Accepted Special Sessions

Special Session 01: IT Supported Collaborative Work in Management and Control: Methods, Tools, Systems, and Applications

Prof. F. G. Filip, The Romanian Academy, Bucharest, Romania, ffilip@acad.ro (https://acad.ro/cv/FilipF/FGF-CV-en.pdf)

Prof. Constantin Bala Zamfirescu, “Lucian Blaga”, University, Sibiu, Romania, zbc@acm.org (https://web.ulbsibiu.ro/constantin.zamfirescu/)

Prof. Cristian Ciurea, Academy of Economic Studies-ASE, Bucharest Romania, cristian.ciurea@ie.ase.ro (https://orcid.org/0000-0002-7327-0007)

In the contemporary context of the hyperconnected world, digital transformation initiatives, and the rather unpredictable developments in economies, societies, and natural environment, new business models have been applied to the management of the present-day public and private organizations. Consequently, the multi-participant collaborative decision-making networks, tools, and activities have gained ever more traction. At the same time, the modern AI-based information tools especially those in the generative AI (GAI) class, and biology inspired approaches have seriously impacted almost all sectors of the economic and social life. Recent trends in combining AI1 (Artificial Intelligence) with AI2 (Augmenting Intelligence [of humans]) within hybrid cognitive units can be noticed. Data collection, consensus building, solution selection, network and crowd working-based decisions also involving collaborative/ social robots in decision-making activities have been supported by modern Information and Communication Technologies (I&CT). Also, the preoccupation for human wellbeing, safety, and resilience, and cultural diversity of the people involved, and environment quality preservation is ever more noticeable in modern management and control settings.

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:
  • and crowd working methods and corresponding platforms
  • Cobots, digital clones of humans, and people with augmented intelligence and their collaboration in a mutualistic synergy manner
  • Modern I&CT enablers for collaborative activities, such as: a) AI-based tools including GAI-based tools and service-oriented cognitive systems, b) multi-agent cooperative schemes, c) data science and analytics, d) cloud, sky, and mobile computing, e) social networks, f) digital twins and co-simulation, f) continuous computing and so on
  • Multi-person MADM and Decision Support Systems (MpDSS) and platforms
  • Special cases of DSS, such as: recommender systems, systems designed to support real-time decision-making in emergency and risky situations and so on.
  • Practical recent applications in: collaborative learning, management of smart cities and sustainable villages, healthcare centres, cultural institutions and events, developments toward Society 5.0
  • Green and trustworthy computing, ethical aspects, and digital humanism

Special Session 02: Network Analysis in Decision Making

Prof. Fuad Aleskerov, HSE University, Russia, fa201204@gmail.com

We consider a wide range of works on the network and decision analysis of various connections and the decisions stemmed from these connections. The aim of this session is to bring together researchers, academics, practitioners, and students who are working on theoretical, computational, and applied aspects that facilitate decision-making process.
The session includes the papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:
  • Analysis of real-world decision processes
  • Theoretical works on Network Analysis
  • Models of complex networks
  • Big Data analysis on social networks
  • Social influence and information diffusion models
  • Link prediction
  • Recommendation systems and networks
  • Influence of Covid-19 to decision processes and the development of the world

Special Session 03: Innovation and Decision-making for Financial and Economic sustainability: Digital and ESG Transformation

Prof. Alexander Karminsky, Higher School of Economics, Moscow, Russia, karminsky@mail.ru

Prof. Mikhail Stolbov, MGIMO-U, Moscow, Russia, stolbov_mi@mail.ru

Financial decisions are the whole show in up-to-date economic processes. To be more effective they require data and analytical & model support. The main aim of this special session will be concentrated at Big Data collection as well as using them for empirical model development mostly connected with sustainable development and ESG trensformation.
We wait proposals for such questions as:
  • Models creation for decisions to financial and economic sustainability
  • Construction Big Data information systems in Banking and Financial markets.
  • Machine learning based models for financial and risk forecasting
  • Digital assets and currency. Innovations in banking and digitalization.
  • Formation of rating system in Business and Finance.
  • ESG ratings and there modeling.
  • Efficiency models to compeer bank and financial companies' usefulness.
  • Ecosystems evolution: Emerging Market Perspectives.
  • Banking and financial innovations at the FinTech platforms.
  • Digitalization of the financial sector and banks.
  • Systemic Risks Assessment including pandemics and restrictions.
  • Risk-controlling and early warning systems in the financial and non-financial sector.
  • Artificial intelligence models in assessing sustainability and creditworthiness

Special Session 04: The 11th Intelligent Decision Making and Extenics based Application

Prof. Xingsen Li, Guangdong University of Technology, China, lixingsen@126.com

Dr. Chang Gao, Guangdong University of Technology, China, gcift@foxnail.com

Dr. Tao Wang, Beijing Institute of Technology, China, wangtao1020@126.com

Dr. Long Tang, Nanjing University of Information Science and Technology, China, tanglong@gdut.edu.cn

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 builds the theory and methods of solving contradictory problems using formalized models to explore the possibility of extension and transformation of things and solve 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 the quality of decision-making. Through ITQM, participants can further discuss the state-of-art technology in the Intelligent Decision Making and Extenics based application 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
  • Web Marketing and CRM taking Extenics as methodology
  • 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 on Extenics
  • Extenics based design technology and applications
  • Intelligent Logistics Management and Web of Things taking Extenics as methodology

Special Session 05: Methods and Tools for Decision Support Systems

Prof. Bogdana Stanojević, Mathematical Institute of the Serbian Academy of Sciences and Arts, Serbia, bgdnpop@mi.sanu.ac.rs

Prof. Boris Delibašić, Faculty of Organizational Sciences, University of Belgrade, Serbia, boris.delibasic@fon.bg.ac.rs

Prof. Sandro Radovanović, Faculty of Organizational Sciences, University of Belgrade, Serbia, sandro.radovanovic@fon.bg.ac.rs

We cordially invite researchers to submit their papers presenting new developed methods and tools that can support decision makers to handle less specified or less structured problems, and to combine models and analytic techniques with the traditional ways to access data. The researchers may contribute by adding features to decision support systems to make them easy to be used in an interactive mode even by computer novices, more flexible and suitable to changes in the decision-making concept of the users.
This session aims to present state-of-the-art research on models and data management in DSSs; research methods that can bring improvements to decision-making activities; and tools for developing the functional aspects of enhanced decision making.
Topics of interest in this session are presented in the following non-exhaustive list:
  • Multiple-Criteria Decision Making in service of Decision Support Systems (DSS);
  • Soft Computing Models for Decision Making;
  • Fuzzy Logic in DSSs;
  • Collaborative Decision-Making Tools;
  • Data Visualization Techniques for Decision Making;
  • Advancements in Artificial Intelligence for Decision Support;
  • Software for DSS;
  • Case Studies in Industry-specific DSSs;
  • Emerging Technologies in DSS (blockchain, IoT, augmented reality, etc.);
  • Future Directions and Challenges in DSS

Special Session 06: Digital Education and Innovation/E-learning

Prof. Xiaodan Yu, University of International Business and Economics, China, xyu@uibe.edu.cn

Prof. Juanqiong Gou, Beijing Jiaotong University, China, jqgou@bjfu.edu.cn

Prof. Jimei Li, Beijing Language and Culture University, China, ljm@blcu.edu.cn

Digital technologies have been effectively leveraged to create engaging, inclusive, and effective learning environments across various contexts, including K-12, higher education, and lifelong learning. Digital education and innovation/E-Learning track explores the transformative potential of digital technologies in education and focuses on innovative approaches to teaching and learning in the digital intelligence age. This track provides a platform for scholars, researchers, and practitioners to share their insights, experiences, and research findings related to the effective integration of technology in education, the development of digital learning environments, the creation of personalized learning experiences and the development of innovative pedagogical strategies. It emphasizes the behavioral aspects of e-learning, technology design, best practices of introducing AI into teaching and learning environments to achieve augmented intelligence between humans and AI.
Topics of interest in this session are presented in the following non-exhaustive list:
  • Behavioral aspects of e-learning and learner engagement
  • User experience (UX) and user interface (UI) design in e-learning platforms
  • Best practices in e-learning course development and delivery
  • Application of artificial intelligence (AI) in e-learning, including adaptive learning, intelligent tutoring systems, and personalized learning
  • Learning analytics and data-driven decision-making in education
  • Gamification and game-based learning
  • Mobile learning and responsive e-learning design
  • Accessibility and inclusivity in e-learning
  • Learning design and instructional strategies for online and blended learning
  • Collaborative and social learning in digital environments
  • Assessment and evaluation in e-learning, including digital badges and micro-credentials
  • Learning management systems (LMS) and virtual learning environments (VLE)
  • Open educational resources (OER) and their impact on e-learning
  • E-learning in corporate training and professional development
  • Quality assurance and best practices in online education
  • Ethical considerations in e-learning, including data privacy and security
  • Cultural and cross-cultural perspectives in e-learning design and delivery
  • Emerging technologies in e-learning, such as virtual reality (VR), augmented reality (AR) and blockchain
  • Case studies and success stories of e-learning implementations in various contexts, including K-12, higher education, lifelong learning, and corporate training

Special Session 07: Decision-Making Behavior and Collaborative Management in Human-Machine Systems

Prof. Yong Shi, Chinese Academy of Sciences, China, yshi@ucas.ac.cn

Prof. Yanzhong Dang, Dalian University of Technology, China yzhdang@dlut.edu.cn

Prof. Xianneng Li, Dalian University of Technology, China, xianneng@dlut.edu.cn

Asso. Prof. Zhaoguang Xu, Dalian University of Technology, China, zhgxu@dlut.edu.cn

In the rapidly evolving landscape of artificial intelligence, its integration with humans is forging a new era of hybrid intelligence. This cutting-edge paradigm marries human intellect and machine intelligence, birthing complex human-machine systems that challenge traditional notions of management. Moving beyond the dated frameworks that depended solely on human or machine decision-makers, the advent of hybrid intelligence heralds a seismic shift in the dynamics of human-machine interactions. This shift is reshaping the foundations of management models and decision-making strategies.
This special session is designed to probe the mechanisms underpinning human-machine behavior from a wide-ranging disciplinary perspective, pioneer new modes of collaborative management within human-machine systems at a systemic level, and scrutinize the nuanced process of hybrid decision-making leveraging data and knowledge. The insights from this inquiry are expected to be instrumental in developing robust theoretical frameworks and methodologies for decision-making and collaborative management within human-machine ecosystems.
This session will cover but not limited to the following topics:
  • Advanced methodologies in harnessing human and machine intelligence
  • Exploring the core mechanisms of human and machine intelligence
  • Strategies for the collaborative management of human-machine systems
  • Innovative approaches to blending human insight and machine intelligence
  • Applications of human-machine systems, decision-making behavior and collaborative management for products such as new energy vehicles
  • Other topics related to human-machine systems

Special Session 08: Intelligent Decision Making and Consensus

Prof. Francisco Javier Cabrerizo, University of Granada, Spain, cabrerizo@decsai.ugr.es

Prof. Juan Antonio Morente-Molinera, University of Granada, Spain, jamoren@decsai.ugr.es

Prof. Ignacio Javier Pérez, University of Granada, Spain, ijperez@decsai.ugr.es

Prof. Enrique Herrera-Viedma, University of Granada, Spain, viedma@decsai.ugr.es

Intelligent decision-making processes are developed by automatic decision-making systems that support individual and organizational decision-making processes using different information technologies (as web and social networks) and artificial intelligence tools. The intelligent decision-making processes involve the use of preference modelling and consensus processes. The preference modelling deals with the representation and modelled of the preferences provided by the experts in the problems. The fuzzy logic is a computational intelligence tool that provides an adequate framework to deal with the uncertainty presented in the user opinions. The fuzzy preference modelling has been satisfactory applied in intelligent decision-making. On the other hand, consensus is an important area of research in intelligent decision-making. Consensus is defined as a state of mutual agreement among members of a group where all opinions have been heard and addressed to the satisfaction of the group. A consensus reaching process is a dynamic and iterative process composed by several rounds where the experts express, discuss and modify their preferences.
The objective of the proposed session is to highlight the ongoing research on intelligent decision-making, fuzzy preference modelling and consensus processes under uncertainty. Focusing on theoretical issues and applications on various domains, such as global green digital economy, ideas on how to solve consensus processes in intelligent decision-making under fuzzy preference modelling, both in research and development and industrial applications, are welcome. Papers describing advanced prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged. Topics appropriate for this special session include, but are not limited to:
  • Fuzzy preference modelling in intelligent decision-making.
  • Intelligent decision-making system applications.
  • Consensus in fuzzy multi-agent decision-making.
  • New models of fuzzy preference modelling.
  • Intelligent decision-making systems for big data.
  • Intelligent decision-making in web.
  • Intelligent decision-making in global green digital economy.
  • Aggregation of preferences.
  • Explainability in intelligent decision-making.
  • Intelligent decision-making with blockchain technologies.
  • Intelligent decision-making in dynamic environments.

Accepted Workshops

Workshop 01: The 3rd Workshop on Quantitative Finance and Risk Management

Prof. Wei Chen, Capital University of Economics and Business, China. ( chenwei@cueb.edu.cn)

A/Prof. Zhensong Chen, Capital University of Economics and Business, China. ( chenzhensong@cueb.edu.cn)

A/Prof. Yinhong Yao, Capital University of Economics and Business, China. ( yaoyinhong@cueb.edu.cn)

Dr. Yanxin Liu, Capital University of Economics and Business, China. ( lyxinnn@cueb.edu.cn)

A/Prof. Xueyong Liu, Capital University of Economics and Business, China. ( liuxueyong@cueb.edu.cn)

With the development of multi-source heterogeneous big data, artificial intelligent methods are being more and more important in various fields within finance. Recently, stock market forecasting, algorithmic trading, risk assessment, portfolio allocation, asset pricing and derivatives market are among the areas where machine learning and deep learning researchers focus on developing models that can provide real-time working solutions for the financial industry. The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of quantitative finance and risk management, to promote in-depth research on frontier theoretical and practical issues in quantitative finance and risk management, to generate new results in this relatively under-researched area, and determine directions for further research. The workshop is interested in topics related to all aspects of quantitative finance and risk management, which includes, but are not limited to, the following:
  • Quantitative investment strategy and technology
  • Quantitative risk management
  • Portfolio optimization and strategy
  • Machine learning based model for financial forecasting
  • Financial complex network
  • Financial distress prediction and bankruptcy prediction

Workshop 02: Risk scenario-based decision making: Methods and Applications

Associate Prof. Weilan Suo, Institutes of Science and Development, Chinese Academy of Sciences, China. ( suoweilan@casisd.cn)

Associate Prof. Jing Zhang, Reserve Security and Emergency Materials Support Center, National Food and Strategic Reserves Administration, Beijing 100834, China. ( olivia_928@msn.com)

Prof. Gang Li, Northeastern University at Qinhuangdao, China. ( ligang@neuq.edu.cn)

Assistant Prof. Mingxi Liu, Institutes of Science and Development, Chinese Academy of Sciences, China. ( liumingxi@casisd.cn)

Prof. Xiaolei Sun, Institutes of Science and Development, Chinese Academy of Sciences, China. ( xlsun@casisd.cn)

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 major public emergency, how to identify the multisource risks and clarify the risk evolution mechanism? Facing the urgent public requirements for emergency supplies, how to assess the resilience of emergency support system to respond the multisource risks? Facing the increasing safety threat of critical infrastructures (CIs), how to prevent multi-source risks and improve CI resilience? Facing the increasing credit demand of real economy, how to conduct credit decision and credit risk management? 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 knowledge graph, machine learning (MI), artificial intelligence (AI), and Large Language Model (LLM), are used to provide an efficient support for risk data collection and processing, visualize the judgment reference for risk identification and diagnosis, improve the accuracy of risk response decision, and solve the problems on risk monitoring and pre-warning. On the other hand, some application issues derived from the process of preventing and resolving critical risks have been studied, such as scenario-driven resilience assessment for risk response of emergency support, and ML/AI/LLM-based modelling for CI risks, 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 and emergency decision making
  • Risk scenario analysis and modeling
  • Risk-based intelligence decision analysis
  • Risk data collection and processing
  • Risk identification and diagnosis
  • Risk monitoring and pre-warning
  • Mechanism analysis for risk evolution
  • Resilience assessment for risk response of emergency support
  • ML/AI/LLM modelling in CI/finance risk management
  • Credit risk decisions
  • Bayesian risk decision
  • Cryptocurrency market risks
  • Fintech risk management

Workshop 03: The 9th workshop on Big Data and Management Science & Outlier Detection in Finance and Economics

Prof. Aihua Li, Central University of Finance and Economics, China. ( aihuali@cufe.edu.cn)

Prof. Zhidong Liu, Central University of Finance and Economics, China. ( liu_phd@163.com)

Prof. Xiaodong Lin, Rutgers University, USA. ( xiaodonglin@gmail.com)

Prof. Fan Meng, Peking University, China. ( mengfan@pku.edu.cn)

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.

Outlier detection in economic and financial markets is a problem faced by managers. In the era of big data, data from various sources are collected to analyze outlier detection, and data fusion methods are often used. Therefore, outlier detection based on data fusion is widely concerned. This workshop focuses on how to detect abnormal pattern with data fusion especially in finance and economics fields. 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 and economic data based on domain knowledge, data fusion, and empirical analysis.

The topics and areas include, but not limited to:
  • Data fusion method and application in financial outlier detection problem;
  • Data fusion method and application in economics outlier detection problem;
  • 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 with data fusion in other field;
  • Quantitative management and decision making in finance;
  • Method, model and application in big data and management science;

Workshop 04: The 11th Workshop on Optimization-based Data Mining

Prof. Yingjie Tian, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. ( tyj@ucas.ac.cn)

Prof. Zhiquan Qi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. ( qizhiquan@ucas.ac.cn)

Dr. Saiji Fu, School of Economics and Management, Beijing University of Posts and Telecommunications, China. ( fusaiji@bupt.edu.cn)

Prof. Yong Shi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. College of Information Science and Technology, University of Nebraska at Omaha, USA. ( yshi@ucas.ac.cn)

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.

Program Committee


Workshop 05: Text-data-mining based Risk Evaluation, Prediction, Prevention, Management and its Applications in Financial Sector and Digital Economy

Prof. Zongrun Wang, School of Business, Central South University, China. ( zrwang0209@sina.com)

Prof. Wen Long, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, China. ( longwen@ucas.ac.cn)

Associate Prof. Na Li, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, China. ( lina@ucas.ac.cn)

Assistant Prof. Yunlong Mi, School of Business, Central South University, China. ( miyunlong17@mails.ucas.ac.cn)

Dr. Yi Qu, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, China. ( quyi17@mails.ucas.ac.cn)

The recent advances of digital economy in China and around the globe have greatly enhanced the development of information technologies, especially the artificial intelligence (AI) tools of machine learning and deep learning algorithms. As one kind of typical unstructured data, text data has rich information related with risk management, enabling various tasks more efficiently or effectively solved, like risk evaluation, prediction, prevention in financial sector and many other digital platforms. It has been widely proven that incorporating vast amounts of textual data, such as news reports, social media posts, and customer feedback, into the analysis, could be beneficial of promoting the performances of AI techniques. In this fast-evolving era of digital economy, combining insightful text information with powerful AI models and other analyzing techniques, leads to better outcomes and efficiency in tasks of identifying risks, forecasting market changes, and optimizing decision-making processes. Towards this end, this workshop aims to explore and investigate both model innovations and applications of text-data-mining in financial sector and digital economy, with a particular focus on risk evaluation, prediction, prevention, and management. By analyzing textual data and extracting useful knowledge from it, critical information can be harnessed to enhance risk management practices, mitigate risks effectively, and support management decision-makings. This workshop covers topics related to all aspects of and in the intersection of text-data-mining, risk management, and digital economy, which includes the following but is not limited to:

Topics of interest include, but are not limited to, the following:
  • Quantitative risk management
  • Text data driven decision making
  • Financial risk measurement, prediction, and management
  • Machine learning and deep learning for text data analysis
  • Forecasting models in risk analysis, prediction, and management
  • Text data mining for digital economy analysis
  • Multi-attribute decision making models for digital economy analysis

Workshop 06: The 2nd Workshop on Operational Research and Artificial Intelligence

Assistant Prof. Ruizhi Zhou, Institute of Operations Research and Information Engineering, Beijing University of Technology, Beijing, China, ( zhourz@bjut.edu.cn)

Assistant Prof. Pei Quan, College of Economics and Management, Beijing University of Technology, Beijing, China, ( quanpei@bjut.edu.cn)

Assistant Prof. Yang Xiao, Faculty of Information Technology, Beijing University of Technology, Beijing, China, ( xiaoyang@bjut.edu.cn)

Associate Prof. Minglong Lei, Faculty of Information Technology, Beijing University of Technology, Beijing, China, ( leiml@bjut.edu.cn)

Prof. Lingfeng Niu, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China, ( niulf@ucas.ac.cn)

New real world applications of artificial intelligence and machine learning have shown that popular methods may appear to be too simple and restrictive. Mining more complex, larger and generally speaking “more difficult” data sets pose new challenges for researchers and ask for novel and more complex approaches. We organize this workshop where we want to promote research and discussion on more complex and advanced methods for the particularly demanding operational research and machine learning problems. Although we welcome submissions concerning methods based on different principles, we also expected to see new research in it about the use of optimization techniques. The new challenges emerging in artificial intelligence are definitely more complex than traditional ones and they could result in more difficult non-convex optimization formulations. We would like to focus interest of artificial intelligence community on various challenging issues which come up while using complex methods to deal with the difficult machine learning problems.

Potential research topics include, but are not limited to the following:
  • Optimization methods for machine learning
  • Combined classifiers for complex learning problems
  • Discussion on using artificial intelligence methods to solve problems in operational research
  • Application of optimization in modeling and algorithm design of artificial intelligence
  • New methods for constructing and evaluating on-line recommendation
  • Mining spatial data and images
  • Complex data representation learning
  • Artificial intelligence driven combinational optimization

Workshop 07: The 14th International Workshop on Computational Methods in Energy Economics

Prof. Lean Yu, School of Business, Sichuan University, China. ( yulean@amss.ac.cn)

Prof. Kaijian He, College of Tourism, Hunan Normal University, China. ( kaijian.he@my.cityu.edu.hk)

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 eleventh 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, Tenth, Eleventh, Twelfth and Thirteenth 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, CMEE2021, CMEE2022 and CMEE2023) were held in Nanjing, Sanya, Huangshan, Kunming, Harbin, rio De Janeiro (Brazil), Asan (Korea), New Dehli (India), Chengdu, Beijing, and Oxford 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, December 8-10, 2017, July 9-11, 2021, December 9-11, 2022, and August 12-14, 2023. To promote the idea-exchange and discussion of this field, the Fourteenth International Workshop on Computational Methods in Energy Economics (CMEE 2024) will be held in Bucharest, Romania. 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)

Workshop 08: The 8th Workshop on Scientific Data Analysis and Decision Making

Prof. Dengsheng Wu, College of Management, Shenzhen University, China. ( wds@casipm.ac.cn)

Prof. Jianping Li, School of Economics and Management, University of Chinese Academy of Sciences, China. ( ljp@ucas.ac.cn)

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. The main objective of this workshop is to provide researchers and practitioners an 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
  • Bibliometrics analysis from scientific data
  • Scientometrics analysis from scientific data

Workshop 09: The 2nd Workshop on Data Essentialization and Financial Innovation

Prof. Jianping Li, University of Chinese Academy of Sciences, China, ( ljp@ucas.ac.cn)

Prof. Rongda Chen, Jiaxing University, China, ( rongdachen@zufe.edu.cn)

Prof. Jin Li, Xi'an Jiaotong University, China, ( jinlimis@xjtu.edu.cn)

Dr. Jun Hao, University of Chinese Academy of Sciences, China. ( haojun@ucas.ac.cn)

With the rapid development of emerging information technology and digital economy, data has become an important element in the national governance system. It is of strategic significance to promote data security governance in a coordinated manner. At the same time, data elements are increasingly becoming an important driving force for high-quality development of the financial industry. Recently, data essentialization and trading, data security and risk assessment, data asset pricing, data governance policy evaluation and financial innovation are among the areas where management science and financial engineering researchers focus on. 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 essentialization and financial innovation. This workshop aims to promote in-depth research on theoretical and practical issues towards data governance and financial innovation, to generate new insights in this relatively under-researched area, and to propose meaningful future research directions. The workshop is interested in topics related to all aspects of data essentialization and financial innovation, which includes, but are not limited to, the following:

The workshop will focus on, but not limited to, the following topics:
  • Data elements and digital finance
  • Data elements and fintech innovation
  • Marketization of data elements and digital transformation of finance
  • Data elements and industrial development
  • Data pricing and accommodation trading mechanism
  • Data security and intelligent monitoring model
  • Data circulation and risk management
  • Intelligent evaluation of data governance policies
  • System complexity of data governance
  • Theories and methods of data element governance
  • Tracking of cutting-edge technologies in data element market

Workshop 10: Business Intelligence

Prof. Chonghui Guo, Institute of Systems Engineering, Dalian University of Technology, Dalian, China. ( dlutguo@dlut.edu.cn)

Associate Prof. Kun Guo, Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, China; Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, China. ( guokun@ucas.ac.cn)

Dr. Yi Qu, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, China. ( quyi17@mails.ucas.ac.cn)

Prof. Yong Shi, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, China, AND, Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, China. ( yshi@ucas.ac.cn)

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

Workshop 11: The 11th Workshop on Intelligent Knowledge Management

Prof. Lingling Zhang, School of Economics and Management, University of Chinese Academy of Sciences, China. ( zhangll@ucas.ac.cn)

Prof. Feng Wang, School of Management and Economics, Kunming University of Science and Technology, China. ( wangfeng@kust.edu.cn)

Prof. Chang Gao, Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, China. ( gaoc@gdut.edu.cn)

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 have 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 ninth International Conference on Information Technology and Quantitative Management (ITQM 2024).

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
  • Wuli-Shili-Renli System Methodology (WSR)
  • Knowledge Synthesis
  • Expert Mining
  • Knowledge Extraction
  • Knowledge Graph
  • Equipment Health Management (EHM)
  • Problem-driven Knowledge Management
  • Knowledge Graph Embedding (KGE)
  • Recommendation Systems
  • Link Prediction
  • Large-scale Engineering Projects Management
  • Data and Knowledge Management Related to Large-scale Research Infrastructures or Scientific Facilities
  • Knowledge Presentation and Visualization
  • Extension
  • Knowledge Sharing
  • Knowledge Spillover
  • Knowledge Evaluation
  • KDD Process and Human Interaction
  • Intelligent Systems and Agents
  • Knowledge Management Support Systems
  • Deep Knowledge Tracing
  • Deep learning
  • Graph Neural Network

Workshop 12: Innovation, Decision-making, and Cultural Heritage Preservation in the Era of Digital Economy and Artificial Intelligence

Associate Prof. Biao Li, Southwestern University of Finance and Economics, China, ( biaoli@swufe.edu.cn)

Assistant Prof. Tie Li, University of Electronic Science and Technology of China, China, ( Lteb2002@uestc.edu.cn)

Prof. Jian Xiong, Southwestern University of Finance and Economics, China, ( xiongjian2017@swufe.edu.cn)

The influence of digital economy and artificial intelligence (AI) is reshaping not only financial and economic landscapes but also the way we preserve and engage with our cultural heritage. This special session invites proposals that explore the transformative potential of these cutting-edge technologies in fostering sustainable decision-making processes and safeguarding our shared cultural legacy.

1. Digitalization for Cultural Heritage Preservation and Access: Models and strategies for digitizing cultural artifacts, monuments, and archives, ensuring their long-term preservation and enhancing global accessibility. Emphasis on the role of AI in automating and optimizing digitization workflows, metadata generation, and content indexing.

2. AI-driven Cultural Analytics and Knowledge Discovery: Applications of machine learning algorithms for pattern recognition, sentiment analysis, and network analysis in cultural datasets.

3. Cultural Heritage Impact Assessment and Sustainability: Development of AI-powered models for evaluating the socio-economic, environmental, and cultural impacts of heritage conservation projects, tourism initiatives, and urban development plans.

4. Smart Tourism and Cultural Experience Design: Utilization of big data, AI, and IoT technologies to create personalized, interactive, and educational tourist experiences. Examination of AI-driven recommendation systems, augmented reality applications, and real-time visitor flow management tools that enhance cultural immersion while minimizing negative impacts on heritage sites.

5. Innovative Financing and Business Models for Cultural Heritage: Analysis of emerging digital finance instruments (e.g., blockchain, digital currencies, crowdfunding) and their potential to mobilize resources, incentivize private sector involvement, and foster community engagement in cultural heritage preservation.

6. Intangible Cultural Heritage Preservation and Revitalization: Deployment of digital platforms and AI technologies to document, transmit, and revitalize endangered languages, oral traditions, performing arts, and traditional knowledge systems.

7. Ethical Considerations and Governance in AI-driven Cultural Heritage Management: Exploration of ethical dilemmas, privacy concerns, and intellectual property issues arising from the use of AI and big data in cultural heritage contexts.

8. Collaborative Networks and Capacity Building for Digital Cultural Heritage: Proposals examining international cooperation, public-private partnerships, and capacity-building initiatives that facilitate knowledge exchange, technological transfer, and resource sharing in the realm of digital cultural heritage.

By delving into these topics, this special session aims to stimulate interdisciplinary dialogue and foster innovative approaches that harness the power of the digital economy and artificial intelligence to advance cultural heritage preservation and sustainable decision-making in an increasingly interconnected world.