From 225facc5af1e32066dabc6181f1da16e0db24682 Mon Sep 17 00:00:00 2001
From: Jason Dai <jason.dai@gmail.com>
Date: Wed, 16 Nov 2022 15:51:28 +0800
Subject: [PATCH] Update README.md (#6641)

---
 README.md | 41 ++++++++++++++++++++++++++---------------
 1 file changed, 26 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index e9b87aa5..584ecda5 100644
--- a/README.md
+++ b/README.md
@@ -35,11 +35,11 @@ flowchart TD;
     Feature2-- Python -->Feature3{{What type of application?}};
     Feature2-- Scala/Java -->ReferDLlib([<em><strong>DLlib</strong></em>]);
     Feature3-- "Distributed Big Data + AI (TF/PyTorch)" -->ReferOrca([<em><strong>Orca</strong></em>]);
-    Feature3-- Accelerating TensorFlow / PyTorch -->ReferNano([<em><strong>Nano</strong></em>]);
+    Feature3-- Accelerate TensorFlow / PyTorch -->ReferNano([<em><strong>Nano</strong></em>]);
     Feature3-- DL for Spark MLlib -->ReferDLlib2([<em><strong>DLlib</strong></em>]);
     Feature3-- High Level App Framework -->Feature4{{Domain?}};
     Feature4-- Time Series -->ReferChronos([<em><strong>Chronos</strong></em>]);
-    Feature4-- Recommendation System -->ReferFriesian([<em><strong>Friesian</strong></em>]);
+    Feature4-- Recommender System -->ReferFriesian([<em><strong>Friesian</strong></em>]);
     
     click ReferNano "https://github.com/intel-analytics/bigdl#nano"
     click ReferOrca "https://github.com/intel-analytics/bigdl#orca"
@@ -371,7 +371,7 @@ The *Chronos* library makes it easy to build end-to-end, large-scale **recommeda
 
 *BigDL PPML* provides a **hardware (Intel SGX) protected** *Trusted Cluster Environment* for running distributed Big Data & AI applications (in a secure fashion on private or public cloud). 
 
-*See PPML [tutorial](https://github.com/intel-analytics/BigDL/blob/main/ppml/README.md) and [user guide](https://bigdl.readthedocs.io/en/latest/doc/PPML/Overview/ppml.html) for more details.* 
+*See PPML [user guide](https://bigdl.readthedocs.io/en/latest/doc/PPML/Overview/ppml.html) and [tutorial](https://github.com/intel-analytics/BigDL/blob/main/ppml/README.md) for more details.* 
 
 ## Getting Support
 
@@ -382,16 +382,27 @@ The *Chronos* library makes it easy to build end-to-end, large-scale **recommeda
 
 ## Citation
 
-If you've found BigDL useful for your project, you may cite the [paper](https://arxiv.org/abs/2204.01715) as follows:
+If you've found BigDL useful for your project, you may cite our papers as follows:
 
-```
-@INPROCEEDINGS{9880257,
-  author={Dai, Jason Jinquan and Ding, Ding and Shi, Dongjie and Huang, Shengsheng and Wang, Jiao and Qiu, Xin and Huang, Kai and Song, Guoqiong and Wang, Yang and Gong, Qiyuan and Song, Jiaming and Yu, Shan and Zheng, Le and Chen, Yina and Deng, Junwei and Song, Ge},
-  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
-  title={BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster}, 
-  year={2022},
-  volume={},
-  number={},
-  pages={21407-21414},
-  doi={10.1109/CVPR52688.2022.02076}}
-```
+- *[BigDL 2.0](https://arxiv.org/abs/2204.01715): Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster*
+  ```
+  @INPROCEEDINGS{9880257,
+    title={BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster}, 
+    author={Dai, Jason Jinquan and Ding, Ding and Shi, Dongjie and Huang, Shengsheng and Wang, Jiao and Qiu, Xin and Huang, Kai and Song, Guoqiong and Wang, Yang and Gong, Qiyuan and Song, Jiaming and Yu, Shan and Zheng, Le and Chen, Yina and Deng, Junwei and Song, Ge},
+    booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
+    year={2022},
+    pages={21407-21414},
+    doi={10.1109/CVPR52688.2022.02076}}
+  ```
+
+- *[BigDL](https://arxiv.org/abs/1804.05839): A Distributed Deep Learning Framework for Big Data*
+  ```
+  @INPROCEEDINGS{10.1145/3357223.3362707,
+  title = {BigDL: A Distributed Deep Learning Framework for Big Data},
+  author = {Dai, Jason Jinquan and Wang, Yiheng and Qiu, Xin and Ding, Ding and Zhang, Yao and Wang, Yanzhang and Jia, Xianyan and Zhang, Cherry Li and Wan, Yan and Li, Zhichao and Wang, Jiao and Huang, Shengsheng and Wu, Zhongyuan and Wang, Yang and Yang, Yuhao and She, Bowen and Shi, Dongjie and Lu, Qi and Huang, Kai and Song, Guoqiong},
+  booktitle = {Proceedings of the ACM Symposium on Cloud Computing (SoCC)},
+  year = {2019},
+  pages = {50–60},
+  doi = {10.1145/3357223.3362707},
+  }
+  ```