Just creating a “model” doesn’t mean you are doing effective MBSE. This webinar will show you how to take MBSE into the 21st century.
A comprehensive introduction to machine learning and deep learning along with application in finance (provided by an example of predicting bank failure). Then, the difference of ML in tech and ML in finance is outlined. Last section is excluded from the file.
The CPE Sectoral Meeting discussed the following:
1. The proposed 2018-2019 Bachelor of Science in Computer Engineering curriculum including introducing Computational Thinking using Python, removing Computer System Organization, and increasing programming subject lab hours to 6 hours per week.
2. Graduate outcomes for Computer Engineering including technical skills, thinking skills and judgment, leadership, and attitude.
3. Elective tracks in systems administration & engineering, signal/image processing and graphics, and software engineering. However, the tracks are lecture-based only which members suggested should include laboratory hours.
4. Faculty trainings and research plan with a budget of less than 7000 from the school. CPE linkages with companies and other universities.
Take a trip into the history and future of systems engineering to better understand how we can improve the discipline.
Your host, Dr. Steve Dam, discusses where systems engineering came from and where it is going. He includes discussions on how:
- complexity has changed our methodology
- systems engineering languages have evolved
- technology improvements enable better systems engineering
Contributions to the multidisciplinarity of computer science and ISSaïd Assar
Les diapos de ma présentation HDR en informatique (CNU section 27) à l'université Paris 1 Panthéon Sorbonne le vendredi 20 janvier 2017. L'enregistrement vidéo de la soutenance est visible sur https://www.youtube.com/watch?v=1ro_iaI-roA
--
Slides of my presentation for Habilitation (HDR) defense in computer science (Informatique section 27 CNU) at University Paris 1 Panthéon Sorbonne on Friday January 2017.
Video recording is visible on https://www.youtube.com/watch?v=1ro_iaI-roA
The document provides an overview of artificial intelligence including its history and key application areas. It discusses how AI research evolved from rationalist traditions in ancient Greece and developments in formal logic in the 19th century. Some of the major application areas discussed include game playing, automated reasoning, expert systems, natural language processing, robotics, and machine learning. The document also covers knowledge engineering concepts such as knowledge acquisition, analysis and representation, validation, and computer-assisted knowledge elicitation techniques.
This is Work-In-Progress. Developing a series of lectures on C++0x. This will augment my presentations on C++ and Design Pattern. First trial run was done at Interra, Noida in 2009
The document discusses different ways to define integer constants in C, including using integer literals, the #define preprocessor directive, enums, and the const qualifier. It provides a table comparing how each option is handled by the C preprocessor, compiler, and debugger. Code examples are given to illustrate the behavior. The key points are that integer literals are replaced directly, #define symbols are replaced textually, enums and const ints create symbols but const ints allow address operations in both the compiler and debugger.
The document discusses digital geometry and provides an overview of the topic. It begins with a brief history of geometry and discusses how the field of digital geometry emerged with the advent of computers and digital images. It then covers some key concepts in digital geometry including tessellations, connectivity in 2D and 3D, and the Jordan curve theorem. The document aims to provide an introduction to digital geometry and its fundamental topics.
The document discusses VLSI education and development in India, including:
1. A chronology of VLSI education from 1979-2005, including government initiatives like SMDP to boost VLSI design manpower and establish academic centers.
2. Surveys by VSI that found a growing gap between projected VLSI manpower needs and current outputs from Indian universities.
3. A workshop discussing goals of university-industry collaboration and feedback that graduating students lack industry readiness in areas like design skills and experience with industrial tools.
This presentation briefs about International Collegiate Programming Contest(ICPC) which is organized by ACM and sponsored by IBM.
This is delivered at VB Siddardha Colleges, Vijayawada on 10th Mar 2015. Somehow Indian participation is not attractive. I am encouraging Indian students to participate in this competition by delivering lectures like this.
1. Machine learning was used to create a decision tree model to diagnose problems in telecommunications networks, achieving 99% accuracy with only 10,000 examples.
2. The model was simplified for comprehensibility, becoming probabilistic and covering 50% of cases with general rules and 50% with specific small disjuncts.
3. Lessons from the success include the importance of model comprehensibility, handling small datasets, addressing systematic errors, and considering future extensions when applying machine learning solutions.
A computational scientist's wish list for tomorrow's computing systemskhinsen
Like many areas of modern life, scientific research has been transformed profoundly by information technology. Most of today's research relies on computers and software for core tasks such as data analysis and model exploration. This has created both new opportunities and new danger zones. The much discussed reproducibility crisis, for example, is largely the result of inappropriate use of computational tools.
To make sure that tomorrow's computing systems provide support for doing research reliably, computational scientists need to establish a dialog with designers of programming languages and systems, and that is my goal with this presentation. I will describe the particularities of computational science: data-centric approaches, situated software, exploration vs. consolidation of computational models, the role of specifications, interfacing independently developed components, and the central scientific requirement of inquirability. I will also outline how today's computing systems are insufficient, and discuss some of my own attempts to contribute to improving them.
This document contains a syllabus for the subject "Design and Analysis of Algorithms". It discusses the following key points:
- The objectives of the course are to learn algorithm analysis techniques, become familiar with different algorithm design techniques, and understand the limitations of algorithm power.
- The syllabus is divided into 5 units which cover topics like introduction to algorithms, brute force and divide-and-conquer techniques, dynamic programming and greedy algorithms, iterative improvement methods, and coping with limitations of algorithmic power.
- Examples of algorithms discussed include merge sort, quicksort, binary search, matrix multiplication, knapsack problem, shortest paths, minimum spanning trees, and NP-complete problems.
- References
This document summarizes a talk on practical machine learning issues. It discusses identifying the right machine learning scenario for a given task, such as classification, regression, clustering, or reinforcement learning. It also addresses common reasons why machine learning models may fail, such as using the wrong evaluation metrics, not having enough labeled training data, or not performing proper feature engineering. The document emphasizes the importance of choosing the appropriate machine learning model, having sufficient high-quality data, and selecting useful features.
A data science observatory based on RAMP - rapid analytics and model prototypingAkin Osman Kazakci
RAMP approach to analytics: Rapid Analytics and Model Prototyping; collaborative data challenges with in-built data science process management tools and analytics; An observatory of data science and scientists. Presented at the Design Theory Special Interest Group of International Design Society. Mines ParisTech and Centre for Data Science.
This document summarizes Jim Gray's 1998 Turing Lecture which discusses remaining challenges in information technology research. It identifies the need for long-term, university-led research projects supported by government funding. Specific challenges mentioned include making parallel programming easier, improving the scalability of databases and transaction processing systems, and advancing the state of artificial intelligence to pass the Turing Test within the next 50 years. The document outlines properties of effective long-term research goals and provides examples like devising an architecture that scales indefinitely.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.
Big Data & Machine Learning - TDC2013 São Paulo - 12/0713Mathieu DESPRIEE
Machine learning and big data technologies enable new types of data analysis. Hadoop is an open-source framework that allows distributed storage and processing of large datasets across clusters of computers. It includes tools for working with structured and unstructured data to power applications in areas like recommendations, customer churn prediction, and more.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
This document provides a high-level overview of the history and trends in computer science:
- It traces the early history of computing from Lady Ada Byron as the first programmer in the 1800s to the development of boolean algebra and its use in computer switching circuits.
- Key developments like the birth of the microcomputer in 1975 and the designation of the computer as "Man of the Year" in 1982 are mentioned.
- Recent trends discussed include exponential increases in processing power according to Moore's Law, the growth of multicore and manycore processors, and shifts toward distributed computing in large data centers and on mobile/pervasive devices like laptops and smartphones.
This document describes the history and evolution of computing systems from the early use of abacuses through modern computers, networks, and software. It discusses the layers of a computing system including hardware and software, and how abstraction is a key concept. The roles of systems programmers who build tools versus applications programmers and users who utilize tools are also distinguished.
This document provides advice on how to prepare for the GATE CSE exam, including recommended books and study materials for each subject. It emphasizes the importance of practicing problems at the end of chapters in reference books to develop technical knowledge and the ability to apply concepts. Specific books and topics are recommended for subjects like Discrete Mathematics, Algorithms, Data Structures, Theoretical Computer Science, and Database Management Systems. It also stresses the value of video lecture courses to supplement reading. The overall message is to focus on understanding fundamental concepts and gaining problem-solving skills through extensive practice.
This is Work-In-Progress. Developing a series of lectures on C++0x. This will augment my presentations on C++ and Design Pattern. First trial run was done at Interra, Noida in 2009
The document discusses different ways to define integer constants in C, including using integer literals, the #define preprocessor directive, enums, and the const qualifier. It provides a table comparing how each option is handled by the C preprocessor, compiler, and debugger. Code examples are given to illustrate the behavior. The key points are that integer literals are replaced directly, #define symbols are replaced textually, enums and const ints create symbols but const ints allow address operations in both the compiler and debugger.
The document discusses digital geometry and provides an overview of the topic. It begins with a brief history of geometry and discusses how the field of digital geometry emerged with the advent of computers and digital images. It then covers some key concepts in digital geometry including tessellations, connectivity in 2D and 3D, and the Jordan curve theorem. The document aims to provide an introduction to digital geometry and its fundamental topics.
The document discusses VLSI education and development in India, including:
1. A chronology of VLSI education from 1979-2005, including government initiatives like SMDP to boost VLSI design manpower and establish academic centers.
2. Surveys by VSI that found a growing gap between projected VLSI manpower needs and current outputs from Indian universities.
3. A workshop discussing goals of university-industry collaboration and feedback that graduating students lack industry readiness in areas like design skills and experience with industrial tools.
This presentation briefs about International Collegiate Programming Contest(ICPC) which is organized by ACM and sponsored by IBM.
This is delivered at VB Siddardha Colleges, Vijayawada on 10th Mar 2015. Somehow Indian participation is not attractive. I am encouraging Indian students to participate in this competition by delivering lectures like this.
1. Machine learning was used to create a decision tree model to diagnose problems in telecommunications networks, achieving 99% accuracy with only 10,000 examples.
2. The model was simplified for comprehensibility, becoming probabilistic and covering 50% of cases with general rules and 50% with specific small disjuncts.
3. Lessons from the success include the importance of model comprehensibility, handling small datasets, addressing systematic errors, and considering future extensions when applying machine learning solutions.
A computational scientist's wish list for tomorrow's computing systemskhinsen
Like many areas of modern life, scientific research has been transformed profoundly by information technology. Most of today's research relies on computers and software for core tasks such as data analysis and model exploration. This has created both new opportunities and new danger zones. The much discussed reproducibility crisis, for example, is largely the result of inappropriate use of computational tools.
To make sure that tomorrow's computing systems provide support for doing research reliably, computational scientists need to establish a dialog with designers of programming languages and systems, and that is my goal with this presentation. I will describe the particularities of computational science: data-centric approaches, situated software, exploration vs. consolidation of computational models, the role of specifications, interfacing independently developed components, and the central scientific requirement of inquirability. I will also outline how today's computing systems are insufficient, and discuss some of my own attempts to contribute to improving them.
This document contains a syllabus for the subject "Design and Analysis of Algorithms". It discusses the following key points:
- The objectives of the course are to learn algorithm analysis techniques, become familiar with different algorithm design techniques, and understand the limitations of algorithm power.
- The syllabus is divided into 5 units which cover topics like introduction to algorithms, brute force and divide-and-conquer techniques, dynamic programming and greedy algorithms, iterative improvement methods, and coping with limitations of algorithmic power.
- Examples of algorithms discussed include merge sort, quicksort, binary search, matrix multiplication, knapsack problem, shortest paths, minimum spanning trees, and NP-complete problems.
- References
This document summarizes a talk on practical machine learning issues. It discusses identifying the right machine learning scenario for a given task, such as classification, regression, clustering, or reinforcement learning. It also addresses common reasons why machine learning models may fail, such as using the wrong evaluation metrics, not having enough labeled training data, or not performing proper feature engineering. The document emphasizes the importance of choosing the appropriate machine learning model, having sufficient high-quality data, and selecting useful features.
A data science observatory based on RAMP - rapid analytics and model prototypingAkin Osman Kazakci
RAMP approach to analytics: Rapid Analytics and Model Prototyping; collaborative data challenges with in-built data science process management tools and analytics; An observatory of data science and scientists. Presented at the Design Theory Special Interest Group of International Design Society. Mines ParisTech and Centre for Data Science.
This document summarizes Jim Gray's 1998 Turing Lecture which discusses remaining challenges in information technology research. It identifies the need for long-term, university-led research projects supported by government funding. Specific challenges mentioned include making parallel programming easier, improving the scalability of databases and transaction processing systems, and advancing the state of artificial intelligence to pass the Turing Test within the next 50 years. The document outlines properties of effective long-term research goals and provides examples like devising an architecture that scales indefinitely.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.
Big Data & Machine Learning - TDC2013 São Paulo - 12/0713Mathieu DESPRIEE
Machine learning and big data technologies enable new types of data analysis. Hadoop is an open-source framework that allows distributed storage and processing of large datasets across clusters of computers. It includes tools for working with structured and unstructured data to power applications in areas like recommendations, customer churn prediction, and more.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
This document provides a high-level overview of the history and trends in computer science:
- It traces the early history of computing from Lady Ada Byron as the first programmer in the 1800s to the development of boolean algebra and its use in computer switching circuits.
- Key developments like the birth of the microcomputer in 1975 and the designation of the computer as "Man of the Year" in 1982 are mentioned.
- Recent trends discussed include exponential increases in processing power according to Moore's Law, the growth of multicore and manycore processors, and shifts toward distributed computing in large data centers and on mobile/pervasive devices like laptops and smartphones.
This document describes the history and evolution of computing systems from the early use of abacuses through modern computers, networks, and software. It discusses the layers of a computing system including hardware and software, and how abstraction is a key concept. The roles of systems programmers who build tools versus applications programmers and users who utilize tools are also distinguished.
This document provides advice on how to prepare for the GATE CSE exam, including recommended books and study materials for each subject. It emphasizes the importance of practicing problems at the end of chapters in reference books to develop technical knowledge and the ability to apply concepts. Specific books and topics are recommended for subjects like Discrete Mathematics, Algorithms, Data Structures, Theoretical Computer Science, and Database Management Systems. It also stresses the value of video lecture courses to supplement reading. The overall message is to focus on understanding fundamental concepts and gaining problem-solving skills through extensive practice.
This document provides information about an algorithms course, including the course syllabus and topics that will be covered. The course topics include introduction to algorithms, analysis of algorithms, algorithm design techniques like divide and conquer, greedy algorithms, dynamic programming, backtracking, and branch and bound. It also covers NP-hard and NP-complete problems. The syllabus outlines 5 units that will analyze performance, teach algorithm design methods, and solve problems using techniques like divide and conquer, dynamic programming, and backtracking. It aims to help students choose appropriate algorithms and data structures for applications and understand how algorithm design impacts program performance.
Automatski - NP-Complete - TSP - Travelling Salesman Problem Solved in O(N^4)Aditya Yadav
The document discusses solving the NP-complete travelling salesman problem (TSP) in O(N^4) time. It begins by introducing Automatski Solutions and their efforts over 25 years to solve some of the toughest computational problems considered unsolvable, including 7 NP-complete problems. It then provides background on the TSP, describing it as one of the most important theoretical problems involving finding the shortest route to visit each of N cities once. The document proceeds to explain Automatski's deterministic algorithm for solving the TSP in O(N^4) time, significantly improving on the best known solution. It claims this proves P=NP and has implications for cryptography such as cracking RSA 2048.
This document provides an overview of an IST 380 data science course. It introduces the instructor, Zach Dodds, and discusses topics that will be covered over the 15 weeks including using R, descriptive statistics, predictive modeling, machine learning algorithms, and a final project. Assignments are due weekly and students can work individually or in pairs. The course aims to provide both specific skills in data analysis and a broad background in data science.
This document provides an overview of an introductory data science course (IST 380). It discusses the course content which includes learning the R programming language, descriptive statistics, predictive modeling, and machine learning algorithms. It also covers course logistics like assignments, grading, and academic honesty policies. The goal of the course is to provide students with practical data science skills that can be applied to real-world problems and datasets.
Similar to Research Roadmap For Cse, Iit Kgp Next 5 Years (20)
Land of Pyramids, Petra, and Prayers - Egypt, Jordan, and Israel Tourppd1961
This is the presentation of photos and history of Land of Pyramids, Petra, and Prayers from our Egypt, Jordan, and Israel Tour during February, 2020. This was prepared and presented to the family and friends on 19th July, 2020.
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++.
This presentation was made in PRISM workshop on Technology Innovations and Trends in IT in the second decade of 21st century. The agenda is from IEEE Computer Society.
This presentation as made as a tutorial at NCVPRIPG (http://www.iitj.ac.in/ncvpripg/) at IIT Jodhpur on 18-Dec-2013.
Kinect is a multimedia sensor from Microsoft. It is shipped as the touch-free console for Xbox 360 video gaming platform. Kinect comprises an RGB Camera, a Depth Sensor (IR Emitter and Camera) and a Microphone Array. It produces a multi-stream video containing RGB, depth, skeleton, and audio streams.
Compared to common depth cameras (laser or Time-of-Flight), the cost of a Kinect is quite low as it uses a novel structured light diffraction and triangulation technology to estimate the depth. In addition, Kinect is equipped with special software to detect human figures and to produce its 20-joints skeletons.
Though Kinect was built for touch-free gaming, its cost effectiveness and human tracking features have proved useful in many indoor applications beyond gaming like robot navigation, surveillance, medical assistance and animation.
The new standard for C++ language has been signed in 2011. This new (extended) language, called C++11, has a number of new semantics (in terms of language constructs) and a number of new standard library support. The major language extensions are discussed in this presentation. The library will be taken up in a later presentation.
The document discusses function call optimization in C++. It provides examples of constructor, base class constructor, and get/set method calls in both debug and release builds. In release builds, the compiler fully optimizes constructor calls and inlines non-virtual functions like get/set methods to improve performance. Only virtual functions cannot be optimized as their call sequence depends on runtime type.
The document discusses the key components of the Standard Template Library (STL) in C++, including containers, iterators, and algorithms. It explains that STL containers manage collections of objects, iterators allow traversing container elements, and algorithms perform operations on elements using iterators. The main STL containers like vector, list, deque, set, and map are introduced along with their basic functionality. Iterators provide a common interface for traversing container elements. Algorithms operate on elements through iterators but are independent of container implementations.
The document discusses object lifetime in C/C++. It covers the fundamentals of object lifetime including construction, use, and destruction. It also describes the different types of objects - static objects which are compiler-managed and have lifetime from program startup to termination, automatic objects which are stack-based and destroyed when they go out of scope, and dynamic objects which are user-managed and allocated on the free store.
This document provides guidance on effective technical documentation. It discusses planning documentation by determining the objective, intended audience, necessary content and approximate length. It also covers tips for clear writing style such as using active voice and avoiding contractions. The goals of technical documentation are clarity, comprehensiveness, conciseness and correctness.
The document provides an overview of reconfigurable computing architectures. It discusses several leading companies in the field including Elixent, QuickSilver, Pact Corp, and Systolix. It then summarizes key reconfigurable computing architectures including D-Fabrix array, Adaptive Computing Machine (ACM), eXtreme Processing Platform (XPP), and PulseDSPTM. The ACM is based on QuickSilver's Self-Reconfigurable Gate Array (SRGA) architecture, which allows fast context switching and random access of the configuration memory.
The document discusses three potential factors that influence women's participation in the workforce: educational systems, technical inclination, and social environment. It explores whether educational systems are a culprit or savior, and whether women have weaker technical skills or are differently abled. Finally, it examines how social environments can be a culprit, through issues like declining sex ratios, workplace discrimination, and domestic discrimination against women with two full-time jobs.
Handling Exceptions In C & C++ [Part B] Ver 2ppd1961
This document discusses exception handling in C++. It provides an overview of how compilers manage exceptional control flow and how functions are instrumented to handle exceptions. It discusses normal vs exceptional function call flows, and the items involved in stack frames like context, finalization, and unwinding. It also summarizes Meyers' guidelines for exception safety, including using destructors to prevent leaks, handling exceptions in constructors, and preventing exceptions from leaving destructors.
The document discusses exception handling in C and C++. It covers exception fundamentals, and techniques for handling exceptions in C such as return values, global variables, goto statements, signals, and termination functions. It also discusses exception handling features introduced in C++ such as try/catch blocks and exception specifications.
The document discusses various models for offshore technology services in the electronics industry. It defines key terms like outsourcing, insourcing, onsite, offsite, and offshore. It describes different software delivery models including the onsite, offsite, offshore, and global delivery models. It discusses factors that determine if work can be done offshore, or is "offshoreable", as well as advantages and disadvantages of outsourcing. It outlines different types of offshore outsourcing like ITO, BPO, and software R&D. Finally, it provides a brief overview of software outsourcing in the electronics industry.
The document discusses various types of smart pointers in C++, including their ownership management policies. It describes common smart pointer policies like destructive copy, deep copy, copy-on-write, and reference counting. Destructive copy transfers ownership on copy by setting the source pointer to null. Deep copy copies the pointed object. Reference counting allows shared ownership by tracking reference counts.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
19. Order of Problems HP-Memory Fabricator Collaboration (2008) Memristor-based Digital Memory 2 nd Solution Problem Order 3 rd 1 st 0 th OPEN – Expected (2012) Memristor Memory has very low MTBF HP’s memristor fabrication (2005) There should be a memristor Chua’s Proof – Charge dependent Flux (1971) There should be a 4 th 2-terminal electrical element