Accepted Special Sessions

Special Session 01: Soft computing methods in quantitative management and decision making processes

Ioan Dzitac, Aurel Vlaicu University of Arad & Agora University of Oradea, Romania, (professor.ioan.dzitac@ieee.org)

Florin Gheorghe Filip,Romanian Academy, Romania, (ffilip@acad.ro)

Misu-Jan Manolescu, Agora University of Oradea, Romania, (mmj@univagora.ro)

Simona Dzitac, University of Oradea, Romania, (simona@dzitac.ro)

In 1965 Lotfi A. Zadeh published "Fuzzy Sets", his pioneering and controversial paper, that now reaches over 101,000 citations. All Zadeh’s papers were cited over 195,000 times. Starting from the ideas presented in that paper, Zadeh founded later the Fuzzy Logic theory, that proved to have useful applications, from consumer to industrial intelligent products. We are presenting general aspects of Zadeh’s contributions to the development of Soft Computing(SC) and Artificial Intelligence(AI).
In accordance with Zadeh’s definition, Soft Computing (SC) consist of computational techniques in computer science, machine learning and some engineering disciplines, which study, model, and analyze very complex reality: those for which more traditional methods have unusable or inefficiently.
SC uses soft techniques, contrasting it with classical artificial intelligence, Hard Computing (HC) techniques), and includes: Fuzzy Logic, Neural Computing, Evolutionary Computation, Machine Learning, and Probabilistic Reasoning.
HC is bound by a Computer Science (CS) concept called NP-Complete, which means that there is a direct connection between the size of a problem and the amount of resources needed to solve that called "grand challenge problem". SC aids to surmount NP-complete problems by using inexact methods to give useful but inexact answers to intractable problems.
SC became a formal CS area of study in the early 1990’s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to HC. It should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive.
SC techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as Boolean logic, or rely heavily on computer-aided numerical analysis (as in finite element analysis).
SC techniques are intended to complement HC techniques. Unlike HC schemes, which strive for exactness and full truth, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. The inductive reasoning plays a larger role in SC than in HC. SC and HC can be used together in certain fusion techniques.
Soft Computing can deal with ambiguous or noisy data and is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for SC is the human mind.
Artificial Intelligence and Computational Intelligence based on SC provide 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 algorithms and procedures in quantitative management and decision making, in order to exchange ideas on problems, solutions, and to work together in a friendly environment.
Decision making in fuzzy environments In many real-world situations, the problems of decision making are subjected to some constraints, objectives and consequences that are not accurately known. After Bellman and Zadeh introduced for the first time (1970) fuzzy sets within multiple-criteria decision-making (MCDM), many researchers have been preoccupied by decision making in fuzzy environments.
The fusion between MCDM and fuzzy set theory has led to a new decision theory, known today as fuzzy multi-criteria decision making (FMCDM), where we have decision-maker models that can deal with incomplete and uncertain knowledge and information. The most important thing is that, when we want to assess, judge or decide we usually use a natural language in which the words do not have a clear, definite meaning. As a result, we need fuzzy numbers to express linguistic variables, to describe the subjective judgement of a decision maker in a quantitative manner. Fuzzy numbers (FN) most often used are triangular FN, trapezoidal FN and Gaussian FN.
We highlight that the concept of linguistic variable introduced by Lotfi A. Zadeh in 1975 allows computation with words instead of numbers and thus linguistic terms defined by fuzzy sets are intensely used in problems of decision theory for modelling uncertain information.
Topics of interest include, but are not limited to, the following:
  • Ant colony optimization algorithms;
  • Artificial intelligence methods for web mining;
  • Bayesian networks and decision graphs; Computational intelligence methods for data mining;
  • Decision support systems for quantitative management;
  • Decision making with missing and/or uncertain data;
  • Fuzzy multi-criteria decision making;
  • 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;
  • Smarter decisions;
  • Support Vector Machine in SC applications.
Bibliography Ioan Dzitac, Florin Gheorghe Filip, Misu-Jan Manolescu, Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh, International Journal of Computers Communications & Control, ISSN 1841-9836, 12(6), 748-789, December 2017. DOI: https://doi.org/10.15837/ijccc.2017.6.3111

Special Session 02: The 6th Intelligent Decision Making and Extenics based Innovation

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

Chunyan Yang, Guangdong University of Technology, China, (fly_swallow@126.com)

Yanwei Zhao, Zhejiang University of Technology, China, (zyw@zjut.edu.cn)

Ping Yuan, NIT, Zhejiang University, China, (yuanping1212@163.com)

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
  • Soft power and soft technology with Extenics
  • Extenics based Big data technology and applications

Special Session 03: Digital Marketing

Vandana Ahuja , Jaypee Business School, India. (vandana.ahuja@jiit.ac.in)

Technological advances and the speed with which new technologies are being embraced by corporates, along with the rising power of the consumers and their ability to get what they want, when they want it, from whomever they want, have opened up new challenges for marketing. With this in mind, the need for understanding the digital world and its application becomes one of the greatest competitive aspects for a business’s survival. The buzzword of globalization holds no meaning without the concept of what is being termed as ‘Digitization’.

This special session on Digital Marketing integrates concepts form the virtual world and analyses how the field of Marketing can benefit from these developments.

The contents of the special session will be as follows:
  • Digital Marketing in a Digital Ecosystem;
  • The Online Marketing Mix;
  • Online Branding;
  • Building Online Traffic;
  • Engagement Marketing through Content Management;
  • The web and the consumer decision making process;
  • Using Online Communities for Marketing;
  • Consumer Generated Media(CGM);
  • Mining CGM;
  • Digital marketing Case Studies;

Dr. Vandana Ahuja has 19 years of experience across the corporate sector and academia. She is the author of Digital Marketing - A book published by Oxford University Press. She has been actively researching the domain of the collaborative web, with focus on its contributions to the fields of Marketing and CRM and has several years of research experience. She has published several manuscripts in International and National Journals. Her research work has found place in the curriculum being offered by the Digital Marketing Institute, Middlesex, UK. She also serves on the Editorial Board of several International Journals. At Jaypee Business School, she is the Area-Chair, Marketing and teaches Sales and Distribution Management, Social Media and E- Marketing, and B2B Marketing. She can be contacted at vandyahuja@yahoo.com, vandana.ahuja@jiit.ac.in.

Special Session 04: Social Networks and Collaborative Decision Making

The Sixth International Conference of Information Technology and Quantitative Management

Enrique Herrera-Viedma, University Of Granada, Spain (viedma@decsai.ugr.es)

Francisco Chiclana, De Montfort University, United Kingdom, (chiclana@dmu.ac.uk)

Yucheng Dong, Sichuan University, China, (ycdong@scu.edu.cn)

Raquel Ureña, De Montfort University, United Kingdom, (rurena@dmu.ac.uk)

Scope and Motivation:
Nowadays we are living the apogee of the Internet based technologies and consequently web 2.0 communities, where a large number of users interact in real time and share opinions and knowledge, is a generalized phenomenon. This type of social networks communities constitute an excellent oportinity from the point of view of collaborative decision making since they involve a huge population in the so called e-collaborative and e-democrazy processes. Therefore, they present an unstimable scenario for goverments and policy makers to leverage the wisdown of the crowds in the decision making processes.

However this scenario also pose several research challenges as it involves a large number of experts coming from different backgrounds and/or with different level of knowledge and influence, and so problems such us scalability, trust definition and propagation, influence assesment, information modeling, among others arise.

The aim of this special session is to bring together the modern Information and communication technologies(ICT), in particular, business intelligence and analytics (BI&A), social networks, big data and mobile cloud computing and what are they role in the development of effective e-collaboration frameworks. Contributions on second generation of ecollaboration activities and systems are expected to be proposed. Theoretical issues and applications on various domains, ideas on how to solve collaborative decision making processes in social networks frameworks, both in research and development and industrial applications, are welcome. 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:
  • Influence assessment in social networks
  • Preference modelling in collaborative decision making in social networks
  • Trust development and propagation in social networks
  • Trust Definition in collaborative decision making
  • Scalability of decision making processes in social netwoks.
  • Information presentation in social network
  • E-democracy
  • E-collaborative platforms
  • Social network based Recommender systems
  • E-health applications
  • E-marketing applications

Accepted Workshops

Workshop 01: The 6th Workshop on Optimization-based Data Mining

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

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

Yong Shi, College of Information Science and Technology, University of Nebraska at Omaha, USA., (yshi@unomaha.edu)

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.

Workshop 02: The 9th Workshop on Risk Correlation Analysis and Risk Measurement (RCARM2018)

Jianping Li, Institutes of Science and Development, Chinese Academy of Sciences, China, (ljp@casipm.ac.cn)

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

Rongda Chen, Zhejiang University of Finance & Economics ,China, (rongdachen@163.com)

Xiaodong Lin, Rutgers University, USA, (lin@business.rutgers.edu)

The analysis of inter-risk correlation and risk aggregation is an important factor to risk measurement, such as the interaction of market risk, credit risk and operational risk. Correlation analysis and risk measurement can be viewed as a Multiple Criteria Decision Making problem in a certain extent, which is the trade-off among different aspects, such as the “project triangle”(cost, quality and schedule).Some mathematical models such as Copula models are used for measuring risk correlation, but risk management must extend far beyond the use of standard measurement in practical operations and applications. An important aspect is to emphasize on the correlation analysis of risks and thus effectively measure all kinds of financial risks. In order to promote the development of risk correlation and measurement, we organize a special workshop dedicated to the topic of “risk correlation analysis and risk measurement” under the Sixth International Conference on Information Technology and Quantitative Management (ITQM 2018) (http://www.itqm-meeting.org/2018/). The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of risk correlation and measurement, to assess the state of knowledge of risk correlation and measurement, to generate new results in this relatively under-researched area, and determine directions for further research, Papers should present modeling approaches/perspectives to risk correlation and measurement. The workshop is interested in topics related to all aspects of risk correlation and measurement. Topics of interest include, but are not limited to, the following:
  • Foundation of risk correlation and dependency
  • Correlation analysis of financial risks
  • Correlation analysis of software risks
  • Correlation analysis of project risks
  • Risk correlation modeling
  • Risk analysis by multiple criteria
  • Risk integrated management and risk correlation
  • Credit scoring, Credit rating
  • Portfolio management
  • New techniques to risk measurement
Original papers are invited from prospective authors with interest on the related areas. Submitted papers must not substantially overlap papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Papers should be at most 8 pages including the bibliography and well-marked appendices. Papers must be received by the submission deadline. We invite you to submit your paper to: EasyChair Login Page for ITQM 2018,RCARM2018 Workshop. Authors of accepted papers must guarantee that their papers will be presented at the conference. Accepted papers will be published in the conference proceedings by Elsevier in their new Procedia Computer Science series. Selected best papers will be published in special issues of high quality journals (Int. J. of Information Technology and Decision Making, Annals of Data Science, and others that are currently under negotiation).
Important Dates: Deadline for paper submission ------ July 1, 2018
Deadline for camera-ready manuscript submission ------ August 1, 2018

Workshop 03: The 3th workshop on Outlier Detection in Financial Data Streams &Big Data and Management Science

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

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

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 fiancé;
  • Method, model and application in big data and management science;

Workshop 04: The 4th Workshop on Scientific Data Analysis and Decision Making

Associate prof. Dengsheng Wu, Institutes of Science and Development, Chinese Academy of Sciences, China (wds@casipm.ac.cn)

Associate prof. Yuanping Chen, Computer Network Information Center, Chinese Academy of Sciences, China (ypchen@cashq.ac.cn)

Prof. Wenbin Jiao, Computer Network Information Center, Chinese Academy of Sciences, China (wbjiao@cashq.ac.cn )

Prof. Jianping Li, Institutes of Science and Development, Chinese Academy of Sciences, China (ljp@casipm.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. The workshop aims to create a communication platform for researchers to share the 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 05: Workshop on Machine Learning and Intelligent Awareness

Xiaofei Zhou, IIE, Chinese Academy of Sciences, China (zhouxiaofei@iie.ac.cn)

Jianlong Tan, IIE, Chinese Academy of Sciences, China (tanjianlong@iie.ac.cn)

Jia Wu, Macquarie University, Australia (jia.wu@mq.edu.au)

With the arrival of the era of big data, machine learning methods are being more and more important in the fields of artificial intelligence and related applications, such as natural language, multimedia, social networks, etc. Intelligent sensing and analysis based on big data is the core task of artificial intelligence in the future. This workshop aims to provide an international forum to discuss data intelligent mining methods, technologies and systems for computational science, artificial intelligence, knowledge engineering and management decision discipline fields. The topics covered will include, but not limited to:
  • Machine Learning and data mining technique: classification, clustering, regression
  • etc.
  • Neural network and deep learning
  • Knowledge extraction and expression
  • Text analysis and nature language process
  • Visualization technologies and analytics
  • Multimedia and Multi-structured Data Analysis
  • Intelligent knowledge in management decision
  • Biometric identification
  • Recommendation Systems
  • Knowledge graph
  • Link and Graph Mining
  • Graph data and networks
  • Social agents for intelligent awareness
  • Algorithms and Systems for Social Media Analysis
  • Mobility and Social Network Data
  • Information filter
  • Object recognition
  • Distributed and Parallel Algorithms
  • Big Data Search Architectures, Scalability and Efficiency
  • Social Data Acquisition, Integration, Cleaning, and Best Practices
  • Cloud/Grid/Stream Data Mining