News (2020)
IEEE Access 8 (Nov 24 2020)
A two-dimensional matrix profile-based anomaly detection technique is used to obtain anomaly weighted images and CT-SS (severity score) from chest CT images. The weighted images perform better than the raw images in training deep learning models for COVID-19 diagnosis. The CT-SS differs between COVID-19 and Non-COVID-19. This difference is partially through the number of underlying diseases using the mediation analysis.
News (2020)
IEEE TrustCom-BigDataSE-CSE-EUC-iSCI 2020
The 14th IEEE International Conference on Big Data Science and Engineering (BigDataSE 2020) is held December 29, 2020-January 01, 2021, Guangzhou (广州), China. Due to COVID-19, it takes place part-virtually as a combination of online and offline event. BigDataSE is held (mostly 08h-21h China Standard Time (UTC+8), i.e., 18h (-1d)-07h Central Standard Time (UTC-6)) with presentation choices like (1) in-person onsite presentation at Guangdong Hotel (广东大厦), (2) prerecorded video presentation on the conference website and offline discussion, (3) prerecorded video presentation and live Q&A on Tencent Meeting, and (4) live presentation and Q&A on Tencent Meeting.
[video: 5.mp4]
Guide2Research
Carson K. Leung
University of Manitoba
Canada
G2R World Ranking 4522nd
G2R Canada Ranking 226th
H-Index & Metrics
Google H-index 43
Number of Google Citations 6,925
Number of Articles on DBLP 212
External Links
Personal Website for Carson K. Leung
Profile was last updated at December 20, 2020, 12:54 pm
Guide2Research Ranking is based on Google Scholar H-Index.
IEEE ISPA-BDCloud-SocialCom-SustainCom-IUCC 2020
The 10th IEEE International Conference on Big Data and Cloud Computing (BDCloud-2020), as well as the 19th International Conference on Ubiquitous Computing and Communications (IUCC-2020), were scheduled to be held Exeter, UK, on October 15-17, and postponed to 17-19 December 2020. Due to the COVID-19 pandemic and associated travel restrictions, they are held virtually (mostly 08h30-19h GMT, i.e., 02h30-13h CST (UTC-6)) in a combined synchronous-asynchronous mode, with prerecorded video presentation on slack and live Q&A on Zoom.
Slack: Exeter_Conference_2020_1
17 December 2020 Thursday
13:30-16:00 (GMT) Session IUCC-2: Ubiquitous Computing (Room-4)
Temporal Data Analytics on COVID-19 Data with Ubiquitous Computing
Yubo Chen, Carson K. Leung, Siyuan Shang, Qi Wen
#bdcloud
[IUCC-115.pptx (33 MB PowerPoint Presentation)]
18 December 2020 Friday
15:10-17:00 (GMT) Session SocialCom_1: Social Network Analysis and Applications (Room-3)
Identifying the Right Person in Social Networks with Double Metaphone Codes
Joshua D. Hamilton, Carson Leung, Sehaj P. Singh
#socialcom
[SocialCom-107.pptx (30 MB PowerPoint Presentation)]
IEEE BIBM 2020
The 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020) was scheduled to be held December 16-19, 2020, in Seoul, South Korea. Due to COVID-19, it takes place virtually and is held (mostly 09h-18h30 KST (UTC+9), i.e., 18h (-1d)-03h30 CST (UTC-6)) online in a combined synchronous-asynchronous mode, with prerecorded video presentation on Underline and live Q&A on Zoom.
16 December
The 7th International Workshop on High... 09:00 - 11:20 KST
IEEE BIBM 2020 / SESSIONS / THE 7TH INTERNATIONAL WORKSHOP ON HIGH PERFORMANCE COMPUTING ON BIOINFORMATICS (HPCB 2020)
Predictive analytics on genomic data with high-performance computing
Carson Leung
Download Slides [7427/slideshow/6410954582b0192264600568949b6d24.pdf]
video abstract
Short bio:
Carson Leung is currently a Professor at the University of Manitoba, Canada. He has contributed more than 250 refereed publications on the topics of big data, bioinformatics, computational intelligence, cognitive computing, data analytics, data mining, data science, fuzzy systems, high-performance computing, machine learning, social network analysis, and visual analytics. He has also served on the Organizing Committee of the ACM CIKM, ACM SIGMOD, IEEE DSAA, IEEE ICDM, and other conferences.
IEEE/WIC/ACM WI-IAT 2020
The 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020) was scheduled to be held 14-17 December 2020, in Melbourne, Australia. Due to the Stage 4 COVID-19 lockdown in Melbourne, it is converted to a fully virtual conference. WI-IAT is held (mostly 08h45-17h30 AEDT (UTC+11), i.e., 17h45 (-1d)-06h30 CST (UTC-6)) in a combined synchronous-asynchronous mode, with prerecorded video presentation on CyberChair and live Q&A on Zoom. Live-stream keynotes are on both YouTube and Tencent Meeting.
IEEE HPCC-SmartCity-DSS-DependSys 2020
The 18th IEEE International Conference on Smart City (SmartCity-2020), together with the Sixth IEEE International Conference on Data Science and Systems (DSS-2020) and the 22nd IEEE International Conference on High Performance Computing and Communications (HPCC-2020), were scheduled to be held 14-16 December 2020, in Fiji. Due to the COVID-19 pandemic and associated travel restrictions, they are held virtually (mostly 08h-19h55 China Standard Time (UTC+8), i.e., 18h (-1d)-05h55 Central Standard Time (UTC-6)) in a combined synchronous-asynchronous mode, with prerecorded video presentation on slack and live Q&A on Zoom.
Slack: Workspace 1 - The IEEE HPCC-2020 Conference
Tuesday December 15, 2020 (09:40-12:10 China Standard Time, Room4)
DSS-1: Data Processing Technology
3. Spatial Data Analytics of COVID-19 Data
Siyuan Shang, Yubo Chen, Carson K. Leung, Adam Pazdor
#pptx-dss
[1570680714.pptx (37 MB PowerPoint Presentation)]
Wednesday December 16, 2020 (10:10-12:15 China Standard Time, Room4)
SmartCity-4: Big City Data and Mining (II)
2. Prediction of Food Preparation Time for Smart City
Calvin Hoi, Carson K. Leung, Joglas Souza
#pptx-smartcity
[1570680718.pptx (33 MB PowerPoint Presentation)]
IEEE BigData 2020
The 2020 IEEE International Conference on Big Data (BigData 2020) was scheduled to be held December 10-13, 2020, in Atlanta, GA, USA. Due to the COVID-19 situation, it transforms into an all-digital conference/virtual event and takes place virtually (mostly 08h30-18h EST (UTC-5), i.e., 07h30-17h CST (UTC-6)) in a combined synchronous-asynchronous mode, with prerecorded video presentation on Underline and live Q&A on Zoom.
11 December
2nd Special Session on Machine Learning... 09:00 - 18:00 EST
IEEE BIGDATA 2020 / SESSIONS / 2ND SPECIAL SESSION ON MACHINE LEARNING ON BIG DATA_1
Machine learning and OLAP on big COVID-19 data
Carson Leung
video abstract
13 December
7th International Workshop on Privacy and... 09:00 - 13:30 EST
IEEE BIGDATA 2020 / SESSIONS / 7TH INTERNATIONAL WORKSHOP ON PRIVACY AND SECURITY OF BIG DATA (PSBD 2020)
Preserving privacy of temporal big data
Carson Leung
video abstract
Short bio:
Carson Leung is currently a Professor at the University of Manitoba, Canada. He has contributed more than 250 refereed publications on the topics of big data, computational intelligence, cognitive computing, data analytics, data mining, data science, fuzzy systems, machine learning, social network analysis, and visual analytics. He has also served on the Organizing Committee of the ACM CIKM, ACM SIGMOD, IEEE DSAA, IEEE ICDM, and other conferences.
IEEE/ACM ASONAM 2020
The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2020), together with International Symposium on Foundations and Applications of Big Data Analytics (FAB) 2020 as its co-located event, was scheduled for 03-06 August in The Hague, Netherlands, and postponed to 07-10 December 2020. They are held (mostly 13h30-20h50 GMT, i.e., 07h30-14h50 CST (UTC-6)) as virtual events in a synchronous mode, with live presentation and Q&A on Zoom. Papers are grouped into sessions geographically (e.g., East Asia/Oceania, hybrid, Americas)
IEEE Access 8 (2020)
Qian Liu, Carson K. Leung, Pingzhao Hu: A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT images. IEEE Access 8: 213718-213728 (2020)
Graphical abstract:
BigDAS 2020
Dr. Carson K. Leung serves as a Program Chair for the Eighth International Conference on Big Data Applications and Services (BigDAS 2020). The conference was originally scheduled to be held August 19-22 in Vladivostok, Russia, and postponed to November 19-21 in Shilla Stay Haeundae, Busan (釜山), South Korea. It further postpones to November 26-28.
IEEE ICDM 2020
Dr. Carson K. Leung serves as a Publicity Chair for the 20th IEEE International Conference on Data Mining (ICDM 2020), which scheduled to be held November 17-20, 2020, in Sorrento, Italy. Due to COVID-19, it takes place completely virtually (mostly 13h-20h CET (UTC+1), i.e., 06h-13h CST (UTC-6)) in a combined synchronous-asynchronous mode, with prerecorded video presentation on Underline and live Q&A on Zoom.
Alfredo Cuzzocrea, Carlo Zaniolo: Message from the ICDM 2020 General Chairs. IEEE ICDM 2020: xxvii
Message from the ICDM 2020 General Chairs
On behalf of the organizing committee of the ICDM 2020 conference, we would like to extend to all of you our warmest welcome to the 2020 IEEE International Conference on Data Mining, and to your virtual visit to Sorrento, Italy, the initial location of the conference.
Due to the COVID-19 outbreak, the ICDM Organizing Committee decided to hold the conference as a virtual event, in order to protect the safety and well-being of all conference participants, which remain the top priority of the ICDM Organizing Committee actions and goals.
Sorrento and the iconic Sorrento Peninsula, initially designated as the location of the conference, are located in Southern Italy and separate the Gulf of Naples to the north from the Gulf of Salerno to the south. The peninsula is named after its main town, Sorrento, which is located on the Gulf of Naples coast. Celebrated tourist attractions that are in close proximity with Sorrento include the Amalfi Coast to the south, then to the west, the island of Capri off the tip of the peninsula in the Tyrrhenian Sea, and finally, Pompei and Herculaneum few miles to the North and... two millennia back in time. The whole area represents a renowned touristic and historical destination.
The organization of a successful conference would not be possible without the dedicated efforts of many individuals. In particular, we would like express our gratitude to the Program Chairs Claudia Plant, University of Vienna, Austria, and Haixun Wang, WeWork, New York, USA; Awards Committee Chair Jiawei Han, University of Illinois, Urbana-Champaign, USA; Demo Chairs Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong, and Arno Siebes, University of Utrecht, The Netherlands; Workshops Chairs Giuseppe Di Fatta, University of Reading, UK, and Victor Sheng, Texas Tech University, USA; Contest Chairs Xindong Wu, Mininglamp Academy of Sciences, China, and Kang Liu, Institute of Automation, Chinese Academy of Sciences, China; Tutorial Chairs Diane Cook, Washington State University, USA, and Min-Ling Zhang, Southeast University, China; Panel Chairs Ranga Vatsavai, North Carolina State University, USA, and Wei Wang, University of New South Wales, Australia; Local Arrangement Chair Giuseppe Polese, University of Salerno, Italy; Publicity Chairs Ting Bai, Beijing University of Posts and Telecommunications, China, Carson Leung, University of Manitoba, Canada, and Washio Takashi, Osaka University, Japan; Sponsor Chair Jieping Ye, DiDi, China; Web Chair Gongqing Wu, Hefei University of Technology, China.
ICDM 2020 is sponsored by the IEEE Computer Society, organized by the University of Calabria, Rende, Italy, and co-organized by Mininglamp Technology, China.
We owe our special thanks to our sponsors. Last but not least, we would like to thank our keynote speakers, panelists, the many authors who submitted research papers to the conference and all the attendees. We are encouraged by your scientific work, passion, and discoveries.
Finally, we thank all researchers, practitioners and students who are working in the field of data mining for their enthusiastic support and promotion of ICDM over the years. We wish to all of you a productive conference with new discoveries, new collaborations and new research exchange opportunities.
Alfredo Cuzzocrea, University of Calabria, Italy, and
Carlo Zaniolo, University of California, Los Angeles, USA
Claudia Plant, Haixun Wang: Message of the Program Co-Chairs. IEEE ICDM 2020: xxviii-xxix.
Message of the Program Co-Chairs
Welcome to ICDM 2020!
This ICDM is a special event in two different ways. It is the 20th anniversary of the IEEE International Conference on Data Mining. The first ICDM took place at San Jose, California in the year 2001. This first ICDM received 365 paper submissions. Since then, the ICDM conference has grown substantially. In the year 2020, we received 930 submissions. With highest quality standards and triple blind review, ICDM has established itself as a major venue for data mining research. Since its beginning, the conference is an important forum of exchange for researchers and practitioners in data mining.
This year, this exchange will happen in a new way, as ICDM 2020 is the first virtual edition of the ICDM conference. We started planning this conference in a meeting with the organizing committee aside of ICDM 2019 in Beijing last November. We planned to see you all in person to celebrate the 20th anniversary of ICDM in November 2020 in Sorrento, Italy. Just a few weeks after our meeting, the COVID-19 pandemic started to change our lives tremendously and, as we know today, with long lasting effects. The pandemic affected authors and reviewers who had to cope with many different challenges during in these times. After affecting China in early 2020, the first wave run over Europe. At the time of the submission deadline in June, the US, Brazil and India have been among the most affected countries. Currently, in October 2020, the number of cases is still rising in many countries, e.g., in Europe, India and in the US.
Since the beginning of ICDM conferences up to 2019, the number of submissions was constantly increasing. In this year, we obtained with 930 submissions. Particularly in the light of the pandemic, this years’ number is remarkably high. We thank all authors who submitted a paper to ICDM 2020 for their extraordinary effort and dedication.
Following the tradition of ICDM, also ICDM 2020 was highly selective. We accepted 91 regular and 92 short papers, which corresponds to a full paper acceptance rate of 9.8% and an overall acceptance rate of 19.7%. At least three Program Committee Members have reviewed each paper. The review process included a discussion phase where reviewers exchanged their opinion about controversial issues. Experienced Area Chairs moderated the discussions and finally gave a recommendation on the acceptance decision. We, the Program Chairs, finalized the decisions based on the reviews, the recommendation of the Area Chair, and the overall view on the status of all papers. We thank all 407 Program Committee Members and 41 Area Chairs for carefully reviewing and discussing the merits of all papers.
ICDM is a highly international and young community. The accepted papers have in total 846 authors from 25 countries. Most authors come from China (347) and from the USA (286) followed by Australia (45), Germany (32) and Japan (14). The first author of 83% of all papers is a student.
The topics of the accepted papers shed light on current hot topics of data mining research and on emerging trends. Most authors chose the topic category “novel data mining algorithms” during the submission process. This reflects that the major focus of ICDM is basic research in data mining. Other frequently chosen categories include
"Mining and linked analysis in networked settings: web, social and computer networks, and online communities"
"mining spatial and temporal datasets";
"data mining in electronic commerce, such as recommendation";
"healthcare, epidemic modeling and clinical research".
To get further insights, let us consider the topics of the ten papers that have obtained the best scores by the reviewers. The Awards Committee is currently in the process of selecting the Best Paper and the Best Student Paper out of these candidates. The best papers focus on representation learning of networks with hierarchical node labels, semantic text matching, alternative clustering of multi-view data, open-world node classification in graphs (i.e. supporting to assign nodes to unseen classes), resolving name disambiguation in academic social networks, feature selection for financial data, and identifying Trojan backdoors in deep neural networks. Hot topics in data mining research are exploring the power of deep neural networks for unsupervised or weakly supervised tasks and developing novel data mining methods for complex and heterogeneous data sets. We can also observe that even after more than 20 years of data mining research, a lot of research effort is still needed to make data mining effective on real-world challenges, as they arise e.g., from finance and medicine.
We are looking forward to a rich and inspiring program that will consist of pre-recorded video presentations and life question-and-answer sessions for all accepted papers. Highlights of the program are life online keynotes by Diane Cook, Sepp Hochreiter and Xuedong Huang. Four online tutorials covering hot topics ranging from multimodal knowledge discovery over algorithmic bias and adversarial robustness in deep learning complement the program of the main conference. As a good tradition of ICDM, we will have on the first day workshops on emerging topics.
Organizing the ICDM 2020 program required the time and expertise of numerous contributors. We want to thank all people from the Steering Committee and the Organizing Committee of ICDM 2020. In particular, we want to thank Xindong Wu for his support, guidance and help with all questions and issues we had. We want to thank the General Chairs Alfredo Cuzzocrea and Carlo Zaniolo for their excellent work. We further want to express our warmest thanks to the Workshop chairs Giuseppe Di Fatta and Victor Cheng, the Tutorial Chairs Diane Cook and Min-Ling Zhang and the Panel Chairs Ranga Vatasavai and Wei Wang. We thank Jiawei Han for serving as Awards Committee Chair. Further thanks go to the Contest Chairs Xindong Wu and Kang Liu, the Local Arrangement Chair Giuseppe Polese and the Publicity Chairs Ting Bai, Carson Leung and Washio Takashi.
I, Claudia Plant want to thank selected people behind the scenes who helped me a lot with the PC Chair work. First, I want to thank my husband and collaboration partner Christian Böhm who helped me in all stages of the organization. Most importantly, he did together with his research group a lot of last minutes reviews for papers that urgently needed one further opinion. I also want to thank the members of my research group Data Mining at University of Vienna especially my PhD students Lena Bauer, Lukas Miklautz and Ylli Sadikaj. Finally, I want to thank Jun (Luke) Huan who served as Co PC-Chair together with me in the beginning but had to resign in early 2020 due to personal reasons. We wish you all an inspiring conference with much fruitful discussions and interactions!
Claudia Plant and Haixun Wang
IEEE SmartData 2020
Dr. Carson K. Leung serves on the Steering Committee for the Sixth IEEE International Conference on Smart Data (SmartData-2020), which was planned to be held as part of the IEEE Cybermatics Congress 2020 (together with IEEE Blockchain, CPSCom, GreenCom and iThings 2020) November 02-06, 2020 in Rhodes Island, Greece. Due to the COVID-19 pandemic and associated travel restrictions, it is held virtually (mostly 08h-19h40 AST (UTC-4), i.e., 06h-17h40 CST (UTC-6)) in a combined synchronous-asynchronous mode, with prerecorded video presentation on slack and live Q&A on Zoom.
On LinkedIn, Dr. Mamoun Alazab (Associate Professor of Cyber Security; IEEE SmartData-2020 General Co-chair) posted:
#CallforPapers Smart #Data aims to filter out the noise and produce valuable data, which can be effectively used by enterprises and governments for planning, operation, monitoring, control, and intelligent decision making. Although unprecedentedly large amounts of sensory data can be collected with the advancement of the #Cyber-Physical-Social systems, the key is to explore how #big_data Data can become #Smart Data and offer #intelligence. Advanced Big Data #modeling and #analytics are indispensable for discovering the underlying structure from retrieved data and further acquiring Smart Data. #ArtificialNeuralNetworks #CyberSecurity #CyberCrime #CFP #BigData #ArtificialIntelligence #CPS #IoT #IIoT #AI #DataScience #KnowledgeGraph #KnowledgeManagement
The 2020 #IEEE International Conference on Smart Data #SmartDataa-2020) https://lnkd.in/gh6rVfB
Alongside with Francisco Herrera & shadi Ibrahim & Laurence T. Yang, FCAE FEIC FIEEE & Carson Leung & Vinayakumar Ravi, PhD
Mamoun Alazab, Francisco Herrera, Shadi Ibrahim: Message from the IEEE SmartData 2020. IEEE iThings-GreenCom-CPSCom-SmartData-Cybermatics 2020: xxxviii.
Message from the IEEE SmartData 2020 General Chairs
iThings-GreenCom-CPSCom-SmartData-Cybermatics 2020
Welcome to the Sixth IEEE International Conference on Smart Data (IEEE SmartData 2020), which was planned to be held on November 02-06, 2020, in Rhodes Island, Greece. Given the COVID-19 pandemic and associated travel restrictions, and as the safety of people is of the highest priority, the conference will be held virtually on November 02-06, 2020. It is held as part of the 2020 IEEE Cybermatics Congress, together with other four conferences: The 3rd IEEE International Conference on Blockchain (Blockchain 2020); The 13th IEEE International Conference on Cyber, Physical and Social Computing (CPSCom 2020); The 13th IEEE International Conference on Internet of Things (iThings 2020); The 16th IEEE International Conference on Green Computing and Communications (GreenCom 2020). IEEE SmartData 2020 is sponsored by IEEE, IEEE Computer Society, IEEE Systems, Man, and Cybernetics Society (SMC), IEEE SMC Technical Committee on Cybermatics, IEEE Technical Committee on Scalable Computing (TCSC).
The IEEE SmartData 2020 is the sixth edition of IEEE International Conference on Smart Data. SmartData provides a regular event of the world that is promoting community-wide discussion to identify the computational intelligence technologies and theories for harvesting Smart Data from Big Data. It covers many topics on SmartData including data science and its foundations, smart/big data infrastructure and systems, smart/big data storage and management, smart/big data processing and analytics, and smart/big data applications.
IEEE SmartData 2020 would not have been possible without the help and effort of many people and organizations. We would like to sincerely thank the Steering Committee Chairs, Prof. Zhikui Chen (Dalian University of Technology, China), Prof. Carson K. Leung (University of Manitoba, Canada), and Prof. Laurence T. Yang (St. Francis Xavier University, Canada) for their valuable advice and suggestions. We would like to express our appreciation to the General Chairs of 2020 IEEE Cybermatics Congress, Prof. Jamal Deen (McMaster University, Canada), Prof. Nicolas Tsapatsoulis (Cyprus University of Technology, Cyprus), and Prof. Klimis Ntalianis (University of West Attica, Greece) for their help and support in organizing the IEEE SmartData 2020 conference as part of the Cybermatics Congress. We would also like to give our special thanks to the Program Committee Chairs, Dr. Anish Jindal (University of Essex, UK), Dr. Huazhong Liu (Huazhong Univeristy of Science and Technology, China), and Dr. Ravi Vinayakumar (University of Cincinnati, USA) for their excellent work and leadership to ensure the success of the conference. We also thank the special sessions chairs, Dr. Jihong Ding (Zhejiang University of Techonlogy, China), Dr. Waheed Iqbal (University of the Punjab, Pakistan), Dr. Isaac Triguero (University of Nottingham, UK), and Dr. Xia Xie (Huazhong Univeristy of Science and Technology, China), and the Program Committee Members for their dedication in the process of reviewing the papers and producing constructive comments to the authors. Thanks also go to Web Chair Zihao Jiang (St. Francis Xavier University, Canada). Last but not least, we thank the IEEE Computer Society's Conference Publishing Services (CPS) team (especially, Lisa O'Conner and Juan Guerrero) for their help in publishing the current proceedings.
Mamoun Alazab, Charles Darwin University, Australia
Francisco Herrera, University of Granada, Spain
Shadi Ibrahim, Inria, France
IEEE SmartData 2020 General Chairs
Huazhong Liu, Ravi Vinayakumar: Message from the IEEE SmartData 2020 Program Chairs. IEEE iThings-GreenCom-CPSCom-SmartData-Cybermatics 2020: xxxix
Message from the IEEE SmartData 2020 Program Chairs
iThings-GreenCom-CPSCom-SmartData-Cybermatics 2020
On behalf of the Program Committee of the 6th IEEE International Conference on Smart Data (SmartData 2020), we would like to welcome you to the virtual meeting of SmartData 2020. SmartData provides a highprofile, leading-edge forum for scientists, engineers and researchers to discuss and exchange novel ideas, results, experiences and work-in-process on all aspects of Smart Data.
This year the conference received lots of submissions. All the submissions were reviewed on the basis of their significance, novelty, technical quality, presentation, and practical impact. After careful reviews by at least three experts in the relevant areas for each paper, 9 regular papers were selected for oral presentation at the conference and included in this IEEE proceedings, with an acceptance rate 27.3%. Besides, 10 short papers were also selected for oral presentation at the conference and included in this IEEE proceedings. The accepted papers in the final program cover a broad range of research topics, including smart data processing and analytics, smart data applications, deep leaning for smart data training and classifications, and data storage.
The SmartData 2020 program also included two special sessions: special session on Medical Big/Smart Data and Artificial Intelligence and special session on Smart Education and Services, which are considered as two parts of the main conference. We would like to express our special appreciation to the special session chairs for special session on Medical Big/Smart Data and Artificial Intelligence: Dr. Xia Xie (Huazhong University of Science and Technology, China) and Dr. Li Guo (Beijjng University of Posts and Telecommunications, China), and the special session chairs for special session on Smart Education and Services: Dr. Jihong Ding (Zhejiang University of Techonlogy, China), Dr. Xiangyang He (Hunan First Normal University, China), and Dr. Wenzheng Yang (Yunnan Normal University, China). SmartData 2020 was made possible by the joint effort of numerous people worldwide. We are deeply grateful to all the PC members for their great efforts in reading, commenting, and finally selecting the papers. We also thank all the external reviewers for assisting the technical program committee in their particular areas of expertise.
We would like to express our sincere appreciation to the Steering Committee Chairs, Prof. Zhikui Chen (Dalian University of Technology, China), Prof. Carson K. Leung (University of Manitoba, Canada), and Prof. Laurence T. Yang (St. Francis Xavier University, Canada); and the General Chairs, Prof. Mamoun Alazab (Charles Darwin University, Australia), Prof. Francisco Herrera (University of Granada, Spain), and Dr. Shadi Ibrahim (Inria, France) for their support and help. It was our pleasure working with them to ensure the success of the conference.
We sincerely thank the authors of all submitted papers and all the conference attendees. We hope you will enjoy the technical program and the virtual meeting of SmartData 2020.
Anish Jindal, University of Essex, UK
Huazhong Liu, Huazhong University of Science and Technology, China
Ravi Vinayakumar, University of Cincinnati, USA
IEEE SmartData 2020 Program Chairs
Zhikui Chen, Carson K. Leung, Laurence T. Yang: Message from the IEEE SmartData Steering Chairs. IEEE iThings-GreenCom-CPSCom-SmartData-Cybermatics 2020: xl
Message from the IEEE SmartData Steering Chairs
iThings-GreenCom-CPSCom-SmartData-Cybermatics 2020
On behalf of the IEEE SmartData Steering Committee and IEEE SmartData 2020 Organizing Committee, we would like to express to all the participants our cordial welcome.
Smart Data aims to filter out the noise and produce valuable data, which can be effectively used by enterprises and governments for planning, operation, monitoring, control, and intelligent decision making. Although unprecedentedly large amount of sensory data can be collected with the advancement of the Cyber-Physical-Social systems, the key is to explore how Big Data can become Smart Data and offer intelligence. Advanced Big Data modeling and analytics are indispensable for discovering the underlying structure from retrieved data and further acquiring Smart Data. The IEEE SmartData 2020 conference is the event in a series of successful International Conferences on Smart Data (SmartData), previously held as IEEE SmartData 2019 (Atlanta, USA, July 2019), IEEE SmartData 2018 (Halifax, Canada, July 2018), IEEE SmartData 2017 (Exeter, UK, June 2017), IEEE SmartData 2016 (Chengdu, China, December 2016), and IEEE SmartData 2015 (New York, USA, August 2015).
An international conference can be organized by supports and great voluntary efforts of many people and organizations and our main responsibility is to coordinate the various tasks carried out with other willing and talented volunteers. We would like to thank the General Chairs Prof. Mamoun Alazab (Charles Darwin University, Australia), Prof. Francisco Herrera (University of Granada, Spain), and Dr. Shadi Ibrahim (Inria, France) for very successful organization of IEEE SmartData 2020. We would also like to express our special thanks to the Program Chairs Dr. Anish Jindal (University of Essex, UK), Dr. Huazhong Liu (Huazhong Univeristy of Science and Technology, China), and Dr. Ravi Vinayakumar (University of Cincinnati, USA) for making excellent technical program. We would like to give a special thanks to the special session chairs and PC members for their great efforts in selecting the papers to be presented in the conference. We also express our appreciation for the excellent local team for the prefect local arrangements and the detailed registration work, as well as for their help on the EDAS submission system and the registration system.
Finally, we also would like to take opportunity to thank all the members of the organizing committee, technical program committee, authors who submitted papers, as well as reviewers who reviewed the submissions.
Zhikui Chen, Dalian University of Technology, China
Carson K. Leung, University of Manitoba, Canada
Laurence T. Yang (Chair), St Francis Xavier University, Canada
IEEE SmartData 2020 Steering Chairs
EUSPN 2020
The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2020), together with its 7th International Symposium on Emerging Information, Communication and Networks (EICN 2020), was scheduled to be held November 02-05, 2020 at the "Vila Gale Santa Cruz" hotel, Madeira, Portugal.
IEEE/ACM/ASA/CCF DSAA 2020
Dr. Carson K. Leung serves as the Regional Chair - North America (Email: rc_na_dsaa2020 @ dsaa.co) for the Seventh IEEE International Conference on Data Science and Advanced Analytics (DSAA 2020) held 06-09 October 2020 in Sydney, Australia. This IEEE conference is cosponsored by ACM, American Statistical Association (ASA), and China Computer Federation (CCF, 中国计算机学会). Due to COVID-19, DSAA is virtual (mostly 24/7) in a combined synchronous-asynchronous mode, with prerecorded video presentation on the DSAA WordPress site and live Q&A in the Zoom Webinar.
UM-RRC-ARF Mitacs Project
Mitacs - Projects:
https://www.mitacs.ca/en/projects/arctic-research-foundation-uxui-searchable-database
Arctic Research Foundation UX/UI searchable database
The Arctic Research Foundation (ARF) is a private, non-profit organization creating a scientific infrastructure for the Canadian Arctic. Over the years, it has generated and collected a huge volume of big data. These valuable big data need to be managed. In response, we design an effective database to (a) integrate these big data (which may be of a wide variety of data types, formats, etc.) from different sources, (b) manage them, (c) catalogue the data, (d) extract useful information from the data, and (e) make the data accessible to researchers and public. By doing so, we centralize these disparate raw data collected by ARF vessels, mobile labs, and equipment from across the Arctic into a universally readable and easily searchable database. Moreover, we add an easily understandable, highly usable user interface (UI) and user experience (UX) elements so that the resulting searchable database can be easily accessible.
Intern: Salweyar Patel; Patience Ngcobo; Hongming Li; Chong Li; Jing Xiao; Anifat Olawoyin
Faculty Supervisor: Ralph Dueck; Reynard Dela Torre; Carson Leung
Province: Manitoba
Partner: Arctic Research Foundation
Sector: Professional, scientific and technical services
Discipline: Computer science
Program: Accelerate
UM Today News & FoS News:
Arctic Research Foundation partners with UM and RRC to revolutionize public access to big Arctic data:
What will big data reveal about Canada’s next frontier—the Canadian Arctic?
October 5, 2020 — A new partnership between the University of Manitoba (UM), Red River College (RRC), and the Arctic Research Foundation (ARF) is setting out to unlock the big data secrets previously hidden in Canada's Arctic.
Ralph Dueck and Reynard Dela Torre from RRC's applied computer education department and Carson Leung in computer science in the UM Faculty of Science have teamed up with the ARF on a joint project that is the first of its kind: it will allow easy access to a quantity of "Arctic-sized" data that will put that data into the hands of northern communities, government, universities, research institutes, and the public.
ARF is a Canadian non-profit organization creating new scientific infrastructure in the Canadian Arctic. It partners with governments, universities, and research institutions to provide access to its Arctic program initiatives and is working to build relationships with Arctic Indigenous peoples to advance understanding of the region through traditional knowledge. ARF has collected a huge volume of "big data" from cutting-edge research vessels and mobile labs including hydrographic and bathymetric assessments, soil and salinity samples, changing ice conditions, animal stock assessments, and data collected through the Naurvik plant production pod.
This collaborative project will gather these disparate data and make them accessible in an Arctic Research Database, unifying research teams in Canada's North and enabling minimally-invasive research practices. Through research and development across UM and RRC, the project team will centralize and catalog these data from across the Arctic through leading-edge methods in data labeling and database design, making them publicly accessible through a universally readable, easily searchable database with a highly usable interface.
"This research collaboration joins the unique strengths of UM and RRC, to tackle challenging questions related to climate change and its impacts on human, animal, and environmental conditions in the Arctic," says Dr. Digvir Jayas, vice-president (research and international) and Distinguished Professor at UM. "Making the research data accessible is a critical aspect of knowledge translation that enables creative approaches to solution-finding."
"Projects like these are integral to giving our students the hands-on experience they need to problem-solve and come up with solutions to real-world issues," says Dr. Christine Watson, Vice President Academic, RRC. "Of course, this year the challenge is even greater due to the pandemic. While students aren’t able to work together and brainstorm in person, we have absolute trust that these student interns will produce exceptional results."
This project will provide students with work-integrated learning opportunities through the Mitacs Accelerate internship program, an initiative within a Canadian Network of Centres of Excellence dedicated to supporting applied and industrial research in mathematical sciences and associated disciplines. College and UM graduate student interns will team up to design a back-end database, develop a User Experience (UX) central user interface and integrate the two for enhanced user experience, following UX integration best practices.
"Mitacs is pleased to make and support the vital connections between the Arctic Research Foundation and top talent within Manitoba's colleges and universities. Connecting this important organization with highly qualified research personnel, while providing public access to Arctic data is a win-win-win for Canada. We are grateful to the Government of Canada and the Government of Manitoba, through Research Manitoba, for providing support for Mitacs internship programs," says John Hepburn, CEO and scientific director, Mitacs.
The resulting Arctic Research Database will enable insight on everything from the mapping of shipping routes to the development of natural resource projects to the growth of food sustainability programs and the improvement of local economies. The project is expected to have powerful and long-lasting impacts on the economic, research, and innovation ecosystems in the Arctic.
"Too much Arctic research is 'siloed,'" explains ARF vice president Tom Henheffer. "It's time to bring it all together so communities, governments, and research institutions can effectively share data and better coordinate work. We're excited to create this database and expect it will be a huge step forward in improving Arctic research, environmental stewardship, and economic development, and in creating a greater public understanding of the North."
UM TODAY STAFF
arctic research, arctic science
LinkedIn:
Jennifer MaRae (2020 Oct 05)
Arctic Research Foundation partners with the University of Manitoba and Red River College to revolutionize public access to big Arctic data. This project will provide students with work-integrated learning through the Mitacs Accelerate internship program.
https://lnkd.in/ddFJ5Sw
#worldchangingscience #umanitobasci #arctic #research #climatechange #indigenous #umanitoba #sustainabledevelopment #datascience
This is a great example of what post-secondary institutions in Manitoba can do when they work together: combining the expertise of Carson Leung's research lab with RRC to work on big data relating to arctic research and climate change.
Twitter: UManitoba Science (@umanitobasci) (Oct 5, 2020)
Arctic Research Foundation @ArcticFocus partners with @umanitoba and @RRC to revolutionize public access to big Arctic data. This project will provide students with work-integrated learning through @MitacsCanada Accelerate internship program
https://news.umanitoba.ca/?p=138432
UManitoba, RRC, ARF partner to allow public access to Arctic data
October 7, 2020
The University of Manitoba, Red River College, and the Arctic Research Foundation (ARF) have partnered to allow access to Arctic data. ARF has compiled “big data” from research vessels, mobile labs, and the Naurvik plant production pod. The project will gather the data and make it accessible to the public in an Arctic Research Database. "Too much Arctic research is 'siloed,'" explained ARF vice president Tom Henheffer. "It's time to bring it all together so communities, governments, and research institutions can effectively share data and better coordinate work." UManitoba (MB)
Mitacs @ U of M (10/08/2020)
U of M and RRC researchers partner to revolutionize big arctic data
A new partnership between the University of Manitoba (led by Prof Carson Leung), Red River College, and the Arctic Research Foundation (ARF) is setting out to unlock the big data secrets previously hidden in Canada’s Arctic with critical support from Mitacs, whose business development representatives helped put the project partners together. Note that this project utilizes an option for universities to partner with colleges to tap in to pure development support, as well as research - all with funding from Mitacs.
https://www.sci.umanitoba.ca/cs/arctic-research-foundation-partners-with-um-and-rrc-to-revolutionize-public-access-to-big-arctic-data/
The Sou'wester - Winnipeg Free Press (Wednesday, October 14, 2020, page S5, local news)
U of M building database for Arctic researchers
By: Susie Strachan (Staff Reporter, University of Manitoba)
Posted: 10/9/2020 2:43 PM
The faculty of science at the University of Manitoba is playing a big part in setting up a database for use by researchers who focus on Canada's Arctic.
The Arctic Research Foundation recently announced the partnership with the U of M and Red River College's applied computer education department, with the goal of unlocking "the big data secrets previously hidden in Canada's Arctic."
Dr. Carson Leung, a U of M computer science professor who runs the database and data mining lab, said this is the beginning of a long-term project that will see the university and college build a searchable database and also help researchers and those living in the arctic to deal with the changing climate in the north.
"We're very excited to be working on this project," Leung said. "This is an example of how the university can help further connections in research being done in the Arctic. It's also an opportunity for us to use data mining to help provide solutions for people living there."
Tom Henheffer, vice-president of the Arctic Research Foundation, said research in the Arctic effectively is stored in silos.
"It's time to bring it all together so communities, governments, and research institutions can effectively share data and better co-ordinate work," Henheffer said. "We're excited to create this database and expect it will be a huge step-forward in improving Arctic research, environmental stewardship, and economic development, and in creating a greater public understanding of the north."
The resulting Arctic Research Database will enable insight on everything from mapping shipping routes to the development of natural resource projects, plus the growth of food sustainability programs and improvement of local economies, Henheffer said.
Through research and development across U of M and RRC, the project team will catalogue data from across the Arctic through leading-edge methods in data labelling and database design, making them publicly accessible through a universally readable, easily searchable database with a highly usable interface.
The project will provide students with work-integrated learning opportunities through the Mitacs Accelerate internship program, an initiative within a Canadian Network of Centres of Excellence dedicated to supporting applied and industrial research in mathematical sciences and associated disciplines. Project funding includes $120,000 from Mitacs and $30,000 from ARF.
RRC and U of M graduate students will design a back-end database, develop a user interface and integrate the two for an enhanced user experience, Leung said.
A Naurvik power pod converts and stores power generated using wind turbines and solar panels, which is used to power a greenhouse. Photo by Thomas Surian.
Professor Dr. Carson Leung at the Unversity of Manitoba's computer science department. Photo by Kira Koop.
University of Manitoba PhD student Anifat Olawoyin.
Jim Bender (from left), Mark Ullikataq, Paul Waechter, Sammy Kogvik, Betty Kogvik, Leanne Wilson, Shawna MacKinnon,Dustin Atkichok, Susie Kununak, Adrian Schimnowski, Quade Digweed and Mark Blackmore inside a newly completed Naurvik grow pod facility in Gjoa Haven, Nunavut. Photo by Thomas Surian/ARF.
University of Manitoba computer science professor Carson Leung (left) will work with Red River College in setting up the Arctic Research Database for the Arctic Research Foundation, which engages in projects in Canada’s north such as the wind and solar-powered greenhouse in a seacan located in Gjoa Haven, Nunavut (right). Supplied photos
Foundation creates scientific infrastructure
The Arctic Research Foundation is a Canadian non-profit organization creating new scientific infrastructure in the Canadian Arctic.
It works with governments, universities and research institutions to provide access to its Arctic program initiatives and is working to build relationships with Arctic Indigenous people to advance understanding of the region through traditional knowledge.
ARF has collected a huge volume of "big data" from cutting-edge research vessels and mobile labs including hydrographic and bathymetric assessments, soil and salinity samples, changing ice conditions, animal stock assessments and data collected through their Naurvik plant production pod. The latter is a solar and wind-powered greenhouse located in Gjoa Haven, Nunavut, that provides fresh vegetables to the community.
"We knew we needed a single repository for the data. And we also need a place for the data being produced by the universities and other researchers in Canada," Henheffer said, adding the Arctic Research Database timeline is at least 20 years.
"During the course of this, we will continue to ask the communities what they need. The Indigenous self-governments will be able to use the data in managing how they interact with the land," he said, adding there is potential down the line to include data from other arctic researchers around the world, including the Stockholm Resilience Centre in Sweden. "No one has attempted a project of this scope before."
UM Today News
https://news.umanitoba.ca/anifat-olawoyin-is-at-the-forefront-of-big-data-arctic-research/
UM PhD student Anifat Olawoyin is at the forefront of big data Arctic research
NOVEMBER 2, 2020—When Anifat Olawoyin was invited to help create the first centralized database for the Canadian Arctic, the Nigerian-born UM PhD student immediately agreed and imagined an empty cold-white horizon and polar bears.
"Do I get to go up North?" was her first question, she says.
Olawoyin was chosen to participate in the Arctic Research Foundation (ARF) project, which is a partnership between the ARF, the University of Manitoba and Red River College.
While a global pandemic has interfered with her dream of Northern travel, Olawoyin, feels a great sense of satisfaction being part of the ARF project. Her participation is made possible by Mitacs, a not-for-profit research network that provides students with research and training opportunities both at UM and internationally.
Olawoyin, who holds both an MBA and MSc in computer science, says her role with the project is to design and implement the database.
"It will be really, really useful because in most cases, people of the North have not seen the outcomes of data collection," she says. "The data is in different spots right now. When we create this database, it's going to be in one spot and accessed by many, many people: researchers, students and people from different domains."
Olawoyin says for years a wide variety of data types has been collected in different formats by ARF vessels, mobile labs, and equipment. Data includes animal stock assessments, weather, changing ice conditions, the salinity (salt content) of the ocean, physical features of coastal areas and the depths and shapes of underwater terrain.
For the first time, huge amounts of data on the rapidly changing Arctic will be available and aid in everything from mapping of shipping routes to the development of natural resource projects to the growth of food sustainability programs and improvement of local economies. Olawoyin says easy access to information can help local communities thrive and improve peoples' lives.
"By making this data available to the public, it will show how different organizations, government and researchers have been very active in the North. Northern communities will see they are not alone, and that a lot of people are working on providing solutions that address the challenges of living in the North."
The second-year doctoral student was asked to join the project by Dr. Carson Leung, her supervisor, professor in the UM department of Computer Science, and head of the Database and Data Mining Lab.
Leung says Olawoyin is “bright and talented” and, through this unique opportunity, will gain experience working in collaboration with the students at Red River College and will develop important skills that will benefit her professionally and academically.
In addition to being a mother and PhD student, Olawoyin works full-time as a business analyst for the City of Winnipeg. Before leaving Nigeria, she received her MBA from the University of Ibadan and did her undergraduate and MSc in Computer Science at the University of Winnipeg.
Olawoyin came to Canada 10 years ago from western Nigeria with her husband and two daughters and had a son in Manitoba. It was -40 C their very first day in the city.
"Humans have an adaptive gene," Olawoyin says. "We adapt to any environment we find ourselves. In Nigeria, the lowest temperature we experienced was +16 C, but now we are used to the cold weather in Canada. Thankfully it's only really cold in the winter."
AIMday Digital Agriculture 2020
UM Today News: https://news.umanitoba.ca/academics-and-industry-tackle-digital-agriculture-challenges/
(Also on MUAP > Success Stories: https://miap.ca/success_story/academics-and-industry-tackle-digital-agriculture-challenges-at-the-first-aimday-event-in-manitoba/)
Academics and industry tackle digital agriculture challenges: First AIMday™ event in Manitoba
NOVEMBER 4, 2020 — ... Questions submitted by companies were the basis for company-led discussions with academics from various disciplines. A structured one-hour discussion around each question enabled industry representatives and academics to assess whether they can work together to find a solution to the specific challenge presented by the company.
Of the 12 proposals submitted by researchers in response to specific industry questions, eight were selected by the companies to receive initial funding of $1,000 each. Upon successful collaboration, additional funding may be available to the researchers to further advance their projects with industry.
The successful projects are:
Carson Leung (UM computer science)
SCF / ICCC 2020
The 2020 International Conference on Cognitive Computing (ICCC 2020), part of the Services Conference Federation (SCF 2020), was originally scheduled to be held June 22-26 in Honolulu, Hawaii, USA, and postponed to August 12-14, and finally adjusted to a fully virtual conference held September 18-20, 2020. ICCC 2020 is held as a part of SCF 2020, which includes ICWS, SCC, Cloud, AIMS, BigData, Services, ICIoT, Edge, ICCC, and ICBC 2020. It is held in an asynchronous mode, with keynotes broadcasted at 08h-11h HST (UTC-10, i.e., 13h-16h CDT (UTC-5)) and repeated 16h-19h HST (i.e., 18h-21h CDT), and prerecorded video presentation on-demand 24/7 for all sessions, on BigMarker.
Sep 17: (ICCC 2020) Cognitive and Predictive Analytics on Big Open Data
[video: 1570651647.mp4]
Abstract: Nowadays, big data are everywhere because huge amounts of valuable data can be easily generated and collected from a wide variety of data sources at a rapid rate. Embedded into these big data are implicit, previously unknown and potentially useful information and valuable knowledge that can be discovered by data science. Due to their value, these big data are often considered as the new oil. In recent years, many governments make their collected big data freely available to their citizens, who could then gain some insights about services available in the city from these open data. In this paper, we present a cognitive and predictive analytic approach to analyze open data for discovering interesting patterns such as tipping patterns. In general, tipping is a voluntary action conceived as social norm that is valuable to service workers in many countries. With the introduction of ride hailing services, traditional taxi services have began facing increased competition. As such, there are increasing interests in factors that are associated with generous tips. Hence, to evaluate the practicality of our approach, we conduct a case study on applying our approach to transportation data (e.g., taxi trip records) from New York City (NYC) to examine and predict tip generosity. Although we conducted the case study on NYC data, our presented approach is expected to be applicable to perform cognitive and predictive analytics on big open data from other cities.
Authors: Kevin Hoang (University of Manitoba, Canada); Carson K. Leung (University of Manitoba, Canada); Matthew R. Spelchak (University of Manitoba, Canada); Bonnie Tang (University of Manitoba, Canada); Duncan P. Taylor-Quiring (University of Manitoba, Canada); Nicholas J. Wiebe (University of Manitoba, Canada)
Email: kleung [AT] cs.umanitoba.ca
Featured Presenters
Carson Leung is currently a Professor at the University of Manitoba, Canada. He has contributed more than 250 refereed publications on the topics of big data, computational intelligence, cognitive computing, data analytics, data mining, data science, fuzzy systems, machine learning, social network analysis, and visual analytics. He has also served on the Organizing Committee of the ACM CIKM, ACM SIGMOD, IEEE DSAA, IEEE ICDM, and other conferences.
Hosted By Services Society
KES 2020
The 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems was originally scheduled to be held 16-18 September 2020 in Verona, Italy. Due to unprecedented times and the global pandemic relating to COVID-19, it is presented as a virtual conference hosted at the KES Virtual Conference Centre. KES 2020 is held (mostly 09h-16h30 BST (UTC+1), i.e., 03h-10h30 CDT (UTC-5)) in a synchronous mode, with live presentation and Q&A on Zoom. Keynotes are on Webinar, and prerecorded video presentations on the KES Online are for a synchronous viewing.
24th International Conference on Knowledge Based and Intelligent information and Engineering Systems > Room 6 > Day 3 > k20is-232 Predictive analytics on open big data for supporting smart transportation services
[YouTube video]
Authors: Mr. Paul Patrick F. Balbin, Mr. Jackson C.R. Barker, Prof. Carson Leung, Mr. Marvin Tran, Mr. Riley P. Wall, Prof. Alfredo Cuzzocrea.
DEXA 2020
The 31st International Conference on Database and Expert Systems Applications (DEXA2020) was originally planned for Comenius University, Bratislava, Slovakia on September 14-17, 2020. Due to safety concerns as well as other restrictions preventing travel and gatherings (related to COVID-19), it is held as a virtual conference (mostly 12h30-17h45 WEST (UTC+1) = 06h30-11h30 CDT (UTC-5)) in a synchronous mode, with live presentation and Q&A on Zoom.
DaWaK 2020
Timakum T., Lee S., Song IY., Song M. (2020) Analyzing the Research Landscape of DaWaK Papers from 1999 to 2019. In: Song M., Song IY., Kotsis G., Tjoa A.M., Khalil I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2020. Lecture Notes in Computer Science, vol 12393. Springer, Cham. https://doi.org/10.1007/978-3-030-59065-9_1
Paper counts 2009-2019
Second most-cited DaWaK 2011 paper (cf. most-cited DaWaK 2011 paper with 35 citations):
Chapter 19 with 21 citations, 10 readers, 1.09K downloads
Leung C.KS., Jiang F. (2011) Frequent Pattern Mining from Time-Fading Streams of Uncertain Data. In: Cuzzocrea A., Dayal U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2011. Lecture Notes in Computer Science, vol 6862. Springer, Berlin, Heidelberg
Second most-cited DaWaK 2012 paper (cf. most-cited DaWaK 2012 paper with 32 citations):
Chapter 24 with 12 citations, 6 readers, 1.79K downloads
Leung C.KS., Tanbeer S.K. (2012) Mining Popular Patterns from Transactional Databases. In: Cuzzocrea A., Dayal U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg
Fifth most-cited DaWaK 2013 paper (cf. top-4 most-cited DaWaK 2013 papers with 17, 15, 11 & 11 citations):
Chapter 18 with 10 citations, 6 readers, 1.18K downloads
Jiang F., Leung C.KS. (2013) Stream Mining of Frequent Patterns from Delayed Batches of Uncertain Data. In: Bellatreche L., Mohania M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg
Fourth & fifth most-cited DaWaK 2014 papers (cf. top-4 most-cited DaWaK 2014 papers with 32, 18 & 14 citations):
Chapter 28 with 13 citations, 6 readers, 1.49K downloads
Jiang F., Leung C.KS. (2014) Mining Interesting "Following" Patterns from Social Networks. In: Bellatreche L., Mohania M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham
Chapter 11 with 10 citations, 7 readers, 1.49K downloads
Leung C.KS., MacKinnon R.K. (2014) BLIMP: A Compact Tree Structure for Uncertain Frequent Pattern Mining. In: Bellatreche L., Mohania M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham
Most-cited DaWaK 2015 paper:
Chapter 10 with 13 citations, 8 readers, 1.51K downloads
Leung C.KS., Jiang F. (2015) Big Data Analytics of Social Networks for the Discovery of “Following” Patterns. In: Madria S., Hara T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science, vol 9263. Springer, Cham
Most-cited DaWaK 2017 paper:
Chapter 10 with 8 citations, 4 readers, 1.09K downloads
Braun P., Cuzzocrea A., Jiang F., Leung C.KS., Pazdor A.G.M. (2017) MapReduce-Based Complex Big Data Analytics over Uncertain and Imprecise Social Networks. In: Bellatreche L., Chakravarthy S. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2017. Lecture Notes in Computer Science, vol 10440. Springer, Cham
Most-cited DaWaK 2018 paper:
Chapter 7 with 3 citations, 3 readers, 688 downloads
Leung C.K., Braun P., Pazdor A.G.M. (2018) Effective Classification of Ground Transportation Modes for Urban Data Mining in Smart Cities. In: Ordonez C., Bellatreche L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science, vol 11031. Springer, Cham
Top citation by year
Rank 17 (among 21 years): 2015 Leung C.KS., Jiang F. (2015) Big Data Analytics of Social Networks for the Discovery of "Following" Patterns. (13 citations, 8 readers, 1510 downloads, Chapter 10)
Rank 18: 2017 Braun P., Cuzzocrea A., Jiang F., Leung C.KS., Pazdor A.G.M. (2017) MapReduce-Based Complex Big Data Analytics over Uncertain and Imprecise Social Networks. (8 citations, 4 readers, 1090 downloads, Chapter 10)
Rank 20: 2018 Leung C.K., Braun P., Pazdor A.G.M. (2018) Effective Classification of Ground Transportation Modes for Urban Data Mining in Smart Cities. (3 citations, 3 readers, 688 downloads, Chapter 7)
Top-10 21 years
Eighth most downloaded DaWaK paper
2012 with 1790 downloads: Chapter 24 by Leung C.KS., Tanbeer S.K.
BioMedInformatics
Dr. Carson K. Leung serves as an Editorial Board Member for BioMedInformatics.
Prof. Dr. Carson K. Leung
Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
Tel. +1(204)474-8677
Website | E-Mail
Interests: data mining and analysis; data visualization and visual analytics; health informatics and electronic health
Special Issues and Collections in MDPI journals:
Special Issue in Sensors: Selected papers from Smart Data 2018 & Big Data Service 2018
Special Issue in Applied Sciences: Big Data Analysis and Visualization
Special Issue in Applied Sciences: Big Data Analysis and Visualization II
IV 2020
24th International Conference Information Visualisation (IV 2020) was originally scheduled to be held (1) 28-31 July 2020 at TU-Wien, Vienna, Austria (for IV2020 @ Vienna) and (2) 24-27 November 2020 at Victoria University, Melbourne, Australia (for IV2020 @ Melbourne). Due to COVID-19, IV/CGiV is rescheduled to be held online on 07-11 September 2020 (mostly 02h-18h CEST (UTC+2) = 10h-02h (+1d) AEST (UTC+9), i.e., 01h-17h BST (UTC+1), i.e., 19h (-1d)-11h CDT (UTC-5)) in a synchronous mode on Zoom. For each session, a sequence of live presentations is given prior to a single Q&A at the end of the session. Prerecorded video presentation on ConfTool is for backup.
Short CV of Presenting Author: Carson Leung is currently a Professor at the University of Manitoba, Canada. He has contributed more than 250 refereed publications on the topics of big data, computational intelligence, data analytics, data mining, data science, fuzzy systems, machine learning, social network analysis, information visualization, and visual analytics. He has also served on the Organizing Committee of the ACM CIKM, ACM SIGMOD, IEEE DSAA, IEEE ICDM, and other conferences.
INCoS 2020
Dr. Carson K. Leung serves as a Track Chair for the 12th International Conference on Intelligent Networking and Collaborative Systems (INCoS 2020) held August 31-September 02, 2020 in Victoria, BC, Canada. He oversees the Data Mining, Machine Learning and Collective Intelligence track.
INCoS-2020 is held in conjunction with NiBS-2020. Paper presentations from the same geographical region are grouped together. For instance, an online session (INCoS-OS2: Online Session 2) is held 09h-10h30 JST (GMT+9) on Sep 01, i.e., 19h-20h30 CDT (GMT-5) on Aug 31, in a synchronous mode, with three live presentations and Q&A on Zoom.
Acting Department Head
Dr. Carson K. Leung serves as the Acting Head of Computer Science August 24-28, 2020.
The Manitoban 107(3) (2020)
August 19, 2020
Page 2 (Index): Research and technology. A new way to excel: data science major to begin fall 2021 ... page 3
Page 3 (Research and technology): U of M announces major in data science: Faculty of science's bachelor of data science to launch in fall 2021
Sarah Doran:
The Manitoban 107(3):
3 (August 18, 2020)
The faculty of science has created a new interdisciplinary bachelor of science with a major in data science degree program, set to launch at the start of the 2021-22 academic year. The program will require 120 credit hours of courses in mathematics, statistics and computer science, as well as courses designed specifically for the major.
Data science is a rapidly growing field involving recording, storing, managing and analyzing data of various kinds. While computers are often involved in the process, computer science is a separate area because the underlying focus of computer science is writing and understanding the logic behind computer programs rather than a specific data component.
The University of Manitoba currently offers joint honours programs, such as between computer science and mathematics and between computer science and statistics, that are cumulatively similar to a data science program but do not have the interdisciplinary focus the new degree will.
Carson Leung, a professor in the department of computer science, has long been interested in data science. He runs the Database and Data Mining Laboratory, which offers both undergraduate and graduate students the opportunity to conduct research on theoretical and practical aspects of data science with real-life applications.
"In addition to participating in research, students interested in data science have had opportunities to acquire knowledge by taking data science-related courses," Leung said.
Several data-related courses are being offered during the 2020-21 academic year throughout a range of programs, including computer science, environmental science, mathematics, management information systems and statistics.
Leung said the U of M is also in the process of creating several data science graduate programs, including a master's in data science and a master of business analytics program.
The wide variety of fields in which data scientists can be employed is a major reason why Manitoba Economic Development and Training approved the creation of the new degree program.
"As data science has emerged as a significant influencer in many sectors of our economy, data scientists are in high demand, but it can be challenging to find skilled employees," Leung said.
["Data scientists are in high demand, but it can be challenging to find skilled employees" — Carson Leung, computer science professor]
Ben Li, faculty of science associate dean of undergraduate programs, explained further in a press release.
"The data science undergraduate degree aligns perfectly with the current needs of major employers and, with the integration of co-operative education as a degree option, students will graduate with a competitive advantage," he said.
"They'll have actual work experience and be able to jump in and solve complex real-world issues from the get-go. [The degree] will also serve as a solid foundation for further studies at the master's and PhD level."
Across town, the University of Winnipeg is also launching its data science program this fall.
However, the program is a specialization within the U of W's existing statistics degree. The course list is heavily focused on statistics and mathematics classes, including a few courses newly created for the program, but students are also required to take at least six applied computer science courses.
The U of M has not yet released the planned degree requirements for its data science major program. Leung said more details will be announced as they become available.
The Manitoban (@themanitoban)
Tuesday, September 1, 2020 at 1:08 PM
The faculty of science has created a new interdisciplinary data science major program, set to launch at the start of the 2021-22 academic year. #science #UniversityOfManitoba #UniversityOfWinnipeg
IEEE CBDCom 2020
Dr. Carson K. Leung serves as a Program Chair for the Sixth IEEE International Conference on Cloud and Big Data Computing (CBDCom 2020)—which co-locates with (1) DASC 2020, (2) PiCom 2020, (3) the Fifth IEEE Cyber Science and Technology Congress (CyberSciTech 2020)—as a part of the IEEE Joint Cyber Science and Technology (IEEE Joint CST 2020). It was originally scheduled to be held June 22-25, then postponed to August 17-24, 2020 (due to COVID-19) as an asynchronous online conference with prerecorded video presentation on Slack.
On LinkedIn, Gautam Srivastava (Associate Professor at Brandon University; IEEE CBDCom 2020 Program Co-chair & Special Issue Chair) posted
Alongside Colleagues Carson Leung, Ladjel Bellatreche, and Xiaokang Zhou, we are inviting high quality submissions to our upcoming Special Issue in Elsevier's Big Data Research (https://lnkd.in/gCZM8dj) entitled "Emerging Trends, Issues and Challenges in Big Data and Cloud Computing" Call for Paper is attached. Deadline for Submissions is July 31st, 2020. The submission site should be open by April 2020. For more details please visit the journal website. This Special Issue is connection to our upcoming conference IEEE Cloud and Big Data Computing (CBDCom 2020) (https://lnkd.in/gd6fKt4).
#IEEE #research #Elsevier #EmergingTrends #CallforPaper #DataResearch #BigData
Anna Kobusinska, Rachid Benlamri, Carson Kai-Sang Leung, Gautam Srivastava: Message from CBDCom 2020. DASC-PICom-CBDCom-CyberSciTech 2020: xxxv.
Message from CBDCom 2020
Program Chairs and General Chairs
The 6th IEEE International Conference on Cloud and Big Data Computing (CBDCom 2020) is another milestone in continuing the tradition of the CBDCom conference series that started in 2015 in Beijing and was held annually. Since its inception, the IEEE CBDCom has become a premier forum for the exchange and dissemination of the latest advances in cloud computing and big data systems and applications among researchers, practitioners, developers, and users who are interested in exploring new ideas, techniques, tools, and in identifying emerging research topics.
The IEEE CBDCom 2020 was to be hosted this year in Calgary, Canada from August 17 to 24. The conference was to be co-located with IEEE PICom 2020, IEEE CyberSciTech, and IEEE DASC 2020, within the IEEE Joint Cyber Science and Technology Congress. However, due to the COVID-19 world pandemic, it is most unfortunate that we are unable to attend this prominent event in person. Nevertheless, due to the committed and hard work of the organizing committee, the CBDCom Steering committee and the CBDCom Technical committee, and in particular the authors, we have been able to host the event virtually. All the events of IEEE Joint CST 2020 will be held online, August 17-24, 2020, Canada MDT time.
Submissions received by IEEE CBDCom 2020 cover a wide range of topics. Each paper was reviewed by at least three experts in the field. Finally, after the rigorous peer review process, 8 regular papers, which reflects an acceptance rate of 27%, were chosen for online presentation and publication in the conference proceedings. Moreover, 4 special session papers, 1 work-in-progress paper, and 1 poster were accepted.
The successful organization of IEEE CBDCom has required dedication and time. We would like to take this opportunity to thank all the members of the organizing and steering committee, especially the Steering Chairs Prof. Jianhua Ma and Prof. Laurence Yang for their great support and guidance in the preparation of the conference. We extend our thanks to the entire local committee members, including General Executive Chair, Prof. Oscar Lin. Also, we thank the Track Chairs, Technical Program Committee and all reviewers for their valuable time and effort in reviewing the papers. Last, but not least, we thank all of the authors for their creative and worthwhile contributions which make the high quality of IEEE CBDCom 2020.
It was our great honor and pleasure to accept the responsibilities and challenges of Conference General and Program Chairs. We trust that you will enjoy the first virtual IEEE CBDCom conference!
General Chairs
Anna Kobusinska, Poznan University of Technology, Poland
Rachid Benlamri, Lakehead University, Canada
Program Chairs
Carson Kai-Sang Leung, University of Manitoba, Canada
Gautam Srivastava, Brandon University, Canada
Bioinformatics-Biostatistics Research Seminar - Summer 2020
On Monday, August 17, 2020, three lab members gave their presentations in the Bioinformatics-Biostatistics Journal Club and Research Seminar Series organized by George & Fay Yee Centre for Healthcare Innovation:
Judah Zammit presented his (Industrial) project on "COVID-19 CT lung image segmentation using deep generative models"
Daryl Fung presented his (Honours) project on "Self-supervised COVID-19 CT lung image segmentation with increasing mask complexity in-painting through multiple networks"
Qian Liu, who enrolled in the Individual Interdisciplinary Studies Program, presented her VADA project on "Deep learning-based COVID-19 diagnosis and mediators of gender, age, and underlying diseases effects on COVID-19 through CT lung imaging phenotypes"
These presentations on "Artificial intelligence-based COVID-19 CT lung image analysis" aim to understand (1) the COVID-19 CT lung image segmentation using deep learning models, (2) the CT lung images for COVID-19 diagnosis and prognosis, and (3) causal mediation analysis with COVID-19 CT lung image data.
IDEAS 2020
Dr. Carson K. Leung serves as a Program Chair for the 24th International Database Engineering & Applications Symposium (IDEAS 2020), which was originally scheduled to be held June 03-05, 2020, in Seoul, South Korea. Due to COVID-19, it is postponed to August 12-14, 2020, with online video conferencing. It is held (mostly 08h-14h30 EDT, i.e., 07h-13h30 CDT) in a synchronous mode, with live presentation and Q&A on Zoom. Slides are available on ConfSys.
JCP
Dr. Carson K. Leung serves as an Editorial Board Member for Journal of Cybersecurity and Privacy (JCP).
Prof. Dr. Carson K. Leung
Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
Tel. +1(204)474-8677
Website | E-Mail
Interests: data mining and analysis; data visualization and visual analytics; health informatics and electronic health
Special Issues and Collections in MDPI journals:
Special Issue in Sensors: Selected papers from Smart Data 2018 & Big Data Service 2018
Special Issue in Applied Sciences: Big Data Analysis and Visualization
Special Issue in Applied Sciences: Big Data Analysis and Visualization II
IEEE WCCI / FUZZ-IEEE 2020
The 2020 IEEE World Congress on Computational Intelligence (IEEE WCCI 2020)—which consists of (1) IJCNN 2020, (2) CEC 2020, and (3) the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020) as three flagship conferences of the IEEE CIS—was scheduled to be held July 19-24, 2020, in Glasgow, Scotland, UK. Due to COVID-19, it is converted to a full virtual conference (mostly 10h-22h BST (UTC+1), i.e., 04h-16h CDT (UTC-5)) in a combined synchronous-asynchronous mode, with prerecorded video presentation on the Confflux platform and live Q&A on Zoom.
FUZZ-IEEE Regular Sessions
Fuzzy Logic in Knowledge Graphs and Factor Space
[video: 22483.mp4]
An Innovative Fuzzy Logic-Based Machine Learning Algorithm For Supporting Predictive Analytics On Big Transportation Data
Author(s): Carson K. Leung, Jonathan D. Elias, Shael M. Minuk, Alddous Roy R. de Jesus, Alfredo Cuzzocrea
ABSTRACT
In the current era of high precision monitoring and big data, many public transit users are still suffering from problems caused by transit delays. To help address this problem, we design and develop an innovative fuzzy logic- based machine learning approach for supporting predictive analytics on big transportation data to helps detect and predict the delay of some modes of public transport. To demonstrate the usefulness of our machine learning approach as a solution to this problem, we use it on heterogeneous data---namely, transit data and weather data---to predict the expected delay of streetcars (aka trolley cars) in the Canadian city of Toronto. To make accurate prediction, our approach takes into account multiple factors such us rain, snow, temperature, time of day, and season. Evaluation results show the effectiveness and usefulness of our fuzzy logic based machine learning approach for predictive analytics on big transportation data, which is promising toward development of a predictive intelligent transport system (ITS).
PRESENTER
Carson K. Leung
University of Manitoba
Biography
Carson Leung is currently a Professor at the University of Manitoba, Canada. He has contributed more than 250 refereed publications on the topics of big data, computational intelligence, data analytics, data mining, data science, fuzzy systems, machine learning, social network analysis, and visual analytics. He has also served on the Organizing Committee of the ACM CIKM, ACM SIGMOD, IEEE DSAA, IEEE ICDM, and other conferences.
Inf Sc 527 (July 2020)
Chris Soo-Hyun Eom, Charles Cheolgi Lee, Wookey Lee, Carson K.Leung:
Effective privacy preserving data publishing by vectorization.
Information Sciences 527:
311-328 (July 2020)
Highlights
We suggested a privacy-preserving data-publishing model to balance data utility and privacy preservation.
The model is based on surrogate vectors.
The model is applicable on grid environments.
The model also protects the private location information of individuals.
The model satisfies ε-differential privacy.
Associate Department Head
Dr. Carson K. Leung takes on the role as an Associate Head (Undergraduate Programs) in Department of Computer Science at University of Manitoba effective July 01, 2020.
Professor, Associate Head (Undergraduate Programs)
Carson Kai-Sang Leung
E2-nnn EITC
kleung [AT] cs. umanitoba. ca
PHONE: (204) 474-nnnn
COVID-19 Resources Canada - Expertise Database
https://covid19resources.ca/expertise.html
Name: Leung, Carson
Institution: University of Manitoba
Department: Computer Science
Expertise: AI, Data Science
Research highlights
Research in my lab focuses on data science. In particular, my research team design and develop data science solutions for (a) big data management, (b) big data analytics and mining, as well as (c) big data visualization and visual analytics. We are open to collaborations on projects/funding applications related to COVID-19 (e.g., disease analytics, healthcare analytics, social network analysis, omics analysis related to the COVID-19 disease and the SARS-CoV-2 virus).
Website: http://dblab.cs.umanitoba.ca/
USRA 2020
Several lab members won undergraduate student research awards:
Each of the following students won a Faculty of Science Undergraduate Student Research Award (USRA) to conduct a full-time 16-week research project in the area of data mining under the academic supervision of Dr. Carson K. Leung:
Second-year second-degree undergraduate student Mr. Jeonghwan Choi, who (i) obtained his B.A. degree in Chinese Language and Literature from Peking University, China, and (ii) is currently enrolled in B.Sc.(Maj.) program in CS;
Second-year second-degree undergraduate student Mr. Hao Zheng, who (i) obtained his B.Eng. degree in Metallurgical Engineering from Curtin University, Perth, Australia, and (ii) is currently enrolled in B.Sc.(Maj.) program in CS;
Third-year undergraduate student Mr. Yubo Chen, who is enrolled in B.C.Sc.(Hons.) program;
Third-year undergraduate student Mr. Siyuan Shang, who is enrolled in B.C.Sc.(Hons.) program; and
Fourth-year undergraduate student Mr. Sehaj Pal Singh, who is enrolled in the B.Sc.(Hons.) with major in CS-Math and minor in Stat program.
Their fellow student, Ms. Yan Wen (third-year B.Sc.(Maj.) student in CS-Chem) was also selected as a winner of this award.
Third-year undergraduate student Ms. Yan Wen, who is enrolled in B.Sc.(Maj.) program with double major in Computer Science and Chemistry, won an UofM Vice-President (Research and International) Undergraduate Research Award (URA) to conduct a full-time 16-week research project in the area of data mining under the academic supervision of Dr. Carson K. Leung. Among ~25,000 undergraduate students across the campus, she was one of 172 UM VP(RI)/UMSU URA winners.
Each of the following students was selected as a winner of a MITACS Globalink Internship awards:
Third-year undergraduate student, Mr. Vishnusai Bhadramraju (aka Vishnu Naidu) from Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India, was selected for the project on "Mining useful information from social networks".
Fourth-year undergraduate student, Mr. Jaime Solis-Soto from Tecnológico de Monterrey - Campus Chihuahua (TEC, Monterrey Institute of Technology and Higher Education (ITESM)), Mexico, was selected for the project on "Big data science and data analytics for useful information". He was an exchange student to The University of Texas at Austin during Winter 2020.
Mitacs Accelerate 2020
Design and development of a big data analytics system to accommodate dynamic risk modeling of emergency responder delays at active rail crossings
In this project, we design and develop a big data analytics system, which is expected to support the advancement in the state-of-the-art in modeling the dynamic risks associated to emergency responders being delayed at active rail crossings. The model will be used to show risk within cities and support prescribed solutions to mitigate the impacts. Specifically, we design and develop a model to evaluate the risk of emergency responders being exposed to active crossings in terms of lost travel time. We also design and develop a state-of-the-art big data management system for efficient computation on generating and regenerating the models at a city level based on thousands of rail crossing blockage events, thousands of emergency response calls, with numerous potential origins and destinations is essential to evaluate the risk factors in varying scenarios.
Intern: Bryan Wodi; Maryam Ghaffari-Dolama
Faculty Supervisor: Carson Leung
Province: Manitoba
University: University of Manitoba
Partner: TRAINFO
Sector: Professional, scientific and technical services
Program: Accelerate
Constrained dynamic pricing for airport parking reservations
https://www.mitacs.ca/en/projects/constrained-dynamic-pricing-airport-parking-reservations
To make best use of technology on available big data (e.g., airport parking reservation data), we design and develop an innovative big data science solution for constrained dynamic pricing for airport parking reservations in the proposed research project undertaken by the intern. From scientific point of view, such a solution is novel in the sense that it will be capable of achieving multiple objectives (e.g., maximize revenue and other objectives) and solving the constrained dynamic pricing problem (in which price is constrained or bounded by some user-specified threshold). Moreover, it will be efficient in integrating big data from a wide variety of heterogeneous sources, as well as analyzing and learning data to make appropriate recommendation on prices. From the business/industrial point of view, such a solution will be beneficial to the partner organization by helping them to get an insight and better understanding of their data and enable it to further enhance its operation. To a further extent, the solution will be applicable and beneficial to other Canadian airports and/or parking facilities in other Canadian communities.
Intern: Deyu Deng
Faculty Supervisor: Carson Leung
Province: Manitoba
University: University of Manitoba
Partner: Winnipeg Airports Authority
Sector: Transportation and warehousing
Discipline: Computer science
Program: Accelerate
Decentralized services for sharing and searching user generated data
https://www.mitacs.ca/en/projects/decentralized-services-sharing-and-searching-user-generated-data
The existing model for applications and services on the internet is a centralized client server model where user information is under the control of the service provider. As such as centralized model, albeit cost-efficient and easy to maintain, has created dire consequences for humanity. Hence, for the proposed research project, we aim to provide tools and algorithms for a decentralized and location-aware experience on the Internet. Individuals, businesses, and institutions will make and qualify connections between one another by considering their trust for each other and the "network-trust" by way of vouching for others within the network. This new paradigm requires further research and development in areas including search, database, performance, and security, all within a decentralized environment to maximize our commercial and social impact.
Intern: Qi Wen
Faculty Supervisor: Carson Leung; Cuneyt Gurcan Akcora
Province: Manitoba
University: University of Manitoba
Partner: Protegra
Sector: Professional, scientific and technical services
Discipline: Computer science
Program: Accelerate
AINA 2020
The 34th International Conference on Advanced Information Networking and Applications (AINA-2020) was scheduled to be held April 15-17, 2020 at University of Campania "Luigi Vanvitelli", Caserta, Italy. As the situation of COVID-19 in Italy is worsening, it is cancelled. The proceedings are published in Springer series AISC.
At the beginning of March 2020, the US Office for Science and Technology Policy (OSTP) initiated a campaign which is supported by the National Science Advisors of 12 other countries with the request to deposit all published articles reporting on research results related to COVID-19 in the repositories of PubMed Central (PMC) and the World Health Organization (WHO). In the framework of these campaigns, Springer Nature has deposited published journal articles and book chapters on coronaviruses. Our AINA 2020 publication on "an innovative big data predictive analytics framework over hybrid big data sources with an application for disease analytics" has been identified and included in these campaigns and made it freely available to help fight the current pandemic.
Dr. Julie Chih-Yu Chen's UM FoS Interdisciplinary Lecture
On her Feb 28 lecture titled "From university to work: an adventure in biological data science", Dr. Julie Chih-Yu Chen mentioned at 43:43-44:08 that
"Here are a little bit of my experience of networking and opportunity. ... I've been presented at meetup, the Winnipeg Data Science Meetup. If you're in data science, it's really great. I'd highly recommend that, and we have Carson presented there too."
IEEE BigComp 2020
Dr. Carson K. Leung serves as a Publicity Chair for the 2020 IEEE International Conference on Big Data and Smart Computing (BigComp 2020) held February 18-22, 2020 in Busan (釜山), South Korea.
MISA-.devClub January 2020 Talk
On Thursday, January 16, 2020, Dr. Carson K. Leung, together with Dave Fulawka (Senior Director of Network Analytics at Bison Transport), presents Data Mining & Analysis, which enables students to learn about the data analytics process that has revolutionized the fields of business and computer science. This presentation brings students a fresh new experience by collaborating with business and computer science professionals.
Facebook - Asper MISA @MISAManitoba
Thank you to Dr. Carson Leung (Computer Science professor from the University of Manitoba) and Dave Fulakwa (Senior Director, Network Analytics from Bison Transport) for spending the evening with us. We really enjoyed learning about data analytics within the fields of business and computer science.
Also, thank you to devClub for co-hosting this event with us.
LinkedIn - MISA Asper
Thank you to Dr. Carson Leung (Computer Science professor from the University of Manitoba) and Dave Fulakwa (Senior Director, Network Analytics from Bison Transport) for spending the evening with us. We really enjoyed learning about data analytics within the fields of business and computer science.
Also, thank you to devClub for co-hosting this event with us.
Carson Leung (Computer scientist at University of Manitoba):
Thanks MISA Asper for co-hosting this event and Dave Fulawka for co-presenting this talk on data analytics
Dave Fulawka (Senior Director, Network Analytics And Development at Bison Transport Inc.):
It's great to collaborate, and share the results of the collaboration. What a great evening. Carson Leung I am privileged to work alongside you. Look forward to doing it again!
Special Appreciation Notice
Thank you to the following individuals and stakeholders for lending us their time and attention during our 2019-20 terms! Without your support, MISA would not be where it is today. Despite the ups and downs, we sincerely appreciate all of the time and attention you gave us.
Thank you to all the external stakeholders:
Dr. Carson Leung
Hoi's, Pazdor's & Wodi's Teaching
Calvin Hoi, a graduate student in our lab, teaches COMP 4380 A02 (Database Implementation) in Winter 2020 on every Tuesday and Thursday at 2:30pm-3:45pm in Armes 201 from January 07 to April 07, 2020. Due to COVID-19, in-person lectures ended March 12, no class on March 17, and switched to online lectures held at the same time from March 19 to April 07, 2020.
Adam G. Pazdor, a graduate student in our lab, teaches
COMP 1020 A02 (Introductory Computer Science 2) in Winter 2020 every Tuesday and Thursday at 10:00am-11:15am in E2-110 EITC from January 07 to April 07, 2020. Due to COVID-19, in-person lectures ended March 12, no class on March 17, and switched to online lectures held at the same time from March 19 to April 07, 2020.
COMP 4380 A01 also in Winter 2020 on every Tuesday and Thursday at 2:30pm-3:45pm in E2-165 EITC from January 07 to April 07, 2020. Due to COVID-19, in-person lectures ended March 12, no class on March 17, and switched to online lectures held at the same time from March 19 to April 07, 2020.
COMP 3380 A02 (Database concepts and usage) in Fall 2020 on every Tuesday and Thursday at 08:30am-09:45am via on-line study from September 10 to December 10, 2020.
By the end of Fall 2020, Pazdor has taught a total of 14 sections of five distinct courses at all four undergraduate year-levels (COMP 1012 six times in Summer 2016, Winter/Summer/Fall 2017, Fall 2018 & Winter 2019; COMP 1020 in Winter 2020; COMP 2130 in Summer 2018; COMP 3380 in Fall 2016/2017/2019/2020; and COMP 4380 in Winter 2018/2020).
Bryan Wodi, a graduate student in our lab, teaches COMP 1020 A03 in Winter 2020 every Monday, Wednesday and Friday at 8:30am-9:20am in E2-105 EITC from January 06 to April 06, 2020. Due to COVID-19, in-person lectures ended March 13, no class on March 16, and switched to online lectures held at the same time from March 18 to April 06, 2020.