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This document presents the πRT-calculus, a calculus for modeling mobile real-time processes. It extends the π-calculus with a timeout operator to model real-time aspects. The document covers the syntax and semantics of the π-calculus and πRT-calculus. It also discusses design choices like having a global clock and discrete time. An example of a mobile video streaming system is used to illustrate the πRT-calculus. The document concludes by discussing future work, like developing timed bisimulation and extending to continuous time.

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Safety Verification of Deep Neural Networks_.pdf

This document presents a framework for verifying the safety of classification decisions made by deep neural networks. It defines safety as the network producing the same output classification for an input and any perturbations of that input within a bounded region. The framework uses satisfiability modulo theories (SMT) to formally verify safety by attempting to find an adversarial perturbation that causes misclassification. It has been tested on several image classification networks and datasets. The framework provides a method to automatically verify safety properties of deep neural networks.

Computing Information Flow Using Symbolic-Model-Checking_.pdf

This document presents methods for computing information flow and quantifying information leakage in non-probabilistic programs using symbolic model checking. It discusses using binary decision diagrams (BDDs) and algebraic decision diagrams (ADDs) to represent program states and calculate fixed points. Algorithms are provided for symbolically computing min-entropy and Shannon entropy leakage by constructing ADDs representing the program summary and sets of possible outputs. The methods were implemented in a tool called Moped-QLeak and evaluated on example programs. Future work includes supporting recursive programs and using other symbolic verification approaches.

Data Structure: Algorithm and analysis

This document discusses algorithms and analysis of algorithms. It covers key concepts like time complexity, space complexity, asymptotic notations, best case, worst case and average case time complexities. Examples are provided to illustrate linear, quadratic and logarithmic time complexities. Common sorting algorithms like quicksort, mergesort, heapsort, bubblesort and insertionsort are summarized along with their time and space complexities.

Time andspacecomplexity

This document discusses time and space complexity analysis of algorithms. It analyzes the time complexity of bubble sort, which is O(n^2) as each pass through the array requires n-1 comparisons and there are n passes needed. Space complexity is typically a secondary concern to time complexity. Time complexity analysis allows comparison of algorithms to determine efficiency and whether an algorithm will complete in a reasonable time for a given input size. NP-complete problems cannot be solved in polynomial time but can be verified in polynomial time.

Speaker Diarization

This document discusses speaker diarization, which is the process of segmenting an audio stream into homogeneous segments according to speaker identity. It covers feature extraction methods like MFCCs, segmentation using Bayesian Information Criteria to compare Gaussian mixture models, and clustering algorithms like k-means and hierarchical agglomerative clustering. Dendrogram visualizations are used to identify natural speaker clusters. The overall goal is to partition audio recordings of discussions or debates into homogeneous segments to attribute speech segments to individual speakers.

Complexity analysis in Algorithms

1) The document discusses complexity analysis of algorithms, which involves determining the time efficiency of algorithms by counting the number of basic operations performed based on input size.
2) It covers motivations for complexity analysis, machine independence, and analyzing best, average, and worst case complexities.
3) Simple rules are provided for determining the complexity of code structures like loops, nested loops, if/else statements, and switch cases based on the number of iterations and branching.

how to calclute time complexity of algortihm

This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.

Computational Complexity

This document discusses computational complexity and analyzing the running time of algorithms. It defines big-O notation, which is used to classify algorithms according to their worst-case performance as the problem size increases. Examples are provided of algorithms with running times that are O(1), O(log N), O(N), O(N log N), O(N^2), O(N^3), and O(2^N). The growth rates of these functions from slowest to fastest are also listed.

Safety Verification of Deep Neural Networks_.pdf

This document presents a framework for verifying the safety of classification decisions made by deep neural networks. It defines safety as the network producing the same output classification for an input and any perturbations of that input within a bounded region. The framework uses satisfiability modulo theories (SMT) to formally verify safety by attempting to find an adversarial perturbation that causes misclassification. It has been tested on several image classification networks and datasets. The framework provides a method to automatically verify safety properties of deep neural networks.

Computing Information Flow Using Symbolic-Model-Checking_.pdf

This document presents methods for computing information flow and quantifying information leakage in non-probabilistic programs using symbolic model checking. It discusses using binary decision diagrams (BDDs) and algebraic decision diagrams (ADDs) to represent program states and calculate fixed points. Algorithms are provided for symbolically computing min-entropy and Shannon entropy leakage by constructing ADDs representing the program summary and sets of possible outputs. The methods were implemented in a tool called Moped-QLeak and evaluated on example programs. Future work includes supporting recursive programs and using other symbolic verification approaches.

Data Structure: Algorithm and analysis

This document discusses algorithms and analysis of algorithms. It covers key concepts like time complexity, space complexity, asymptotic notations, best case, worst case and average case time complexities. Examples are provided to illustrate linear, quadratic and logarithmic time complexities. Common sorting algorithms like quicksort, mergesort, heapsort, bubblesort and insertionsort are summarized along with their time and space complexities.

Time andspacecomplexity

This document discusses time and space complexity analysis of algorithms. It analyzes the time complexity of bubble sort, which is O(n^2) as each pass through the array requires n-1 comparisons and there are n passes needed. Space complexity is typically a secondary concern to time complexity. Time complexity analysis allows comparison of algorithms to determine efficiency and whether an algorithm will complete in a reasonable time for a given input size. NP-complete problems cannot be solved in polynomial time but can be verified in polynomial time.

Speaker Diarization

This document discusses speaker diarization, which is the process of segmenting an audio stream into homogeneous segments according to speaker identity. It covers feature extraction methods like MFCCs, segmentation using Bayesian Information Criteria to compare Gaussian mixture models, and clustering algorithms like k-means and hierarchical agglomerative clustering. Dendrogram visualizations are used to identify natural speaker clusters. The overall goal is to partition audio recordings of discussions or debates into homogeneous segments to attribute speech segments to individual speakers.

Complexity analysis in Algorithms

1) The document discusses complexity analysis of algorithms, which involves determining the time efficiency of algorithms by counting the number of basic operations performed based on input size.
2) It covers motivations for complexity analysis, machine independence, and analyzing best, average, and worst case complexities.
3) Simple rules are provided for determining the complexity of code structures like loops, nested loops, if/else statements, and switch cases based on the number of iterations and branching.

how to calclute time complexity of algortihm

This document discusses algorithm analysis and complexity. It defines key terms like asymptotic complexity, Big-O notation, and time complexity. It provides examples of analyzing simple algorithms like a sum function to determine their time complexity. Common analyses include looking at loops, nested loops, and sequences of statements. The goal is to classify algorithms according to their complexity, which is important for large inputs and machine-independent. Algorithms are classified based on worst, average, and best case analyses.

Computational Complexity

This document discusses computational complexity and analyzing the running time of algorithms. It defines big-O notation, which is used to classify algorithms according to their worst-case performance as the problem size increases. Examples are provided of algorithms with running times that are O(1), O(log N), O(N), O(N log N), O(N^2), O(N^3), and O(2^N). The growth rates of these functions from slowest to fastest are also listed.

EuroPython 2017 - PyData - Deep Learning your Broadband Network @ HOME

45 min talk about collecting home network performance measures, analyzing and forecasting time series data, and building anomaly detection system.
In this talk, we will go through the whole process of data mining and knowledge discovery. Firstly we write a script to run speed test periodically and log the metric. Then we parse the log data and convert them into a time series and visualize the data for a certain period.
Next we conduct some data analysis; finding trends, forecasting, and detecting anomalous data. There will be several statistic or deep learning techniques used for the analysis; ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short Term Memory).

Algorithm And analysis Lecture 03& 04-time complexity.

This document discusses algorithm efficiency and complexity analysis. It defines key terms like algorithms, asymptotic complexity, Big O notation, and different complexity classes. It provides examples of analyzing time complexity for different algorithms like loops, nested loops, and recursive functions. The document explains that Big O notation allows analyzing algorithms independent of machine or input by focusing on the highest order term as the problem size increases. Overall, the document introduces methods for measuring an algorithm's efficiency and analyzing its time and space complexity asymptotically.

Introduction to Algorithms

This document discusses algorithm analysis and complexity. It introduces algorithm analysis as a way to predict and compare algorithm performance. Different algorithms for computing factorials and finding the maximum subsequence sum are presented, along with their time complexities. The importance of efficient algorithms for problems involving large datasets is discussed.

Algorithm Analyzing

This slide explain complexity of an algorithm. Explain from theory perspective. At the end of slide, I also show the test result to prove the theory. Pleas, read this slide to improve your code quality .
This slide is exported from Ms. Power
Point to PDF.

Analysis of Algorithm

The document discusses approximation algorithms and genetic algorithms for solving optimization problems like the traveling salesman problem (TSP) and vertex cover problem. It provides examples of approximation algorithms for these NP-hard problems, including algorithms that find near-optimal solutions within polynomial time. Genetic algorithms are also presented as an approach to solve TSP and other problems by encoding potential solutions and applying genetic operators like crossover and mutation.

Complexity of Algorithm

This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.

Performance analysis(Time & Space Complexity)

The document discusses algorithms analysis and design. It covers time complexity and space complexity analysis using approaches like counting the number of basic operations like assignments, comparisons etc. and analyzing how they vary with the size of the input. Common complexities like constant, linear, quadratic and cubic are explained with examples. Frequency count method is presented to determine tight bounds of time and space complexity of algorithms.

Algorithm analysis

This document provides an overview of algorithm analysis. It discusses how to analyze the time efficiency of algorithms by counting the number of operations and expressing efficiency using growth functions. Different common growth rates like constant, linear, quadratic, and exponential are introduced. Examples are provided to demonstrate how to determine the growth rate of different algorithms, including recursive algorithms, by deriving their time complexity functions. The key aspects covered are estimating algorithm runtime, comparing growth rates of algorithms, and using Big O notation to classify algorithms by their asymptotic behavior.

Unit i basic concepts of algorithms

The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.

Introduction to Algorithms Complexity Analysis

The document provides an overview of algorithms, including definitions, types, characteristics, and analysis. It begins with step-by-step algorithms to add two numbers and describes the difference between algorithms and pseudocode. It then covers algorithm design approaches, characteristics, classification based on implementation and logic, and analysis methods like a priori and posteriori. The document emphasizes that algorithm analysis estimates resource needs like time and space complexity based on input size.

Fundamentals of the Analysis of Algorithm Efficiency

This document discusses analyzing the efficiency of algorithms. It introduces the framework for analyzing algorithms in terms of time and space complexity. Time complexity indicates how fast an algorithm runs, while space complexity measures the memory required. The document outlines steps for analyzing algorithms, including measuring input size, determining the basic operations, calculating frequency counts of operations, and expressing efficiency in Big O notation order of growth. Worst-case, best-case, and average-case time complexities are also discussed.

Analysis of Algorithum

The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts like best case, worst case, and average case running times. It explains that worst case analysis is most important and easiest to analyze. The document covers analyzing algorithms using pseudocode, counting primitive operations, and determining asymptotic running time using Big-O notation. Examples are provided to illustrate these concepts, including analyzing algorithms for finding the maximum element in an array and computing prefix averages.

Dsp lab _eec-652__vi_sem_18012013

i. The linear convolution of two sequences was calculated using the conv command in MATLAB. The input sequences, individual sequences, and convolved output were plotted.
ii. Linear convolution was also calculated using the DFT and IDFT. The sequences were padded with zeros and transformed to the frequency domain using FFT. The transformed sequences were multiplied and inverse transformed using IFFT to obtain the circular convolution result.
iii. The circular convolution result using DFT/IDFT was the same as the linear convolution using the conv command, demonstrating the equivalence between linear and circular convolution in the frequency domain.

Europy17_dibernardo

Slides of my talk at #EuroPython2017 about "Big Data Analytics at the Max Planck Computing and Data Facility: GPU Crystallography with Python"

Dsp manual

This document discusses the TMS320C6713 digital signal processor (DSP) development kit (DSK). The DSK features the high-performance TMS320C6713 floating-point DSP chip capable of 1350 million floating point operations per second. The DSK allows for efficient development and testing of applications for the C6713 DSP. It includes onboard memory, an analog interface circuit for data conversion, I/O ports, and JTAG emulation support. The DSK also includes a stereo codec for analog audio input/output.

Lec7

The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental studies vs theoretical analysis, pseudocode, primitive operations, counting operations, big-O notation, and analyzing algorithms to determine asymptotic running time. As an example, it analyzes two algorithms for computing prefix averages - one with quadratic running time O(n^2) and one with linear running time O(n).

Algorithem complexity in data sructure

The document discusses algorithms and their complexity. It provides an example of a search algorithm and analyzes its time complexity. The dominating operations are comparisons, and the data size is the length of the array. The algorithm's worst-case time complexity is O(n), as it may require searching through the entire array of length n. The average time complexity depends on the probability distribution of the input data.

Dsp lab manual

The document describes MATLAB software and its uses for signal processing. MATLAB is a matrix-based program for scientific and engineering computation. It provides built-in functions for technical computation, graphics, and animation. The Signal Processing Toolbox contains functions for filtering, Fourier transforms, convolution, and filter design. The document lists some important MATLAB commands and frequently used signal processing functions, along with their syntax and purpose. It also describes the basic windows of the MATLAB interface and provides examples of generating common continuous and discrete time signals using MATLAB code.

Asymptotics 140510003721-phpapp02

The presentation covered time and space complexity, average and worst case analysis, and asymptotic notations. It defined key concepts like time complexity measures the number of operations, space complexity measures memory usage, and worst case analysis provides an upper bound on running time. Common asymptotic notations like Big-O, Omega, and Theta were explained, and how they are used to compare how functions grow relative to each other as input size increases.

Parallel algorithms

This document discusses parallel algorithms and models of parallel computation. It begins with an overview of parallelism and the PRAM model of computation. It then discusses different models of concurrent versus exclusive access to shared memory. Several parallel algorithms are presented, including list ranking in O(log n) time using an EREW PRAM algorithm and finding the maximum of n elements in O(1) time using a CRCW PRAM algorithm. It analyzes the performance of EREW versus CRCW models and shows how to simulate a CRCW algorithm using EREW in O(log p) time using p processors.

5_2019_01_12!09_25_57_AM.ppt

This document provides an overview of various topics related to control system modeling and analysis using MATLAB. It begins with introducing different ways to build models for linear time-invariant systems including continuous and discrete time transfer functions and state-space models. It then discusses how to combine models using block diagram manipulation functions in MATLAB. The document covers analyzing the transient response using step, impulse and ramp inputs. It also introduces frequency response analysis using Bode plots and stability analysis using Nyquist plots. The overall summary is that the document provides an introduction to modeling and analyzing control systems in MATLAB focusing on transfer functions, state-space models, transient response and frequency domain analysis.

Linux capacity planning

Rodrigo Campos presented on Linux systems capacity planning. He discussed performance monitoring tools like Sysstat and common metrics like CPU usage. He explained concepts from queueing theory like utilization, Little's Law, and using modeling tools like PDQ to create what-if scenarios of system performance. Campos provided an example of modeling a web application using a customer behavior model to understand and optimize performance bottlenecks.

EuroPython 2017 - PyData - Deep Learning your Broadband Network @ HOME

45 min talk about collecting home network performance measures, analyzing and forecasting time series data, and building anomaly detection system.
In this talk, we will go through the whole process of data mining and knowledge discovery. Firstly we write a script to run speed test periodically and log the metric. Then we parse the log data and convert them into a time series and visualize the data for a certain period.
Next we conduct some data analysis; finding trends, forecasting, and detecting anomalous data. There will be several statistic or deep learning techniques used for the analysis; ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short Term Memory).

Algorithm And analysis Lecture 03& 04-time complexity.

This document discusses algorithm efficiency and complexity analysis. It defines key terms like algorithms, asymptotic complexity, Big O notation, and different complexity classes. It provides examples of analyzing time complexity for different algorithms like loops, nested loops, and recursive functions. The document explains that Big O notation allows analyzing algorithms independent of machine or input by focusing on the highest order term as the problem size increases. Overall, the document introduces methods for measuring an algorithm's efficiency and analyzing its time and space complexity asymptotically.

Introduction to Algorithms

This document discusses algorithm analysis and complexity. It introduces algorithm analysis as a way to predict and compare algorithm performance. Different algorithms for computing factorials and finding the maximum subsequence sum are presented, along with their time complexities. The importance of efficient algorithms for problems involving large datasets is discussed.

Algorithm Analyzing

This slide explain complexity of an algorithm. Explain from theory perspective. At the end of slide, I also show the test result to prove the theory. Pleas, read this slide to improve your code quality .
This slide is exported from Ms. Power
Point to PDF.

Analysis of Algorithm

The document discusses approximation algorithms and genetic algorithms for solving optimization problems like the traveling salesman problem (TSP) and vertex cover problem. It provides examples of approximation algorithms for these NP-hard problems, including algorithms that find near-optimal solutions within polynomial time. Genetic algorithms are also presented as an approach to solve TSP and other problems by encoding potential solutions and applying genetic operators like crossover and mutation.

Complexity of Algorithm

This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.

Performance analysis(Time & Space Complexity)

The document discusses algorithms analysis and design. It covers time complexity and space complexity analysis using approaches like counting the number of basic operations like assignments, comparisons etc. and analyzing how they vary with the size of the input. Common complexities like constant, linear, quadratic and cubic are explained with examples. Frequency count method is presented to determine tight bounds of time and space complexity of algorithms.

Algorithm analysis

This document provides an overview of algorithm analysis. It discusses how to analyze the time efficiency of algorithms by counting the number of operations and expressing efficiency using growth functions. Different common growth rates like constant, linear, quadratic, and exponential are introduced. Examples are provided to demonstrate how to determine the growth rate of different algorithms, including recursive algorithms, by deriving their time complexity functions. The key aspects covered are estimating algorithm runtime, comparing growth rates of algorithms, and using Big O notation to classify algorithms by their asymptotic behavior.

Unit i basic concepts of algorithms

The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.

Introduction to Algorithms Complexity Analysis

The document provides an overview of algorithms, including definitions, types, characteristics, and analysis. It begins with step-by-step algorithms to add two numbers and describes the difference between algorithms and pseudocode. It then covers algorithm design approaches, characteristics, classification based on implementation and logic, and analysis methods like a priori and posteriori. The document emphasizes that algorithm analysis estimates resource needs like time and space complexity based on input size.

Fundamentals of the Analysis of Algorithm Efficiency

This document discusses analyzing the efficiency of algorithms. It introduces the framework for analyzing algorithms in terms of time and space complexity. Time complexity indicates how fast an algorithm runs, while space complexity measures the memory required. The document outlines steps for analyzing algorithms, including measuring input size, determining the basic operations, calculating frequency counts of operations, and expressing efficiency in Big O notation order of growth. Worst-case, best-case, and average-case time complexities are also discussed.

Analysis of Algorithum

The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts like best case, worst case, and average case running times. It explains that worst case analysis is most important and easiest to analyze. The document covers analyzing algorithms using pseudocode, counting primitive operations, and determining asymptotic running time using Big-O notation. Examples are provided to illustrate these concepts, including analyzing algorithms for finding the maximum element in an array and computing prefix averages.

Dsp lab _eec-652__vi_sem_18012013

i. The linear convolution of two sequences was calculated using the conv command in MATLAB. The input sequences, individual sequences, and convolved output were plotted.
ii. Linear convolution was also calculated using the DFT and IDFT. The sequences were padded with zeros and transformed to the frequency domain using FFT. The transformed sequences were multiplied and inverse transformed using IFFT to obtain the circular convolution result.
iii. The circular convolution result using DFT/IDFT was the same as the linear convolution using the conv command, demonstrating the equivalence between linear and circular convolution in the frequency domain.

Europy17_dibernardo

Slides of my talk at #EuroPython2017 about "Big Data Analytics at the Max Planck Computing and Data Facility: GPU Crystallography with Python"

Dsp manual

This document discusses the TMS320C6713 digital signal processor (DSP) development kit (DSK). The DSK features the high-performance TMS320C6713 floating-point DSP chip capable of 1350 million floating point operations per second. The DSK allows for efficient development and testing of applications for the C6713 DSP. It includes onboard memory, an analog interface circuit for data conversion, I/O ports, and JTAG emulation support. The DSK also includes a stereo codec for analog audio input/output.

Lec7

The document discusses algorithm analysis and asymptotic analysis. It introduces key concepts such as algorithms, running time analysis, experimental studies vs theoretical analysis, pseudocode, primitive operations, counting operations, big-O notation, and analyzing algorithms to determine asymptotic running time. As an example, it analyzes two algorithms for computing prefix averages - one with quadratic running time O(n^2) and one with linear running time O(n).

Algorithem complexity in data sructure

The document discusses algorithms and their complexity. It provides an example of a search algorithm and analyzes its time complexity. The dominating operations are comparisons, and the data size is the length of the array. The algorithm's worst-case time complexity is O(n), as it may require searching through the entire array of length n. The average time complexity depends on the probability distribution of the input data.

Dsp lab manual

The document describes MATLAB software and its uses for signal processing. MATLAB is a matrix-based program for scientific and engineering computation. It provides built-in functions for technical computation, graphics, and animation. The Signal Processing Toolbox contains functions for filtering, Fourier transforms, convolution, and filter design. The document lists some important MATLAB commands and frequently used signal processing functions, along with their syntax and purpose. It also describes the basic windows of the MATLAB interface and provides examples of generating common continuous and discrete time signals using MATLAB code.

Asymptotics 140510003721-phpapp02

The presentation covered time and space complexity, average and worst case analysis, and asymptotic notations. It defined key concepts like time complexity measures the number of operations, space complexity measures memory usage, and worst case analysis provides an upper bound on running time. Common asymptotic notations like Big-O, Omega, and Theta were explained, and how they are used to compare how functions grow relative to each other as input size increases.

Parallel algorithms

This document discusses parallel algorithms and models of parallel computation. It begins with an overview of parallelism and the PRAM model of computation. It then discusses different models of concurrent versus exclusive access to shared memory. Several parallel algorithms are presented, including list ranking in O(log n) time using an EREW PRAM algorithm and finding the maximum of n elements in O(1) time using a CRCW PRAM algorithm. It analyzes the performance of EREW versus CRCW models and shows how to simulate a CRCW algorithm using EREW in O(log p) time using p processors.

EuroPython 2017 - PyData - Deep Learning your Broadband Network @ HOME

EuroPython 2017 - PyData - Deep Learning your Broadband Network @ HOME

Algorithm And analysis Lecture 03& 04-time complexity.

Algorithm And analysis Lecture 03& 04-time complexity.

Introduction to Algorithms

Introduction to Algorithms

Algorithm Analyzing

Algorithm Analyzing

Analysis of Algorithm

Analysis of Algorithm

Complexity of Algorithm

Complexity of Algorithm

Performance analysis(Time & Space Complexity)

Performance analysis(Time & Space Complexity)

Algorithm analysis

Algorithm analysis

Unit i basic concepts of algorithms

Unit i basic concepts of algorithms

Introduction to Algorithms Complexity Analysis

Introduction to Algorithms Complexity Analysis

Fundamentals of the Analysis of Algorithm Efficiency

Fundamentals of the Analysis of Algorithm Efficiency

Analysis of Algorithum

Analysis of Algorithum

Dsp lab _eec-652__vi_sem_18012013

Dsp lab _eec-652__vi_sem_18012013

Europy17_dibernardo

Europy17_dibernardo

Dsp manual

Dsp manual

Lec7

Lec7

Algorithem complexity in data sructure

Algorithem complexity in data sructure

Dsp lab manual

Dsp lab manual

Asymptotics 140510003721-phpapp02

Asymptotics 140510003721-phpapp02

Parallel algorithms

Parallel algorithms

5_2019_01_12!09_25_57_AM.ppt

This document provides an overview of various topics related to control system modeling and analysis using MATLAB. It begins with introducing different ways to build models for linear time-invariant systems including continuous and discrete time transfer functions and state-space models. It then discusses how to combine models using block diagram manipulation functions in MATLAB. The document covers analyzing the transient response using step, impulse and ramp inputs. It also introduces frequency response analysis using Bode plots and stability analysis using Nyquist plots. The overall summary is that the document provides an introduction to modeling and analyzing control systems in MATLAB focusing on transfer functions, state-space models, transient response and frequency domain analysis.

Linux capacity planning

Rodrigo Campos presented on Linux systems capacity planning. He discussed performance monitoring tools like Sysstat and common metrics like CPU usage. He explained concepts from queueing theory like utilization, Little's Law, and using modeling tools like PDQ to create what-if scenarios of system performance. Campos provided an example of modeling a web application using a customer behavior model to understand and optimize performance bottlenecks.

Tutorial: The Role of Event-Time Analysis Order in Data Streaming

This document provides a tutorial on the role of event-time order in data streaming analysis. The agenda covers motivations and examples of data streaming and stream processing engines, causes of out-of-order data and solutions to enforce total ordering, pros and cons of total ordering, and relaxation of total ordering using watermarks. Enforcing total ordering through techniques like sorting tuples is computationally expensive but provides benefits like determinism and synchronization. However, it may be an overkill for some applications and increase latency.

10 Discrete Time Controller Design.pptx

This document discusses digital control system design. It begins with an overview of discretization methods and the effect of zero-order hold. Examples are provided to illustrate discretization and digital controller design. Design of PI and PID digital controllers via pole placement is covered. An example designs a cruise control system for a car using a digital PI controller. The controller is designed by deriving specifications from the design problem, discretizing the plant, determining controller parameters, and simulating the closed-loop response. The controller meets specifications when applied to both the discretized and actual continuous-time plant.

Robust PID Controller Design for Non-Minimum Phase Systems using Magnitude Op...

This document discusses two approaches for designing a controller for non-minimum phase systems: 1) the magnitude optimum and multiple integration method, and 2) a numerical optimization approach. The magnitude optimum method uses areas calculated from the process step response to determine the PID controller parameters, eliminating the need to estimate process parameters directly. The numerical optimization approach formulates the controller design as an optimization problem to minimize sensitivity functions in the closed-loop system. Both approaches are presented as ways to design robust controllers for non-minimum phase systems.

RxJava applied [JavaDay Kyiv 2016]

The document provides an overview of RxJava and its advantages over traditional Java streams and callbacks. It discusses key RxJava concepts like Observables, Observers, and Subscriptions. It demonstrates how to create Observables, subscribe to them, and compose operations like filter, map, and zip. It shows how to leverage schedulers to control threading. The document also provides examples of using RxJava with HTTP requests and the Twitter API to asynchronously retrieve user profiles and tweets. It highlights scenarios where RxJava is useful, like handling asynchronous operations, and discusses some pitfalls like its learning curve and need to understand backpressure.

Optimization of Continuous Queries in Federated Database and Stream Processin...

The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.

Keynote: Building and Operating A Serverless Streaming Runtime for Apache Bea...

Apache Beam is Flink’s sibling in the Apache family of streaming processing frameworks. The Beam and Flink teams work closely together on advancing what is possible in streaming processing, including Streaming SQL extensions and code interoperability on both platforms.
Beam was originally developed at Google as the amalgamation of its internal batch and streaming frameworks to power the exabyte-scale data processing for Gmail, YouTube and Ads. It now powers a fully-managed, serverless service Google Cloud Dataflow, as well as is available to run in other Public Clouds and on-premises when deployed in portability mode on Apache Flink, Spark, Samza and other runners. Users regularly run distributed data processing jobs on Beam spanning tens of thousands of CPU cores and processing millions of events per second.
In this session, Sergei Sokolenko, Cloud Dataflow product manager, and Reuven Lax, the founding member of the Dataflow and Beam team, will share Google’s learnings from building and operating a global streaming processing infrastructure shared by thousands of customers, including:
safe deployment to dozens of geographic locations,
resource autoscaling to minimize processing costs,
separating compute and state storage for better scaling behavior,
dynamic work rebalancing of work items away from overutilized worker nodes,
offering a throughput-optimized batch processing capability with the same API as streaming,
grouping and joining of 100s of Terabytes in a hybrid in-memory/on-desk file system,
integrating with the Google Cloud security ecosystem, and other lessons.
Customers benefit from these advances through faster execution of jobs, resource savings, and a fully managed data processing environment that runs in the Cloud and removes the need to manage infrastructure.

EO notes Lecture 27 Project Management 2.ppt

The document discusses project management techniques including PERT and CPM. It explains that PERT and CPM are used to plan, schedule, and coordinate large projects by graphically displaying project activities, estimating project duration, identifying critical activities, and determining float. The framework involves defining activities and relationships, drawing network diagrams, estimating activity times, computing the critical path, and using the network to plan and control the project. Key terms and how to draw network diagrams are also covered.

Automated Parameterization of Performance Models from Measurements

This is a tutorial presented in ICPE 2016 (https://icpe2016.spec.org/). In this tutorial, we present the problem of estimating parameters of performance models from measurements of real systems and discuss algorithms that can support researchers and practitioners in this task. The focus lies on performance models based on queueing systems, where the estimation of request arrival rates and service demands is a required input to the model. In the tutorial, we review existing estimation methods for service demands and present models to characterize time-varying arrival processes. The tutorial also demonstrates the use of relevant tools that automate demand estimation, such as LibRede, FG and M3A.

Rx workshop

Workshop slides from the Alt.Net Seattle 2011 workshop. Presented by Wes Dyer and Ryan Riley. Get the slides and the workshop code at http://rxworkshop.codeplex.com/

Design, analysis and controlling of an offshore load transfer system Dimuthu ...

MAS501 Control Theory2-Autumn 2013 Design, analysis and controlling of an offshore load transfer system Dimuthu Dharshana

Super COMPUTING Journal

This document discusses a supercomputer called HYPE-2 built by Santosh Pandey, Ram Sharan Chaulagain, and Prakash Gyawali under the supervision of Prof. Dr. Subarna Shakya. It provides an overview of multiprocessor and multicore systems and discusses how HYPE-2 uses a distributed memory architecture with dynamic scaling to achieve high performance computing capabilities for research applications like cryptography, data mining, and weather forecasting. Performance tests showed near-linear speedup as nodes were added, with the system able to handle complex computations through inter-process communication, though it is not as powerful as larger supercomputers.

DSP_2018_FOEHU - Lec 05 - Digital Filters

The document discusses digital filters and their design process. It explains that the design process involves four main steps: approximation, realization, studying arithmetic errors, and implementation.
For approximation, direct and indirect methods are used to generate a transfer function that satisfies the filter specifications. Realization generates a filter network from the transfer function. Studying arithmetic errors examines how quantization affects filter performance. Implementation realizes the filter in either software or hardware.
The document also outlines the basic building blocks of digital filters, including adders, multipliers, and delay elements. It introduces linear time-invariant digital filters and explains their input-output relationship using difference equations and the z-transform.

My Postdoctoral Research

Automatic differentiation, specializing in compiler technology, parallel network computing, numerical methods and discrete algorithms.

[EUC2016] FFWD: latency-aware event stream processing via domain-specific loa...

Tools and applications for event stream processing and real-time analytics are getting a huge hype these days on a wide range of application scenarios, from the smallest Internet of Things (IoT) embedded sensor to the most popular Social Network feed. Unfortunately, dealing with this kind of input rises some issues that can easily mine the real-time analysis requirement due to an unexpected overload of the system; this happens as the processing time may strongly depend on the single event content, while the event arrival rate may vary unpredictably over time. In this work, we propose Fast Forward With Degradation (FFWD), a latency-aware load shedding framework that exploits performance degradation techniques to adapt the throughput of the application to the size of the input, allowing the system to have a fast and reliable response time in case of overloading. Moreover, we show how different domain-specific policies can guarantee a reasonable accuracy of the aggregated output metrics.
Full paper: http://ieeexplore.ieee.org/document/7982234/

Simulation of Signal Reflection in Digital Design

A project made for SJSU-CMPE296B to simulate signal reflection phenomenon along signal line in digital design.

Discretizing of linear systems with time-delay Using method of Euler’s and Tu...

Delays deteriorate the control performance and could destabilize the overall system in the theory of discretetime
signals and dynamic systems. Whenever a computer is used in measurement, signal processing or control
applications, the data as seen from the computer and systems involved are naturally discrete-time because a
computer executes program code at discrete points of time. Theory of discrete-time dynamic signals and systems
is useful in design and analysis of control systems, signal filters, state estimators and model estimation from
time-series of process data system identification. In this paper, a new approximated discretization method and
digital design for control systems with delays is proposed. System is transformed to a discrete-time model with
time delays. To implement the digital modeling, we used the z-transfer functions matrix which is a useful model
type of discrete-time systems, being analogous to the Laplace-transform for continuous-time systems. The most
important use of the z-transform is for defining z-transfer functions matrix is employed to obtain an extended
discrete-time. The proposed method can closely approximate the step response of the original continuous timedelayed
control system by choosing various of energy loss level. Illustrative example is simulated to demonstrate
the effectiveness of the developed method.\

ODSC 2019: Sessionisation via stochastic periods for root event identification

In todays world majority of information is generated by self sustaining systems like various kinds of bots, crawlers, servers, various online services, etc. This information is flowing on the axis of time and is generated by these actors under some complex logic. For example, a stream of buy/sell order requests by an Order Gateway in financial world, or a stream of web requests by a monitoring / crawling service in the web world, or may be a hacker's bot sitting on internet and attacking various computers. Although we may not be able to know the motive or intention behind these data sources. But via some unsupervised techniques we can try to infer the pattern or correlate the events based on their multiple occurrences on the axis of time. Associating a chain of events in order of time helps in doing a root event analysis. In certain cases a time ordered correlation and root event identification is good enough to automatically identify signatures of various malicious actors and take appropriate corrective actions to stop cyber attacks, stop malicious social campaigns, etc.
Sessionisation is one such unsupervised technique that tries to find the signal in a stream of events associated with a timestamp. In the ideal world it would resolve to finding periods with a mixture of sinusoidal waves. But for the real world this is a much complex activity, as even the systematic events generated by machines over the internet behave in a much erratic manner. So the notion of a period for a signal also changes in the real world. We can no longer associate it with a number, it has to be treated as a random variable, with expected values and associated variance. Hence we need to model "Stochastic periods" and learn their probability distributions in an unsupervised manner.
The main focus of this talk will be to showcase applied data science techniques to discover stochastic periods. There are many ways to obtain periods in data, so the journey would begin by a walk through of existing techniques like FFT (Fast Fourier Transform) then discuss about Gaussian Mixture Models. After highlighting the short comings of these techniques we will succinctly explain one of the most general non-parametric Bayesian approaches to solve this problem. Without going too deep in the complex math, we will get back to applied data science and discuss a much simpler technique that can solve the same problem if certain assumptions are satisfied.
In this talk we will demonstrate some time based pattern we discovered while working on a security analytics use case that uses Sessionisation. In the talk we will demonstrate such patterns based on an open source malware attack datasets that is available publicly.
Key concepts explained in talk: Sessionisation, Bayesian techniques of Machine Learning, Gaussian Mixture Models, Kernel density estimation, FFT, stochastic periods, probabilistic modelling, Bayesian non-parametric methods

VET4SBO Level 3 module 3 - unit 2 - v0.9 en

This document discusses semantic interoperability and reasoning techniques for heterogeneous IoT devices and data in smart buildings. It describes using ontologies and semantic annotations to model building components, properties, and their relationships. Semantic matching of component inputs and outputs can then enable automatic configuration of monitoring and control systems based on the available devices. Reasoning over the semantic knowledge graph allows reconfiguration when devices are added, removed or properties change over time.

5_2019_01_12!09_25_57_AM.ppt

5_2019_01_12!09_25_57_AM.ppt

Linux capacity planning

Linux capacity planning

Tutorial: The Role of Event-Time Analysis Order in Data Streaming

Tutorial: The Role of Event-Time Analysis Order in Data Streaming

10 Discrete Time Controller Design.pptx

10 Discrete Time Controller Design.pptx

Robust PID Controller Design for Non-Minimum Phase Systems using Magnitude Op...

Robust PID Controller Design for Non-Minimum Phase Systems using Magnitude Op...

RxJava applied [JavaDay Kyiv 2016]

RxJava applied [JavaDay Kyiv 2016]

Optimization of Continuous Queries in Federated Database and Stream Processin...

Optimization of Continuous Queries in Federated Database and Stream Processin...

Keynote: Building and Operating A Serverless Streaming Runtime for Apache Bea...

Keynote: Building and Operating A Serverless Streaming Runtime for Apache Bea...

EO notes Lecture 27 Project Management 2.ppt

EO notes Lecture 27 Project Management 2.ppt

Automated Parameterization of Performance Models from Measurements

Automated Parameterization of Performance Models from Measurements

Rx workshop

Rx workshop

Design, analysis and controlling of an offshore load transfer system Dimuthu ...

Design, analysis and controlling of an offshore load transfer system Dimuthu ...

Super COMPUTING Journal

Super COMPUTING Journal

DSP_2018_FOEHU - Lec 05 - Digital Filters

DSP_2018_FOEHU - Lec 05 - Digital Filters

My Postdoctoral Research

My Postdoctoral Research

[EUC2016] FFWD: latency-aware event stream processing via domain-specific loa...

[EUC2016] FFWD: latency-aware event stream processing via domain-specific loa...

Simulation of Signal Reflection in Digital Design

Simulation of Signal Reflection in Digital Design

Discretizing of linear systems with time-delay Using method of Euler’s and Tu...

Discretizing of linear systems with time-delay Using method of Euler’s and Tu...

ODSC 2019: Sessionisation via stochastic periods for root event identification

ODSC 2019: Sessionisation via stochastic periods for root event identification

VET4SBO Level 3 module 3 - unit 2 - v0.9 en

VET4SBO Level 3 module 3 - unit 2 - v0.9 en

socradar-q1-2024-aviation-industry-report.pdf

SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.

How to write a program in any programming language

How to write a program in any programming language

GreenCode-A-VSCode-Plugin--Dario-Jurisic

Presentation about a VSCode plugin from Dario Jurisic at the GSD Community Stage meetup

Measures in SQL (SIGMOD 2024, Santiago, Chile)

SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374

Transform Your Communication with Cloud-Based IVR Solutions

Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony

2024 eCommerceDays Toulouse - Sylius 2.0.pdf

Sylius 2.0 New features
Unvealing the future

What is Master Data Management by PiLog Group

PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.

Webinar On-Demand: Using Flutter for Embedded

Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.

LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM

Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.

SMS API Integration in Saudi Arabia| Best SMS API Service

Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.

AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App

AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
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✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
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#AIFusionBuddyforBeginners,
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#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks

KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD

Charla impartida en el evento de "KuberTENes Birthday Bash Guadalajara" para celebrar el 10mo. aniversario de Kubernetes #kuberTENes #celebr8k8s #k8s

OpenMetadata Community Meeting - 5th June 2024

The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E

Need for Speed: Removing speed bumps from your Symfony projects ⚡️

No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.

DDS-Security 1.2 - What's New? Stronger security for long-running systems

DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.

Artificia Intellicence and XPath Extension Functions

The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.

Fundamentals of Programming and Language Processors

Fundamentals of Programming and Language Processors

A Study of Variable-Role-based Feature Enrichment in Neural Models of Code

Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia

Empowering Growth with Best Software Development Company in Noida - Deuglo

Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.

LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx

WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.

socradar-q1-2024-aviation-industry-report.pdf

socradar-q1-2024-aviation-industry-report.pdf

How to write a program in any programming language

How to write a program in any programming language

GreenCode-A-VSCode-Plugin--Dario-Jurisic

GreenCode-A-VSCode-Plugin--Dario-Jurisic

Measures in SQL (SIGMOD 2024, Santiago, Chile)

Measures in SQL (SIGMOD 2024, Santiago, Chile)

Transform Your Communication with Cloud-Based IVR Solutions

Transform Your Communication with Cloud-Based IVR Solutions

2024 eCommerceDays Toulouse - Sylius 2.0.pdf

2024 eCommerceDays Toulouse - Sylius 2.0.pdf

What is Master Data Management by PiLog Group

What is Master Data Management by PiLog Group

Webinar On-Demand: Using Flutter for Embedded

Webinar On-Demand: Using Flutter for Embedded

LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM

LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM

SMS API Integration in Saudi Arabia| Best SMS API Service

SMS API Integration in Saudi Arabia| Best SMS API Service

AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App

AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App

KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD

KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD

OpenMetadata Community Meeting - 5th June 2024

OpenMetadata Community Meeting - 5th June 2024

Need for Speed: Removing speed bumps from your Symfony projects ⚡️

Need for Speed: Removing speed bumps from your Symfony projects ⚡️

DDS-Security 1.2 - What's New? Stronger security for long-running systems

DDS-Security 1.2 - What's New? Stronger security for long-running systems

Artificia Intellicence and XPath Extension Functions

Artificia Intellicence and XPath Extension Functions

Fundamentals of Programming and Language Processors

Fundamentals of Programming and Language Processors

A Study of Variable-Role-based Feature Enrichment in Neural Models of Code

A Study of Variable-Role-based Feature Enrichment in Neural Models of Code

Empowering Growth with Best Software Development Company in Noida - Deuglo

Empowering Growth with Best Software Development Company in Noida - Deuglo

LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx

LORRAINE ANDREI_LEQUIGAN_HOW TO USE WHATSAPP.pptx

- 1. A calculus of mobile Real-Time processes A calculus of mobile Real-Time processes lotﬁ.larbaoui@polymtl.ca 16 novembre 2017
- 2. A calculus of mobile Real-Time processes Introduction The π-calculus General design choices and properties The πRT-calculus Example Conclusion
- 3. A calculus of mobile Real-Time processes Introduction Introduction The π-calculus General design choices and properties The πRT-calculus Example Conclusion
- 4. A calculus of mobile Real-Time processes Introduction Modelling Real-time system : c Building models which faithfully represent complex system is a non trivial problem and a prerequisite to the application of formal analysis .d Joseph Sifakis 2001 Process calculi (CCS and pi-calculus etc) : c A mathematical treatment of communicating and concurrent entities that could rival the known theories of sequential programming .d Robin Milner 2010 Real-time process algebra : πRT-calculus Modeling the real-time aspect of systems in a mobile environment and reasoning about dynamic temporal behaviour and dynamic conﬁgurations of systems.
- 5. A calculus of mobile Real-Time processes Introduction Background
- 6. A calculus of mobile Real-Time processes The π-calculus Introduction The π-calculus General design choices and properties The πRT-calculus Example Conclusion
- 7. A calculus of mobile Real-Time processes The π-calculus Syntax P ::= | 0 Inaction | αi .P Action | P1 + P2 Choice | P1|P2 Parallel composition | (νx).P Scoping | [x = y]P Match | A(x1, ..., xn) Agent identiﬁer | !P replication Names : x , y , z The actions αi are : τ Internal , ¯xy Output a name , x(y) Input a name ,¯x(y) Output a reference
- 8. A calculus of mobile Real-Time processes The π-calculus Semantics The semantics of π-calculus is deﬁned by : 1. Reduction binary relations : P → Q 2. Labeled transition : P α −→ Q The inference rules deﬁning the reduction relations and labeled transition. The semantics of the reductions use the notion of Structural congruence
- 9. A calculus of mobile Real-Time processes The π-calculus Structural congruence
- 10. A calculus of mobile Real-Time processes The π-calculus Transition rules
- 11. A calculus of mobile Real-Time processes The π-calculus Transition rules
- 12. A calculus of mobile Real-Time processes The π-calculus Interactions between processes
- 13. A calculus of mobile Real-Time processes General design choices and properties Introduction The π-calculus General design choices and properties The πRT-calculus Example Conclusion
- 14. A calculus of mobile Real-Time processes General design choices and properties Model Features Global clock Discrete time Separation of actions and time events : interleaving model Synchronous time event : Time progresses in a synchronous fashion Time transmission : can result in dynamic temporal behaviour of processes.
- 15. A calculus of mobile Real-Time processes General design choices and properties Model Properties Maximal progress : if P τ −→ P for some P , then for t > O, P (t) −−→ P for no P . Timelock freeness : for all agent P and P α −→ P for some P and α = τ , then for t > 0, P (t) −−→ P for some P . Time visibility : for t > 0, if P (t) −−→ P , then for any name x free in P , (νx)P (t) −−→ (νx)P
- 16. A calculus of mobile Real-Time processes General design choices and properties Model Properties Time determinacy : for t > 0, whenever P (t) −−→ P and P (t) −−→ P , then P ≡ P Time continuity : for t > 0 and u > 0 , P (t+u) −−−−→ P iﬀ there exists P such that P (t) −−→ P and P (u) −−→ P Action persistency : for t > 0, if P (t) −−→ P and P α −→ Q then P α −→ Q for some Q
- 17. A calculus of mobile Real-Time processes General design choices and properties Atomic actions and Idling Non-controllable action (above) and controllable action (below)
- 18. A calculus of mobile Real-Time processes The πRT-calculus Introduction The π-calculus General design choices and properties The πRT-calculus Example Conclusion
- 19. A calculus of mobile Real-Time processes The πRT-calculus πRT-calculus P ::= | 0 Inaction | αi .P Action | P1 + P2 Choice | P1|P2 Parallel composition | P t Q The timeout operator | (νx).P Scoping | [x = y]P Match | A(x1, ..., xn) Agent identiﬁer | !P replication Names : x , y , z . - The actions αi are : τ Internal , ¯xy Output a name , x(y) Input a name ,¯x(y) is not deﬁned ! ! !.
- 20. A calculus of mobile Real-Time processes The πRT-calculus Semantics
- 21. A calculus of mobile Real-Time processes The πRT-calculus Operational semantics
- 22. A calculus of mobile Real-Time processes The πRT-calculus Operational semantics
- 23. A calculus of mobile Real-Time processes Example Introduction The π-calculus General design choices and properties The πRT-calculus Example Conclusion
- 24. A calculus of mobile Real-Time processes Example Network conﬁguration for the mobile streaming video player
- 25. A calculus of mobile Real-Time processes Example Ports of the video player Player def = attach(t, r).Attached(t, r) Attached(t, r) def = 0 t Playing(r) Playing(r) def = video.(0 1 .τplay.Playing(r)) + attach(t, r).rel.Attached(t, r)
- 26. A calculus of mobile Real-Time processes Example Ports of the routers Router(t, rel) def = attach t, rel .rel.Router(t, rel) RouterA def = Router(3, rel1) RouterB def = Router(5, rel2)
- 27. A calculus of mobile Real-Time processes Example Port of the video server Server def = video.Server
- 28. A calculus of mobile Real-Time processes Example The whole system is encoded from the user’s point of view and is as follows : System def = (νvideo)(Server|(νattach, rel1, rel2) (Player|RouterA|RouterB)) .
- 29. A calculus of mobile Real-Time processes Example The whole system is encoded from the user’s point of view and is as follows : System def = (νvideo)(Server|(νattach, rel1, rel2) (Player|RouterA|RouterB)) . Suppose the player ﬁrst attaches to router A. Router A transmits the transmission delay from the server which is 3 time units plus a release link to the player. System τ → (νvideo)(Server|(νattach, rel1, rel2) (Attached(3, rel1)|rel1.Router(3, rel1)|RouterB)) .
- 30. A calculus of mobile Real-Time processes Example The whole system is encoded from the user’s point of view and is as follows : System def = (νvideo)(Server|(νattach, rel1, rel2) (Player|RouterA|RouterB)) . Suppose the player ﬁrst attaches to router A. Router A transmits the transmission delay from the server which is 3 time units plus a release link to the player. System τ → (νvideo)(Server|(νattach, rel1, rel2) (Attached(3, rel1)|rel1.Router(3, rel1)|RouterB)) . System (3) → System .
- 31. A calculus of mobile Real-Time processes Example The whole system is encoded from the user’s point of view and is as follows : System def = (νvideo)(Server|(νattach, rel1, rel2) (Player|RouterA|RouterB)) . Suppose the player ﬁrst attaches to router A. Router A transmits the transmission delay from the server which is 3 time units plus a release link to the player. System τ → (νvideo)(Server|(νattach, rel1, rel2) (Attached(3, rel1)|rel1.Router(3, rel1)|RouterB)) . System (3) → System . where System def = (νvideo)(Server|(νattach, rel1, rel2) (Playing(rel1)|rel1.Router(3, rel1)|RouterB))
- 32. A calculus of mobile Real-Time processes Example After the transmission delay, the player gets the ﬁrst video clip, decodes and processes it for 1 time unit and then plays the clip to the user. System τ → (νvideo)(Server|(νattach, rel1, rel2) (0 1 .τplay.Playing(rel1))| rel1.Router(3, rel1)|RouterB)) .
- 33. A calculus of mobile Real-Time processes Example After the transmission delay, the player gets the ﬁrst video clip, decodes and processes it for 1 time unit and then plays the clip to the user. System τ → (νvideo)(Server|(νattach, rel1, rel2) (0 1 .τplay.Playing(rel1))| rel1.Router(3, rel1)|RouterB)) . (1) → (νvideo)(Server|(νattach, rel1, rel2) (τplay.Playing(rel1))|rel1.Router(3, rel1)|RouterB)) . τ → (νvideo)(Server|(νattach, rel1, rel2) (play.Playing(rel1))|rel1.Router(3, rel1)|RouterB)) play → System
- 34. A calculus of mobile Real-Time processes Example The player moves away from router A and attaches to router B. Router B transmits the new transmission delay from the server which is now 5 time units plus a release link to the player. .
- 35. A calculus of mobile Real-Time processes Example The player moves away from router A and attaches to router B. Router B transmits the new transmission delay from the server which is now 5 time units plus a release link to the player. . System τ → (νvideo)(Server|(νattach, rel1, rel2) (rel1.Attached(5, rel2)| rel1.Router(3, rel1)|rel2.Router(5, rel2))) .
- 36. A calculus of mobile Real-Time processes Example The player moves away from router A and attaches to router B. Router B transmits the new transmission delay from the server which is now 5 time units plus a release link to the player. . System τ → (νvideo)(Server|(νattach, rel1, rel2) (rel1.Attached(5, rel2)| rel1.Router(3, rel1)|rel2.Router(5, rel2))) . τ → (νvideo)(Server|(νattach, rel1, rel2) (Attached(5, rel2)| RouterA|rel2.Router(5, rel2))) (5) → System System def = (νvideo)(Server|(νattach, rel1, rel2) (Playing(rel2)|RouterA|rel2.Router(5, rel2)))
- 37. A calculus of mobile Real-Time processes Example When the player moves away from router B and back to router A again : . System τ → (νvideo)(Server|(νattach, rel1, rel2) (0 1 .τplay.Playing(rel2))| RouterA|rel2.Router(5, rel2))) .
- 38. A calculus of mobile Real-Time processes Example When the player moves away from router B and back to router A again : . System τ → (νvideo)(Server|(νattach, rel1, rel2) (0 1 .τplay.Playing(rel2))| RouterA|rel2.Router(5, rel2))) . (1) → (νvideo)(Server|(νattach, rel1, rel2) (τplay.Playing(rel2))|RouterA|rel2.Router(5, rel2))) .
- 39. A calculus of mobile Real-Time processes Example When the player moves away from router B and back to router A again : . System τ → (νvideo)(Server|(νattach, rel1, rel2) (0 1 .τplay.Playing(rel2))| RouterA|rel2.Router(5, rel2))) . (1) → (νvideo)(Server|(νattach, rel1, rel2) (τplay.Playing(rel2))|RouterA|rel2.Router(5, rel2))) . τ → (νvideo)(Server|(νattach, rel1, rel2) (play.Playing(rel2))|RouterA|rel2.Router(5, rel2))) play → System
- 40. A calculus of mobile Real-Time processes Example After the transmission delay, the player can start receiving and playing the stream : .
- 41. A calculus of mobile Real-Time processes Example After the transmission delay, the player can start receiving and playing the stream : . System τ → (νvideo)(Server|(νattach, rel1, rel2) (rel2.Attached(3, rel1)| rel1.Router(3, rel1)|rel2.Router(5, rel2))) .
- 42. A calculus of mobile Real-Time processes Example After the transmission delay, the player can start receiving and playing the stream : . System τ → (νvideo)(Server|(νattach, rel1, rel2) (rel2.Attached(3, rel1)| rel1.Router(3, rel1)|rel2.Router(5, rel2))) . τ → (νvideo)(Server|(νattach, rel1, rel2) (Attached(3, rel1)| rel1.Router(3, rel1)|RouterB)) (3) → System
- 43. A calculus of mobile Real-Time processes Conclusion Introduction The π-calculus General design choices and properties The πRT-calculus Example Conclusion
- 44. A calculus of mobile Real-Time processes Conclusion Conclusion The πRT-calculus is a temporal extension of a π-calculus by introducing a timeout operator. To continue : Developing timed bisimulation equivalence relation Extending the π-calculus with continuous time Deﬁn the broadcast commnication in the πRT-calculus . This formalism : is simple real-time process algebra is not suitable for real-time programming their mechanisms are too abstract
- 45. A calculus of mobile Real-Time processes Conclusion References Robin Milner A Calculus of Communicating Systems Springer-Verlag, 1980 Davide Sangiorgi,David Walker The Pi-Calculus : A Theory of Mobile Processes Cambridge University Press, 2003 Xavier Nicollin, Joseph Sifakis An Overview and Synthesis on Timed Process Algebras Proceedings of CAV 91, LNCS 575, 1991. Jeremy Y. Lee John Zic On Modeling Real-time Mobile Processes Australian Computer Science Communications Vol. 24 (2002)