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proj

Proj4：改进LiteOS中物理内存分配算法

Proj4：改进LiteOS中物理内存分配算法 实验目的 掌握LiteOS系统调用的自定义方法 实验环境 Ubantu和IMX6ULL mini 实验内容 （从代码角度详细描述实验的步骤和过程） 原先代码: 1 /* 2 3 * Description : find suitable free bl ......
算法 物理 内存 LiteOS Proj4

搭建GDAL JAVA环境；DXF转KML；坐标转换；PROJ: proj_create_from_database 错误解决

搭建JAVA GDAL环境 GDAL是一个栅格和矢量地理空间数据格式的转换库，由开源地理空间基金会按照MIT开源协议发布。作为一个库，它向应用程序为所有支持的数据格式提供统一的栅格抽象数据模型和矢量抽象数据模型。它还提供了用于数据转换和处理的各种有用的命令行实用工具。 GDAL官网： GDAL — ......
proj_create_from_database 坐标 database 错误 环境

Proj4：改进LiteOS中物理内存分配算法

记录一下,操作系统课上老师讲的proj4做法 给的参考资料 LiteOS中的物理内存分配采用了TLSF算法，该算法较好地解决了最坏情况执行时间不确定(not bounded)或者复杂度过高(bounded with a too important bound")，以及碎片化问题(fragmentat ......
算法 物理 内存 LiteOS Proj4

Proj. Unknown: Deciding Differential Privacy of Online Algorithms with Multiple Variables

Paper https://arxiv.org/abs/2309.06615 Abstract 背景： 自动机A被称作查分隐私自动机：当对某些D，对任何隐私预算ε>0,该自动机是Dε-differentially private( A DiP automaton is a parametric au ......
Differential Algorithms Variables Deciding Multiple

Proj CDeepFuzz Paper Reading: POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis

Abstract 本文： PolyCruise Method: 跨编程语言的holistic dynamic information flow analysis(DIFA) use a light language-specific analysis和language-agnostic online ......
Cross-Language Information POLYCRUISE CDeepFuzz Analysis

Proj CDeepFuzz Paper Reading: NYX: Greybox Hypervisor Fuzzing using Fast Snapshots and Affine Types

Abstract 背景：hypervisor(virtual machine monitor, VMM) 保障了不同虚拟机之间的安全隔离(security boundaries) 用户：攻击场景：在云服务上运行自身的VM instances, 提升权限 本文：Nyx 目的：coverage guid ......
Hypervisor CDeepFuzz Snapshots Greybox Fuzzing

Proj. CRR Paper Reading: Optimal Speedup of Las Vegas Algorithms, Adaptive restart for stochastic synthesis

Title Adaptive restart for stochastic synthesis PLDI 2021 Task Distribute the power between multiple runs in stochastic program synthesis to accelerat ......
Algorithms stochastic synthesis Adaptive Optimal

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

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

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

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

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

【Python&GIS】GDAL、OGR报错ERROR 1: PROJ: proj_create_from_database: Cannot find proj.db

解决ｇｄａｌ、ｏｇｒ报错信息：         ERROR 1: PROJ: proj_create_from_database: Cannot find proj.db         ERROR 1: PROJ: proj_identify: Cannot find proj.db       ... ......
proj_create_from_database proj database Python Cannot

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

Proj CDeepFuzz Paper Reading: Automatic differentiation in PyTorch

## Abstract 本文：描述automatic differentiation module of PyTorch 包括：Lua Torch, Chainer, HIPS Autograd Task: Provides a high-performance environment on dif ......
differentiation CDeepFuzz Automatic Reading PyTorch
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