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diff --git a/External/Activity Recognition/README.md b/External/Activity Recognition/README.md
@@ -6,20 +6,20 @@
 **Overview**: https://github.com/ermongroup/Wifi_Activity_Recognition \
 **Direct Link**: https://drive.google.com/file/d/19uH0_z1MBLtmMLh8L4BlNA0w-XAFKipM/view?usp=sharing \
 **Publication**: https://doi.org/10.1109/MCOM.2017.1700082 \
-**Citation**: S. Yousefi, H. Narui, S. Dayal, S. Ermon and S. Valaee, "A Survey on Behavior Recognition Using WiFi Channel State Information," in IEEE Communications Magazine, vol. 55, no. 10, pp. 98-104, Oct. 2017, doi: 10.1109/MCOM.2017.1700082. \
+**Citation**: S. Yousefi, H. Narui, S. Dayal, S. Ermon and S. Valaee, "A Survey on Behavior Recognition Using WiFi Channel State Information," in IEEE Communications Magazine, vol. 55, no. 10, pp. 98-104, Oct. 2017, doi: 10.1109/MCOM.2017.1700082.
 
 **Name**: Channel state information (WiFi traces) for 6 activities \
 **Author(s)**: Jeroen Klein Brinke \
 **Description**: Data captures for 6 activities across several subjects on different days. \
 **Overview**: https://data.4tu.nl/articles/dataset/Channel_state_information_WiFi_traces_for_6_activities/12692816/1 \
 **Direct Link**: https://data.4tu.nl/ndownloader/articles/12692816/versions/1 \
 **Publication**: https://dl.acm.org/doi/10.1145/3363347.3363362 \
-**Citation**: Klein Brinke, Jeroen (2019): Channel state information (WiFi traces) for 6 activities. 4TU.ResearchData. Dataset. https://doi.org/10.4121/uuid:42bffa4c-113c-46eb-84a1-c87b6a31a99f \
+**Citation**: Klein Brinke, Jeroen (2019): Channel state information (WiFi traces) for 6 activities. 4TU.ResearchData. Dataset. https://doi.org/10.4121/uuid:42bffa4c-113c-46eb-84a1-c87b6a31a99f
 
 **Name**: Dataset for Wi-Fi based human-to-human interaction \
 **Author(s)**: Rami Alazrai, Ali Awad, Baha' Alsaify, Mohammad Hababeh, Mohammad I. Daoud \
 **Description**: Data captures from 40 subjects performing 12 human-to-human interactions. \
 **Overview**: https://data.mendeley.com/datasets/3dhn4xnjxw/1 \
 **Direct Link**: https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/3dhn4xnjxw-1.zip \
 **Publication**: https://doi.org/10.1016/j.dib.2020.106534 \
-**Citation**: Baha’A, A., Almazari, M.M., Alazrai, R. and Daoud, M.I., 2020. A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments. Data in Brief, 33, p.106534. \
+**Citation**: Baha’A, A., Almazari, M.M., Alazrai, R. and Daoud, M.I., 2020. A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments. Data in Brief, 33, p.106534.
diff --git a/External/Communication/README.md b/External/Communication/README.md
@@ -6,4 +6,4 @@
 **Overview**: http://tns.thss.tsinghua.edu.cn/sun/researches/Cross-TechnologyCommunication.html \
 **Direct Link**: http://tns.thss.tsinghua.edu.cn/sun/static/data/zigfi.zip \
 **Publication**: https://doi.org/10.1109/TNET.2019.2962707 \
-**Citation**: Guo, X., He, Y., Zheng, X., Yu, L. and Gnawali, O., 2020. Zigfi: Harnessing channel state information for cross-technology communication. IEEE/ACM Transactions on Networking, 28(1), pp.301-311. \
+**Citation**: Guo, X., He, Y., Zheng, X., Yu, L. and Gnawali, O., 2020. Zigfi: Harnessing channel state information for cross-technology communication. IEEE/ACM Transactions on Networking, 28(1), pp.301-311.
diff --git a/External/Fall Detection/README.md b/External/Fall Detection/README.md
@@ -6,12 +6,12 @@
 **Overview**: https://github.com/dmsp123/FallDeFi \
 **Direct Link**: https://mega.nz/#!9tFA1JrR!lEVzKwNRkS3PcOd0ssb8V3tOi0ZA5Gs9EOU0drtFYcg \
 **Publication**: https://dl.acm.org/doi/10.1145/3161183 \
-**Citation**: Palipana, Sameera & Rojas, David & Agrawal, Piyush & Pesch, Dirk. (2018). FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices. PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). 1. 10.1145/3161183. \
+**Citation**: Palipana, Sameera & Rojas, David & Agrawal, Piyush & Pesch, Dirk. (2018). FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices. PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). 1. 10.1145/3161183.
 
 **Name**: ToiFall \
 **Author(s)**: Ziqi Wang, Zhihao Gu, Junwei Yin, Zhe Chen, Yuedong Xu \
 **Description**: Data captures of fall behaviours in bathroom environments with relation to stroke. \
 **Overview**: http://medianet.azurewebsites.net/toifall-ubicomp18/ \
 **Direct Link**: https://drive.google.com/file/d/1RJvLL58m__km6vPTZbW2IV3FqgplvOj4/view?usp=sharing \
 **Publication**: https://doi.org/10.1145/3267305.3267650 \
-**Citation**: Wang, Z., Gu, Z., Yin, J., Chen, Z. and Xu, Y., 2018, October. Syncope detection in toilet environments using Wi-Fi channel state information. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 287-290). \
+**Citation**: Wang, Z., Gu, Z., Yin, J., Chen, Z. and Xu, Y., 2018, October. Syncope detection in toilet environments using Wi-Fi channel state information. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 287-290).
diff --git a/External/Gesture Recognition/README.md b/External/Gesture Recognition/README.md
@@ -6,12 +6,12 @@
 **Overview**: https://yongsen.github.io/SignFi/ \
 **Direct Link**: https://wm1693.box.com/s/z9vsrn3998n4xyzkpqtj89yclk28eatp \
 **Publication**: https://doi.org/10.1145/3310194 \
-**Citation**: Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign Language Recognition Using WiFi. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 23 (March 2018), 21 pages. DOI: https://doi.org/10.1145/3191755 \
+**Citation**: Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign Language Recognition Using WiFi. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 23 (March 2018), 21 pages. DOI: https://doi.org/10.1145/3191755
 
 **Name**: WiAR \
 **Author(s)**: Linlin Guo, Lei Wang, Jialin Liu, and Wei Zhou \
 **Description**: Data captures for 16 gestures performed by 10 subjects. \
 **Overview**: https://github.com/linteresa/WiAR \
 **Direct Link**: https://github.com/linteresa/WiAR/archive/master.zip \
 **Publication**: https://doi.org/10.1109/HealthCom.2017.8210783 \
-**Citation**: L. Guo et al., "A novel benchmark on human activity recognition using WiFi signals," 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, 2017, pp. 1-6, doi: 10.1109/HealthCom.2017.8210783 \
+**Citation**: L. Guo et al., "A novel benchmark on human activity recognition using WiFi signals," 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, 2017, pp. 1-6, doi: 10.1109/HealthCom.2017.8210783
diff --git a/External/Human Identification/README.md b/External/Human Identification/README.md
@@ -6,4 +6,4 @@
 **Overview**: https://zenodo.org/record/3882104 \
 **Direct Link**: https://zenodo.org/record/3882104/files/EyeFi_Dataset.zip?download=1 \
 **Publication**: https://doi.org/10.1109/DCOSS49796.2020.00022 \
-**Citation**: Fang, S., Islam, T., Munir, S. and Nirjon, S., 2020, May. EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching. In 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 59-68). IEEE. \
+**Citation**: Fang, S., Islam, T., Munir, S. and Nirjon, S., 2020, May. EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching. In 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 59-68). IEEE.