Special Session 1:The Special Session on Data Marketing as Cross-disciplinary Data Exchange and Collaboration (CDEC2020)
Special Session 2:6th Special Session on Intelligent Data Mining
Special Session 3:3rd Special Session on HealthCare Data
Special Session 4:Machine Learning on Big Data (MLBD 2020)
Special Session 5:Information Granulation in Data Science and Scalable Computin
Special Session 6:Explainable Artificial Intelligence in Safety Critical Systems
The Special Session on Data Marketing as Cross-disciplinary Data Exchange and Collaboration (CDEC2020)December 10-13, 2020 Atlanta, GA, USA
The recent social movement of big data and artificial intelligence has resulted in a tremendous increase in the importance of data. In view of these expectations, there are the externalizations of interdisciplinary issues. Many papers about data utilization have been published, and several approaches for analyzing data have been shared widely. However, there are only a limited number of studies on the process of cross-disciplinary data exchange and collaboration and its ecosystem. Since this process encompasses various activities of different stakeholders, it is difficult to evaluate the patterns or processes quantitatively. Moreover, in the data market, there are not only big data but also small data necessary for decision making. It is essential to discuss the dynamics of such a network of heterogeneous data in different fields.
To address these gaps, we propose to hold a special session to discuss the processes and interactions among data, humans, and society –Data Marketing as Cross-disciplinary Data Exchange and Collaboration (CDEC). The topics taken up at CDEC will involve practical issues such as the analytical tasks performed using data, solutions for challenging social issues, and cross-disciplinary data collaboration and its process. Our special session will target not only cleanly formatted homogenous data, but also heterogeneous data that affect human behaviors, thoughts, and intentions across different domains. We will also focus on a discussion to obtain tacit knowledge of artificial intelligence and data mining by analysis and synthesis. In addition to these research fields, we will attempt to take a cognitive approach toward observing the processes of knowledge discovery and data exchange. It is expected that conflicts and inconsistencies may arise owing to differences in opinion when stakeholders from different knowledge domains have discussions on data-driven decision making. We believe that this special session focusing on the process of cross-disciplinary data exchange and collaboration will have great significance, not only on academia, but also on the society as a whole.
We call for anyone interested in the following topics related to CDEC.
Data-oriented Application Areas
- Statistical Graphics and Mathematics
- Financial Security, and Business
- Physical Sciences and Engineering
- Earth, Space, and Environmental Sciences
- Geographic/Geospatial/ Terrain Data Mining
- Text, Documents, and Software
- Social, Ambient, and Information Sciences
- Multimedia (Image/Video/Music) Mining
Case Studies on Data Exchange and Collaboration
- Methods for Data Evaluation and Utilization
- Data Management and Curation
- Risks, Limitations, and Challenges of Data Exchange
- Trust, Resilience, Privacy, and Security Issues
- Design of Data
Empirical and Comprehension Focused Data Utilization
- Modeling of Machine Learning for Social Data
- Machine Learning and Data Mining Methods Based on Empirical Knowledge
- Ontology and Dictionary
- Business Efficiency
- Cognition and Perception Issues
- Natural Language Processing, and Text Mining
- Retrieval/recommender systems
Data Focused Visualization Research
- High-Dimensional Data, Dimensionality Reduction, and Data Compression
- Multidimensional Multi-Field, Multi-Modal, Multi-Resolution, and Multivariate Data
- Causality and Uncertainty Data
- Time Series, Time Varying, and Streaming Data
- Point-Based Data, and Large Scale Data
Data Focused Cognitive Research
- Human-Computer Interaction, Cognitive Science, and Behavioral Science and Modeling (including quantitative and qualitative results)
- Theoretical Models, Technological Advances and Experimental Methods in Human-Computer Interaction, Cognitive Science, and Behavioral Science and Modeling
Market of Data
- Process and Technologies for Data Exchange
- Representation of Knowledge and Requirements
- Pricing and Evaluating Mechanism of Data
- Design of Data Platform
- Data Acquisition, and Sensors
Teruaki Hayashi (PhD, Assistant Professor)
Yukio Ohsawa (PhD, Professor)
Yoshiaki Fukami (PhD, Associate Professor)
Fabrice Tocco (co-Founder and co-CEO of Dawex)
Shusaku Tsumoto (PhD, Professor)
Hayashi Teruaki (Co-chair)
Department of Systems Innovation, School of Engineering, University of Tokyo (7-3-1, Hongo, Bunkyo-ku, Tokyo, JAPAN)
6th Special Session on Intelligent Data MiningDecember 10-13, 2020 Atlanta, GA, USA
After the successes of the first, second, third, fourth and fifth editions of Special Session on Intelligent Data Mining in Santa Clara, CA (2015), Washington, DC (2016), Boston, MA (2017), Seattle, WA, (2018), Los Angeles, CA, (2019) and the sixth Special Session on Intelligent Data Mining in Atlanta, GA (2020) will continue promoting and disseminating the knowledge concerning several topics and technologies related to data mining science.
Artificial Intelligence (AI) & Machine Learning (ML) fields are interdisciplinary, including computer science, mathematics, psychology, linguistics, philosophy, neuroscience etc. This interdisciplinary special session seeks scientific understanding on data and intelligence.
This session may help to create scientific evolution to propose robust and powerful schemes between human nature and big data processing.
Intelligent Data Mining session open to every researcher as well as industrial partners.
The aims of this Special Session on Intelligent Data Mining are to:
- Bring researchers and experts together to discuss and share their experiences
- Share the current and new research topics and ideas
- Improve and enhance personal, enterprise, national and international awareness
- Provide a platform to present and discuss recent advancements
- Increase international collaborations among university–industry-institutions
In the fields of theory and applications of data mining, artificial intelligence, computer science, mathematics, psychology, linguistics, philosophy, neuroscience and other disciplines to discuss better understanding of big data and intelligence.
The papers submitted to this special session might be in a large range of topics that include theory, application and implementation of artificial intelligence, machine learning and data mining including but not limited to the topics given below.
- Data Mining, Data Science and Big Data
- Data Warehouse, Clustering, Visualization
- Big Data and Services
- Graph Mining
- Data Security and Privacy
- Homeland Security and Data Analysis
- Coin Mining and GPU Applications
- Deep Learning
- Scalable Computing, Cloud Computing
- Knowledge Discovery, Integration, Transformation
- Information Retrieval
- Data Classification, Regression, Cleaning
- Smart Cities & Energy
- Social Media, Social Networking, Social Data
- Semantic Computing
- IoT, Autonomous Systems and Agents
- Mobile Computing
- Sensors, Networks, Devices
- Neuroscience and Bioinformatics
- HPCC and Hadoop
- Recent Theory, Trends, Technologies and Applications
- Future Directions and Challenges in Intelligent Data Mining
- Industrial Challenges in Intelligent Data Mining
- Demo Applications
Extended versions of all session papers will be published on the International Journal of Data Mining Science
Papers should be submitted for this special session by Sept 5, 2020, at the conference special session submission system.Papers should be submitted as a PDF in 2-column IEEE format. Detailed instructions for the authors can be found at the conference website. Accepted papers will be published in the conference proceedings. All accepted papers must be presented by one of the author/s in the conference to include the article in the proceedings (http://cci.drexel.edu/bigdata/bigdata2020). Submission System via https://wi-lab.com/cyberchair/2020/bigdata20/scripts/ws_submit.php?subarea=SP
If you have any question about this special session, please do not hesitate to direct your question to the special session organizer Asst. Prof. Dr. Uraz Yavanoglu (email@example.com, firstname.lastname@example.org, email@example.com)
Special Session Organizer:
Asst. Prof. Dr. Uraz Yavanoglu
Department of Computer Engineering
Gazi University, Turkey
The important dates for this special session are:
Full Paper Submission Deadline : Oct 2, 2020 11:59pm PST
Notification of Acceptance : Oct 16, 2020
Camera-ready papers & Pre-registration : Nov 10, 2020, 11:59pm PST
Conference Dates : Dec 10-13, 2020
3rd Special Session on HealthCare DataDecember 10-13, 2020 Atlanta, GA, USA
Health data differs from other industries' data in terms of structure, context, importance, volatility, availability, traceability, liquidity, change speed, usage and sources from which it is collected. As medicine is a constantly developing science, healthcare sector also. In this new emerging research area which stands at the intersection of several different discipline such as Medicine, Behavioral Science, Supply Chain Management or Big Data Analytics, techniques, methods, applications and devices are continuously developed to be used for the acquisition, storage, processing, analysis, standardization and optimization of every process in the health sector. As the healthcare sector is so challenging and related data are consistently explosive, healthcare organizations are focusing to become smarter in order to overcome the industry’s inefficiencies to improve quality of care. “To become smarter” requires impeccable data analytics. All stakeholders in the sector should reveal the deep value of this valuable data in order to apply insights to improve quality of care, clinical outcomes and deliver personalized healthcare value, while reducing medical costs, collaborate across care settings to deliver integrated, personalized care experiences, prevent disease, promote wellness and manage care, build flexibility into operations to support cost reduction and excellence in clinical and business performance and practices.
The general purpose of this special session in IEEE BigData 2020 conference is to bring together researchers, academicians and sector employees from different fields and disciplines and provide them an independent platform to exchange information on their researches, ideas and findings about healthcare data and its analytics. It is also aimed to encourage debate on how big data can effectively support the healthcare in terms of diagnosis, treatment and population health, and to develop a common understanding for research conducted in this multidisciplinary field.
Topics of interest include, but are not limited to, the following:
- Healthcare Data
- Health data collection and analysis
- Problems in health data processing
- Protection and security of personal health data
- Electronic health records and standards
- Healthcare Information Systems
- Medical Imaging Systems
- Medical Applications
- Mobile Solutions
- Pervasive Healthcare Information Systems and Services
- Sensor nodes
- Wearable health information
- Information solutions developed for the disabled
- Process Management in Health Informatics Systems
- Health Decision Support Systems
- E-health Applications
- Public health information application
Dr. Sultan Turhan
Department of Computer Engineering, Galatasaray University
Dr. Ozgun Pinarer
Department of Computer Engineering, Galatasaray University
The important dates for this special session are:
Full paper submission: Oct 9, 2020
Notification of paper acceptance: Oct 16, 2020
Camera-ready of accepted papers: Nov 16, 2020
Conference Dates : Dec 10-13, 2020
Machine Learning on Big Data (MLBD 2020)December 10-13, 2020, Atlanga, GA, USA
Best Papers of MLBD 2020 will be Invited for Extended Submission to a Top-Quality Journal
Aim and Scope
The Special Session “Machine Learning on Big Data” (MLBD 2020) of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020) follows the great success of three previous editions co-located with the IEEE BigData and IEEE ICMLA conference series and focuses on machine learning models, techniques and algorithms related to Big Data, a vibrant and challenging research context playing a leading role in the Machine Learning and Data Mining research communities. Big data is gaining attention from researchers, being driven among others by technological innovations (such as cloud interfaces) and novel paradigms (such as social networks). Devising and developing machine learning models, techniques and algorithms for big data represent a fundamental problem stirred-up by the tremendous range of critical applications incorporating machine learning tools in their core platforms. For example, in application settings where big data arise and machine is useful, we recognize, among other things: (i) machine-learning-based processing (e.g., acquisition, knowledge discovery, and so forth) over large-scale sensor networks introduces important advantages over classical datamanagement- based approaches; similarly, (ii) medical and e-heath information systems usually include successful machine learning tools for processing and mining very large graphs modelling patient-to-disease, patient-to-doctor, and patient-to-therapy networks; (iii) genome data management and mining can gain important benefits from machine learning algorithms. Some hot topics in machine learning on big data include: (i) machine learning on unconventional big data sources (e.g., large-scale graphs in scientific applications, stronglyunstructured social networks, and so forth); (ii) machine learning over massive big data in distributed settings; (iii) scalable machine learning algorithms; (iv) deep learning – models, principles, issues; (v) machine-learning-based predictive approaches; (vi) machine-learningbased big data analytics; (vii) privacy-preserving machine learning on big data; (viii) temporal analysis and spatial analysis on big data; (ix) heterogeneous machine learning on big data; (x) novel applications of machine learning on big data (e.g., healthcare, cybersecurity, smart cities, and so forth).
The MLBD 2020 special session focuses on all the research aspects of machine learning on Big Data. Among these, an unrestricted list includes:
The Special Session “Machine Learning on Big Data” (MLBD 2020) of the 2020 IEEE
International Conference on Big Data (IEEE BigData 2020) will be held in Atlanta, GA, USA,
during December 10-13, 2020, and it aims to synergistically connect the research community
and industry practitioners. It provides an international forum where scientific domain experts
and Machine Learning and Data Mining researchers, practitioners and developers can share
their findings in theoretical foundations, current methodologies, and practical experiences on
Machine Learning on Big Data. MLBD 2020 will provide a stimulating environment to
encourage discussion, fellowship, and exchange of ideas in all aspects of research related to
Machine Learning on Big Data. This includes both original research contributions and insights
from practical system design, implementation and evaluation, along with new research
directions and emerging application domains in the target area. An expected outcome from
MLBD 2020 is the identification of new problems in the main topics, and moves to achieve
consolidated solutions to already-known problems. Other goals are to help in creating a focused
community of scientists who create and drive interest in the area of Machine Learning on Big
Data, and additionally to continue on the success of the event across future years.
Special Session Location
Atlanta Marriott Marquis, Atalanta, GA, USA
Submission Guidelines and Instructions
Contributions are invited from prospective authors with interests in the indicated session topics and related areas of application. All contributions should be high quality, original and not published elsewhere or submitted for publication during the review period.
Submitted papers should strictly follow the IEEE official template. Maximum paper length allowed is 10 pages.
Submitted papers will be thoroughly reviewed by members of the Special Session Program Committee for quality, correctness, originality and relevance. All accepted papers must be presented by one of the authors, who must register.
Papers must be submitted via the CyberChair System by selecting the track “Special Session on Machine Learning on Big Data”.
Accepted papers will appear in the proper Big Data 2020 proceedings, published by IEEE. Authors of selected papers from the workshop will be invited to submit an extended version of their paper to a special issue of a high-quality international journal.
Paper submission: September 15, 2020
Notification of acceptance: October 15, 2020
Camera-ready paper due: November 10, 2020
Alfredo Cuzzocrea, University of Calabria, Italy
Michelangelo Ceci, University of Bari, Italy
Alfredo Cuzzocrea, University of Trieste and ICAR-CNR, Italy
Joao Gama, University of Porto, Portugal
Marwan Hassani, TU Eindhoven, The Netherlands
Mark Last, Ben-Gurion University of the Negev, Israel
Rocco Langone, Deloitte, Belgium
Carson K. Leung, University of Manitoba, Canada
Sofian Maabout, LABRI, Bordeaux University, France
Anirban Mondal, Shiv Nadar University, India
Enzo Mumolo, University of Trieste, Italy
Apostolos Papadopoulos, Aristotle University of Thessaloniki, Greece
For more information and any inquire, please contact Alfredo Cuzzocrea.
Information Granulation in Data Science and Scalable ComputingDecember 10-13, 2020, Atlanta, GA, USA
Granular Computing is a general computation approach for effectively using information granules such as classes, clusters, sets, groups, intervals and networks to build an efficient computational model for complex applications with Big Data, such as huge amounts of data, information and knowledge. Though the label is relatively recent, the notions and principles of Granular Computing and Information Granulation, under different names, have appeared in many related fields, such as information hiding in programming, granularity in artificial intelligence, divide and conquer paradigms in theoretical computer science, interval computing, cluster analysis, fuzzy and rough set systems, neuro computing, quotient spaces, belief functions, approximate analytics, approximate computing, deep neural networks and many others. Thus, "Information granule = Fundamental Piece of Human Knowledge": As a meta-mathematical methodology, computation of information granules gives a theoretical framework of big data analytics.
Special Session on Information Granulation in Data Science and Scalable Computing will continue to address the theory and practice of computation of information granules. It will provide researchers from universities, laboratories and industry with the means to present state-of-the-art research results and methodologies for information granules. The session will also make it possible for scientists and developers to highlight their new research directions and new interactions with novel computing models for information granulation.
The session will focus particularly on currently important research tracks such as social network computing, cloud computing, cyber-security, data mining, machine learning, knowledge management, AI-based systems, intelligent systems and soft computing (neural networks, fuzzy systems, evolutionary computation, rough sets, self-organizing systems), e-Intelligence (Web intelligence, semantic Web, Web informatics), business informatics, bioinformatics and medical informatics.
-- The session is organized as a part of the IEEE Big Data 2020 conference (December 10-13, Atlanta Marriott Marquis, 265 Peachtree Center Ave NE, Atlanta, GA 30303 USA), which is a well-established and highly competitive international event targeted at modern trends in big data processing and analytics.
-- The session is intended to be a forum for discussing concepts, issues, and methods by the leading researchers in the fundamental problems of Information Granulation and Granular Computing in an atmosphere promoting the exchange of ideas and viewpoints.
-- Papers accepted to the session will be published in the IEEE Big Data 2019 conference proceedings, together with papers submitted and accepted to the main conference track.
-- Organizers are planning a special issue on mathematical framework of big data analytics in some journal.
Submission Deadline: October 5, 2020
Acceptance Notification: October 19, 2020
Camera-Ready: November 9, 2020
Special Session Organizer:
Department of Medical Informatics, Faculty of Medicine,
Shimane University, Japan
Institute of Informatics,
University of Warsaw
Department of Computer Science and Information Engineering
National University of Kaohsiung
S. L. Wang
Department of Information Management,
National University of Kaohsiung
School of Information Science and Technology,
Explainable Artificial Intelligence in Safety Critical SystemsDecember 10-13, 2020, Atlanta, GA, USA
Dramatic success in machine learning has led to a new wave of artificial intelligence applications that offer extensive benefits to our daily lives. AI is finding its way into a broad range of industries such as education, manufacturing, law enforcement, healthcare, and finance. We are finding that the need to trust these AI based systems with all manner of decision making and predictions is paramount, especially for safety critical systems. However, this is limited by the machine’s current inability to explain their decisions and actions to human users.
In order to explore how the AI research community can handle the lack of explainability and trust for AI systems, we organize this special track on Explainable Artificial Intelligence (XAI) in Safety Critical Systems.
XAI aims to create a suite of machine learning techniques that: produce more explainable models, while maintaining a high level of learning performance, extract comprehensible knowledge and actionable plans from AI systems, and enable human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.
The special track will focus on technological framework that allows the adoption of explainable artificial intelligence (XAI) in safety-critical systems, which are required to meet governance criteria concerning safety, transparency, accountability, responsibility, and fairness for multiple application areas such as finance, business, and healthcare.
Technical issues include but not limit to: prediction accuracy which means models will explain how conclusions are reached to improve future decision making, decision understanding and trust from human users and operators, as well as inspection and traceability of actions undertaken by the AI systems.
Efforts should be done to arrive at common, standard understandings of levels of explainability to facilitate communication between end users and technology vendors. Thus, the special track intends to provide a forum to discuss the open problems and share the most recent and path-breaking work on the study and application of XAI. It will also serve as a venue for networking. Researchers from different communities interested in this problem will have ample time to share thoughts and experience, promoting possible long-term collaborations. Both theoretical and application-based contributions are welcome.
The special track seeks to explore new ideas with particular focus on addressing the following challenges:
• Is the data sources used reasonable and how to audit outcomes?
• How to enable humans to get into AI decision loops and have the ability to stop or control its tasks whenever need arises?
• Can the whole process and intention surrounding the model be properly accounted for?
• Does the system give a transparent report of why it took specific conclusions?
• Can we extract actionable decisions from AI models so as to facilitate the production of desirable outcomes?
• How to make the tradeoff decision between explainability and accuracy whose balance depends on various factors like end-user, legal liability, technicality of the concerned parties?
• How to customize different XAI systems in various safety critical areas?
We welcome submissions on recent advances in interpretability problem, info-obesity and explainable artificial intelligence in safety critical systems. All accepted papers will be presented during the conference. At least one author of each accepted paper is expected to register for and attend the conference.
Topics include but not limit to:
• Transparent machine learning models
• Post-hoc explainability techniques for machine learning models
• Theory and methods for extracting actionability
• Models with structural interpretability
• Compression and simplification of complex models
• Shallow models and deep learning
• Model-agnostic techniques
• Text explanations
• Visual explanation
• Local explanations
• Explanations by example
• Explanations by simplification
• Feature relevance explanation
• Standards of model explainability
• Adversarial machine learning
• Output confidence
• Critical data studies
• Strength/weakness assessment
• User Satisfaction
• Explainable recommendation
• Causality analysis
• Logic based explainable AI
Electronic submission of full papers: Sep 15, 2020
Notification of paper acceptance: Oct 16, 2020
Camera-ready of accepted papers: Nov 10, 2020
Conference: Dec 10-13, 2020
Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system. Paper Submission: Page Papers should be formatted according to the IEEE Computer Society Proceedings Manuscript Formatting Guidelines (https://www.ieee.org/conferences/publishing/templates.html)
Formatting Instructions 8.5" x 11" (DOC, PDF)
LaTex Formatting Macros
Prof. Qiang Yang (firstname.lastname@example.org)
Lixin Fan (WeBank, China)
Yixin Chen (email@example.com)