C programming_MSBTE_Diploma_Pranoti DokePranoti Doke
"1.1 Structure of ‘C’program, Assembler, Linker, Compiler, Interpreter.
1.2 ‘C’character set-keywords, identifiers, types of constants (Integer, single character, string, and real) variables, scope of variables, concept of ASCII.
1.3 Data types: integer- unsigned, signed, long, float- float, double, character char, string, octal, hexadecimal
1.4 Algorithm and flow chart.
1.5 Formatted input and output statements. Input and output function.
1.6 Operators and expressions:
a. Operators in ‘C’- arithmetic, logical, assignment, relational, increment and decrement, conditional, bit wise, special operators
b. Expressions
c. Precedence and associatively."
"2.1 Decision making if statement (if, if-else, nested if-else), switch –case statement.
2.2 Repetition in ‘C’ (loop control
statement) while, do-while and for loop, break and continue statement, nested loops
"
"3.1 Introduction to Array and its types
3.2 Declaration, initialization of array,
accessing elements of an array, adding,
deleting, sorting & searching.
3.3 Introduction to string Initializing,
declaring and display of string
3.4 String handling functions from standard library (strlen (), strcpy (), strcat (), strcmp(), strlwr(),strupr()):
"
"4.1 Concept and need of functions
4.2 Library functions: Math functions,
String handling functions, other
miscellaneous functions.
4.3 Writing User defined functions, scope of variables.
4.4 Parameter passing: call by value, call by reference.
4.5 Recursive functions
"
"5.1 Concept of pointer and pointer variables, initialization of pointer, call-by reference.
5.2 Pointer arithmetic.
5.3 Handling arrays using pointers
5.4 Handling functions using pointers
"
"6.1 Introduction and Features and Syntax of structure
6.2 Declaration and Initialization of
Structures
6.3 Initializing, assessing structure members using pointers
6.4 Type def, Enumerated Data Type,
using structures in C Program
6.5 Operations on structure."
C programming_MSBTE_Diploma_Pranoti DokePranoti Doke
"1.1 Structure of ‘C’program, Assembler, Linker, Compiler, Interpreter.
1.2 ‘C’character set-keywords, identifiers, types of constants (Integer, single character, string, and real) variables, scope of variables, concept of ASCII.
1.3 Data types: integer- unsigned, signed, long, float- float, double, character char, string, octal, hexadecimal
1.4 Algorithm and flow chart.
1.5 Formatted input and output statements. Input and output function.
1.6 Operators and expressions:
a. Operators in ‘C’- arithmetic, logical, assignment, relational, increment and decrement, conditional, bit wise, special operators
b. Expressions
c. Precedence and associatively."
"2.1 Decision making if statement (if, if-else, nested if-else), switch –case statement.
2.2 Repetition in ‘C’ (loop control
statement) while, do-while and for loop, break and continue statement, nested loops
"
"3.1 Introduction to Array and its types
3.2 Declaration, initialization of array,
accessing elements of an array, adding,
deleting, sorting & searching.
3.3 Introduction to string Initializing,
declaring and display of string
3.4 String handling functions from standard library (strlen (), strcpy (), strcat (), strcmp(), strlwr(),strupr()):
"
"4.1 Concept and need of functions
4.2 Library functions: Math functions,
String handling functions, other
miscellaneous functions.
4.3 Writing User defined functions, scope of variables.
4.4 Parameter passing: call by value, call by reference.
4.5 Recursive functions
"
"5.1 Concept of pointer and pointer variables, initialization of pointer, call-by reference.
5.2 Pointer arithmetic.
5.3 Handling arrays using pointers
5.4 Handling functions using pointers
"
"6.1 Introduction and Features and Syntax of structure
6.2 Declaration and Initialization of
Structures
6.3 Initializing, assessing structure members using pointers
6.4 Type def, Enumerated Data Type,
using structures in C Program
6.5 Operations on structure."
Approaches and techniques for statically finding a multitude of issues in source code have been developed in the past. A core property of these approaches is that they are usually targeted towards finding only a very specific kind of issue and that the effort to develop such an analysis is significant. This strictly limits the number of kinds of issues that can be detected.
In this paper, we discuss a generic approach based on the detection of infeasible paths in code that can discover a wide range of code smells ranging from useless code that hinders comprehension to real bugs. Code issues are identified by calculating the difference between the control-flow graph that contains all technically possible edges and the corresponding graph recorded while performing a more precise analysis using abstract interpretation.
We have evaluated the approach using the Java Development Kit as well as the Qualitas Corpus (a curated collection of over 100 Java Applications) and were able to find thousands of issues across a wide range of categories.
This PPT is intended to provide a thorough coverage of verilog HDL concepts based on fundamental principles of digital design. This is the basic fundamental concept for the programming of the digital electronics.
Keywords, identifiers ,datatypes in C++Ankur Pandey
Everything about Keywords, Identifiers,
Datatypes in C++, in this module you gain knowledge about what is Keywords, Identifiers,
Datatypes in C++ (Object oriented programming).
Approaches and techniques for statically finding a multitude of issues in source code have been developed in the past. A core property of these approaches is that they are usually targeted towards finding only a very specific kind of issue and that the effort to develop such an analysis is significant. This strictly limits the number of kinds of issues that can be detected.
In this paper, we discuss a generic approach based on the detection of infeasible paths in code that can discover a wide range of code smells ranging from useless code that hinders comprehension to real bugs. Code issues are identified by calculating the difference between the control-flow graph that contains all technically possible edges and the corresponding graph recorded while performing a more precise analysis using abstract interpretation.
We have evaluated the approach using the Java Development Kit as well as the Qualitas Corpus (a curated collection of over 100 Java Applications) and were able to find thousands of issues across a wide range of categories.
This PPT is intended to provide a thorough coverage of verilog HDL concepts based on fundamental principles of digital design. This is the basic fundamental concept for the programming of the digital electronics.
Keywords, identifiers ,datatypes in C++Ankur Pandey
Everything about Keywords, Identifiers,
Datatypes in C++, in this module you gain knowledge about what is Keywords, Identifiers,
Datatypes in C++ (Object oriented programming).
Like most imperative languages in the ALGOL tradition, C has facilities for structured programming and allows lexical variable scope and recursion, while a static type system prevents many unintended operations. In C, all executable code is contained within subroutines, which are called "functions" (although not in the strict sense of functional programming). Function parameters are always passed by value. Pass-by-reference is simulated in C by explicitly passing pointer values. C program source text is free-format, using the semicolon as a statement terminator and curly braces for grouping blocks of statements.
These are the outline slides that I used for the Pune Clojure Course.
The slides may not be much useful standalone, but I have uploaded them for reference.
Slideshare hasn't imported my notes, so here's the link to the Google Presentation: https://goo.gl/Gl4Vhm
Haskell is a statically typed, non strict, pure functional programming language. It is often talked and blogged about, but rarely used commercially. This talk starts with a brief overview of the language, then explains how Haskell is evaluated and how it deals with non-determinism and side effects using only pure functions. The suitability of Haskell for real world data science is then discussed, along with some examples of its users, a small Haskell-powered visualization, and an overview of useful packages for data science. Finally, Accelerate is introduced, an embedded DSL for array computations on the GPU, and an ongoing attempt to use it as the basis for a deep learning package.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
2. 7) Which of following are true with respect to arrays?
a) Dynamic arrays are useful for contiguous collection of variables whose number
keeps varying
b) Associative arrays can be used when size of an array is not known as it can be built
as key/value pairs
c) Dynamic arrays can be resized after size is allocated once
d) All of above
8) Which of the following implementation using a systemverilog queue will give you a FIFO
implementation?
a) Use push_front() to put an entry in queue and use pop_front() to get entry from
queue
b) Use push_back() to put an entry in queue and use pop_front() to get entry from
queue
c) Use push_back() to put an entry in queue and use pop_back() to get entry from
queue
9) Identify which of following are packed and which are unpacked arrays?
a) logic status[31:0]
b) reg[31:0] registers[128];
c) Integer data [8][32]
10) Write SystemVerilog code for following operations
a) Create a dynamic array of integers
b) Initialize the array to 10 integers with value 0 to 9
c) Use array methods to randomize the order of array
d) Print the array contents
e) Now use array methods to sort in ascending order
f) Print the array contents
3. Threads and Interprocess communication
1) In following code what happens to threads A() and B() if C() finishes first ?
fork
A();
B();
C();
join_any
2) Analyze following code and answer following:
a) How many concurrent process will this code spawn?
b) If task4() finishes first, how many child processes will be disabled by the
“disable fork” ?
fork
task1();
begin
task2();
task3();
end
begin
task4();
end
join_any
disable fork;
3) How many concurrent processes will be generated by code?
fork
for (int i=0; i < 10; i++ ) begin
ABC();
end
join
4) Which of the following are true with respect to comparing a queue and a mailbox?
a) A mailbox is just a queue with no real difference
b) A queue supports adding, removing or modifying any entry in the queue at any
given time while a mailbox can only be accessed at head of it
c) A mailbox size can be bounded while a queue has no limits in size
5) What is difference between get() and peek() methods in mailbox?
6) What is an event data type and what are its uses ?
5.
Lab Exercises:
1) Simulate a few examples to understand concepts of associative arrays and queues
a) Create a simple associative array of integers indexed with a string
b) Add 3 entries to the associative array
c) Print the full associative array contents using assoc array methods
2) Simulate a few examples to understand concepts of class definition, objects
a) Create a simple packet class with following data members a 32 bit source and
destination address, a dynamic array of data bytes and a 32 bit CRC field
b) Create some sample methods to print the class contents, set the data bytes to a
random size with random values
c) Create a test module and create 10 instances of above packet and call the print
method to display contents of packet
3) Simulate a few examples to understand concept of class inheritance
a) Continue from above exercise
b) Derive an error packet class from packet class defined in previous exercise with
a new data member as “bit error”
Class ErrPacket;
c) In the test module, create an instance of derived packet
d) Declare a base class pointer and assign the derived class object to base class
pointer
e) What happens when you try to access the data member “error” using base class
pointer ?
4) Simulate an example to understand concept of virtual methods
a) Use a reference code where a base class and a derived class has a virtual
method and a nonvirtual method
b) Simulate and understand which of the methods gets called when referencing
each object using different pointers
5) Simulate a few examples to understand constraints and randomization
[TBD]
6) Fork.. join / join_any / join_none example