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computation further reading theory

What We’re Reading: What makes you happy?

Hi everybody, As I enjoy the last dregs of summer in Chicago, I bask in the nearly-perfect pre-winter weather, the joy in returning my kids to school, ......
What Reading makes happy you

Proj CDeepFuzz Paper Reading: Metamorphic Testing of Deep Learning Compilers

## Abstract 背景：Compiling DNN models into high-efficiency executables is not easy: the compilation procedure often involves converting high-level model ......
Metamorphic CDeepFuzz Compilers Learning Reading

解决npm ERR! Cannot read properties of null (reading ‘pickAlgorithm‘)报错问题

转载自：https://www.cnblogs.com/zhyp/p/16920380.html 解决方法：在终端中运行命令：npm cache clear --force 然后重新运行 npm i 命令，再次安装安装完成，没有出现报错npm run serve 运行项目，项目可以正常启动了。 安装 ......
pickAlgorithm properties reading Cannot 问题

Proj CDeepFuzz Paper Reading: A Comprehensive Study of Deep Learning Compiler Bugs

## Abstract 背景：深度学习编译器处理的深度学习模型与命令式程序有根本的不同，因为深度学习模型中的程序逻辑是隐式的。（the DL models processed by DL compilers differ fundamentally from imperative programs ......
Comprehensive CDeepFuzz Compiler Learning Reading

Proj CDeepFuzz Paper Reading: DeepMutation: Mutation Testing of Deep Learning Systems

## Abstract 本文：DeepMutation Github: https://github.com/berkuva/mutation-testing-for-DNNs Task: mutation testing framework specialized for DL systems t ......
DeepMutation CDeepFuzz Mutation Learning Reading

Proj CDeepFuzz Paper Reading: Testing Deep Neural Networks

## Abstract 本文:DeepCover Github: https://github.com/TrustAI/DeepCover Task: propose 4 novel test criteria to test DNNs Method: inspired by MC/DC cover ......
CDeepFuzz Networks Reading Testing Neural

Proj CDeepFuzz Paper Reading: TensorFlow: a system for Large-Scale machine learning

## Abstract 本文:Tensorflow Github: https://github.com/tensorflow/tensorflow Task: Detail on Tensorflow dataflow model 特点: 1. operates at large scale an ......
Large-Scale TensorFlow CDeepFuzz learning Reading

Proj CDeepFuzz Paper Reading: SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks

## Abstract 本文：SparseProp Github: https://github.com/IST-DASLab/sparseprop Task: a back-propagation algo for sparse training data, a fast vectorized i ......
Backpropagation SparseProp CDeepFuzz Efficient Networks

Proj CDeepFuzz Paper Reading: PyTorch: an imperative style, high-performance deep learning library

## Abstract 本文： PyTorch Task: detail the implementation and architecture of PyTorch Github: https://github.com/pytorch/pytorch 特点： 1. PyTorch同时关注可用性和速 ......
high-performance performance imperative CDeepFuzz learning

Proj CDeepFuzz Paper Reading: Software Testing with Large Language Model: Survey, Landscape, and Vision

## Abstract 本文: Task: Review on the use of LLMs in software testing Method: 1. analyzes 52 relevant studies ## 1. Intro ![](https://img2023.cnblogs.co ......
CDeepFuzz Landscape Language Software Reading

docker启动mysql报错Can't read dir of '/etc/mysql/conf.d/'

执行命令： docker run -p 3306:3306 --privileged=true -v /mysql/data:/var/lib/mysql -v /mysql/log:/var/log/mysql -v /mysql/conf:/etc/mysql-e MYSQL_ROOT_PASS ......
mysql 39 docker conf read

Proj CDeepFuzz Paper Reading: PELICAN: Exploiting Backdoors of Naturally Trained Deep Learning Models In Binary Code Analysis

## Abstract 背景： 1. 本文研究的不是被恶意植入的后门，而是products of defects in training 2. 攻击模式: injecting some small fixed input pattern(backdoor) to induce misclassifi ......
Exploiting CDeepFuzz Backdoors Naturally Analysis

Proj CDeepFuzz Paper Reading: Decompiling x86 Deep Neural Network Executables

## Abstract 本文: BTD github: https://github.com/monkbai/DNN-decompiler/ Task: a decompiler for DNN models to output DNN specifications including: opera ......
Decompiling Executables CDeepFuzz Network Reading

Python文件读取方法：read()、readline()和readlines()的区别

![在这里插入图片描述](https://img-blog.csdnimg.cn/1bab73a81f954fbabbe802c443e6aa39.png) 在Python中，读取文件是一项常见的任务。Python提供了多种方法来读取文件内容，其中包括read()、readline()和readli ......
readlines readline 文件 方法 Python

CF1374E2 Reading Books(hard version) 题解

# CF1374E2 Reading Books(hard version) 这道题是在 [CF1374E1 Reading Books(easy version)](https://www.luogu.com.cn/problem/CF1374E1) 的基础上出的，而且仅仅增加了一个 $m$ 的限 ......
题解 Reading version 1374E Books

通过ENQUEUE_READ判断单据是否被锁定

## 使用场景 公司的外向交货单需要传到WMS进入出库操作，传输成功后调用BAPI外向交货单的一个状态,如果不判断单据是否被锁定，那么下传wms成功后改变单据状态时就会报错，进而导致状态修改失败，从而导致wms和SAP数据不一致的问题。 ## 解决方式 通过ENQUEUE_READ函数判断某个单据是 ......
单据 ENQUEUE_READ ENQUEUE READ

Proj CDeepFuzz Paper Reading: Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests

## Abstract 背景：In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning mod ......
Non-Deterministic Effectiveness Deterministic CDeepFuzz Balancing

Proj CDeepFuzz Paper Reading: NeuRI: Diversifying DNN Generation via Inductive Rule Inference

## Abstract 背景：The correctness of DL systems is crucial for trust in DL applications 本文: NeuRI BaseTool: FreeFuzz Github: https://github.com/ise-uiuc/ ......
Diversifying Generation CDeepFuzz Inductive Inference

Proj CDeepFuzz Paper Reading: Natural attack for pre-trained models of code

## Abstract 背景：目前大多数的adversarial attack method on pre-trained models of code忽略了perturbations should be natural to human judges(naturalness requirement ......
pre-trained CDeepFuzz Natural Reading trained

Proj CDeepFuzz Paper Reading: COMET: Coverage-guided Model Generation For Deep Learning Library Testing

## Abstract 背景：已有的方法(Muffin, Lemon, Cradle) can cover at most 34.1% layer inputs, 25.9% layer parameter values, and 15.6% layer sequences. 本文：COMET Gi ......
Coverage-guided Generation CDeepFuzz Coverage Learning

Proj CDeepFuzz Paper Reading: IvySyn: Automated Vulnerability Discovery in Deep Learning Frameworks

## Abstract 本文：IvySyn Task: discover memory error vulnerabilities in DL frameworks BugType: memory safety errors, fatal runtime errors Method: 1. 利用na ......
Vulnerability Frameworks CDeepFuzz Automated Discovery

vue中computed和watch的区别

在一样的情况下，computed的性能会高于watch，所以大部分情况使用computed会更好。 但是，它们也有各自的优缺点： computed计算属性： 1. 能够实时监听data里面绑定的数据（包括vueX），但是其余数据的改变是监听不到的。 2. 适用于需要计算的一个值被多个数据影响的情况， ......
computed watch vue

MAPF Paper Reading Note

随便写写记录一下 ## 1. 2005-Cooperative Pathfinding ### 1.1. LRA* local repair A* - 依次做A* - 即将开始碰撞时，replan - a general replan solution: 每次重规划时，新增noise，按照比例加入$ ......
Reading Paper MAPF Note

Proj CDeepFuzz Paper Reading: Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness

## Abstract 本文： Task: 1. prove invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations 2. prove that ......
Invariance-inducing transformations regularization Invariance robustness

Proj CDeepFuzz Paper Reading: Framework for Evaluating Faithfulness of Local Explanations

## Abstract 本文: Task: 1. study the faithfulness of an explanation system to the underlying prediction model on consistency and sufficiency 2. introduc ......
Faithfulness Explanations Evaluating CDeepFuzz Framework

computed和watch的区别

1、computed是计算属性；watch是监听，监听data中的数据变化。 2、computed支持缓存，当其依赖的属性的值发生变化时，计算属性会重新计算，反之，则使用缓存中的属性值；watch不支持缓存，当对应属性发生变化的时候，响应执行。 3、computed不支持异步，有异步操作时无法监听数 ......
computed watch

Proj CDeepFuzz Paper Reading: Differential Testing of Cross Deep Learning Framework APIs: Revealing Inconsistencies and Vulnerabilities

## Abstract 背景：目前对cross-framework conversion中的inconsistencies和security bugs的研究少有 本文：TensorScope Task: test cross-frame APIs in Machine Learning Librar ......
Inconsistencies Vulnerabilities Differential CDeepFuzz Framework

Proj CDeepFuzz Paper Reading: DeepTest: automated testing of deep-neural-network-driven autonomous cars

## Abstract 本文: DeepTest Task: a systematic testing tool for DNN-driven vehicles Method: 1. generated test cases with real-world changes like rain, fo ......
deep-neural-network-driven autonomous CDeepFuzz automated DeepTest

Proj CDeepFuzz Paper Reading: DeepGauge: multi-granularity testing criteria for deep learning systems

## Abstract 本文: DeepGauge Task: provide multi-granularity testing criteria for DL systems Method: multi-granularity testing criteria for DL systems: 1 ......
multi-granularity granularity CDeepFuzz DeepGauge criteria

Proj CDeepFuzz Paper Reading: Combinatorial Testing for Deep Learning Systems

## Abstract 本文：DeepCT Task: Testing DL Models with Combinatorial Testing Method: 1. 将输出值的空间离散化为区间，以便覆盖每个区间,对不同层内的神经元交互进⾏采样，并减少必须执⾏的测试输⼊的数量。 2. a set o ......
Combinatorial CDeepFuzz Learning Reading Testing
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