Update golden_last.csv

golden_last.csv

@@ -1,6 +1,6 @@
-software,repo_name,readme_url,portal,stars,selection,categories,,date_collection,date_submission,content,plan,steps,seq_order,optional_steps,extra_info_optional
-vcr-video-representation-for-contextual,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/oronnir/VCR/main/README.md,machine_learning,1,latest,,,13/02/2024,12/2/24,,,,,,
-ensuring-trustworthy-and-ethical-behaviour-in,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/AAAI-DISIM-UnivAQ/DALI/master/README.md,machine_learning,15,latest,,,13/02/2024,12/2/24,"## Installation
+software,repo_name,readme_url,content,plan,steps,optional_steps,extra_info_optional
+vcr-video-representation-for-contextual,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/oronnir/VCR/main/README.md,,,,,
+ensuring-trustworthy-and-ethical-behaviour-in,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/AAAI-DISIM-UnivAQ/DALI/master/README.md,"## Installation
 
 **OS X & Linux:**
 1.  To download and install SICStus Prolog (it is needed), follow the instructions at https://sicstus.sics.se/download4.html.
@@ -23,32 +23,32 @@ bash startmas.sh
      You will see different windows opening:
 *      Prolog LINDA server (active_server_wi.pl)
 *      Prolog FIPA client (active_user_wi.pl) 
-*      1 instance of DALI metaintepreter for each agent (active_dali_wi.pl)","binary, source","[Binary] 1.  To download and install SICStus Prolog (it is needed), follow the instructions at https://sicstus.sics.se/download4.html.
-[Source] 2.  Then, you can download DALI and test it by running an example DALI MAS:
+*      1 instance of DALI metaintepreter for each agent (active_dali_wi.pl)","binary, source","[plan binary]>>step1. follow the instructions at https://sicstus.sics.se/download4.html.
+[plan source]>>step 2. download DALI. step3. test it by running an example DALI MAS:
 ```sh
 git clone https://github.com/AAAI-DISIM-UnivAQ/DALI.git
 cd DALI/Examples/advanced
 bash startmas.sh
-```","1,2","**Windows:**
+```","**Windows:**
 1.  To download and install SICStus Prolog (it is needed), follow the instructions at https://sicstus.sics.se/download4.html.
 2.  Then, you can download DALI from https://github.com/AAAI-DISIM-UnivAQ/DALI.git.
 3.  Unzip the repository, go to the folder ""DALI/Examples/basic"", and test if DALI works by duble clicking ""startmas.bat"" file (this will launch an example DALI MAS). \","You will see different windows opening:
 Prolog LINDA server (active_server_wi.pl)
 Prolog FIPA client (active_user_wi.pl) 
 1 instance of DALI metaintepreter for each agent (active_dali_wi.pl)"
-synthesizing-sentiment-controlled-feedback,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/MIntelligence-Group/CMFeed/main/README.md,machine_learning,0,latest,,,13/02/2024,12/2/24,,,,,,
-only-the-curve-shape-matters-training,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/cfeng783/GTT/main/README.md,machine_learning,0,latest,,,13/02/2024,12/2/24,"## Getting Started
+synthesizing-sentiment-controlled-feedback,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/MIntelligence-Group/CMFeed/main/README.md,,,,,
+only-the-curve-shape-matters-training,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/cfeng783/GTT/main/README.md,"## Getting Started
 
 #### Install dependencies (with python 3.10) 
 
 ```shell
 pip install -r requirements.txt
-```",source,1. Install dependencies with pip install -r requirements.txt,,,
-from-uncertainty-to-precision-enhancing,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/fer-agathe/calibration_binary_classifier/main/README.md,machine_learning,0,latest,,,13/02/2024,12/2/24,,,,,,
-stochastic-gradient-flow-dynamics-of-test,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/rodsveiga/sgf_dyn/main/README.md,machine_learning,,,,,13/02/2024,12/2/24,,,,,,
-accuracy-of-textfooler-black-box-adversarial,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/zero-one-loss/wordcnn01/main/LICENSE*,machine_learning,0,,,,13/02/2024,12/2/24,,,,,,
-differentially-private-decentralized-learning-1,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/totilas/DPrandomwalk/main/README.md,machine_learning,,,,,13/02/2024,12/2/24,,,,,,
-aydiv-adaptable-yielding-3d-object-detection,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/sanjay-810/AYDIV2/main/README.md,machine_learning,1,,,,13/02/2024,12/2/24,"### **Installation**
+```",source,[plan source]>> [INCOMPLETE] step1. Install dependencies with ```pip install -r requirements.txt```,,
+from-uncertainty-to-precision-enhancing,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/fer-agathe/calibration_binary_classifier/main/README.md,,,,,
+stochastic-gradient-flow-dynamics-of-test,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/rodsveiga/sgf_dyn/main/README.md,,,,,
+accuracy-of-textfooler-black-box-adversarial,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/zero-one-loss/wordcnn01/main/LICENSE,,,,,
+differentially-private-decentralized-learning-1,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/totilas/DPrandomwalk/main/README.md,,,,,
+aydiv-adaptable-yielding-3d-object-detection,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/sanjay-810/AYDIV2/main/README.md,"### **Installation**
 1.  Prepare for the running environment. 
 
     You can use the docker image provided by [`OpenPCDet`](https://github.com/open-mmlab/OpenPCDet). Our experiments are based on the
@@ -91,16 +91,12 @@ aydiv-adaptable-yielding-3d-object-detection,https://paperwithcode.com/paper/,ht
     cd pcdet/ops/iou3d/cuda_op
     python setup.py develop
     cd ../../../..
-    ```","source,docker","[source]: step1. Prepare for the running environment.
-step2. prepare for the data:```
-    cd Aydiv
-    python depth_to_lidar.py
-    ```
-[docker]: step1. You can use the docker image provided by [`OpenPCDet`](https://github.com/open-mmlab/OpenPCDet)","1,2",,
-cartesian-atomic-cluster-expansion-for,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/BingqingCheng/cace/main/README.md,machine_learning,4,latest,,,13/02/2024,12/2/24,"## Installation
+    ```","source,docker","[plan source]>> step1. Prepare for the running environment. step2. prepare for the data:```cd Aydiv python depth_to_lidar.py ```
+[plan docker]>> step1. You can use the docker image provided by [`OpenPCDet`](https://github.com/open-mmlab/OpenPCDet)",,
+cartesian-atomic-cluster-expansion-for,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/BingqingCheng/cace/main/README.md,"## Installation
 
-Please refer to the `setup.py` file for installation instructions.",source,[source] step1. please refer to the `setup.py` file for installation instructions.,,,
-teller-a-trustworthy-framework-for,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/less-and-less-bugs/Trust_TELLER/main/README.md,machine_learning,1,latest,,,13/02/2024,12/2/24,"## Getting Started
+Please refer to the `setup.py` file for installation instructions.",source,[plan source]>>[INCOMPLETE] step1. please refer to the `setup.py` file for installation instructions.,,
+teller-a-trustworthy-framework-for,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/less-and-less-bugs/Trust_TELLER/main/README.md,"## Getting Started
 
 Step 1: Download the dataset folder from onedrive by [data.zip](https://portland-my.sharepoint.com/:u:/g/personal/liuhui3-c_my_cityu_edu_hk/EfApQlFP3PhFjUW4527STo0BALMdP16zs-HPMNgwQVFWsA?e=zoHlW2). Unzip this folder into the project  directory.  You can find four orginal datasets,  pre-processed datasets (i.e., val.jsonl, test.jsonl, train.jsonl in each dataset folder) and the files incuding questions and answers 
 
@@ -108,13 +104,13 @@ Step 2: Place you OpenAI key into the file named api_key.txt.
 
 ```
 openai.api_key = """"
-```",binary,"[binary] step1: Download the dataset folder from onedrive by https://portland-my.sharepoint.com/:u:/g/personal/liuhui3-c_my_cityu_edu_hk/EfApQlFP3PhFjUW4527STo0BALMdP16zs-HPMNgwQVFWsA?e=zoHlW2. 
+```",binary,"[plan binary]>> step1: Download the dataset folder from onedrive by https://portland-my.sharepoint.com/:u:/g/personal/liuhui3-c_my_cityu_edu_hk/EfApQlFP3PhFjUW4527STo0BALMdP16zs-HPMNgwQVFWsA?e=zoHlW2. 
 step2. Unzip this folder into the project  directory.
 step3. Place you OpenAI key into the file named api_key.txt.
 ```
 openai.api_key = """"
-```","1,2,3",,
-continuous-time-radar-inertial-and-lidar,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/utiasASRL/steam_icp/master/README.md,computer_science,77,latest,robotics,,13/02/2024,9/2/24,"## Installation
+```",,
+continuous-time-radar-inertial-and-lidar,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/utiasASRL/steam_icp/master/README.md,"## Installation
 
 Clone this repository and its submodules.
 
@@ -145,19 +141,14 @@ docker run -it --name steam_icp \
 
 ```bash
 bash build.sh
-```",source,"[source] step1. Clone this repository and its submodules.
-step2. We use docker to install dependencies The recommended way to build the docker image is
-```bash
-docker build -t steam_icp \
+```",source,"[plan source]>> step1. clone this repository and its submodules. step2.  Use docker to install dependencies  ```docker build -t steam_icp \
   --build-arg USERID=$(id -u) \
   --build-arg GROUPID=$(id -g) \
   --build-arg USERNAME=$(whoami) \
   --build-arg HOMEDIR=${HOME} .
+``` 
+step3. mount the code, dataset, and output directories to proper locations in the container.
 ```
-step3. When starting a container, remember to mount the code, dataset, and output directories to proper locations in the container.
-An example command to start a docker container with the image is
-
-```bash
 docker run -it --name steam_icp \
   --privileged \
   --network=host \
@@ -165,14 +156,12 @@ docker run -it --name steam_icp \
   -v /tmp/.X11-unix:/tmp/.X11-unix \
   -v ${HOME}:${HOME}:rw \
   steam_icp
-
 step4.(Inside Container) Go to the root directory of this repository and build STEAM-ICP
-
 ```bash
 bash build.sh
-```","1,2,3,4",,
-towards-a-thermodynamical-deep-learning,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/fedezocco/ThermoVisMedRob/main/README.md,computer_science,0,latest,robotics,,13/02/2024,8/2/24,,,,,,
-robust-parameter-fitting-to-realistic-network,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/PFischbeck/parameter-fitting-experiments/main/Readme.md,computer_science,0,latest,Social and Information Networks Data Structures and Algorithms,,13/02/2024,8/2/24,"# Installation
+```",,
+towards-a-thermodynamical-deep-learning,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/fedezocco/ThermoVisMedRob/main/README.md,,,,,
+robust-parameter-fitting-to-realistic-network,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/PFischbeck/parameter-fitting-experiments/main/Readme.md,"# Installation
 
 - Make sure you have Python, Pip and R installed.
 - Checkout this repository
@@ -191,7 +180,7 @@ R -e 'install.packages(c(""ggplot2"", ""reshape2"", ""plyr"", ""dplyr"", ""scale
 ```
 
 - Download the file `konect-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `input_data/konect`
-- Optional: Download the file `output-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `output_data`. This way, you can access all experiment results without running them yourself.",source,"[source] step1. Make sure you have Python, Pip and R installed.
+- Optional: Download the file `output-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `output_data`. This way, you can access all experiment results without running them yourself.",source,"[plan source]>> step1. Make sure you have Python, Pip and R installed.
 step2. Checkout this repository
 step3. Install the python dependencies with
 ```
@@ -203,14 +192,13 @@ step5. Install the R dependencies (used for plots) with
 R -e 'install.packages(c(""ggplot2"", ""reshape2"", ""plyr"", ""dplyr"", ""scales""), repos=""https://cloud.r-project.org/"")'
 ```
 step6. Download the file `konect-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `input_data/konect`
-step7. Optional: Download the file `output-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `output_data`. This way, you can access all experiment results without running them yourself.","1,2,3,4,5,6,7","step7. Optional: Download the file `output-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `output_data`. This way, you can access all experiment results without running them yourself.",
-get-tok-a-genai-enriched-multimodal-tiktok,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/gabbypinto/GET-Tok-Peru/main/README.md,computer_science,1,latest,Social and Information Networks Computers and Society Human-Computer Interaction ,,13/02/2024,8/2/24,"## Installation
+step7. Optional: Download the file `output-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `output_data`. This way, you can access all experiment results without running them yourself.","step7. Optional: Download the file `output-data.zip` from [Zenodo](https://doi.org/10.5281/zenodo.10629451) and extract its contents into the folder `output_data`. This way, you can access all experiment results without running them yourself.",
+get-tok-a-genai-enriched-multimodal-tiktok,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/gabbypinto/GET-Tok-Peru/main/README.md,"## Installation
 pip install -r requirements.txt 
 
-*Note: I did not us a virtual environment so the packages in the requirements.txt file are probably not reflective of all the packages used in this project. If some issues pop up please don't hesitate to email me at: gpinto@usc.edu*",packagemanager,"step1.
-pip install -r requirements.txt ",,,*Note: I did not us a virtual environment so the packages in the requirements.txt file are probably not reflective of all the packages used in this project. If some issues pop up please don't hesitate to email me at: gpinto@usc.edu*
-a-longitudinal-study-of-italian-and-french,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/orsoFra/LS_FRIT_UKR/main/README.md,computer_science,0,latest,Social and Information Networks Computers and Society,,13/02/2024,7/2/24,,,,,,
-geometric-slosh-free-tracking-for-robotic,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/jonarriza96/gsft/main/README.md,computer_science,1,latest,robotics,,13/02/2024,7/2/24,"## Installation
+*Note: I did not us a virtual environment so the packages in the requirements.txt file are probably not reflective of all the packages used in this project. If some issues pop up please don't hesitate to email me at: gpinto@usc.edu*",packagemanager,[plan packagemanager]>>step1. pip install -r requirements.txt ,,*Note: I did not us a virtual environment so the packages in the requirements.txt file are probably not reflective of all the packages used in this project. If some issues pop up please don't hesitate to email me at: gpinto@usc.edu*
+a-longitudinal-study-of-italian-and-french,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/orsoFra/LS_FRIT_UKR/main/README.md,,,,,
+geometric-slosh-free-tracking-for-robotic,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/jonarriza96/gsft/main/README.md,"## Installation
 
 ### Dependencies
 
@@ -236,13 +224,12 @@ To ensure that Python paths are properly defined, update the `~/.bashrc` by addi
 ```
 export GSFT_PATH=/path_to_gsfc
 export PYTHONPATH=$PYTHONPATH:/$GSFT_PATH
-```",source,"[source] step1. Check dependencies
-step2. Initialize git submodules with
+```",source,"[plan source]>> step1. Check dependencies. step2. Initialize git submodules with
 ```
     git submodule init
     git submodule update
 ```
-step3. Install the specific versions of every package from `requirements.txt` in a new conda environment:
+step3. Create conda environment and install requirements:
 ```
 conda create --name gsft python=3.9
 conda activate gsft
@@ -252,16 +239,16 @@ step4. Create variables to ensure that Python paths are properly defined, update
 ```
 export GSFT_PATH=/path_to_gsfc
 export PYTHONPATH=$PYTHONPATH:/$GSFT_PATH
-```","1,2,3,4",,
-real-time-line-based-room-segmentation-and,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/EricssonResearch/Line-Based-Room-Segmentation-and-EDF/release/README.md,computer_science,0,latest,robotics,,13/02/2024,7/2/24,"## Installation
+```",,
+real-time-line-based-room-segmentation-and,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/EricssonResearch/Line-Based-Room-Segmentation-and-EDF/release/README.md,"## Installation
 The project can be installed by running the following command in your terminal:
 ```bash
 pip install -r requirements.txt
-```",source,"[source] step1. Run the command in your terminal:
+```",source,"[plan source]>>[INCOMPLETE]step1. Run the command in your terminal:
 ```
 pip install -r requirements.txt
-```",1,,
-viga,https://bio.tools/,https://raw.githubusercontent.com/viralInformatics/VIGA/master/README.md,,7,https://bio.tools/t?sort=citationDate&ord=desc,,command_line,13/02/2024,last week,"## Installation
+```",,
+viga,https://bio.tools/,https://raw.githubusercontent.com/viralInformatics/VIGA/master/README.md,"## Installation
 
 ### Step1: Download VIGA
 
@@ -317,74 +304,70 @@ Base on python 3.6.8
 ```
 pip install pandas=1.1.5 numpy=1.19.5  matplotlib=3.3.4  biopython=1.79
 ```
-",source,"[source] step1. Download VIGA with Git from GitHub:
+",source,"[plan source]>> step1. Download VIGA with Git from GitHub:
 ```
 git clone https://github.com/viralInformatics/VIGA.git
-```
-or Download ZIP to local
+(stepOptional). or Download ZIP to local
 step2.download Database:
 step2.1.download taxdmp.zip: https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/ and unzip taxdmp.zip and put it in ./db/
 step2.2.download ""prot.accession2taxid"" file from https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/accession2taxid/
 step2.3.download ""RefSeqVirusProtein"" file from
-wget -c ftp.ncbi.nlm.nih.gov/refseq/release/viral/viral.1.protein.faa.gz
+```wget -c ftp.ncbi.nlm.nih.gov/refseq/release/viral/viral.1.protein.faa.gz
 gzip -d viral.1.protein.faa.gz
-mv viral.1.protein.faa RefSeqVirusProtein
-step2.4. download ""nr"" file from
+mv viral.1.protein.faa RefSeqVirusProtein```
+step2.4. download ""nr"" file from```
 wget -c ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nr.gz
 or ascp -T  -i  asperaweb_id_dsa.openssh --host=ftp.ncbi.nih.gov --user=anonftp --mode=recv /blast/db/FASTA/nr.gz ./
-gzip -d nr.gz
-step2.5.use Diamond v2.0.11.149 to create two separate databases as the indexing libraries in the current version are incompatible with each other.
+gzip -d nr.gz```
+step2.5.use Diamond v2.0.11.149 to create two separate databases.
 step2.6.In order to set up a reference database for DIAMOND, the makedb command needs to be executed with the following command line:
 diamond makedb --in YourPath/RefSeqVirusProtein  -d Diamond_RefSeqVirusProtein --taxonmap YourPath/prot.accession2taxid --taxonnodes YourPath/nodes.dmp
 diamond makedb --in nr -d Dimond_nr --taxonmap YourPath/prot.accession2taxid --taxonnodes YourPath/nodes.dmp
 ```
-step3. installation of dependent software
-step3.1. installing Some Software Using Conda
+step3. installing  requirements via conda
 ```
 conda install fastp=0.12.4 trinity=2.8.5 diamond=2.0.11.149 ragtag=2.1.0 quast=5.0.2
 ```
-step3.2. manual Installation of MetaCompass
-https://github.com/marbl/MetaCompass
-step4: install Python Dependencies
-step4.1.base on python 3.6.8
+step4: install Python dependencies base on python 3.6.8
 ```
 pip install pandas=1.1.5 numpy=1.19.5  matplotlib=3.3.4  biopython=1.79
-```","1,2,3,4,5,6",,
-lncrtpred,https://bio.tools/,https://raw.githubusercontent.com/zglabDIB/LncRTPred/main/README.md,,,https://bio.tools/t?sort=citationDate&ord=desc,,command_line,13/02/2024,8 months,,,,,,
-nrn-ez,https://bio.tools/,https://raw.githubusercontent.com/scimemia/NRN-EZ/master/README.md,,,,,Script,13/02/2024,last week,"**INSTALLATION FOR VERSION 1.1.6**
+```",,"manual Installation of MetaCompass
+https://github.com/marbl/MetaCompass"
+lncrtpred,https://bio.tools/,https://raw.githubusercontent.com/zglabDIB/LncRTPred/main/README.md,,,,,
+nrn-ez,https://bio.tools/,https://raw.githubusercontent.com/scimemia/NRN-EZ/master/README.md,"**INSTALLATION FOR VERSION 1.1.6**
 
 NRN-EZ was built with PyInstaller 3.6, and requires the following languages and libraries:
 
-�	Python 3.6.9 and higher (currently up to 3.10)
+ÔøΩ	Python 3.6.9 and higher (currently up to 3.10)
 
-�	PyQt 5.10.1
+ÔøΩ	PyQt 5.10.1
 
-�	PyQtGraph 0.11.0
+ÔøΩ	PyQtGraph 0.11.0
 
 Installation instructions for Linux (Ubuntu and Pop!_OS): download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. 
 
 Installation instructions for Mac OS: download the Mac zip file and copy the NRN-EZ app to the Applications folder. 
 
-Installation instructions for Windows: download the Win zip file and run the installation wizard.",binary,"[binary] step1. install requirements:
+Installation instructions for Windows: download the Win zip file and run the installation wizard.",binary,"[plan binary]>> step1. install requirements:
 Python 3.6.9 and higher (currently up to 3.10)
 PyQt 5.10.1
 PyQtGraph 0.11.0
-step2. for linux:download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. ",,"2. for linux:download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. 
+step2. for linux:download the Linux zip file and, from the command window. step3. run a bash command for the install.sh file in the corresponding installation folder. ",,"2. for linux:download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. 
 2. for Mac OS: download the Mac zip file and copy the NRN-EZ app to the Applications folder. 
-2. for Windows: download the Win zip file and run the installation wizard.",
-causnet,https://bio.tools/,https://raw.githubusercontent.com/nand1155/CausNet/main/README.md,,0,https://bio.tools/t?sort=citationDate&ord=desc,,Library,13/02/2024,two years,"## Installation
+2. for Windows: download the Win zip file and run the installation wizard."
+causnet,https://bio.tools/,https://raw.githubusercontent.com/nand1155/CausNet/main/README.md,"## Installation
 
 You can install the development version from GitHub with:
 
 ``` r
 require(""devtools"")
 install_github(""https://github.com/nand1155/CausNet"")
-```",source,"[source]: step1.install the development version from GitHub with:
+```",source,"[source]>>step1.install the development version from GitHub with:
 ``` r
 require(""devtools"")
 install_github(""https://github.com/nand1155/CausNet"")
-```",,,
-viralcc,https://bio.tools/,https://raw.githubusercontent.com/dyxstat/Reproduce_ViralCC/main/README.md,,0,https://bio.tools/t?sort=citationDate&ord=desc,,command_line,13/02/2024,8 months,"""# Instruction of reproducing results in ViralCC paper
+```",,
+viralcc,https://bio.tools/,https://raw.githubusercontent.com/dyxstat/Reproduce_ViralCC/main/README.md,"""# Instruction of reproducing results in ViralCC paper
 We take the cow fecal datasets for example. The other two datasets were processed following the same procedure.
 
 Scripts to process the intermediate data and plot figures are available in the folder [Scripts](https://github.com/dyxstat/Reproduce_ViralCC/tree/main/Scripts).
@@ -463,59 +446,52 @@ python ./viralcc.py pipeline -v final.contigs.fa COW_MAP_SORTED.bam viral.txt ou
 ```
 python concatenation.py -p out_cow/VIRAL_BIN -o viralCC_cow_bins.fa
 checkv end_to_end viralCC_cow_bins.fa output_checkv_viralcc_cow -t 16 -d /panfs/qcb-panasas/yuxuandu/checkv-db-v1.0
-```""",source,"[source]step1.download and preprocess the raw data**
-Note: NCBI may update its links for downloading the database. Please check the latest link at [NCBI](https://www.ncbi.nlm.nih.gov/) if you meet the download error.
+```""",source,"[plan source]>>step1.download and preprocess the raw data.
 ```
 wget https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos2/sra-pub-run-13/ERR2282092/ERR2282092.1
 wget https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos2/sra-pub-run-13/ERR2530126/ERR2530126.1
 wget https://sra-downloadb.be-md.ncbi.nlm.nih.gov/sos2/sra-pub-run-13/ERR2530127/ERR2530127.1
-
 fastq-dump --split-files --gzip ERR2282092.1
 fastq-dump --split-files --gzip ERR2530126.1
 fastq-dump --split-files --gzip ERR2530127.1
-
 bbduk.sh  in1=ERR2282092.1_1.fastq.gz in2=ERR2282092.1_2.fastq.gz out1=COWSG1_AQ.fastq.gz out2=COWSG2_AQ.fastq.gz ref=/home1/yuxuandu/cmb/SOFTWARE/bbmap/resources/adapters.fa ktrim=r k=23 mink=11 hdist=1 minlen=50 tpe tbo
 bbduk.sh  in1=ERR2530126.1_1.fastq.gz in2=ERR2530126.1_2.fastq.gz out1=S3HIC1_AQ.fastq.gz out2=S3HIC2_AQ.fastq.gz ref=/home1/yuxuandu/cmb/SOFTWARE/bbmap/resources/adapters.fa ktrim=r k=23 mink=11 hdist=1 minlen=50 tpe tbo
 bbduk.sh  in1=ERR2530127.1_1.fastq.gz in2=ERR2530127.1_2.fastq.gz out1=M1HIC1_AQ.fastq.gz out2=M1HIC2_AQ.fastq.gz ref=/home1/yuxuandu/cmb/SOFTWARE/bbmap/resources/adapters.fa ktrim=r k=23 mink=11 hdist=1 minlen=50 tpe tbo
-
 bbduk.sh  in1=S3HIC1_AQ.fastq.gz in2=S3HIC2_AQ.fastq.gz out1=S3HIC1_CL.fastq.gz out2=S3HIC2_CL.fastq.gz trimq=10 qtrim=r ftm=5 minlen=50
 bbduk.sh  in1=M1HIC1_AQ.fastq.gz in2=M1HIC2_AQ.fastq.gz out1=M1HIC1_CL.fastq.gz out2=M1HIC2_CL.fastq.gz trimq=10 qtrim=r ftm=5 minlen=50
 bbduk.sh  in1=COWSG1_AQ.fastq.gz in2=COWSG2_AQ.fastq.gz out1=COWSG1_CL.fastq.gz out2=COWSG2_CL.fastq.gz trimq=10 qtrim=r ftm=5 minlen=50
-
 bbduk.sh in1=S3HIC1_CL.fastq.gz in2=S3HIC2_CL.fastq.gz out1=S3HIC1_trim.fastq.gz out2=S3HIC2_trim.fastq.gz ftl=10
 bbduk.sh in1=M1HIC1_CL.fastq.gz in2=M1HIC2_CL.fastq.gz out1=M1HIC1_trim.fastq.gz out2=M1HIC2_trim.fastq.gz ftl=10
-
 clumpify.sh in1=S3HIC1_trim.fastq.gz in2=S3HIC2_trim.fastq.gz out1=S3HIC1_dedup.fastq.gz out2=S3HIC2_dedup.fastq.gz dedupe
 clumpify.sh in1=M1HIC1_trim.fastq.gz in2=M1HIC2_trim.fastq.gz out1=M1HIC1_dedup.fastq.gz out2=M1HIC2_dedup.fastq.gz dedupe
 cat S3HIC1_dedup.fastq.gz M1HIC1_dedup.fastq.gz > HIC1.fastq.gz
 cat S3HIC2_dedup.fastq.gz M1HIC2_dedup.fastq.gz > HIC2.fastq.gz
 ```
-step2.assemble contigs and align processed Hi-C reads to contigs**
+step2.assemble contigs and step3. align processed Hi-C reads to contigs**
 ```
 megahit -1 COWSG1_CL.fastq.gz -2 COWSG2_CL.fastq.gz -o COW_ASSEMBLY --min-contig-len 1000 --k-min 21 --k-max 141 --k-step 12 --merge-level 20,0.95
-
 bwa index final.contigs.fa
 bwa mem -5SP final.contigs.fa HIC1.fastq.gz HIC2.fastq.gz > COW_MAP.sam
 samtools view -F 0x904 -bS COW_MAP.sam > COW_MAP_UNSORTED.bam
 samtools sort -n COW_MAP_UNSORTED.bam -o COW_MAP_SORTED.bam
 ```
-step3. identify viral contigs from assembled contigs**
+step4. identify viral contigs from assembled contigs:
 ```
 perl removesmalls.pl 3000 final.contigs.fa > cow_3000.fa
 wrapper_phage_contigs_sorter_iPlant.pl -f cow_3000.fa --db 1 --wdir output_directory --ncpu 16 --data-dir /panfs/qcb-panasas/yuxuandu/virsorter-data
 Rscript find_viral_contig.R
 ```
-step4. run ViralCC**
+step5. run ViralCC:
 ```
 python ./viralcc.py pipeline -v final.contigs.fa COW_MAP_SORTED.bam viral.txt out_cow
 ```
-step5. evaluation draft viral genomes using CheckV**
+step6. evaluation draft viral genomes using CheckV:
 ```
 python concatenation.py -p out_cow/VIRAL_BIN -o viralCC_cow_bins.fa
 checkv end_to_end viralCC_cow_bins.fa output_checkv_viralcc_cow -t 16 -d /panfs/qcb-panasas/yuxuandu/checkv-db-v1.0
-```",,,
-DRaW,https://bio.tools/,https://raw.githubusercontent.com/BioinformaticsIASBS/DRaW/main/README.md,,0,https://bio.tools/t?sort=citationDate&ord=desc,,,,,"# Running DRaW on COVID-19 datasets
-The DRaW has been applied on three COVID-19 datasets, DS1, DS2, and DS3. There are three subdirectories, �DS1_repur�, �DS2_repur�, and �DS3_repur�, in the �Drug-Repurposing� directory. Each subdirectory has been assigned to one of the mentioned datasets. We put the Draw implementation file for each dataset in each subdirectory separately. This is due to keep the corresponding hyperparameters of each dataset. 
+```",, (extra comment: NCBI may update its links for downloading the database. Please check the latest link at [NCBI](https://www.ncbi.nlm.nih.gov/) if you meet the download error)
+DRaW,https://bio.tools/,https://raw.githubusercontent.com/BioinformaticsIASBS/DRaW/main/README.md,"# Running DRaW on COVID-19 datasets
+The DRaW has been applied on three COVID-19 datasets, DS1, DS2, and DS3. There are three subdirectories, ÔøΩDS1_repurÔøΩ, ÔøΩDS2_repurÔøΩ, and ÔøΩDS3_repurÔøΩ, in the ÔøΩDrug-RepurposingÔøΩ directory. Each subdirectory has been assigned to one of the mentioned datasets. We put the Draw implementation file for each dataset in each subdirectory separately. This is due to keep the corresponding hyperparameters of each dataset. 
 We use Adam as the optimizer with a learning rate equal to 0.001, beta1 = 0.9, beta2 = 0.999, and epsilon = 1e_7. The dropout rate is set to 0.5. The batch size is chosen by the number of samples per dataset. This hyperparameter for DS1 is equal to 8, and those for DS2 and DS3 are set to 32.
 To run the model, it is enough to execute ""Drug-Repurposing.py"" script in the command line. After that, execute ""score.py"". The repurposed drugs will be stored in the ""meanScore.csv"" spreadsheet. It contains the average of ach drug ranking. The lower, the better. For example, to run the DRaW on DS1:
 ```bash
@@ -525,40 +501,40 @@ python score.py
 ```
 Same goes for other datasets. Just change the directory path.
 # Performance analysis
-In order to analysis the performance, there is a one extra directory in the root, �Performance_analysis�. By running following command the model is trained on a given dataset and returns its performance metrics, AUC-ROC, AUPR, F1 score, etc.   
-The input parameter �dataset_name� is one the following five datasets� name. The first one is COVID-19 DS3 and other four are golden benchmarks. 
+In order to analysis the performance, there is a one extra directory in the root, ÔøΩPerformance_analysisÔøΩ. By running following command the model is trained on a given dataset and returns its performance metrics, AUC-ROC, AUPR, F1 score, etc.   
+The input parameter ÔøΩdataset_nameÔøΩ is one the following five datasetsÔøΩ name. The first one is COVID-19 DS3 and other four are golden benchmarks. 
 'DS3','ic','nr','gpcr','e'
 
 ```bash
 cd Performance_analysis
 python main.py dataset_name
-```",source,"[source]step1.execute ""Drug-Repurposing.py"" script in the command line. step2. after that, execute ""score.py"". The repurposed drugs will be stored in the ""meanScore.csv"" spreadsheet. It contains the average of ach drug ranking. The lower, the better. For example, to run the DRaW on DS1:
+```",source,"[plan source]>>step1.execute ""Drug-Repurposing.py"" script in the command line. step2. after that, execute ""score.py"":
 ```bash
 cd Drug-Repurposing\DS1_repur
 python Drug-Repurposing.py 
 python score.py
-```",,,
-NRN-EZ,https://bio.tools/,https://raw.githubusercontent.com/scimemia/NRN-EZ/master/README.md,,,,,,,6 months,"**INSTALLATION FOR VERSION 1.1.6**
+```",," The repurposed drugs will be stored in the ""meanScore.csv"" spreadsheet. It contains the average of ach drug ranking. The lower, the better. For example, to run the DRaW on DS1"
+NRN-EZ,https://bio.tools/,https://raw.githubusercontent.com/scimemia/NRN-EZ/master/README.md,"**INSTALLATION FOR VERSION 1.1.6**
 
 NRN-EZ was built with PyInstaller 3.6, and requires the following languages and libraries:
 
-�	Python 3.6.9 and higher (currently up to 3.10)
+ÔøΩ	Python 3.6.9 and higher (currently up to 3.10)
 
-�	PyQt 5.10.1
+ÔøΩ	PyQt 5.10.1
 
-�	PyQtGraph 0.11.0
+ÔøΩ	PyQtGraph 0.11.0
 
 Installation instructions for Linux (Ubuntu and Pop!_OS): download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. 
 
 Installation instructions for Mac OS: download the Mac zip file and copy the NRN-EZ app to the Applications folder. 
 
-Installation instructions for Windows: download the Win zip file and run the installation wizard.",source,"[source] step1. install the requirements:Python 3.6.9 and higher (currently up to 3.10), PyQt 5.10.1, PyQtGraph 0.11.0
+Installation instructions for Windows: download the Win zip file and run the installation wizard.",source,"[plan source]>>step1. install the requirements:Python 3.6.9 and higher (currently up to 3.10), PyQt 5.10.1, PyQtGraph 0.11.0
 step2. for Linux: download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. 
 step2. for Mac OS: download the Mac zip file and copy the NRN-EZ app to the Applications folder. 
-step2. for Windows: download the Win zip file and run the installation wizard.",,"step2. for Linux: download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. 
+step2. for Windows: download the Win zip file and run the installation wizard.","step2. for Linux: download the Linux zip file and, from the command window, run a bash command for the install.sh file, in the corresponding installation folder. 
 step2. for Mac OS: download the Mac zip file and copy the NRN-EZ app to the Applications folder. 
 step2. for Windows: download the Win zip file and run the installation wizard.",
-guiding-instruction-based-image-editing-via,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/apple/ml-mgie/main/README.md,computer_science,3089,top,,,13/02/2024,29/09/2023,"## Requirements
+guiding-instruction-based-image-editing-via,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/apple/ml-mgie/main/README.md,"## Requirements
 ```
 conda create -n mgie python=3.10 -y
 conda activate mgie
@@ -584,7 +560,7 @@ pip install -U pydrive2 gdown wget
 cd ..
 cp mgie_llava.py LLaVA/llava/model/llava.py
 cp mgie_train.py LLaVA/llava/train/train.py
-```",source,"[source]"" step1. create conda environment ```
+```",source,"[plan source]>> step1. create conda environment ```
 conda create -n mgie python=3.10 -y
 conda activate mgie
 conda update -n base -c defaults conda setuptools -y
@@ -608,8 +584,8 @@ pip install -U pydrive2 gdown wget
 cd ..
 cp mgie_llava.py LLaVA/llava/model/llava.py
 cp mgie_train.py LLaVA/llava/train/train.py
-```","1,2,3,4",,
-self-play-fine-tuning-converts-weak-language,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/uclaml/SPIN/main/README.md,computer_science,430,top,,,13/02/2024,2/1/24,"## Setup
+```",,
+self-play-fine-tuning-converts-weak-language,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/uclaml/SPIN/main/README.md,"## Setup
 The following steps provide the necessary setup to run our codes.
 1. Create a Python virtual environment with Conda:
 ```
@@ -628,7 +604,7 @@ python -m pip install flash-attn --no-build-isolation
 4. Login to your huggingface account for downloading models
 ```
 huggingface-cli login --token ""${your_access_token}""
-```",source,"step1.create a Python virtual environment with Conda:
+```",source,"[plan source]>>step1.create a Python virtual environment with Conda:
 ```
 conda create -n myenv python=3.10
 conda activate myenv
@@ -645,8 +621,8 @@ python -m pip install flash-attn --no-build-isolation
 step4.login to your huggingface account for downloading models
 ```
 huggingface-cli login --token ""${your_access_token}""
-```",,,
-genegpt-teaching-large-language-models-to-use,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/ncbi/GeneGPT/main/README.md,computer_science,214,top,,,13/02/2024,19/04/2023,"# Requirements
+```",,
+genegpt-teaching-large-language-models-to-use,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/ncbi/GeneGPT/main/README.md,"# Requirements
 
 The code has been tested with Python 3.9.13. Please first install the required packages by:
 ```bash
@@ -671,12 +647,11 @@ To run GeneGPT-slim, simply use:
 ```bash
 python main.py 001001
 ```
-which will only use the Dm.1 and Dm.4 for in-context learning.",source,"step1.install requirements:
-The code has been tested with Python 3.9.13. Please first install the required packages by:
+which will only use the Dm.1 and Dm.4 for in-context learning.",source,"[plan source]>>step1.install requirements:
 ```bash
 pip install -r requirements.txt
 ```
-step2.set OpenAI API key to run GeneGPT with Codex. Replace the placeholder with your key in `config.py`:
+step2.set OpenAI API key to run GeneGPT with Codex. replace the placeholder with your key in `config.py`:
 ```bash
 $ cat config.py 
 API_KEY = 'YOUR_OPENAI_API_KEY'
@@ -687,13 +662,12 @@ After setting up the environment, one can run GeneGPT on GeneTuring by:
 python main.py 111111
 ```
 where `111111` denotes that all Documentations (Dc.1-2) and Demonstrations (Dm.1-4) are used.
-To run GeneGPT-slim, simply use:
+step4. To run GeneGPT-slim, simply use:
 ```bash
 python main.py 001001
-```
-which will only use the Dm.1 and Dm.4 for in-context learning.",,,
-the-boundary-of-neural-network-trainability,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/Sohl-Dickstein/fractal/main/README.md,computer_science,41,top,,,13/02/2024,9/2/24,,,,,,
-learning-to-fly-in-seconds,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/arplaboratory/learning-to-fly/master/README.MD,computer_science,201,top,,,13/02/2024,22/11/2023,"## Instructions to run the code
+```",,The code has been tested with Python 3.9.13
+the-boundary-of-neural-network-trainability,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/Sohl-Dickstein/fractal/main/README.md,,,,,
+learning-to-fly-in-seconds,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/arplaboratory/learning-to-fly/master/README.MD,"## Instructions to run the code
 ### Docker (isolated)
 We provide a pre-built Docker image with a simple web interface that can be executed using a single command (given that Docker is already installed on your machine):
 ```
@@ -829,17 +803,17 @@ docker run -it --rm -v $(pwd)/checkpoints/multirotor_td3/2023_11_16_14_46_38_d+o
 This should build the firmware using the newly trained policy and output the binary to `build_firmware/cf2.bin`. After that we can use the `cfclient` package to flash the firmware (find the installation instructions [here](https://www.bitcraze.io/documentation/repository/crazyflie-clients-python/master/installation/install/))
 ```
 cfloader flash build_firmware/cf2.bin stm32-fw -w radio://0/80/2M
-```","source,docker","[plan1. Docker (isolated)]
+```","source,docker","[plan1>>Docker (isolated)]
 step1: Execute a single command (given that Docker is already installed on your machine):
 ```
 docker run -it --rm -p 8000:8000 arpllab/learning_to_fly
 ```
-step2. the container is running, now step3 navigate to [https://0.0.0.0:8000](https://0.0.0.0:8000) and step 4 you should see something like (after starting the training):
+step2. the container is running, now step3. navigate to [https://0.0.0.0:8000](https://0.0.0.0:8000) and step 4. you should see something like (after starting the training):
 <div align=""center"">
 <img src=""https://github.com/arplaboratory/learning_to_fly_media/blob/master/simulator_screenshot.png"" />
 </div>
 Note that to make this Docker image compatible with a broad range of CPUs, some optimizations have been turned off. For full speed we recommend a [Native installation](#Native-installation). 
-[Docker installation (isolated)]
+[plan>>Docker installation (isolated)]
 step1. install Docker on your machine. step2. Then move to the original directory `learning_to_fly` and step3. build the Docker image:
 ```
 docker build -t arpllab/learning_to_fly .
@@ -860,7 +834,7 @@ docker run -it --rm -p 6006:6006 arpllab/learning_to_fly training_headless
 ```
 Navigate to [https://0.0.0.0:6006](https://0.0.0.0:6006) with your browser to investigate the Tensorboard logs.
 
-[plan2]. Native installation
+[plan>>Native installation]
 step1. clone this repository:
 ```
 git clone https://github.com/arplaboratory/learning-to-fly learning_to_fly
@@ -884,9 +858,7 @@ optional. As an alternative to openblas you can also install [Intel MKL](https:/
 ```
 brew install hdf5 protobuf boost
 ```
-Please make sure that `brew` links the libraries correctly. If not you might have to link e.g. `protobuf` manually using `brew link protobuf`.
-
-Going back to the main directory (`learning_to_fly`), we can now configure the build of the code:
+step5.  Configure the build of the code:
 ```
 cd ../../
 mkdir build
@@ -895,13 +867,11 @@ cd build
 - Ubuntu + OpenBLAS: `cmake .. -DCMAKE_BUILD_TYPE=Release -DRL_TOOLS_BACKEND_ENABLE_OPENBLAS:BOOL=ON`
 - Ubuntu + MKL: `cmake .. -DCMAKE_BUILD_TYPE=Release -DRL_TOOLS_BACKEND_ENABLE_MKL:BOOL=ON`
 - macOS (tested on Sonoma): `cmake .. -DCMAKE_BUILD_TYPE=Release`
-
-Finally, we can build the targets:
+step6. build the targets:
 ```
 cmake --build . -j8
 ```
-
-After successfully building the targets, we can run the code (in the original directory `learning_to_fly`):
+step7. run the code (in the original directory `learning_to_fly`):
 ```
 cd ..
 ./build/src/training_headless 
@@ -922,34 +892,17 @@ To run the training with the UI, we download the JavaScript dependencies in the
 cd src/ui
 ./get_dependencies.sh
 ```
-
-After that we can execute the UI binary from the root folder:
+step8.execute the UI binary from the root folder:
 ```
 cd ../../
 ./build/src/ui 0.0.0.0 8000
 ```
-Now you should be able to navigate to [http://0.0.0.0:8000](http://0.0.0.0:8000) in your browser and start the training.
-
-To run the benchmark (with UI, checkpointing and Tensorboard logging turned off):
+step9. navigate to [http://0.0.0.0:8000](http://0.0.0.0:8000) in your browser and start the training.
+step10. run the benchmark (with UI, checkpointing and Tensorboard logging turned off):
 ```
 sudo nice -n -20 ./build/src/training_benchmark
-```
-
-## Deploying trained policies on a Crazyflie
-Train a policy, e.g. using the Docker image with the UI:
-```
-docker run -it --rm -p 8000:8000 -v $(pwd)/checkpoints:/learning_to_fly/checkpoints arpllab/learning_to_fly 
-```
-The checkpoints are placed in the current working directory's `checkpoints` folder. Inspect the logs of the container to find the path of the final log, e.g., `checkpoints/multirotor_td3/2023_11_16_14_46_38_d+o+a+r+h+c+f+w+e+_002/actor_000000000300000.h`. 
-We can mount this file into the container `arpllab/learning_to_fly_build_firmware` for building the firmware, e.g.: 
-```
-docker run -it --rm -v $(pwd)/checkpoints/multirotor_td3/2023_11_16_14_46_38_d+o+a+r+h+c+f+w+e+_002/actor_000000000300000.h:/controller/data/actor.h:ro -v $(pwd)/build_firmware:/output arpllab/learning_to_fly_build_firmware
-```
-This should build the firmware using the newly trained policy and output the binary to `build_firmware/cf2.bin`. After that we can use the `cfclient` package to flash the firmware (find the installation instructions [here](https://www.bitcraze.io/documentation/repository/crazyflie-clients-python/master/installation/install/))
-```
-cfloader flash build_firmware/cf2.bin stm32-fw -w radio://0/80/2M
-```",,,
-/LargeWorldModel/LWM,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/LargeWorldModel/LWM/main/README.md,,2098,top,,,13/02/2024,,"## Setup
+```",,
+/LargeWorldModel/LWM,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/LargeWorldModel/LWM/main/README.md,"## Setup
 Install the requirements with:
 ```
 conda create -n lwm python=3.10
@@ -959,7 +912,7 @@ pip install -r requirements.txt
 or set up TPU VM with:
 ```
 sh tpu_requirements.sh
-```","packagemanager, source","step1.install the requirements with:
+```","packagemanager, source","[plan packagemanager]>>step1.install the requirements with:
 ```
 conda create -n lwm python=3.10
 pip install -U ""jax[cuda12_pip]==0.4.23"" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
@@ -968,8 +921,11 @@ pip install -r requirements.txt
 optional. set up TPU VM with:
 ```
 sh tpu_requirements.sh
-```",,,
-,,https://raw.githubusercontent.com/microsoft/UFO/main/README.md,,830,top,,,,,"### ___ Step 1: Installation
+```",,"optional. set up TPU VM with:
+```
+sh tpu_requirements.sh
+```"
+microsoft/UFO,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/microsoft/UFO/main/README.md,"### ___ Step 1: Installation
 UFO requires **Python >= 3.10** running on **Windows OS >= 10**. It can be installed by running the following command:
 ```bash
 # [optional to create conda environment]
@@ -1035,71 +991,49 @@ You can find the screenshots taken and request & response logs in the following
 ```
 ./ufo/logs/<your_task_name>/
 ```
-You may use them to debug, replay, or analyze the agent output.",source,"step1: Installation
-UFO requires **Python >= 3.10** running on **Windows OS >= 10**. It can be installed by running the following command:
-```bash
-# [optional to create conda environment]
-# conda create -n ufo python=3.10
-# conda activate ufo
-# clone the repository
+You may use them to debug, replay, or analyze the agent output.",source,"[plan source]>>step1: Run the following command:
+```
+conda create -n ufo python=3.10
+conda activate ufo
+clone the repository
 git clone https://github.com/microsoft/UFO.git
-cd UFO
-# install the requirements
-pip install -r requirements.txt
+cd UFO```
+step2. install the requirements:
+```pip install -r requirements.txt
 ```
-__ Step 2: Configure the LLMs
-Before running UFO, you need to provide your LLM configurations. Taking OpenAI as an example, you can configure `ufo/config/config.yaml` file as follows. 
-#### OpenAI
+step 3: configure the LLMs `ufo/config/config.yaml` file as follows:
+for OpenAI
 ```
 API_TYPE: ""openai"" 
 OPENAI_API_BASE: ""https://api.openai.com/v1/chat/completions"" # The base URL for the OpenAI API
 OPENAI_API_KEY: ""YOUR_API_KEY""  # Set the value to the openai key for the llm model
 OPENAI_API_MODEL: ""GPTV_MODEL_NAME""  # The only OpenAI model by now that accepts visual input
 ```
-
-#### Azure OpenAI (AOAI)
+for Azure OpenAI (AOAI):
 ```
 API_TYPE: ""aoai"" 
 OPENAI_API_BASE: ""YOUR_ENDPOINT"" # The AOAI API address. Format: https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/completions?api-version={api-version}
 OPENAI_API_KEY: ""YOUR_API_KEY""  # Set the value to the openai key for the llm model
 OPENAI_API_MODEL: ""GPTV_MODEL_NAME""  # The only OpenAI model by now that accepts visual input
 ```
-
-
-### __ Step 3: Start UFO
-
-#### __ You can execute the following on your Windows command Line (CLI):
-
-```bash
+step4: Start UFO using command line:
+```
 # assume you are in the cloned UFO folder
 python -m ufo --task <your_task_name>
 ```
-
-This will start the UFO process and you can interact with it through the command line interface. 
-If everything goes well, you will see the following message:
-
+step5. check installation. If everything goes well, you will see the following message in the console:
 ```bash
 Welcome to use UFO__, A UI-focused Agent for Windows OS Interaction. 
- _   _  _____   ___
-| | | ||  ___| / _ \
-| | | || |_   | | | |
-| |_| ||  _|  | |_| |
- \___/ |_|     \___/
-Please enter your request to be completed__:
-```
-#### __Reminder:  ####
+```",,"#### __Reminder:  ####
 - Before UFO executing your request, please make sure the targeted applications are active on the system.
 - The GPT-V accepts screenshots of your desktop and application GUI as input. Please ensure that no sensitive or confidential information is visible or captured during the execution process. For further information, refer to [DISCLAIMER.md](./DISCLAIMER.md).
-
-
 ###  Step 4 __: Execution Logs 
-
 You can find the screenshots taken and request & response logs in the following folder:
 ```
 ./ufo/logs/<your_task_name>/
 ```
-You may use them to debug, replay, or analyze the agent output.",,,
-,,https://raw.githubusercontent.com/catid/dora/main/README.md,,135,top,,,,,"## Demo
+You may use them to debug, replay, or analyze the agent output."
+/catid/dora,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/catid/dora/main/README.md,"## Demo
 
 Install conda: https://docs.conda.io/projects/miniconda/en/latest/index.html
 
@@ -1112,8 +1046,16 @@ conda create -n dora python=3.10 -y && conda activate dora
 pip install -U -r requirements.txt
 
 python dora.py
-```",source,,,,
-,,https://raw.githubusercontent.com/AILab-CVC/YOLO-World/master/README.md,,,,,,,,"### 1. Installation
+```",source,"[plan source]>>step1. install conda:https://docs.conda.io/projects/miniconda/en/latest/index.html. step2. clone the repository and move to the folder:
+```bash
+git clone https://github.com/catid/dora.git
+cd dora
+step3. create conda environment:```
+conda create -n dora python=3.10 -y && conda activate dora```
+step4. install requirements:```
+pip install -U -r requirements.txt```
+step5. execute the script: ```python dora.py```",,
+YOLO-World,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/AILab-CVC/YOLO-World/master/README.md,"### 1. Installation
 
 YOLO-World is developed based on `torch==1.11.0` `mmyolo==0.6.0` and `mmdetection==3.0.0`.
 
@@ -1127,8 +1069,15 @@ git clone --recursive https://github.com/AILab-CVC/YOLO-World.git
 ```bash
 pip install torch wheel -q
 pip install -e .
-```",source,,,,
-,,https://raw.githubusercontent.com/FasterDecoding/BitDelta/main/README.md,,63,top,,,,,"## Install
+```",source,"[plan source]>>step1. clone repository:
+```
+git clone --recursive https://github.com/AILab-CVC/YOLO-World.git
+```
+step2. install module:
+pip install torch wheel -q
+pip install -e .
+```",,
+FasterDecoding/BitDelta,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/FasterDecoding/BitDelta/main/README.md,"## Install
 
 1. Clone the repo and navigate to BitDelta:
 
@@ -1144,7 +1093,7 @@ conda create -yn bitdelta python=3.9
 conda activate bitdelta
 
 pip install -e .
-```",source,"step1.clone the repo and navigate to BitDelta:
+```",source,"[plan source]>>step1.clone the repo and navigate to BitDelta:
 ```
 git clone https://github.com/FasterDecoding/BitDelta
 cd BitDelta
@@ -1154,8 +1103,8 @@ step2.set up environment:
 conda create -yn bitdelta python=3.9
 conda activate bitdelta
 pip install -e .
-```",,,
-,,https://raw.githubusercontent.com/tensorflow/tensorflow/master/README.md,,180724,greatest,,,,,"## Install
+```",,
+tensorflow,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/tensorflow/tensorflow/master/README.md,"## Install
 
 See the [TensorFlow install guide](https://www.tensorflow.org/install) for the
 [pip package](https://www.tensorflow.org/install/pip), to
@@ -1185,9 +1134,7 @@ commands.
 
 *Nightly binaries are available for testing using the
 [tf-nightly](https://pypi.python.org/pypi/tf-nightly) and
-[tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.*",packagemanager,"step1. To install the current release, which includes support for
-[CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu) *(Ubuntu and
-Windows)*:
+[tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.*",packagemanager,"[plan packagemanager]>>via pip. step1.:
 ```
 $ pip install tensorflow
 ```
@@ -1198,11 +1145,10 @@ $ pip install tensorflow-cpu
 step3. optional.
 To update TensorFlow to the latest version, add `--upgrade` flag to the above
 commands.
-
-*Nightly binaries are available for testing using the
+[plan binary]>> binaries are available for testing using the
 [tf-nightly](https://pypi.python.org/pypi/tf-nightly) and
-[tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.*",,,
-,,https://raw.githubusercontent.com/huggingface/transformers/main/README.md,,120272,greatest,,,,,"## Installation
+[tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.",,
+transformers,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/huggingface/transformers/main/README.md,"## Installation
 
 ### With pip
 
@@ -1235,8 +1181,7 @@ conda install conda-forge::transformers
 
 Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
 
-> **_NOTE:_**  On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).",packagemanager,"Plan1. With pip
-requirements >> This repository is tested on Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+.
+> **_NOTE:_**  On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).",packagemanager,"[plan packagemanager]>>via pip:
 step1. install __ Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html).(extra information) If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 step2. create a virtual environment with the version of Python you're going to use and activate it.
 step3.  install at least one of Flax, PyTorch, or TensorFlow.
@@ -1246,15 +1191,15 @@ step4. When one of those backends has been installed, __ Transformers can be ins
 pip install transformers
 ```
 extrainformation. If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source).
-plan1.2. With conda
+[plan packagemanager]>>via conda:
 step1.
 ```shell script
 conda install conda-forge::transformers
 ```
 > **_NOTE:_** Installing `transformers` from the `huggingface` channel is deprecated.
 Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
-> **_NOTE:_**  On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).",,,
-,,https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md,,120270,greatest,,,,,"## Quick Install
+> **_NOTE:_**  On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).",,"requirements >> This repository is tested on Python 3.8+, Flax 0.4.1+, PyTorch 1.11+, and TensorFlow 2.6+."
+langchain,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/langchain-ai/langchain/master/README.md,"## Quick Install
 
 With pip:
 ```bash
@@ -1264,15 +1209,15 @@ pip install langchain
 With conda:
 ```bash
 conda install langchain -c conda-forge
-```",packagemanager,"Plan1. With pip. Step1:
+```",packagemanager,"[plan packagemanager]>>step1: via pip
 ```bash
 pip install langchain
 ```
-Plan2. Step1: With conda:
+[plan packagemanager]>>step1: via conda:
 ```bash
 conda install langchain -c conda-forge
-```",,,
-,,https://raw.githubusercontent.com/divelab/DIG/dig-stable/README.md,,1912,topic,drug discovery,task libraries,,,"## Installation
+```",,
+DIG/dig-stable,https://paperwithcode.com/paper/,https://raw.githubusercontent.com/divelab/DIG/dig-stable/README.md,"## Installation
 
 ### Install from pip
 The key dependencies of DIG: Dive into Graphs are PyTorch (>=1.10.0), PyTorch Geometric (>=2.0.0), and RDKit.
@@ -1316,46 +1261,25 @@ If you want to try the latest features that have not been released yet, you can
 git clone https://github.com/divelab/DIG.git
 cd DIG
 pip install .
-```",packagemanager,"step 1. Install from pip
-The key dependencies of DIG: Dive into Graphs are PyTorch (>=1.10.0), PyTorch Geometric (>=2.0.0), and RDKit.
-
-1. Install [PyTorch](https://pytorch.org/get-started/locally/) (>=1.10.0)
-
-```shell script
-$ python -c ""import torch; print(torch.__version__)""
->>> 1.10.0
+```",packagemanager,"[plan packagemanager]>>step 1. Install [PyTorch](https://pytorch.org/get-started/locally/) (>=1.10.0)
+```python -c ""import torch; print(torch.__version__)""
+```
+step2. Install [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html#) (>=2.0.0)
 ```
-
-
-
-
-2. Install [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html#) (>=2.0.0)
-
-```shell script
 $ python -c ""import torch_geometric; print(torch_geometric.__version__)""
->>> 2.0.0
+``` 
+step3. Install DIG: Dive into Graphs.
 ```
-    
-3. Install DIG: Dive into Graphs.
-
-```shell script
 pip install dive-into-graphs
 ```
-
-
-After installation, you can check the version. You have successfully installed DIG: Dive into Graphs if no error occurs.
-
-``` shell script
-$ python
+step4. check the version installed.
+```
+python
 >>> from dig.version import __version__
 >>> print(__version__)
 ```
-
-### Install from source
-If you want to try the latest features that have not been released yet, you can install dig from source.
-
-```shell script
+[plan source] step1. Clone repository ```
 git clone https://github.com/divelab/DIG.git
-cd DIG
-pip install .
-```",,,
+``` 
+step2. move to the folder ```cd DIG```
+step3. install the module ```pip install .```",,