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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.

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A calculus of mobile Real-Time processes

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.

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.

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.

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.

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.

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.

VAE-type Deep Generative Models

This document provides an overview of VAE-type deep generative models, especially RNNs combined with VAEs. It begins with notations and abbreviations used. The agenda then covers the mathematical formulation of generative models, the Variational Autoencoder (VAE), variants of VAE that combine it with RNNs (VRAE, VRNN, DRAW), a Chainer implementation of Convolutional DRAW, other related models (Inverse DRAW, VAE+GAN), and concludes with challenges of VAE-like generative models.

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.

A calculus of mobile Real-Time processes

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.

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.

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.

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.

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.

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.

VAE-type Deep Generative Models

This document provides an overview of VAE-type deep generative models, especially RNNs combined with VAEs. It begins with notations and abbreviations used. The agenda then covers the mathematical formulation of generative models, the Variational Autoencoder (VAE), variants of VAE that combine it with RNNs (VRAE, VRNN, DRAW), a Chainer implementation of Convolutional DRAW, other related models (Inverse DRAW, VAE+GAN), and concludes with challenges of VAE-like generative models.

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.

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.

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.

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.

Dynamic Programming - Part II

Dynamic Programming design technique is one of the fundamental algorithm design techniques, and possibly one of the ones that are hardest to master for those who did not study it formally. In these slides (which are continuation of part 1 slides), we cover two problems: maximum value contiguous subarray, and maximum increasing subsequence.

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.

Predicting organic reaction outcomes with weisfeiler lehman network

This document discusses neural message passing networks for modeling quantum chemistry. It defines message passing networks as having message functions that update node states based on neighboring node states, vertex update functions that update node states based to accumulated messages, and a readout function that produces an output for the full graph. It provides examples of specific message, update, and readout functions used in existing message passing models like interaction networks and molecular graph convolutions.

Deep Learning: R with Keras and TensorFlow

An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.

Deep Learning, Scala, and Spark

This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).

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.

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.

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 pdf

The document is a laboratory manual for a digital signal processing lab. It contains instructions for students on how to conduct experiments in the lab. It lists 18 experiments related to topics in digital signal processing, such as generating signals, filtering, sampling rate conversion, and analyzing random processes. It provides details on the aim, theory, procedure and outputs for Experiment 1 on generating basic signals using MATLAB.

pptx - Psuedo Random Generator for Halfspaces

This document summarizes research on constructing pseudorandom generators for halfspaces. The key results are:
1) The researchers developed a pseudorandom generator for halfspaces over arbitrary product distributions on Rn, requiring only that E[xi4] is constant. This improves on prior work that only handled the uniform distribution on {-1,1}n.
2) Their generator can simulate intersections of k halfspaces using a seed of length k log(n), and arbitrary functions of k halfspaces using a seed of length k2 log(n).
3) The generator exploits a "dichotomy" among halfspaces - they are either "dictator" functions depending on few variables, or

Static Analysis and Verification of C Programs

Static Analysis and Verification of C ProgramsNew York City College of Technology Computer Systems Technology Colloquium

Recent years have seen the emergence of several static analysis techniques for reasoning about programs. This talk presents several major classes of techniques and tools that implement these techniques. Part of the presentation will be a demonstration of the tools.
Dr. Subash Shankar is an Associate Professor in the Computer Science department at Hunter College, CUNY. Prior to joining CUNY, he received a PhD from the University of Minnesota and was a postdoctoral fellow in the model checking group at Carnegie Mellon University. Dr. Shankar also has over 10 years of industrial experience, mostly in the areas of formal methods and tools for analyzing hardware and software systems.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.

Asymptotic Notation and Data Structures

This is the second lecture in the CS 6212 class. Covers asymptotic notation and data structures. Also outlines the coming lectures wherein we will study the various algorithm design techniques.

Scala and Deep Learning

An introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and Tensorboard, and then some code samples with Scala and TensorFlow.

C++ and Deep Learning

This document provides an overview and introduction to deep learning concepts including linear regression, activation functions, gradient descent, backpropagation, hyperparameters, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and TensorFlow. It discusses clustering examples to illustrate neural networks, explores different activation functions and cost functions, and provides code examples of TensorFlow operations, constants, placeholders, and saving graphs.

Gaussian processing

Robot의 Gait optimization, Gesture Recognition, Optimal Control, Hyper parameter optimization, 신약 신소재 개발을 위한 optimal data sampling strategy등과 같은 ML분야에서 약방의 감초 같은 존재인 GP이지만 이해가 쉽지 않은 GP의 기본적인 이론 및 matlab code 소개

Neural networks

- The document presents a neural network model for recognizing handwritten digits. It uses a dataset of 20x20 pixel grayscale images of digits 0-9.
- The proposed neural network has an input layer of 400 nodes, a hidden layer of 25 nodes, and an output layer of 10 nodes. It is trained using backpropagation to classify images.
- The model achieves an accuracy of over 96.5% on test data after 200 iterations of training, outperforming a logistic regression model which achieved 91.5% accuracy. Future work could involve classifying more complex natural images.

COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...

Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks innon-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. Itdiscussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rateand error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better thanlogistic regression model.

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.

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.

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.

Dynamic Programming - Part II

Dynamic Programming design technique is one of the fundamental algorithm design techniques, and possibly one of the ones that are hardest to master for those who did not study it formally. In these slides (which are continuation of part 1 slides), we cover two problems: maximum value contiguous subarray, and maximum increasing subsequence.

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.

Predicting organic reaction outcomes with weisfeiler lehman network

This document discusses neural message passing networks for modeling quantum chemistry. It defines message passing networks as having message functions that update node states based on neighboring node states, vertex update functions that update node states based to accumulated messages, and a readout function that produces an output for the full graph. It provides examples of specific message, update, and readout functions used in existing message passing models like interaction networks and molecular graph convolutions.

Deep Learning: R with Keras and TensorFlow

An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.

Deep Learning, Scala, and Spark

This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).

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.

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.

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 pdf

The document is a laboratory manual for a digital signal processing lab. It contains instructions for students on how to conduct experiments in the lab. It lists 18 experiments related to topics in digital signal processing, such as generating signals, filtering, sampling rate conversion, and analyzing random processes. It provides details on the aim, theory, procedure and outputs for Experiment 1 on generating basic signals using MATLAB.

pptx - Psuedo Random Generator for Halfspaces

This document summarizes research on constructing pseudorandom generators for halfspaces. The key results are:
1) The researchers developed a pseudorandom generator for halfspaces over arbitrary product distributions on Rn, requiring only that E[xi4] is constant. This improves on prior work that only handled the uniform distribution on {-1,1}n.
2) Their generator can simulate intersections of k halfspaces using a seed of length k log(n), and arbitrary functions of k halfspaces using a seed of length k2 log(n).
3) The generator exploits a "dichotomy" among halfspaces - they are either "dictator" functions depending on few variables, or

Static Analysis and Verification of C Programs

Static Analysis and Verification of C ProgramsNew York City College of Technology Computer Systems Technology Colloquium

Recent years have seen the emergence of several static analysis techniques for reasoning about programs. This talk presents several major classes of techniques and tools that implement these techniques. Part of the presentation will be a demonstration of the tools.
Dr. Subash Shankar is an Associate Professor in the Computer Science department at Hunter College, CUNY. Prior to joining CUNY, he received a PhD from the University of Minnesota and was a postdoctoral fellow in the model checking group at Carnegie Mellon University. Dr. Shankar also has over 10 years of industrial experience, mostly in the areas of formal methods and tools for analyzing hardware and software systems.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.

Asymptotic Notation and Data Structures

This is the second lecture in the CS 6212 class. Covers asymptotic notation and data structures. Also outlines the coming lectures wherein we will study the various algorithm design techniques.

Scala and Deep Learning

An introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and Tensorboard, and then some code samples with Scala and TensorFlow.

C++ and Deep Learning

This document provides an overview and introduction to deep learning concepts including linear regression, activation functions, gradient descent, backpropagation, hyperparameters, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and TensorFlow. It discusses clustering examples to illustrate neural networks, explores different activation functions and cost functions, and provides code examples of TensorFlow operations, constants, placeholders, and saving graphs.

Gaussian processing

Robot의 Gait optimization, Gesture Recognition, Optimal Control, Hyper parameter optimization, 신약 신소재 개발을 위한 optimal data sampling strategy등과 같은 ML분야에서 약방의 감초 같은 존재인 GP이지만 이해가 쉽지 않은 GP의 기본적인 이론 및 matlab code 소개

how to calclute time complexity of algortihm

how to calclute time complexity of algortihm

Algorithm And analysis Lecture 03& 04-time complexity.

Algorithm And analysis Lecture 03& 04-time complexity.

Complexity of Algorithm

Complexity of Algorithm

Dynamic Programming - Part II

Dynamic Programming - Part II

Introduction to Algorithms Complexity Analysis

Introduction to Algorithms Complexity Analysis

Predicting organic reaction outcomes with weisfeiler lehman network

Predicting organic reaction outcomes with weisfeiler lehman network

Deep Learning: R with Keras and TensorFlow

Deep Learning: R with Keras and TensorFlow

Deep Learning, Scala, and Spark

Deep Learning, Scala, and Spark

Unit i basic concepts of algorithms

Unit i basic concepts of algorithms

Algorithm analysis

Algorithm analysis

Lec7

Lec7

Algorithem complexity in data sructure

Algorithem complexity in data sructure

Dsp lab pdf

Dsp lab pdf

pptx - Psuedo Random Generator for Halfspaces

pptx - Psuedo Random Generator for Halfspaces

Static Analysis and Verification of C Programs

Static Analysis and Verification of C Programs

Fundamentals of the Analysis of Algorithm Efficiency

Fundamentals of the Analysis of Algorithm Efficiency

Asymptotic Notation and Data Structures

Asymptotic Notation and Data Structures

Scala and Deep Learning

Scala and Deep Learning

C++ and Deep Learning

C++ and Deep Learning

Gaussian processing

Gaussian processing

Neural networks

- The document presents a neural network model for recognizing handwritten digits. It uses a dataset of 20x20 pixel grayscale images of digits 0-9.
- The proposed neural network has an input layer of 400 nodes, a hidden layer of 25 nodes, and an output layer of 10 nodes. It is trained using backpropagation to classify images.
- The model achieves an accuracy of over 96.5% on test data after 200 iterations of training, outperforming a logistic regression model which achieved 91.5% accuracy. Future work could involve classifying more complex natural images.

COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...

Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks innon-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. Itdiscussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rateand error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better thanlogistic regression model.

A simple framework for contrastive learning of visual representations

Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
If you'd like to discuss something, text me on LinkedIn, IG, or Twitter. To support me, please use my referral link to Robinhood. It's completely free, and we both get a free stock. Not using it is literally losing out on free money.
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This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)

COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...

Cluster analysis of graph related problems is an important issue now-a-day. Different types of graph
clustering techniques are appeared in the field but most of them are vulnerable in terms of effectiveness
and fragmentation of output in case of real-world applications in diverse systems. In this paper, we will
provide a comparative behavioural analysis of RNSC (Restricted Neighbourhood Search Clustering) and
MCL (Markov Clustering) algorithms on Power-Law Distribution graphs. RNSC is a graph clustering
technique using stochastic local search. RNSC algorithm tries to achieve optimal cost clustering by
assigning some cost functions to the set of clusterings of a graph. This algorithm was implemented by A.
D. King only for undirected and unweighted random graphs. Another popular graph clustering
algorithm MCL is based on stochastic flow simulation model for weighted graphs. There are plentiful
applications of power-law or scale-free graphs in nature and society. Scale-free topology is stochastic i.e.
nodes are connected in a random manner. Complex network topologies like World Wide Web, the web of
human sexual contacts, or the chemical network of a cell etc., are basically following power-law
distribution to represent different real-life systems. This paper uses real large-scale power-law
distribution graphs to conduct the performance analysis of RNSC behaviour compared with Markov
clustering (MCL) algorithm. Extensive experimental results on several synthetic and real power-law
distribution datasets reveal the effectiveness of our approach to comparative performance measure of
these algorithms on the basis of cost of clustering, cluster size, modularity index of clustering results and
normalized mutual information (NMI).

DAOR - Bridging the Gap between Community and Node Representations: Graph Emb...

Slides of the presentation given at BigData'19, special session on Information Granulation in Data Science and Scalable Computing.
The fully automatic (i.e., without any manual tuning) graph embedding (i.e., network representation learning, unsupervised feature extraction) performed in near-linear time is presented. The resulting embeddings are interpretable, preserve both low- and high-order structural proximity of the graph nodes, computed (i.e., learned) by orders of magnitude faster and perform competitively to the manually tuned best state-of-the-art embedding techniques evaluated on diverse tasks of graph analysis.

Towards neuralprocessingofgeneralpurposeapproximateprograms

Did validation of one of the machine learning algorithms of neural networks,and compared the results for its implementation on hardware (FPGA) using xilinx, with that of a sequential code execution(using FANN).

NeuralProcessingofGeneralPurposeApproximatePrograms

The document discusses using neural networks to accelerate general purpose programs through approximate computing. It describes generating training data from programs, using this data to train neural networks, and then running the neural networks at runtime instead of the original programs. Experimental results show the neural network implementations provided speedups of 10-900% compared to the original programs with minimal loss of accuracy. An FPGA implementation of the neural networks was also able to achieve further acceleration, running a network 4x faster than software.

X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...

The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.

X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...

In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.

Keynote at IWLS 2017

Machine learning techniques can be applied in formal verification in several ways:
1) To enhance current formal verification tools by automating tasks like debugging, specification mining, and theorem proving.
2) To enable the development of new formal verification tools by applying machine learning to problems like SAT solving, model checking, and property checking.
3) Specific applications include using machine learning for debugging and root cause identification, learning specifications from runtime traces, aiding theorem proving by selecting heuristics, and tuning SAT solver parameters and selection.

Making Robots Learn

In this deck, Pieter Abbeel from UC Berkeley describes his group research into making robots learn.
Watch the video: https://wp.me/p3RLHQ-hf7
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

HW2-1_05.doc

This document provides instructions for two machine learning homework assignments involving time series prediction and classification. For the first assignment, students are asked to use neural networks to predict chaotic time series data from the Mackey-Glass equation, comparing performance of linear and nonlinear models. For the second assignment, students must classify iris flower types from the Iris data set using a neural network with four input nodes, three output nodes, and logistic output units, evaluating performance through cross-validation and testing.

00463517b1e90c1e63000000

This document discusses parallelizing object detection in videos for many-core systems. It presents an object detection algorithm that includes frame differencing, background differencing, post-processing, and background updating. The algorithm is parallelized by vertically partitioning video frames across cores, with some pixel overlap between partitions to reduce communication overhead. The parallel implementation achieves a speedup of 37.2x on a 64-core Tilera system processing 18 full-HD frames per second. A performance prediction equation is also developed and shown to accurately model the real performance results.

A detailed analysis of the supervised machine Learning Algorithms

A detailed analysis of the supervised machine Learning AlgorithmsNIET Journal of Engineering & Technology (NIETJET)

ABSTRACT: In the field of computer science known as "machine learning," a computer makes predictions about
the tasks it will perform next by examining the data that has been given to it. The computer can access data via
interacting with the environment or by using digitalized training sets. In contrast to static programming
algorithms, which require explicit human guidance, machine learning algorithms may learn from data and
generate predictions on their own. Various supervised and unsupervised strategies, including rule-based
techniques, logic-based techniques, instance-based techniques, and stochastic techniques, have been presented in
order to solve problems. Our paper's main goal is to present a comprehensive comparison of various cutting-edge
supervised machine learning techniques.
AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...

With the increase in Internet users the number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the network. This has led to the research area of intrusion detection which essentially analyzes the network traffic and tries to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard NSL-KDD intrusion dataset using some neural network based techniques for predicting possible intrusions. Four most effective classification methods, namely, Radial Basis Function Network, SelfOrganizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been applied. In order to enhance the performance of the classifiers, three entropy based feature selection methods have been applied as preprocessing of data. Performances of different combinations of classifiers and attribute reduction methods have also been compared.

An ann approach for network

This document discusses using artificial neural networks for network intrusion detection. Specifically, it proposes a hybrid classification model that uses entropy-based feature selection to reduce the dataset, followed by four neural network techniques (RBFN, SOM, SMO, PART) for classification. It provides details on each neural network technique and the overall methodology, which uses 10-fold cross validation to evaluate performance based on standard criteria. The goal is to build an efficient intrusion detection system with low false alarms and high detection rates.

All projects

The document discusses several projects and implementations done by Karishma Jain related to computer vision and deep learning. These include visual question answering using CNNs and RNNs, parallelizing an ADABOOST classifier on different platforms, designing a lane departure warning system using monocular camera, and implementing various CNN architectures for MNIST classification achieving up to 97.74% accuracy.

Log polar coordinates

This document outlines an assignment for a computer vision course. Students are asked to implement 4 vision algorithms: 2 using OpenCV and 2 using MATLAB. The algorithms are the log-polar transform, background subtraction, histogram equalization, and contrast stretching. Students must also answer 3 short questions about orthographic vs perspective projection, efficient filtering, and sensors beyond cameras for computer vision.

Backbone search for object detection for applications in intrusion warning sy...

In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.

Introduction to Deep Learning and Tensorflow

A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)

Neural networks

Neural networks

COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...

COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...

A simple framework for contrastive learning of visual representations

A simple framework for contrastive learning of visual representations

COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...

COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...

DAOR - Bridging the Gap between Community and Node Representations: Graph Emb...

DAOR - Bridging the Gap between Community and Node Representations: Graph Emb...

Towards neuralprocessingofgeneralpurposeapproximateprograms

Towards neuralprocessingofgeneralpurposeapproximateprograms

NeuralProcessingofGeneralPurposeApproximatePrograms

NeuralProcessingofGeneralPurposeApproximatePrograms

X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...

X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...

X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...

X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...

Keynote at IWLS 2017

Keynote at IWLS 2017

Making Robots Learn

Making Robots Learn

HW2-1_05.doc

HW2-1_05.doc

00463517b1e90c1e63000000

00463517b1e90c1e63000000

A detailed analysis of the supervised machine Learning Algorithms

A detailed analysis of the supervised machine Learning Algorithms

AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...

AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE S...

An ann approach for network

An ann approach for network

All projects

All projects

Log polar coordinates

Log polar coordinates

Backbone search for object detection for applications in intrusion warning sy...

Backbone search for object detection for applications in intrusion warning sy...

Introduction to Deep Learning and Tensorflow

Introduction to Deep Learning and Tensorflow

A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...

In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.

Quarter 3 SLRP grade 9.. gshajsbhhaheabh

Curriculum map

Enhanced Screen Flows UI/UX using SLDS with Tom Kitt

Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.

8 Best Automated Android App Testing Tool and Framework in 2024.pdf

Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.

The Rising Future of CPaaS in the Middle East 2024

Explore "The Rising Future of CPaaS in the Middle East in 2024" with this comprehensive PPT presentation. Discover how Communication Platforms as a Service (CPaaS) is transforming communication across various sectors in the Middle East.

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

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

Enums On Steroids - let's look at sealed classes !

These are slides for my session"Enums On Steroids - let's look at sealed classes !" - delivered among others, on Devoxx UK 2024 conference

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.

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

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

Unveiling the Advantages of Agile Software Development.pdf

Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.

How Can Hiring A Mobile App Development Company Help Your Business Grow?

ToXSL Technologies is an award-winning Mobile App Development Company in Dubai that helps businesses reshape their digital possibilities with custom app services. As a top app development company in Dubai, we offer highly engaging iOS & Android app solutions. https://rb.gy/necdnt

E-commerce Development Services- Hornet Dynamics

For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.

UI5con 2024 - Bring Your Own Design System

How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.

Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...

Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media

"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样

原版定制【微信:bwp0011】《(hull学位证书)英国赫尔大学毕业证硕士文凭》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。

一比一原版(USF毕业证)旧金山大学毕业证如何办理

USF硕士毕业证成绩单【微信95270640】一比一伪造旧金山大学文凭@假冒USF毕业证成绩单+Q微信95270640办理USF学位证书@仿造USF毕业文凭证书@购买旧金山大学毕业证成绩单USF真实使馆认证/真实留信认证回国人员证明
#一整套旧金山大学文凭证件办理#—包含旧金山大学旧金山大学本科毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
一：旧金山大学旧金山大学本科毕业证成绩单毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理旧金山大学旧金山大学本科毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640奈一次次令他失望山娃今年岁上五年级识得很多字从走出小屋开始山娃就知道父亲的家和工地共有一个很动听的名字——天河工地的底层空空荡荡很宽阔很凉爽在地上铺上报纸和水泥袋父亲和工人们中午全睡在地上地面坑坑洼洼山娃曾多次绊倒过也曾有长铁钉穿透凉鞋刺在脚板上但山娃不怕工地上也常有五六个从乡下来的小学生他们的父母亲也是高楼上的建筑工人小伙伴来自不同省份都操着带有浓重口音的普通话可不知为啥山娃不仅很快与他们熟识了

E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies

Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.

The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...

In today's business landscape, digital integration is ubiquitous, demanding swift innovation as a necessity rather than a luxury. In a fiercely competitive market with heightened customer expectations, the timely launch of flawless digital products is crucial for both acquisition and retention—any delay risks ceding market share to competitors.

A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...

A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...

Quarter 3 SLRP grade 9.. gshajsbhhaheabh

Quarter 3 SLRP grade 9.. gshajsbhhaheabh

Migration From CH 1.0 to CH 2.0 and Mule 4.6 & Java 17 Upgrade.pptx

Migration From CH 1.0 to CH 2.0 and Mule 4.6 & Java 17 Upgrade.pptx

Enhanced Screen Flows UI/UX using SLDS with Tom Kitt

Enhanced Screen Flows UI/UX using SLDS with Tom Kitt

8 Best Automated Android App Testing Tool and Framework in 2024.pdf

8 Best Automated Android App Testing Tool and Framework in 2024.pdf

The Rising Future of CPaaS in the Middle East 2024

The Rising Future of CPaaS in the Middle East 2024

Measures in SQL (SIGMOD 2024, Santiago, Chile)

Measures in SQL (SIGMOD 2024, Santiago, Chile)

KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD

KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD

Enums On Steroids - let's look at sealed classes !

Enums On Steroids - let's look at sealed classes !

Webinar On-Demand: Using Flutter for Embedded

Webinar On-Demand: Using Flutter for Embedded

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

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

Unveiling the Advantages of Agile Software Development.pdf

Unveiling the Advantages of Agile Software Development.pdf

How Can Hiring A Mobile App Development Company Help Your Business Grow?

How Can Hiring A Mobile App Development Company Help Your Business Grow?

E-commerce Development Services- Hornet Dynamics

E-commerce Development Services- Hornet Dynamics

UI5con 2024 - Bring Your Own Design System

UI5con 2024 - Bring Your Own Design System

Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...

Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...

如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样

如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样

一比一原版(USF毕业证)旧金山大学毕业证如何办理

一比一原版(USF毕业证)旧金山大学毕业证如何办理

E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies

E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies

The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...

The Key to Digital Success_ A Comprehensive Guide to Continuous Testing Integ...

- 1. Safety Verification of Deep Neural Networks Alexandre Hua - Lotfi Larbaoui Bruno Roy - Anne Laurence Thoux 5 avril 2018
- 2. [2]
- 3. Outline ● Introduction ● Literature Review ● Definitions ● Framework verification ● Experimental results ● Comparison ● Conclusion
- 4. Abstract Research Safety artificial intelligence Machine learning Deep learning Architecture Deep neural network Application Self-driving car Framework Automated verification Method Satisfiability modulo theories (SMT) Objective Safety of classification decision
- 5. Introduction ● Working with classifiers ● Small perturbations can cause the network to misclassify the image ● Framework for automated verification of safety classification decisions [2]
- 6. 1993 Extracting Rules from Artificial Neural Networks First method to verify the specification of a neural network. Verification and validation of neural networks for safety-critical applications Present an analysis techniques that can be used for verification of polynomial neural network (PNN). 2002 2010 An Abstraction-Refinement Approach to Verification of Artificial Neural Networks First paper demonstrating that the output class is constant across a desired neighborhood. 2016 Safety Verification of Deep Neural Networks Present a novel framework that find a misclassification if found if it exists, using SMT. 2017 Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Suggest a method to extend SMT solvers, allowing for the verification of constraints on deep neural networks. Literature Review
- 7. Definitions
- 8. Definitions
- 9. The safety of classification decisions Intuition :
- 10. The safety of classification decisions Intuition :
- 11. The safety of classification decisions Intuition :
- 12. The safety of classification decisions Intuition :
- 13. The safety of classification decisions Intuition : Adversarial example
- 14. The safety of classification decisions Formally : Region
- 15. Definition of a manipulation
- 16. Minimal manipulation and bounded variation (1) (2) (3)
- 19. Boolean satisfiability problem (SAT) ● SAT: given a formula A(x1, x2,..., xn), are there any Boolean values xi of xi who make A true? ● VALID: given a formula A(x1, x2, …, xn), A is true for all Boolean values xi of xi? ● VALID(A)⟷ ¬SAT(¬A) SAT is a fundamental problem of computer science and mathematics, with applications everywhere It is the prototype of the NP-complete problem to which many other problems are reduced
- 20. Work with formulas mixing logic and theories . ((a = 1)∨(a = 2))∧(a ≥ 3)∧((b ≤ 3)∨(b ≥ 2)) logic + arithmetic ((f (a) = 1)∨(a - 3 = 2))∧(g(a) ≥ 3)∧((B[0] ≤ 3)∨(B[1] ≥ 2)) logic + arithmetic + functions + tables Satisfiability : there is a model,i.e., a value of unknowns in the theories that makes the formula true . Validity : the formula is true for any model ⟺ his negation is not satisfactory. Satisfiability modulo theories (SMT)
- 21. Uninterpreted function Example : for x,y,z are integers and f is an integer function the following formula may be true ? (x = y )∧(x × (f(y)+f(x)) = t)∧(y× (f(x)+f(x)) ≠ t) No, because the extensional equality is written : x=y ⇒ f(x) = f(y ) So (x = y )∧(x × (f (y )+f (x)) = t)⇒(y × (f(x)+f(x)) = t) and the initial formula is false
- 22. [5]
- 23. [5]
- 24. [5]
- 25. [5]
- 27. Layer-by-Layer Refinement Figure : Complete refinement in general safety and safety wrt manipulations
- 30. Experimental Results ● Experimentations on trained classification neural network ● Using well-known image dataset to feed input to classifier such as ○ MNIST ○ CIFAR-10 ○ ImageNet ○ GTSRB
- 31. Two-Dimensional Point Classification Network Input Layer First Hidden Layer [1]
- 32. Image Classification Network for the MNIST Handwritten Image Dataset [1]
- 33. Image Classification Network for the CIFAR-10 Small Image Dataset Misclassified as a truck [1]
- 34. Image Classification Network for the ImageNet Dataset Adversarial example found after 6346 dimensional changes No adversarial example found after 20 000 dimensional changes => report as safe [1] [1]
- 35. Image Classification Network for the GTSRB dataset [1] [1] [1]
- 36. Comparison
- 37. DLV vs FGSM vs JSMA • FGSM (Fast Gradient Step Method) calculates the optimal attack for a linear approximation of the network cost • JSMA (Jacobian Saliency Map Algorithm) finds a set of dimensions in the input layer to manipulate, according to the linear approximation (by computing the Jacobian matrix) of the model from current output to a nominated target output
- 38. DLV vs FGSM vs JSMA FGSM JSMA DLV Misclassed [1] [1] [1]
- 39. DLV vs FGSM vs JSMA [1]
- 40. Conclusion ● Framework for automated verification of safety (for classification decisions) ● Using the Satisfiability Modulo Theory (SMT) ● Framework that finds a misclassification if it exists ● Framework can be generalized to other tasks
- 41. References ● [1] : Xiaowei Huang, Marta Kwiatkowska, Sen Wang and Min Wu, "Safety Verification of Deep Neural Networks" [Online]. Available: http://qav.comlab.ox.ac.uk/papers/hkww17.pdf, 2016. ● [2] : Uber self-driving system should have spotted woman, experts say (22 march 2018) CBC. [Online]. Available: http://www.cbc.ca/news/world/uber-self-driving-accident-video-1.4587439 ● [3] : How Adversarial Attacks Work. (2017) Emil Mikhailov and Roman Trusov. [Online]. Available: https://blog.ycombinator.com/how-adversarial-attacks-work/ ● [4] https://www.pyimagesearch.com/2017/03/20/imagenet-vggnet-resnet-inception-xception-keras/ ● [5] http://www.cleverhans.io/security/privacy/ml/2017/06/14/verification.html