Accepted Special Sessions

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

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 context of the hyperconnected world, characterized by extensive digitalization, and the rather unpredictable developments in industries, societies, and natural environment, new business and diffusion-based AI models have been applied to the management of the present-day public and private organizations. Since 2022, Generative AI (Gen AI) has taken the center stage. 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 GenAI class, and biology inspired approaches, have seriously impacted almost all sectors of the economic and social life. The 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 ever more 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 more and more noticeable in modern management and control settings. The multi-modal search, AI agents and video generation make the biggest impact in the industries, healthcare, finance and transportation, smart city, and culture sector.

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:
  • Network 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 GenAI-based ones 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, decision authorized and secure access, and so on
  • Multi-attribute Decision Models and Multi-person Decision Support Systems 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/knowledge-based cities and sustainable villages, healthcare centers, cultural institutions and events, developments toward Society 5.0
  • Gen AI, Interoperable LLs, Custom GPTs and AI agentic flows
  • Multimodal AI, Agentic AI, AI Ethics Policy, New AI Strategies
  • Digital enlightening, hallucination mitigation, and digital humanism in the AI era

Special Session 02: Innovation and Decision-making for Financial and Economic sustainability: Digital and ESG Transformation

Prof. Fuad Aleskerov, Higher School of Economics, Moscow, Russia, fa201204@gmail.com

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

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

Today's economy requires the integration of technology and analytics for effective financial decisions. Big data helps to create models that support sustainable growth and take into account ESG principles. The session will explore innovations for financial sustainability, including decision support, risk assessment and predictive analytics. Particular attention will be given to digital assets and their impact on banks, as well as the development of rating systems that take into account ESG criteria for more accurate analysis of the sustainability of companies. The session will be organized at hybrid format.
We are waiting for proposals on issues such as:
  • Creating models to decision making for financial and economic sustainability
  • Building big data information systems in banking and financial markets
  • Models based on machine learning for financial and risk forecasting
  • Digital assets and currency. Innovations in banking and digitalization
  • Formation of a rating system in business and finance
  • ESG ratings and their modeling
  • Efficiency models for comparing the usefulness of banks and financial companies
  • Evolution of ecosystems: prospects for emerging markets
  • Banking and financial innovations on FinTech platforms
  • Digitalization of the financial sector and banks

Special Session 03: The 12th Intelligent Decision Making and Extenics based Application

Prof. 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

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

Dr. Yiqing Yan, Guangdong University of Technology, China, yanlolo2012@gmail.com

With the rapid development of information technology, knowledge acquisition through data mining becomes one of the most important direction 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
  • Extenics Education and its technology

Special Session 04: Human-Machine Decision-Making with LLMs and GAI

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

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

The rapid evolution of Large Language Models (LLMs) and Generative AI (GAI) has opened new frontiers in human-machine system decision-making and collaborative management across various industries. These technologies are revolutionizing the way complex, dynamic processes in fields such as manufacturing, healthcare, finance, logistics, and more are managed, enabling deeper levels of automation, optimization, and human-AI collaboration. By integrating LLMs and GAI into decision-making environments, organizations can enhance accuracy, optimize resource allocation, and streamline workflows, while maintaining a human-centric approach to system governance. This session will explore the application of LLMs and GAI in diverse settings, emphasizing their impact on decision behavior, management strategies, and the future of collaborative governance across industries.
This session will cover but not limited to the following topics:
  • Generative AI for Real-Time Decision-Making: Exploring the role of GAI in optimizing decision-making in various sectors such as production, healthcare, finance, and logistics.
  • Human-in-the-Loop (HITL) for Complex Decision-Making: Examining the role of HITL models to enhance decision-making in high-stakes environments such as healthcare and operations management.
  • Transparency and Explainability in AI-Driven Decision-Making: Exploring the importance of explainable AI (XAI) in ensuring that decisions made by LLMs and GAI systems are understandable and justifiable to human stakeholders.
  • Case Studies of AI-Driven Decision-Making Across Industries: Analyzing real-world applications and outcomes of LLMs and GAI in sectors like manufacturing, finance, healthcare, and beyond.
  • Other topics related to human-machine systems in decision-making: Exploring emerging trends and innovations in human-machine collaboration, with applications across various industries.

Special Session 05: Innovation, Domain-Specific Applications, and Fine-Tuning in the Era of AI: Advancing LLMs and VLLMs

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

Associate Prof. Tie Li, University of Electronic Science and Technology of China, Chengdu, China, Lteb2002@uestc.edu.cn

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

The rapid evolution of artificial intelligence, particularly in the realm of large language models (LLMs) and vision-language models (VLLMs), is reshaping industries, research paradigms, and technological applications. This special session invites contributions that examine the innovative methodologies, domain-specific adaptations, and fine-tuning strategies that enhance the performance and applicability of AI models in diverse fields.
1. Advancements in Large Language Models (LLMs) and Vision-Language Models (VLLMs): Exploration of state-of-the-art techniques for improving LLM and VLLM capabilities, including model scaling, multi-modal integration, and emergent behaviors.
2. Domain-Specific AI Adaptations: Strategies for fine-tuning and adapting AI models to specialized domains such as healthcare, finance, law, education, and creative industries. Emphasis on transfer learning, prompt engineering, and task-specific optimization.
3. AI-Driven Knowledge Discovery and Representation Learning:Applications of deep learning, graph-based methods, and unsupervised learning in extracting insights from structured and unstructured data across various domains.
4. Human-AI Collaboration and Decision-Making: Investigation of AI's role in augmenting human intelligence, improving decision-making processes, and facilitating collaborative workflows in professional and research environments.
5. Ethical Considerations and Bias Mitigation in AI Models: Examination of fairness, transparency, and accountability in LLMs and VLLMs, with a focus on mitigating biases and ensuring ethical deployment in real-world applications.
6. Computational Efficiency and Optimization in Model Fine-Tuning: Techniques for reducing computational costs, improving model efficiency, and leveraging techniques such as parameter-efficient tuning, distillation, and quantization.
7. Cross-Modal AI Applications and Multimodal Interaction: Development and application of AI models that integrate text, image, speech, and video data for enhanced user experiences and improved contextual understanding.
8. Innovative Business Models and AI-driven Industry Transformation: Analysis of how AI-powered innovations are transforming industries, enabling new business models, and driving economic growth through automation, enhanced analytics, and personalized services.
By addressing these themes, this session aims to foster interdisciplinary discussions and contribute to the advancement of AI research and its practical applications in various domains, ensuring that LLMs and VLLMs are harnessed effectively to drive innovation and societal progress.

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. Alanah Mitchell, Drake University, USA, alanah.mitchell@drake.edu

Digital technologies have been leveraged in education to create engaging, inclusive, and effective learning environments across various contexts, including K-12, higher education, and lifelong learning. The 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. Track submissions may emphasize 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.
Submissions may focus on, but are not limited to, the following topics:
  • 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: AI Frontiers in Business - Consumer and Organizational Perspectives

Prof. Nitin Upadhyay, IT Systems & Analytics, Indian Institute of Management Jammu, India nitin@iimj.ac.in

Artificial Intelligence (AI) is fundamentally reshaping the business landscape, driving unprecedented changes in both consumer behaviour and organizational strategies. From enhancing customer experiences through personalized recommendations to optimizing operational efficiencies with predictive analytics, AI has become a crucial component of modern business. However, while AI offers immense opportunities, it also raises significant concerns, ethical dilemmas, and operational challenges that businesses and consumers must navigate. The need for this special session stems from the rapid evolution of AI technologies and their widespread adoption across industries. Businesses are leveraging AI for automation, decision-making, and data-driven strategies, yet concerns over privacy, algorithmic bias, and workforce displacement persist. Additionally, while AI can enhance personalization and efficiency, there are conflicting views on its ethical implications, such as data security risks, misinformation, and the reinforcement of existing biases in AI-driven decision-making.
On one hand, proponents argue that AI significantly transform business operations by improving efficiency, reducing costs, and enabling better customer engagement. For instance, AI-powered recommendation engines in e-commerce have significantly enhanced customer satisfaction by providing personalized experiences. In the financial sector, AI-driven fraud detection systems have reduced financial losses and increased security. Moreover, AI in healthcare has led to early disease detection and precision medicine, improving patient outcomes. On the other hand, critics highlight AI’s limitations, such as the potential for biased decision-making, job displacement due to automation, and the erosion of human touch in customer service. There are also concerns about data privacy, particularly in industries such as finance and healthcare, where sensitive consumer information is processed. Some experts argue that excessive reliance on AI-driven analytics may lead to over-automation, where critical business decisions lack human intuition and ethical considerations.
Given these conflicting perspectives, this special session invites high-quality research that explores the multifaceted role of AI in business, particularly from both consumer and organizational standpoints across various industries and sectors. We welcome empirical, theoretical, and conceptual papers that examine the opportunities, challenges, and future implications of AI adoption in business. This session aims to foster an interdisciplinary dialogue among researchers, industry practitioners, and policymakers to advance knowledge and practical applications of AI in business environments.
We seek papers that address, but are not limited to, the following themes:
Consumer Perspectives:
  • AI-driven personalization and consumer decision-making
  • Trust, ethics, and consumer acceptance of AI in marketing
  • AI and customer engagement in digital platforms
  • The impact of chatbots, virtual assistants, and generative AI on consumer interactions
  • AI in shaping consumer behaviour and brand loyalty
  • AI-driven consumer behaviour, perspectives, and decision-making in various industries (For instance, Banking and Financial Services, Tourism, Healthcare, Manufacturing, Hospitality, Education, etc.)
Organizational Perspectives:
  • AI-driven business intelligence and decision support systems
  • AI in supply chain and operations management
  • AI and strategic management in enterprises
  • Challenges in AI adoption: ethical considerations, bias, and transparency
  • AI in crisis communication and management
  • AI governance and regulatory frameworks in business settings
  • AI-driven management and decision-making in various industries (For instance, Banking and Financial Services, Tourism, Healthcare, Manufacturing, Hospitality, Education, etc.)

Special Session 08: Decentralized Science and Decentralized Autonomous Organizations: Challenges and Future Trends

Asso. Prof. Seyed Mojtaba Hosseini Bamakan, Masaryk University, Czech Republic, smhosseini@muni.cz

Asso. Prof. Ahad ZareRavasan, Masaryk University, Czech Republic, ahad.zareravasan@econ.muni.cz

Prof. Qiang Qu, Shenzhen Institutes of Advanced Technology, China, qiang@siat.ac.cn

This special issue aims to explore the emerging paradigms, current challenges, and future trajectories of decentralized models in scientific research, funding, and collaboration. Traditional scientific research and organizational structures are undergoing radical transformation through blockchain technologies, smart contracts, and tokenization mechanisms. Decentralized science promises to democratize research funding, increase transparency, facilitate open access to data, and create novel incentive structures for scientific collaboration. Similarly, DAOs offer new governance models that could reshape how scientific communities organize, allocate resources, and make decisions.
This special issue seeks original research, case studies, theoretical frameworks, and critical analyses that examine the potential and limitations of these decentralized approaches in advancing scientific progress and organizational innovation.
Topics of Interest
Submissions addressing the following topics are especially encouraged:
  • DeSci Frameworks and Implementation
    • Novel architectures for decentralized research platforms
    • Tokenization of scientific contributions and intellectual property
    • Blockchain-based peer review systems
    • Open science protocols leveraging distributed ledger technologies
  • DAO Governance in Scientific Contexts
    • Decision-making mechanisms for research fund allocation
    • Stakeholder representation and voting systems in scientific DAOs
    • Balancing decentralization with scientific rigor and accountability
    • Legal and regulatory considerations for science-focused DAOs
  • Economic Models and Incentive Structures
    • Token economics for sustainable scientific research
    • Funding models beyond traditional grants and institutional support
    • Incentivizing data sharing and collaboration across institutions
    • Reputation systems and non-monetary rewards in decentralized science
  • Technical Challenges and Solutions
    • Scalability issues in decentralized scientific infrastructures
    • Privacy and security concerns in open science environments
    • Interoperability between decentralized science platforms
    • Data sovereignty and management in DeSci ecosystems
  • Social and Ethical Implications
    • Inclusivity and accessibility in decentralized scientific communities
    • Power dynamics and potential new centralization risks
    • Cross-cultural aspects of globally distributed scientific DAOs
    • Digital divides and participation barriers in DeSci
  • Case Studies and Applications
    • Evaluations of existing DeSci projects and scientific DAOs
    • Domain-specific applications (e.g., in medicine, climate science, etc.)
    • Comparative analyses with traditional research organizations
    • Failures, successes, and lessons learned from early implementations

Special Session 09: 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.
  • Intelligent decision-making in dynamic environments.