diff --git a/docs/readthedocs/source/doc/Application/powered-by.md b/docs/readthedocs/source/doc/Application/powered-by.md
index 3cf880bf..637deb17 100644
--- a/docs/readthedocs/source/doc/Application/powered-by.md
+++ b/docs/readthedocs/source/doc/Application/powered-by.md
@@ -2,7 +2,8 @@
 ---
 
 * __Alibaba__
-  <br>• [Deploy Analytics Zoo in Aliyun EMR](https://partners-intl.aliyun.com/help/doc-detail/93155.htm)
+  <br>• [Alibaba Cloud and Intel synergize BigDL PPML and Alibaba Cloud Data Trust to protect E2E privacy of AI and big data](https://www.intel.com/content/www/us/en/customer-spotlight/stories/alibaba-cloud-ppml-customer-story.html)
+  <br>• [Better Together: Alibaba Cloud Realtime Compute and Distributed AI Inference](https://www.intel.cn/content/dam/www/central-libraries/cn/zh/documents/better-together-alibaba-cloud-realtime-compute-and-distibuted-ai-inference.pdf) (in Chinese)
   <br>• [Better Together: Privacy-Preserving Machine Learning](https://www.intel.com/content/www/us/en/artificial-intelligence/posts/alibaba-privacy-preserving-machine-learning.html)
 * __AsiaInfo__
   <br>• [Network AI Applications using BigDL and oneAPI toolkit on Intel Xeon](https://www.intel.cn/content/www/cn/zh/customer-spotlight/cases/asiainfo-taps-intelligent-network-applications.html)
@@ -15,7 +16,8 @@
   <br>• [How Intel and Burger King built an order recommendation system that preserves customer privacy](https://venturebeat.com/2021/04/06/how-intel-and-burger-king-built-an-order-recommendation-system-that-preserves-customer-privacy/)
   <br>• [Burger King: Context-Aware Recommendations (video)](https://www.intel.com/content/www/us/en/customer-spotlight/stories/burger-king-ai-customer-story.html)
 * __Capgemini__
-  <br>• [Intelligent 5G L2 MAC Scheduler: Powered by Capgemini NetAnticipate 5G on Intel Architecture](https://networkbuilders.intel.com/solutionslibrary/intelligent-5g-l2-mac-scheduler-powered-by-capgemini-netanticipate-5g-on-intel-architecture)
+<br>• [Project Bose: A smart way to enable sustainable 5G networks in Capgemini](https://www.capgemini.com/insights/expert-perspectives/project-bose-a-smart-way-to-enable-sustainable-5g-networks/)
+<br>• [Intelligent 5G L2 MAC Scheduler: Powered by Capgemini NetAnticipate 5G on Intel Architecture](https://networkbuilders.intel.com/solutionslibrary/intelligent-5g-l2-mac-scheduler-powered-by-capgemini-netanticipate-5g-on-intel-architecture)
 * __China Unicom__
   <br>• [Cloud Data Center Power Saving using BigDL Chronos in China Unicom](https://www.intel.cn/content/www/cn/zh/customer-spotlight/cases/china-unicom-bigdl-chronos-framework-5gc.html)
 * __CERN__
@@ -35,6 +37,7 @@ Learning on Apache Spark and Analytics Zoo](https://www.dellemc.com/resources/en
 <br>• [Goldwind SE: Intelligent Power Prediction Solution](https://www.intel.com/content/www/us/en/customer-spotlight/stories/goldwind-customer-story.html)
 <br>• [Intel big data analysis + AI platform helps GoldWind to build a new energy intelligent power prediction solution](https://www.intel.cn/content/www/cn/zh/analytics/artificial-intelligence/create-power-forecasting-solutions.html)
 * __Inspur__
+<br>• [Inspur’s Big Data Intelligent Computing AIO Solution Based on Intel Architecture](https://dpgresources.intel.com/asset-library/inspur-insight-big-data-platform-solution-icx-prc/)
 <br>• [Inspur E2E Smart Transportation CV application](https://jason-dai.github.io/cvpr2021/slides/Inspur%20E2E%20Smart%20Transportation%20CV%20application%20-CVPR21.pdf)
 <br>• [Inspur End-to-End Smart Computing Solution with Intel Analytics Zoo](https://dpgresources.intel.com/asset-library/inspur-end-to-end-smart-computing-solution-with-intel-analytics-zoo/)
 * __JD__
diff --git a/docs/readthedocs/source/doc/Application/presentations.md b/docs/readthedocs/source/doc/Application/presentations.md
index 0dd0764b..2a87e6f5 100644
--- a/docs/readthedocs/source/doc/Application/presentations.md
+++ b/docs/readthedocs/source/doc/Application/presentations.md
@@ -13,13 +13,19 @@
 - Building Deep Learning Applications on Big Data Platforms, [CVPR 2018](https://cvpr2018.thecvf.com/) [tutorial](https://jason-dai.github.io/cvpr2018/), June 2018 ([slides](https://jason-dai.github.io/cvpr2018/slides/BigData_DL_Jason-CVPR.pdf))
 
 **Talks:**
+- BigDL 2.0: Seamlessly scaling end-to-end AI pipelines, [Ray Summit 2022](https://www.anyscale.com/ray-summit-2022/agenda/sessions/174), August 2022 ([slides](https://github.com/analytics-zoo/analytics-zoo.github.io/blob/master/presentations/BigDL-2.0-Seamlessly-scaling-end-to-end-AI-pipelines.pdf))
+
+- Exploration on Confidential Computing for Big Data & AI, [oneAPI DevSummit for AI 2022](https://www.oneapi.io/event-sessions/exploration-on-confidential-computing-for-big-data-ai-ai-2022/), July 2022 ([slides](https://simplecore.intel.com/oneapi-io/wp-content/uploads/sites/98/Qiyuan-Gong-and-Chunyang-Hui-Exploration-on-Confidential-Computing-for-Big-Data-AI.pdf))
+
+- Privacy Preserving Machine Learning and Big Data Analytics Using Apache Spark, [Data + AI Summit 2022](https://www.databricks.com/dataaisummit/session/privacy-preserving-machine-learning-and-big-data-analytics-using-apache-spark), June 2022 ([slides](https://microsites.databricks.com/sites/default/files/2022-07/Privacy-Preserving-Machine-Learning-and-Big-Data-Analytics-Using-Apache-Spark.pdf))
+
 - E2E Smart Transportation CV application in Inspur (using Insight Data-Intelligence platform), [CVPR 2021](https://jason-dai.github.io/cvpr2021/), July 2021 ([slides](https://jason-dai.github.io/cvpr2021/slides/Inspur%20E2E%20Smart%20Transportation%20CV%20application%20-CVPR21.pdf))
 
 - Mobile Order Click-Through Rate (CTR) Recommendation with Ray on Apache Spark at Burger King, [Ray Summit 2021](https://www.anyscale.com/events/2021/06/22/mobile-order-click-through-rate-ctr-recommendation-with-ray-on-apache-spark-at-burger-king), June 2021 ([slides](https://files.speakerdeck.com/presentations/1870110b5adf4bfc8f0c76255a417f09/Kai_Huang_and_Luyang_Wang.pdf))
 
-- Deep Reinforcement Learning Recommenders using RayOnSpark, **Data + AI Summit 2021**, May 2021 ([slides](https://github.com/analytics-zoo/analytics-zoo.github.io/blob/master/presentations/210527DeepReinforcementLearningRecommendersUsingRayOnSpark2.pdf))
+- Deep Reinforcement Learning Recommenders using RayOnSpark, *Data + AI Summit 2021*, May 2021 ([slides](https://github.com/analytics-zoo/analytics-zoo.github.io/blob/master/presentations/210527DeepReinforcementLearningRecommendersUsingRayOnSpark2.pdf))
 
-- Cluster Serving: Deep Learning Model Serving for Big Data, **Data + AI Summit 2021**, May 2021 ([slides](https://github.com/analytics-zoo/analytics-zoo.github.io/blob/master/presentations/210526Cluster-Serving.pdf))
+- Cluster Serving: Deep Learning Model Serving for Big Data, *Data + AI Summit 2021*, May 2021 ([slides](https://github.com/analytics-zoo/analytics-zoo.github.io/blob/master/presentations/210526Cluster-Serving.pdf))
 
 - Offer Recommendation System with Apache Spark at Burger King, [Data + AI Summit 2021](https://databricks.com/session_na21/offer-recommendation-system-with-apache-spark-at-burger-king), May 2021 ([slides](https://github.com/analytics-zoo/analytics-zoo.github.io/blob/master/presentations/20210526Offer%20Recommendation.pdf))