目录
- Contributor License Agreement
- Reporting a Vulnerability
- General Workflow
- logkafka - Collect logs and send lines to Apache Kafka 0.8+
- Introduction 中文文档
- Features
- Differences with other log aggregation and monitoring tools
- Users of logkafka
- Supported operating systems
- Requirements
- Build
- Usage
- Benchmark
- Third Party
- Developers
- TODO
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Overview
- Security
- License
- Documentation
- Setup and Usage
- Connection String Syntax
- Schema Discovery
- Schema Management
- SQL and JDBC Limitations
- Troubleshooting Guide
- Security Notice
- Contributor’s Getting Started Guide
- Improved Octo Waddle
- Install notes
- Developer notes
- Fragment insertion
- GC Signals
- Overview
- Installation
- News: updates in BTyper3 v3.2.0, v3.3.0, and v3.4.0 – new species just dropped!
- Citation
- Quick Start
- Command Structure
- Sample Commands
- Perform all default analyses, using an assembled genome (complete or draft) in (multi-)FASTA format as input (assumes fastANI is in the user’s path):
- Perform all default analyses, using an assembled genome (complete or draft) in (multi-)FASTA format as input (fastANI is not in the user’s path):
- Perform all default analyses, plus pseudo-gene flow unit assignment, using an assembled genome (complete or draft) in (multi-)FASTA format as input (assumes fastANI is in the user’s path):
- Perform seven-gene MLST only, using user-supplied MLST gene sequences and the latest version of the PubMLST B. cereus s.l. database (sequences can be in multi-FASTA format, or concatenated into a single sequence in FASTA format):
- Perform panC group assignment only, using a user-supplied panC gene sequence in FASTA format:
- Perform virulence factor and Bt toxin-encoding gene detection in a plasmid sequence in FASTA format:
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
go-uaa 
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- Installation
- Description
- Output file description
- Filtering and Refiltering
- Examples and recipes
- Whole genome somatic SV and indel detection
- Whole genome germline SV and indel detection
- Targeted detection
- Targeted local assembly
- Assemble all reads
- Snapshot of where svaba run is currently operating
- Debug a local assembly and produce the assembly graph
- View all of the ASCII alignments
- View a particular contig with read to contig alignments
- Make a function to sort and index contigs
- Contributors
- Why gitlink-cli?
- Features
- Installation & Quick Start
- Usage Examples
- Global Parameters
- Branch Conventions
- AI Agent Skills
- Project Structure
- Documentation
- FAQ
- Q: How do I use gitlink-cli in scripts?
- Q: How does automatic owner/repo resolution work?
- Q: What if my token expires?
- Q: How do I use gitlink-cli in CI/CD or non-interactive environments (e.g. Trae sandbox)?
- Q: What if npm installs successfully but
gitlink-clireports a missing binary? - Q: Where are credentials stored on Windows?
- Q: Where can I find the full API reference?
- License
dsb: normalize and denoise antibody-derived-tag data from cite-seq, asap-seq, tea-seq and related assays.'>
dsb: Normalize and denoise antibody-derived-tag data from CITE-seq, ASAP-seq, TEA-seq and related assays.- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- ApesterKit
- Requirements
- Integration
- Apester Strip View
- Implementaion:
- 1 - declare variable of type
APEStripView: - 2 - initiate a strip style configuration
APEStripStyle. configure the strip view style, i.e shape, size, padding, shadow, title header and more…. - 3 - initiate a strip configuration
APEStripConfiguration. set the channel token, style and bundle parameters …. - 4 - initiate the strip view instance with the parameter value.
- 5 - The channel strip in a container view
- 6 - Implemet The
APEStripViewDelegateto observe the stripView updates when success, failure or height updates.
- 1 - declare variable of type
- Implementaion:
- Apester Unit View
- 1 - declare variable of type
APEUnitView: - 2 - initiate a unit params
APEUnitParams. Set the media id or the channel token for playlist. - 3 - initiate a unit configuration
APEUnitConfiguration. set the unit params and bundle - optional settings:
- 1 - declare variable of type
- APEViewService
- Event subscription:
- Best practices:
- Example:
- Installation
- License
- Video Introduction
- Main Features
- Solve The Problem
- Application Scenarios And Benefits
- EXTENSION
- updates">
Updates - :desktop_computer: Installation
- 🔥 Pre-trained Model
- data preparation">
Data Preparation - avatar creation">
Avatar Creation - :running_woman: Evaluation on InterHand2.6M
- prior learning on interhand2.6m">
Prior learning on InterHand2.6M - :love_you_gesture: Citation
- :newspaper_roll: License
- acknowledgements">
Acknowledgements
- Installation
- Usage
- Compatibility
- Dependencies
- Contributing
- License
- Write HTML — in Swift!
- Applying attributes
- Type safety built-in
- Components
- Using the component environment
- Inline control flow
- Custom elements and attributes
- Rendering a document
- RSS feeds, podcasting, and site maps
- System requirements
- Installation
- Navigating Plot’s API and implementation
- Compatibility with standards
- Credits, alternatives and focus
- Contributions and support
1.简介
本项目主要是基于置信学习技术，对apache项目中的警告数据集进行去噪。
本项目的工作主要分为两个阶段：第一个阶段，利用github上现有的项目
findbugs-violation ，收集github上的apache项目的警告数据集。第二个阶段，利用cleanlab这一python包，对数据集进行去噪，并对模型进行训练。
其中过程报告.pdf介绍了我们的工作过程和心路历程，代码分析.pdf介绍了本项目的各项代码以及数据文件，同时也介绍了如何使用本项目进行置信学习，实验结果.pdf汇总了本次实验的结果以及对结果进行的分析。可以通过折三个文件全方位地了解本项目。
2.写在最后
自动化测试这门课不同于之前学习过的所有课程之处在于，我们是在一个没有完全成熟的领域里面探索，这样的学习方式增添了学习过程中的未知性、困难性也增添了乐趣：我们永远不知道我们会碰到什么险阻，也永远不知道会得到什么意外的财富。
通过这个项目，我们小组了解到了现在前沿的技术——置信学习。我们也学习了这个项目所依赖的领域知识，例如深度学习，大数据分析等等。同时，我们的收获已经超出了这个项目本身。虽然说我们对于这个领域还涉世未深、无法做出开拓性的贡献，但是我们可以保证的是我们尽全力在把这个项目做到最好。
最后十分感谢老师们、学长学姐们能为我们提供这样一个平台，让我们有幸体验科研探索的过程。
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2022-自动化测试-警告识别