In the context of new business models applied to the present-day enterprises and organizations, multi-participant decision-making activities have gained ever more traction. Over the latest decades, the vast majorities of decisions have been made by collaborative groups and collectivities, and even negotiation panels. The pandemic caused adaptation of the working style to a new situation. Data collection, consensus building, solution selection, and crowdsourcing-based decisions have been supported by modern Information and Communication Technologies (I&CT). The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:

• Methods for Consensus Building and Selection;
• Crowdsourcing Methods and Platforms;
• Computer Supported Collaboration and Collaboration Engineering;
• Modern ICT Enabling Effective Collaborative Decision-making Activities, such as: a) AI-based tools including service-oriented cognitive systems and multi-agent cooperative schemes, b) Big Data analytics software, c) cloud and mobile computing, d) social networks, e) biometric systems for e-collaboration
• Multi-person Decision Support Systems (DSS) and Platforms;
• Special Cases of DSS, such as: recommender systems and systems designed to support real-time decision-making in crisis situations.
• Practical Applications including Pandemic Related Cases.

With the rapid development of information technology, knowledge acquisition through data mining becomes one of the most important directions of scientific decision-making. Extenics is a new inter-discipline of mathematics, information, philosophy, and engineering including Extension theory, extension innovation methods and extension engineering. It is dedicated to exploring the theory and methods of solving contradictory problems uses formalized models to explore the possibility of extension and transformation of things and solve contradictory problems intelligently. The intelligent methods aim to provide targeted decision-making on the transformation of the practice which is facing the challenges of data explosion. Artificial intelligence and intelligent systems beyond big data offer efficient mechanisms that can significantly improve decision-making quality. Through ITQM, participants can further discuss the state-of-art technology in the Intelligent Decision Making and Extenics based Innovation field as well as the problems or issues occurred during their research. The topics and areas include, but not limited to:

• Extenics based Information methods and technology
• Intelligent knowledge management based on Extenics
• Intelligent Information management and Problem Solving on Extenics
• Knowledge Mining on E-business
• Intelligent Systems and its Applications based on Extenics
• Intelligent Logistics Management and Web of Things combined with Extenics
• Web Marketing and CRM
• Intelligent Data Analysis and Financial Management
• Intelligent technology and Tourism Management
• Innovation theory and Extenics based Methods
• Extenics based Decision Making
• Extension data mining and its Applications
• Web Intelligence and Innovation on big data
• Knowledge based Systems and decision-making theory combined with Extenics
• Extenics based Big data technology and applications

L.A. Zadeh was the first one who used the Soft Computing term. According to his definition, the soft computing methods are those techniques used in computer science, machine learning and several engineering fields which, opposing to hard computing methods, are able to study, model and analyze a very complex reality that have not been efficiently managed by the traditional methods. Soft Computing can handle ambiguous data, and is tolerant of imprecision, uncertainty, partial truth, and approximation. The human mind is the crucial model for Soft Computing.

Soft Computing includes: (1) Fuzzy Logic; (2) Neural Computing: Perceptions, Artificial Neural Networks, Neuro-Fuzzy Systems; (3) Evolutionary Computation: Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Life (AL); (4) Machine Learning: Intelligent Agents, Expert Systems, Data Mining; (5) Probabilistic Reasoning: Bayesian Networks, Markov Networks, Belief Networks.

Soft Computing provides the background for the development of smart management systems and decisions in case of ill-posed problems.

The goal of this special session is to bring together researchers interested in applications of soft computing methods in quantitative management and decision making, in order to exchange ideas, discuss significant findings, share experiences, and work together in a friendly environment.

The topics of interest include, but are not limited to:

• colony optimization algorithms.
• Artificial intelligence methods for web mining.
• Computational intelligence methods for data mining.
• Decision support systems for quantitative management.
• Decision making with missing and/or uncertain data.
• Fuzzy and neuro-fuzzy modelling and simulation.
• Fuzzy numbers applications to decision making.
• Fuzzy-sets-based models in operation research.
• Knowledge Discovery in Databases.
• Machine learning for intelligent support of quantitative management.
• Neural networks in decision making tools.
• Support Vector Machine in SC applications.

Financial decisions are the whole show in up-to-date economic processes. To be more effective they require data and model support. So the aim of our special session will be concentrated at Big Data collection as well as using them for empirical model development.

We propose to consider proposals for such questions as:

• Construction Big Data information systems in Banking and Financial markets;
• Creation of Risk Management models for financial decisions;
• Formation of rating system in Business and Finance;
• Using credit ratings and there models as creditworthiness measure;
• Consider efficiency models to compeer bank and financial companies’ usefulness;
• Evolution of the functions of financial and banking ecosystems;
• Impact of banking innovations on the development of banking products and services, assessment of the level of competitiveness;
• Evaluation of the results of financial digitalization of the financial sector and banks on the basis of business informatization;
• Financial stability assessment and prudential regulation, including ESG sustainability.

Human decision making can depend on many factors, some of them are the result of our present life history, and others depend on our changing dynamic preferences. Efforts are made to apply an engineering approach to neuro-cognitive processes, assuming that there is prior knowledge and experience about the physiological and neurological elements and/or components of the systems which we aim to highlight.

Since the discovery of the neurobiological foundations which impact the process of learning and memory, and the explosive development of neuroscience through what has been called the era of the brain, the amount of knowledge that is being accumulated and processed progressively, makes it unavoidable not to consider brain functioning and knowledge about how it works of great and useful value. On the other hand, the vertiginous development of technology involving brain stimulation and scanning, functional brain imaging and image analysis have contributed to the development of this new discipline.

One of the consequences of the development of neurobiology in the decision-making process was the emergence of Neuromanagement and Neuromarketing. The use of electrophysiological devices to capture human physiological activity during purchase decisions, or the process of learning about customer purchasing preferences, as well as simulation of choice and neural processes involved, introduce a broad spectrum of research fields in these areas.

With this session we will highlight how common cognitive biases influence the decision-making process and have an impact on behaviour. We will also show how these biases can be studied and understood using tools such as eyetracking, EEG, physiological sensors such as GSR and other tools to measure human behaviour.

It is in our interest to present a joint research work that associates the neurocognitive research of human behavior with empirical findings of Neuromanagement and Neuromarketing in decision-making. We seek the development of novel and diverse ways of analyzing, visualizing and interpreting human physiological data in order to characterize the functional processes of the brain in different temporal scales during the accomplishment of different tasks.

So far, the focus of Neuromarketing has been the stimulus-response paradigm. However, the approach of neurocognitive engineering is looking for medium-and long-term responses to the change in human behaviour as a decision-maker. This means deepening the learning processes as central processes and procedures of human communication, education and culture.

There has been a great revolution in the development of the applications for technology used in human sciences in its diverse fields, which allows us to include the following topics of interest:

• Process of learning about customer purchasing preferences.
• Cognitive biases and Simulation process involved in decision making.
• Theoretical and practical solutions to capture human physiological activity during decision-making processes and purchase decisions.
• Qualitative and quantitative methods used in new applications.
• Software and systems for research in Neuromanagement and Neuromarketing.

In the scope of decision support systems, which were at first designed as individual tools. Individual tools have quickly demonstrated to be limited, in the sense that in today’s organizations several decision makers are involved in most of the decision processes. To deal with such decision problems, a promising approach is to unify decision support systems and Artificial Intelligence (AI). AI attempts to mimic human decision making in some capacity, and advances in AI have shown important promise in improving and assisting human decision making, in particular in real-time and complex environments. When AI techniques are used, the resulting systems are referred to as intelligent decision support systems.

The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in the designing of intelligent decision support systems that use AI techniques, such as machine learning, Bayesian networks, neural networks, fuzzy logic, and others, to improve and enhance support for decision makers in solving difficult applied problems that involve large amounts of data, are often real-time, and benefit from complex reasoning. Approaches describing advanced prototypes, systems, tools and techniques and state of the art surveys pointing out future research directions are also encouraged. Some of the appropriate topics, but no limited, to be discussed in this special session include:

• Group decision making
• Fuzzy group decision making
• Fuzzy linguistic modelling
• Soft consensus
• Intelligent decision making based on computational intelligence
• Aggregation operators
• Social group decision making
• Large scale group decision making
• Dynamics decision making
• Intelligent decision making and applications

With the development of information technology, more and more data are stored in many fields and different industries. Big data is not only a definition for data but also for the technology and idea to deal with big data. For decision makers, there are still drowned in data but lack of knowledge. Management Science is a subject to solve the problem in management with qualitative and quantitative method. New idea and method in Big data put new energy for management science. Thus, there are new methods and technology in the field big data and management science in recent years.

Data stream is one of the important data types in the financial sector, and it is with the following characteristics, such as arriving quickly, unstable, huge and so on. Traditional analysis methods and theories can’t meet the requirements of financial data stream analysis due to these characteristics. This workshop focuses on how to detect abnormal pattern in data streams especially in financial data streams. In addition, theoretical system and methods of outlier detection would be needed to be proposed. This topic includes outlier detection theory, method and application for financial data stream based on domain knowledge, outlier detection for data stream and empirical analysis.

The topics and areas include, but not limited to:

• Outlier detection based on classification method;
• Outlier detection based on clustering method;
• Outlier detection based on domain knowledge;
• Data preprocessing method for data streams;
• Domain knowledge and risk management in finance;
• Data mining and knowledge discovery in finance;
• Outlier detection method in other field;
• Quantitative management and decision making in finance;
• Method, model and application in big data and management science;

Preventing and resolving critical risks has been an important national strategic plan. To response the requirements of the national strategic plan, scientists are exploring new methods, models, and tools to address the issues arising from the various risk scenarios in different fields. Facing the intensified geographic conflicts, how to improve the security guarantee system under the Belt and Road Initiative? Facing the increasing credit demand of real economy under the COVID-19 pandemic, how to conduct credit decision and credit risk management? Facing the increasing safety threat of critical infrastructures, how to prevent multi-source risks and improve critical infrastructure resilience? Facing the increasingly prosperous cryptocurrency market brought about by the rapid development of financial technology, how to look ahead and judge the risks of the volatile digital currency market? ……

In the process of preventing and resolving critical risks, some general issues on risk management and decision-making have attracted much attention. For example, risk scenarios analysis is conducted from the perspectives of scenario elements, effect mechanism, and potential consequence. For another example, risk characterization is represented in the form of multi-source, interdependency, and dynamicity, which are modeled by some quantification tools. Recently, some intelligence technologies, such as deep learning (DL), ensemble learning (EL), artificial intelligence (AI) and text mining, are used to provide an efficient support for risk data collection and processing, visualize the judgment reference the for risk identification and diagnosis, improve the accuracy of credit decision, and solve the problems on warning and management for financial risks. On the other hand, some application issues derived from the process of preventing and resolving critical risks have been studied, such as scenario-based model for critical infrastructure risks, and ML/AI model for credit risks and Fintech risks. The main objective of this workshop is to facilitate the exchange of ideas and knowledge among practitioners, scholars, teachers, and others interested in applications of decision-analysis methods in various risk scenarios.

Topics of interest include, but are not limited to, the following:

• Risk scenario analysis
• Risk management and decision-making
• Risk characterization and modeling
• Risk-based intelligence decision analysis
• Risk data collection and processing
• Risk identification and diagnosis
• Scenario-based model for critical infrastructure risks
• ML/AI in finance risk management
• Credit decisions
• Credit rating and credit scoring
• Bayesian risk decision
• Cryptocurrency market risks
• Fintech risk management

The fields of data mining and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most data mining approaches. For last several years, the researchers have extensively applied quadratic programming into classification, known as V. Vapnik’s Support Vector Machine, as well as various applications. However, using optimization techniques to deal with data separation and data analysis goes back to more than many years ago. According to O. L. Mangasarian, his group has formulated linear programming as a large margin classifier in 1960’s. In 1970’s, A. Charnes and W.W. Cooper initiated Data Envelopment Analysis where a fractional programming is used to evaluate decision making units, which is economic representative data in a given training dataset. From 1980’s to 1990’s, F. Glover proposed a number of linear programming models to solve discriminant problems with a small sample size of data. Then, since 1998, the organizer and his colleagues extended such a research idea into classification via multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MQLP), which differs from statistics, decision tree induction, and neural networks. So far, there are more than 100 scholars around the world have been actively working on the field of using optimization techniques to handle data mining and web intelligence problems. This workshop intends to promote the research interests in the connection of optimization, data mining and web intelligence as well as real-life applications.

Shingo Aoki
Osaka Prefecture University, Japan aoki@ie.osakafu-u.ac.jp
Wanpracha Art Chaovalitwongse
Rutgers, the State University of New Jersey, USA wchaoval@rci.rutgers.edu
Masato Koda
University of Tsukuba, Japan Koda@sk.tsukuba.ac.jp
Gang Kou
University of Electronic Science and Technology of China, China kougang@uestc.edu.cn
Kin Keung Lai
City University of Hong Kong, Hong Kong, China mskklai@cityu.edu.hk
Heeseok Lee
Korea Advanced Institute Science and Technology, Korea
David Olson
University of Nebraska at Lincoln, USA Dolson3@unl.edu
Jiming Peng
University of Illinois at Urbana-Champaign, USA pengj@uiuc.edu
Yi Peng
University of Electronic Science and Technology of China, China pengyi@uestc.edu.cn
Lingfeng Niu
Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science niulfster@gmail.com
John Wang
Montclair State University, USA wangj@mail.montclair.edu
Ning Zhong
Maebashi Institute of Technology, Japan zhong@maebashi-it.ac.jp
Xiaofei Zhou
Institute of Computing Technology, Chinese Academy of Sciences, China exiaofei@gmail.com
Jianping Li
Institute of Policy & Management, Chinese Academy of Sciences, China ljp@casipm.ac.cn
Peng Zhang
Institute of Computing Technology, Chinese Academy of Sciences, China nczhang1999@163.com

Intelligent control is a popular control technology, which is developed to deal with the uncertainty and complexity of the controlled objects in terms of various environments, control objectives and tasks. Intelligent control can automatically measure the controlled quantity of the controlled object and derive the deviation from the expected value. Meanwhile, it collects the information of the input environment, and then makes inferences based on the obtained input information and existing knowledge to produce the output control of the controlled object to minimize or eliminate the deviation. The following artificial intelligence control methods are generally used such as neural networks, fuzzy logic, machine learning, evolutionary computing, genetic algorithms, etc.

The purpose of this workshop is to take the opportunity to bring together the peers engaged in area of Intelligent control. It mainly focuses on the current development, new progress, the results (theory, experiment), and challenges of related control technologies in the fields of Intelligent Control from both theoretical and practical perspectives. We warmly welcome all experts, scholars and researchers to contribute and participate in the ITQM Intelligent Control Workshop. Technical topics of interest include, but are not limited to the following fields:

• Artificial intelligence and its applications in control engineering
• Fuzzy control
• Neural networks and their applications
• Computer control systems
• Adaptive control
• Monitoring and fault diagnosis in control engineering
• Game and its applications
• Inner and outer control loops
• General control systems

For more information on this workshop, please contact Dr. Hao Chen: h.chen@swun.edu.cn

In recent years, financial fraud events occurred frequently at home and abroad, which seriously threatened the stability of the financial system and affected the healthy development of the capital market. Existing literature has done good research on the identification, analysis, and measurement of financial fraud risk based on the structured quantitative data. However, the quantitative data only contain limited information, it is difficult to break through the decision-making bottleneck under incomplete information. The advent of the information age has brought us a huge amount of multi-source data, such as news reports, images, videos, financial analysts’ reports, and textual risk disclosures in financial statements. These big data contain a wealth of risk information, which can be used as an effective supplement to traditional quantitative data. Based on the comprehensive information from multi-source data, a more effective and accurate fraud risk analysis is expected.

Big data has the typical characteristics of massive, multi-source, and heterogeneous. How to obtain and deal with fraud-related big data? How to gather and fuse these multi-source data? How to extract the key risk information from multi-source data? How to use advanced technology for better financial fraud analysis? Especially, the current COVID pandemic results in new risks and new characteristics for financial fraud. What types of new data and new methods can be used to better identify and analyze financial fraud? This workshop intends to collect fraud risk analysis papers based on multi-source data, to promote in-depth exchanges among scholars, industry, and regulatory agencies in this field.

Topics of interest include, but are not limited to, the following:

• Insurance fraud risk analysis
• Financial statement fraud analysis
• Credit fraud analysis
• Credit rating and scoring
• Risk data collection and processing
• Risk data integration and fusion
• Risk information extraction
• Machine learning in fraud analysis
• Artificial intelligence in fraud analysis
• Knowledge graph analysis and reasoning
• Big data analysis
• Text mining method
• Fintech risk management

To be competitive and innovative in the global digital economy, organizations have to invest in digital technologies and digital marketing. The contemporary diffusion of digital technologies in terms of cloud computing, distributed computing, mobile computing, social media, artificial intelligence, business data analytics, FinTech, etc. have triggered digital marketing, resulting in the business value achievement.

It is evident that digital technologies and digital marketing generate business value by influencing organizational innovation, entrepreneurship, and transformation. Work is increasingly being digitalized or virtualized. Several new digital forms such as crowdsourcing, crowdfunding, have been emerged. Additionally, new business models of the shared economy have disrupted traditional organizations and created new marketplaces.

While digital technologies and digital marketing have brought many advantages of efficiency, effectiveness, convenience, and competiveness, these advantages are only possible if organizational information systems are aligned with these new digital technologies and new digital marketing.

Our workshop welcomes rigorous and relevant theoretical and empirical research to reassess traditional assumptions and create new theories about digital technologies, digital marketing, and business value. This research challenges require the joint effort from fields of information systems research, management science, organizational studies, business value or other disciplines.

With the development of big data and artificial intelligence technology, the digital transformation of enterprises has become an inevitable trend. Business intelligence plays an important role in discovering the knowledge behind the data and providing the management decision support for enterprises. Especially in the era of big data, how to use the new artificial intelligence technology to improve the quantitative management ability of enterprises has become the key point to the digital transformation of enterprises and the improvement of management decision-making ability.

The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:

• New Algorithm of Business intelligence
• Business intelligence based on big data
• Business intelligence for enterprise management
• Business intelligence for financial management
• Business intelligence in complexity system

Chonghui Guo is a professor of the Institute of Systems Engineering, Dalian University of Technology, Dalian, China. He is the Head of Center for Big Data and Intelligent Decision Making, Dalian University of Technology. He received a B.S. degree in mathematics from Liaoning University in 1995, an M.S. degree in operational research and control theory in 1999, and a Ph.D. degree in management science and engineering from Dalian University of Technology in 2002. He was a postdoctoral research fellow in the Department of Computer Science at Tsinghua University, Beijing, China. His studies concentrate on data mining and knowledge discovery. He has published over 160 peer-reviewed papers in academic journals and conferences, besides 5 text-books and 2 monographs. He has been the principal investigator on over 20 research projects from the government and the industry.

Kun Guo is an associate professor in Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences and Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences. She received her PhD in management science and engineering from UCAS in 2011. Her research covers financial market, big data application in economy analysis, fictitious economy and complex system. She has published over 30 peer-reviewed journal articles on Journal of Forecasting, Industrial Management & Data Systems, Applied Economics, Finance Research Letters, Knowledge-Based Systems, etc. She is also secretary general of Business Intelligence Society, Chinese Academy of Management.

Yong Shi is the Director of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences and Deputy Director of National Engineering Laboratory of Big Data Analysis System. He is State Council Counsellor, Academician of Academy of Sciences for Developing Countries and President of International Academy of Information Technology and Quantitative Management (IAITQM). His major research areas include data mining, big data analysis and management decision-making.

Credit risk, with the characteristics of potential, cumulative and destructive, is the main risk undertaken by the financial institution，investor and counterparty. Credit risk management, one of the core issues of financial management, is the biggest subject in the financial industry must be considered in any business model. It is necessary to evaluate credit risk, so as to effectively manage and control it. In today’s complex global social-economic environment, credit risk evaluation and management has become one of the core issues of academic circles at home and abroad.

In order to promote the development of credit risk evaluation and management, we organize a special workshop dedicated to the topic of “credit risk evaluation and management” under The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020&2021) (http://www.itqm-meeting.org/2021/). The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of credit risk evaluation and management, and determine directions for further research. Papers should present modeling approaches/perspectives to credit risk evaluation and management.

Potential research topics include, but are not limited to the following:

• Credit risk evaluation
• The influencing factors of credit risk
• The contagion effect of credit risk
• Associated credit risk evaluation and management
• Credit risk management
• Portfolio risk management
• New techniques to credit risk measurement
• Credit rating and analysis
• The impact of COVID-19 on credit risk

With the development of the times, science and technology continue to update and progress. And today's society has gradually entered the era of big data. The widespread application of information technology has promoted the development of artificial intelligence and communication technology. Artificial intelligence can play an important role in the communication technology network in the era of big data, providing people with a better communication experience.

This workshop is mainly focus on the application of artificial intelligence technology in communication networks, including its application in communication network security management, traffic communication systems, and communication video services provisioning. In the management of communication network security, the submitted articles are supposed to investigate the way to improve the security code for increasing the overall security of the computer network system and the way to upgrade the identification method with the use of artificial intelligence technology. In the traffic communication system, the submitted articles can investigate the way to use artificial intelligence technology for providing users with real-time road traffic conditions, and the way to use artificial intelligence technology to realize the intelligent operation of electronic bulletin boards, monitoring equipment, and toll stations in the traffic communication system, as well as the use of artificial intelligence technology in in-vehicle systems. In provisioning communication video services, the submitted articles can investigate how to use artificial intelligence technology to realize intelligent screening and loading of video services. In addition, this workshop will also welcome the articles about other application issues of artificial intelligence in communication networks.

The topics and areas include, but not limited to:

• Artificial intelligence technology based management for communication network;
• Novel security code employing artificial intelligence technology in communication;
• Novel identification method with the use of artificial intelligence technology;
• Novel technology to provide users with real-time road traffic conditions with artificial intelligence;
• Intelligent operation in the traffic communication system;
• Intelligent in-vehicle systems;
• Intelligent screening for video services
• Intelligent loading of video services

Novel coronavirus outbreak in 2019, Forced global digital transformation to accelerate. China has used a lot of digital solutions at different stages of the COVID-19 to ensure that government can respond quickly and mitigate the impact. Digital empowerment has greatly improved the ability of epidemic management of Chinese government. In post epidemic period, how to strengthen digital empowerment, strengthen the ability of grass-roots governance, and promote the modernization of grass-roots governance is a major issue which worthy of in-depth consideration.

The normalization of novel coronavirus pneumonia governance has brought great challenges to digital empower of grassroots governance, and also provided important opportunities. In the future, the demand of grassroots governance for digital empowerment will be further enhanced, and digital empowerment will promote the quality of grassroots governance more strongly. This seminar focuses on "after COVID-19，How does digital empower grassroots governance". Topics and areas include but are not limited to:

• Research on connotation characteristics of digital empowerment based on grassroots governance.
• How can digital empowerment help grassroots governments cope with the uncertainty of external environment？
• Research on dynamic mechanism between digital empowerment and grassroots governance.
• Research on new methods of digital empowerment and grassroots governance.
• How does digital empowerment eliminate the information barrier in the grassroots governance system？
• How does digital empowerment promote the participation of multiple-subjects in grassroots governance？
• Research on the innovation ability of digital empowerment grassroots governance.
• How to improve the digital and intelligent level of grassroots governance？

Knowledge or hidden patterns discovered by data mining from large databases has great novelty, which is often unavailable from experts’ experience. Its unique irreplaceability and complementarity has brought new opportunities for decision-making and it has become important means of expanding knowledge bases to derive business intelligence in the information era. The challenging problem, however, is whether the results of data mining can be really regarded as “knowledge”. To address this issue, the theory of knowledge management should be applied.

Intelligent Knowledge Management is the management of how rough knowledge and human knowledge can be combined and upgraded into intelligent knowledge. Intelligent Knowledge Management aims to bridge the gap between these two fields. In addition, Knowledge Graph can express knowledge through visualization method and explore and analyze the relationship between knowledge, reveal the dynamic development law of knowledge field, and then realize knowledge sharing and reusing, which can provide rich semantic knowledge network and auxiliary information source for knowledge management and knowledge recommendation. This study not only promotes more significant research beyond data mining, but also enhances the quantitative analysis of knowledge management on hidden patterns from data mining.

In order to promote intelligent knowledge management research, we want to organize a special workshop dedicated to the topic of “Intelligent Knowledge Management” under the eighth International Conference on Information Technology and Quantitative Management (ITQM 2020&2021).

The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data mining, expert mining, knowledge graph, recommendation system and intelligent knowledge management, to generate new methods to determine directions for further research. Papers should present modeling approaches/perspectives to intelligent knowledge. The workshop is interested in topics related to all aspects of expert mining and intelligent knowledge. Topics of interest include, but are not limited to, the following:

• Intelligent Knowledge Management
• Knowledge synthesis
• Expert Mining
• Knowledge Extraction
• Knowledge Graph
• Link Prediction
• Knowledge Graph Embedding (KGE)
• Recommendation Systems
• Equipment Health Management (EHM)
• Knowledge Creation/Emergence
• Large-scale Engineering Projects
• Knowledge Sharing
• Knowledge Spillover
• Knowledge Presentation and Visualization
• Knowledge Evaluation
• Extension
• KDD Process and Human Interaction
• Intelligent Systems and Agents
• Knowledge Management Support Systems

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Please, note that papers must not exceed eight pages in length, a paper without figures can be around 4500 words maximally. For editorial inquiry and correspondence, please contact the workshop chairs.

As E-science has emerged as a persistent and increasingly large part of the research enterprise, scientists are exploring new roles, services, staffing, and resources to address the issues arising from this new mode of research. Scientists use computer modeling and simulation programs to test and produce new theories and experimental techniques, often generating and accumulating vast amounts of data. Ideally, that data could be shared with other scientists, for re-use and re-analysis, ultimately speeding up the process of scientific discovery. The collection and utilization of scientific data are the two primary features that characterize e-Science. The scientific data are generated by different aspects and departments in the management activities of research institutions and are decentralized-managed and separated-stored, which generates the difficult to share and manage the scientific data. Furthermore, the global sharing of data has promoted interdisciplinary teamwork on complex problems and has enabled other researchers to use data for different purposes. Recently, the scientific data analysis for ecological animal husbandry has attracted much attention. Some empirical research, such as the big data management platform for ecological animal husbandry in Qinghai Province China, has generated significant benefits. The main objective of this workshop is to provide researchers and practitioners and opportunity to share the most recent advances in the area of data science and decision analysis for e-science, especial the ecological animal husbandry. The workshop aims to create a communication platform for researchers to share recent and significant developments in the general area.

Topics of interest include, but are not limited to, the following:

• Metadata standard for scientific data
• Scientific data quality analyzing
• Scientific data integration and sharing
• ETL process for scientific data
• Disambiguation of scientific data
• Scientific data visualization
• Decision analysis modeling from scientific data
• Network analysis from scientific data
• Data integration for ecological animal husbandry
• Analysis modeling for ecological animal husbandry
• Bibliometrics analysis from scientific data
• Scientometrics analysis from scientific data

With the developments of modern information and communication technology (ICT), mobile Internet, multi-media and intelligent terminal devices, the district financial risk management issue becomes more complicated than ever before. Whilst, the artificial intelligence technology and big data analyzing technology are changing the means of district financial risk management. Risk management is becoming more complex and more unstructured data-heavy, such as media news, financial organization announcement, etc. Many, if not most, economic data are stored in an unstructured format. The unstructured data analysis needs more alternative or advanced methods along with applications.

This workshop will address the following topics of interest (but not limited to):

• Investigate the indices system of district financial risk
• Construct the framework of district financial risk management
• Develop intelligent early warning and prevention systems for district financial risk management based on scenario simulations, and emergency response mechanisms etc.
• Study on the mechanism of district financial risk infection
• Build up models of identifying the financial risk using artificial intelligence and big data analyzing technologies
• Analyze the financial risk and its evolvement in multi-media.
• Study on the methodologies and methods of financial risk big-data collecting, cleaning, integration, storage optimization and security access
• Design decision making system of unexpected and abnormal financial risk event
• Intelligent Decision Support System
• Financial risk preference and culture
• State-owned enterprise debt risk management and control
• Risk quantitative assessment system
• Unstructured data mining tool
• Non-relational database
• Text mining
• Natural language processing (NLP)

IoT plays a central role in digital transformation through connecting and integrating physical objects with the internet. In the last couple of years, varied applications of the Internet of Things (IoT) are growing rapidly across the world. The implementation and usage of IoTs expedite the process of digitization and building information society worldwide. IoT is not only revolutionizing the way we live but also transforming the business model and processes by organizations. Among other technologies, such as artificial intelligence, big data analytics, and cloud computing, etc., IoT is expected to have the most impact on society and the economy.

The growing trend in emphasizing digital transformation worldwide is prospective, predictive, and underlying these trends is the increasing digitization of physical things or objects such as machines, buildings, vehicles, and products through the internet. With the use of IoT, we are witnessing fundamental differences in the scale, scope, and speed of data collection, analysis, and usage. Despite the prosperity of IoT businesses, scaling the digital transformation idea to drive growth remains challenging. Significant factors contributing to such challenge can range from too much focus on technology, lack of solid business case, ineffective cross-domain collaboration to the public's divergent perceptions toward IoT services.

Thus, this workshop's objectives focus on the fundamental issues with IoTs and how IoT-related opportunities can support digital transformation in varied areas, such as healthcare, retail, manufacturing, building, government, and smart home etc. We aim to discuss the design, development, deployment, and usage of IoT in broad areas. As many significant firms are developing platforms such that multitude of devices can be connected, we are also interested in the innovative and successful usage of these IoT platforms in real business cases as well as related teaching cases in the educational setting.

We welcome submissions on varied topics around IoT and digital transformation, such as (but not limited to):

• Formal verification and model-checking for Internet of Things applications
• Knowledge representation models in the Internet of Things
• Business information processing and business models in the Internet of Things
• Software engineering in the Internet of Things
• Enterprise knowledge management in the Internet of Things
• Privacy protection and security issues of the Internet of Things
• Intelligent applications of the Internet of Things in varied domains (healthcare, logistics, manufacturing, crisis management, government, project management)
• Technologies of data management and integration in the Internet of Things
• IoT user experience
• IoT education
• IoT blockchain
• Other emerging issues of IoT and digital transformation

A new era of data has dawned. Many services are being developed by using public and private data, such as a power grid system, an autonomous vehicle, a humanoid robot, an intelligent personal assistant, and a smart home device. Artificial Intelligence (AI) is in the middle of these momentous changes. The Innovation of AI is increasingly taking place not only at the government level, but also at private companies. All attention is focused on how much value AI creates by integrating with the business process. This workshop seeks the research that promotes technical, theoretical and behavioral applications as well as emerging applications in AI. Other topics that have relevance to value creation and theoretical/practical implications in AI and business analytics fields are also welcome.

Topics of interest include, but are not limited to, the following:

• Infrastructure and management of AI
• Machine learning, robotics, deep learning, RPA and text mining from various data sources
• Data analytics applications in various business domains, including operations, SCM, marketing, finance, healthcare, energy, etc.
• Business process management applications such as process mining. knowledge management, and digital innovation
• Privacy, security, standard and legal issues in analytics and big data
• Innovative analytics and big data applications in sharing economy

As is known to all, energy economics is a subfield of economics that focuses on energy relationships as the foundation of all other relationships. The field can arise from a number of disciplines, including economic theory, financial economics, computational economics, statistics, econometrics, operational research and strategic modeling. A wide interpretation of the subject includes, for example, issues related to forecasting, financing, pricing, investment, development, conservation, policy, regulation, security, risk management, insurance, portfolio theory, taxation, fiscal regimes, accounting and the environments. In these listed issues there are a large number of computational problems to be solved for the energy systems, particular for energy risk measurement and management. This will be the eighth workshop for such a subject that provides a premier and open forum for the dissemination of innovative computational methods as well as original research results in energy economics and energy risk management.

In order to provide an academic exchange platform, the First International Workshop on Computational Methods in Energy Economics (CMEE 2007) was held in Beijing on May 27-30, 2007. Subsequently, the Second, Third, Fourth, Fifth, Sixth, Seventh, Eighth, Ninth and Tenth International Workshop on Computational Methods in Energy Economics (CMEE 2008, CMEE 2009, CMEE 2010, CMEE 2011, CMEE 2012, CMEE 2013, CMEE 2015, CMEE 2016, and CMEE 2017) were held in Nanjing, Sanya, Huangshan, Kunming, Harbin, rio De Janeiro (Brazil), Asan (Korea) New Dehli (India) on June 27-30, 2008, April 24-26, 2009, May 28-31, 2010, April 15-19, 2011, June 24-26, 2012, May 16-18, 2013, July 21-24, 2015, August 16-18, 2016, and December 8-10, 2017. To promote the idea-exchange and discussion of this field, the Eleventh International Workshop on Computational Methods in Energy Economics (CMEE 2021) will be held in Chengdu, China, July 9-11, 2021. The organizers solicit all interested academic researchers and industrial practitioners to submit their recent research results to this workshop within the scope of the following topics.

The workshop will provide an open forum for research papers concerned with the computational problems on energy economics and energy risk management, including economic and econometric modeling, computation, and analysis issues in energy systems. The workshop will focus on, but not limited to, the following topics:

• Forecasting models for energy prices (oil, coal, gas, electricity)
• Pricing models in energy markets (mean reversion, jump diffusion)
• Investment analysis models in energy projects (portfolio theory)
• Econometric modeling for energy demands
• Energy and environment policy modeling
• Modeling strategic behavior for energy security
• Hybrid energy-economy models for energy policy simulation
• Statistical analysis of energy cost, energy consumption and economic growth
• Energy risk management (risk measurement, hedging strategy and instruments)

With the acceleration of globalization, the ties between countries, industries, enterprises, and people are getting closer. Various risks are intertwined and rapidly spreading, increasing the uncertainty and complexity of the economic and social environment. In 2009, Nobel Prize winner Robert Engle proposed a new paradigm “Anticipating Correlations” for risk analysis. In reality, various risks and their interactions in different fields are also extremely different, especially in the fields of enterprise risk management, financial risk management, overseas investment and trade decision-making. This brings great challenges to the accuracy of risk measurement and management.

Affected by the COVID-19, the global turbulence sources and risks have increased. Various inherent and nascent, domestic and transnational risks resonate and spread, presenting a complex system characteristic of risk networks. To systematically interpret the current new risk topics and risk scenarios in global politics, economy, finance and other fields, there is also an urgent need for new developments in risk analysis and measurement technology.

Topics of interest include, but are not limited to, the following:

• Risk correlation
• Risk perception
• Risk and reliability analysis
• Risk integration
• Crisis and emergency
• Financial risk
• Country risk
• Social risk
• Program risk