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, (raquel.urena@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


Special Session 05: High Performance Data Analysis

Vassil Alexandrov, ICREA Research Professor in Computational Science at Barcelona Supercomputing Centre, Spain, (vassil.alexandrov@bsc.es)

Ying Liu, Professor, University of Chinese Academy of Sciences, China, (yingliu@ucas.ac.cn)

Big data is active research area in the past few years. There is a clear need to analyze huge amounts of unstructured and structured complex data, historic data as well as data coming from real time feeds (e.g. Business data, meteorological ones from sensors, remote sensing data, etc.). It is beyond the capability of traditional data processing techniques and tools. The challenges include data capture, storage, search, sharing, transfer, analysis, and visualization. In order to meet the requirement of big data analysis, computational science and high performance computing methods and algorithms are in real demand to solve the above challenges, including scalable mathematical methods and algorithms, parallel and distributed computing, cloud computing, etc. This workshop will focus on the issues of high performance data analysis. Theoretical advances, mathematical methods, algorithms and systems, as well as diverse application areas will be in the focus of the special session.

This year the session aims at organizing a special theme session exploring emerging trends in high performance data analysis. We welcome papers on all aspects of high performance data analysis, including, but not limited to:

  • Data processing exploiting hybrid architectures and accelerators (multi/many-core, CUDA-enabled GPUs, FPGAs)
  • Data processing exploiting dedicated HPC machines and clusters
  • Data processing exploiting cloud
  • Deep Learning
  • High performance data-stream mining and management
  • Efficient, scalable, parallel/distributed data mining methods and algorithms for diverse applications
  • Advanced methods and algorithms for big data visualization
  • Parallel and distributed KDD frameworks and systems
  • Theoretical foundations and mathematical methods for mining data streams in parallel/distributed environments
  • Applications of parallel and distributed data mining in diverse application areas such as business, science, engineering, medicine, and other disciplines

Program Committee
  • Di Zhao, Computer Network and Information Center, Chinese Academy of Sciences, China
  • Xiaofei Zhou, Institute of Information Engineering, Chinese Academy of Sciences, China
  • Steve Chiu, Idaho State University
  • Jayaprakash Pisharath, Intel Cooperation
  • Yang Gao, SenseTime Co.
  • Jun Xu, Institute of Computer Techniques, Chinese Academy of Sciences
  • Ying Liu, University of Chinese Academy of Sciences, China
  • Vassil Alexandrov, ICREA-BSC, Spain
  • Svetlana Chuprina, Perm University, Russia
  • BingQiang Wang, Sun Yat-sen University


Bios:
Vassil Alexandrov is an ICREA Research Professor in Computational Science at BSC (Barcelona Supercomputing Center) since September 2010. He holds an MSc in Applied Mathematics from Moscow State University, Russia (1984) and a PhD in Parallel Computing from Bulgarian Academy of Sciences (1995). He is also a Distinguished Visiting Professor in Data and Computational Science at Monterrey Tech (ITESM), Mexico since January 2015. He is a member of the Editorial Board of the Journal of Computational Science and Guest Editor of Mathematics and Computers in Simulation. He has published over 120 papers in renowned refereed journals and international conferences and workshops in the area of his research expertise. His research interests are in the area of Computational Science encompassing Parallel and High Performance Computing, Scalable Algorithms for advanced Computer Architectures, Monte Carlo methods and algorithms.

Ying Liu received her B.S. degree from Peking University, China, in 1999, the M.S. degree and the Ph.D. degree from Northwestern University, Evanston, IL, USA, in computer engineering in 2001 and 2005, respectively. She is currently a professor in School of Computer and Control Engineering, University of Chinese Academy of sciences. She also holds an adjunct appointment with the Key Lab of Big Data Mining and Knowledge Management of Chinese Academy of Sciences. Her research interests include data mining, high-performance computing, deep learning, etc. She is a member of the Editorial Board of the Data Science Journal and Guest Editor of Annuals of Data Science. She has published 70 papers in renowned refereed journals and international conferences. She served as the workshop chairman for the workshop on High Performance Data Analysis on ITQM 2014-2017 and served as the workshop chair for workshop on High Performance Data Mining with 7th International Conference on Data Mining (ICDM), 2007, and workshop on High Performance Data Mining with 7th International Conference on Computational Science ( ICCS), 2007.

Special Session 06: Cloud, Big Data and Analytics for a Successful Organization

Nitin Upadhyay, Chair and Head Centre for Innovation, Big Data & Analytics, Information Technology Goa Institute of Management Goa, India (upadhyay.nitin@gmail.com or nitin@gim.ac.in)

Cloud, Big Data and Analytics impeccably contribute to the success of organization on a modern, client/audience-driven marketplace, and are perceived as a very interesting research area from theoretical and practical perspectives. The conference session entitled “Cloud, Big Data and Analytics for a Successful Organization” is expected to exchange ideas and thoughts about impacts of Cloud, Big Data and Analytics research on the state of the art as well as upcoming trends of issues related to research and applications of these solutions for an organization that successfully faces modern market, organizational and societal challenges in a creative, innovative way. It provides a platform for the participants to present and discuss the most recent, innovative and significant findings and experiences in the field of Cloud, Big Data and Analytics research and practice.
Topics of the session include, but is not limited to, the following:
  • Data Driven Decision Making;
  • Competition and Intelligence, Competing on Analytics;
  • Data Driven Marketing and Decision Making;
  • Creativity and Innovativeness based on Big Data;
  • Managing Analytical People;
  • Building an Analytical Capability;
  • Cloud and Big Data Applications (Marketing, Logistics, Finance, Banking, Insurance, HR, Government, People, Culture, Communication, Leadership, Performance);
  • Temporal Big Data;
  • Cloud-Based Business Intelligence;
  • Models, methods and tools for Big Data and Analytics;
  • Data mining, Text mining, Opinion Mining;
  • Cloud and Big Data Systems’ Architectures;
  • Cloud Service Management and Decision Making;
  • Algorithms for Big Data Analysis/Processing;
  • Big Data Visualization.

Session Chair:
Dr. Nitin Upadhyay is a researcher, inventor, innovator, consultant, leader, coach, academician and a prolific writer. Over the years he has engaged with top Fortune 500 companies. He is a leading authority and speaker on innovation, design, cloud computing, big data & analytics, future technology and user experience. He is currently the Chair and Head of the Centre for Innovation at Goa Institute of Management. He is also working in the area of information technology and is a core member of the Big Data Analytic programme, Goa Institute of Management, India. He has wide industry, academic, consultancy and research experience and is an Executive member and Chair of Cloud SLAs (service-level agreements) for the Cloud Computing Innovation Council of India. As Startup coach, he provide innovative insights, roadmap and action plans for successfully materializing innovative process and new products.

He has contributed to numerous peer-reviewed publications/presentations/posters and talks and nine books. He is associated with various journals and societies of repute as Deputy Editor-in-Chief/Editor/Editorial Board ember/Reviewer/Member. He has received many awards and recognition nationally and internationally. He is listed in the Who’s Who in the World, Who’s Who in the Asia, Top Innovators of the World (IBC Cambridge), and received multiple awards such as - Research Shepherd, Star Researcher and Innovator, Outstanding Scientist, Lifetime achievement award USA, Best Research (Intl. Conf. South Korea).

Special Session 07: Data Acquisition Architecture and Management for Traceability Analytics (DAAMTA)

Jing He, Swinburne University of Technology, Australia and Nanjing University of Finance and Economics, (lotusjing@gmail.com)

Zhiwang Zhang, Ludong University, (zzwmis@163.com)

Bo Mao, Nanjing University of Finance and Economics, China, (bo.mao@njue.edu.cn)

Hai Liu, School of Computer, South China Normal University, (liuhai@scnu.edu.cn)

Guangyan Huang, Deakin University, (guangyan.huang@deakin.edu.au)

Yimu Ji, Nanjing University of Posts and Telecommunications, (jiym@njupt.edu.cn)

1.Overview
In the era of wireless technology, robotics, web service, there are many computing technologies being introduced. With the recent development and progress of IoT (Internet of Things), it is possible to get information about how a system is operating and its real-time status in details. For example, RFID can track the distribution of goods, different sensors can monitor the environment, and GPS can send the location and time back. Based on the information, we could have a log for the monitored system and implement the traceability analysis. Traceability is the ability to verify the history, location, or application of an item. It is especially critical for some industries such as food processing, logistics, supply chain and e-business. The two key technologies for the traceability analysis are data acquisition and management. In the age of cloud computing, they are two promising fields. Although there are several solutions already in place, many challenges remain to be investigated and tackled.

The purpose of this special session is to not only discuss the existing topics in data acquisition architecture such as single chip and hot chips and management for traceability analysis, but also focus on the new rapidly growing area from the integration of big data analytics and traceability analysis for significant mutual promotion. We intend to discuss the recent and significant developments in the general area and to promote cross-fertilization of techniques. The participants in this special session will benefit as they will learn the latest research results of high performance chips, data acquisition architecture and management of IoT and big data analytics based traceability system, as well as the novel idea of merging them.

2.History of this workshop
We have successfully organized one workshop at the 2nd ITQM conference at Moscow. Seven authors have shown up and given the presentation at Higher School of Economics. In 2015, the special session on traceability analysis was hold at the 3rd ITQM conference at Brazil where 5 papers were presented. In 2016, we continued the special session in Korea and 6 papers were accepted and four authors represented their work. In 2017, our special session attracted four papers at India. This will be the 5th DAAMTA special session with ITQM.

3.Goal
The special session is interdisciplinary and provides a platform for researchers, industry practitioners and students from engineering, sociology, computer science, information systems share, exchange, learn, and develop new research results, concepts, ideas, principles, and methodologies, aiming to bridge the gaps between paradigms, encourage interdisciplinary collaborations, advance and deepen our understanding of IoT, big data analytics, traceability and the related data management method.

There are two major topics of interest for this workshop: (1) Traceability data acquisition, (2) Data management and mining for the generated IoT data. Comprehensive tutorials and surveys are also expected. The general topics include, but are not limited to

  • Traceability Data Management
    • Visualization of IoT based Traceability system
    • Intelligent Data Fusion and Aggregation
    • Storage Management Technologies
    • Deep Learning
    • Big (Sensor) Data
    • Pattern Discovery
    • Multiple Representation Structure
    • Spatiotemporal Data Management and Analysis
  • IoT based Traceability Data Acquisition and Architecture
    • Chips
    • Operating System
    • Computer System Architecture
    • RFID Related Technologies;
    • Wireless Sensor Network
    • Online Quality Estimation;
    • Data Acquisition based on Smart Phones
    • User Analysis based on Social Network
    • Communication system

More specially, details about recommended topics include, but are not limited to, the following:
  • Advanced Cloud Computing Solutions for Traceability Systems
  • Agent-based approaches to Cloud Services for Traceability Systems
  • Self-Organizing Agents for Service Composition and Orchestration in Traceability Systems
  • Self-service cloud and self-optimization in Traceability Systems
  • Trust in Cloud computing for Traceability System
  • Trace-ability Systems related Workflow Design and Optimization
  • Emerging Areas of Trace-ability Applications in the frontier of web and cloud computing
  • Advanced Cloud Computing Solutions for Traceability Systems
  • Agent-based approaches to Cloud Services for Traceability Systems
  • Self-Organizing Agents for Service Composition and Orchestration in Traceability Systems
  • Self-service cloud and self-optimization in Traceability Systems
  • Cloud resource allocation approaches
  • Privacy Preserving in Cloud Computing for Traceability Systems
  • Trust in Cloud computing for Traceability Systems
  • Traceability Systems related Workflow Design and Optimization
  • Advanced IT Solutions for Traceability Systems
  • Agent-based approaches to ICT Services for Traceability Systems
  • Self-Organizing Agents for Service Composition and Orchestration in Traceability Systems
  • Self-service cloud and self-optimization in Traceability Systems
  • Information resource allocation approaches
  • Privacy Preserving for Traceability Systems
  • Trust in Cloud Computing for Traceability Systems
  • Trace-ability Systems related Workflow Design and Optimization
  • Emerging Areas of Traceability Applications in the frontier of web and cloud computing
  • Hot chips
  • Virtual Reality
  • Optical Equipment
  • Single chip
  • Communication system


4.Special issues
The selected paper will be recommended to International Journal of Information Technology & Decision Making (SCI).

5.Important dates
Research Paper Abstract Submission: 15th July. 2018
Research Paper Submission: 20th July. 2018
Paper Notification of Acceptance: 1st September 2018

6.Special track chairs Professor Jing He, Swinburne University of Technology, Australia and Nanjing University of Finance and Economics, lotusjing@gmail.com
A/P Zhiwang Zhang, Ludong University, zzwmis@163.com
A/P Bo Mao, Nanjing University of Finance and Economics, China, bo.mao@njue.edu.cn
A/P Hai Liu, School of Computer, South China Normal University liuhai@scnu.edu.cn
Dr. Guangyan Huang, Deakin University, Guangyan Huang guangyan.huang@deakin.edu.au
Dr. Yimu Ji, Nanjing University of Posts and Telecommunications, jiym@njupt.edu.cn

7.Short Bio for co-chairs
Dr. Jing He, Swinburne University of Technology, Australia
Dr. Jing He is currently a Professor in the department of software and electronical engineering, Swinburne University of Technology. She used to work as a professor at College of Engineering and Science, Victoria University from 2008 to 2018. She has been awarded a PhD degree from Academy of Mathematics and System Science, Chinese Academy of Sciences in 2006. Prior to joining to Victoria University, she worked in University of Chinese Academy of Sciences, China during 2006-2008. She has been active in areas of Data Mining, Web service/Web search, Spatial and Temporal Database, Multiple Criteria Decision Making, Intelligent System, Scientific Workflow and some industry field such as E-Health, Petroleum Exploration and Development, Water recourse Management and e-Research. She has published over 40 research papers in refereed international journals and conference proceedings including ACM transaction on Internet Technology (TOIT), IEEE Transaction on Knowledge and Data Engineering (TKDE), Information System, The Computer Journal, Computers and Mathematics with Applications, Concurrency and Computation: Practice and Experience, International Journal of Information Technology & Decision Making, Applied Soft Computing, and Water Resource Management. She received research fund from ARC early career researcher award (DECRA), ARC discovery, ARC Linkage, National Science Foundation of China, Youth Science Fund of Chinese Academy of Sciences, Grant-in aid for Scientific Research of Japan. She has filed nine U.S. patents and served on three program committees of international conferences: International Conference on Computational Science (ICCS), The IEEE International Conference on Data Mining (ICDM), and International Symposium on Knowledge and Systems Science (KSS), as well as the workshop co-chair on APWeb 2008, WI 2009, MCDM 2009. In addition, she has been serving as external reviewers for several international journals and conferences, such as Information Sciences, The Computer Journal, IEEE Transaction on Systems, Man, Cybernetics, International Journal of Information Technology and Decision Making, Journal of Management Review (in Chinese), Decision Support System, Science (in China), ICDE, ICCS, ICDM, KSS, WISE, HIS, APWeb etc.
A/P. Zhiwang Zhang, School of Information and Electrical Engineering, Ludong University
Dr. Zhang received the PhD degree in computer science from Chinese Academy of Sciences in 2009. He is currently a researcher and associate professor with the Department of Computer Science at Ludong University, China. His research interests are in the areas of data science, big data analytic and knowledge discovery, forecasting, machine learning, optimization, artificial intelligence and natural language processing. He has published over 30 academic papers in various international journals and conferences, including Neurocomputing, Soft Computing, Computer Speech and Language, Knowledge-Based Systems, European Journal of Operational Research, Applied Soft Computing, and Expert Systems with Applications, and so on.
Dr Bo Mao, Nanjing University of Finance and Economics
Dr. Bo Mao is currently an Associate Professor Nanjing University of Finance and Economics, China. He has been awarded a PhD degree from Royal Institute of Technology-KTH, Sweden in 2012. He has been active in areas of 3D City model generalization, Online Visualization, Data Mining, Spatial and Temporal analysis, and some industry field such as Food traceability system and e-business. He has published over 30 research papers in refereed international journals and conference proceedings including ISPRS Journal of Photogrammetry and Remote Sensing (ISPRS J), Computers, Environment and Urban Systems (CEUS), Science China Earth Sciences, World Wide Web Journal (WWWJ), International Conference on Geographic Information Science (GIScience), ACM conference on Recommender systems (RecSys). He received research fund from National Science Foundation of China and Jiangsu Doctor Convergence Program. He served on program committees of International conference on Advanced Data Mining and Applications (ADMA). In addition, He has been serving as external reviewers for several international journals and conferences, such as ISPRS J, CEUS, IJGIS, ADMA etc.
A/P. Hai Liu, School of Computer, South China Normal University
Dr. Hai Liu is now a researcher at south china normal university. His research interests include Machine learning, Data mining, Ontology Engineer (Description Logic), Classification Clustering, Matrix Factorization, Topic modeling, and Recommender Systems. Education M.S.2001.Computer Science and Engineering, South China Normal University. Ph.D.2010.Computer Science and Engineering, SUN YAT-SEN University Research Field: Description Logic, Data Mining (Machine Learning), Personized Recommendation
Dr. Guangyan Huang, Deakin University
Dr. Guangyan Huang currently is a senior lecturer in the School of Information Technology, Deakin University. She is also an ARC Discovery Early Career Researcher Award (DECRA) fellow. She was awarded a PhD degree in Computer Science from Victoria University (VU) in 2012. Before she joined Deakin, she was a senior lecturer (2014) and research fellow (2012-2013) in VU. She has 80 publications mainly in data mining (e.g., sensor stream data mining, text mining, spatial temporal data mining, sequence mining, pattern recognition and image analysis), wireless sensor networks (e.g., routing, sensor network operation system, multimedia sensor networks and sensor data quality), Web services/Internet Technology (e.g., Web search, Internet of things, cloud computing, parallel algorithms for big data) and software testing/reliability. She also has 5 years' research experiences in Chinese Academy of Sciences. She was a research assistant from 2003 to 2007 in the Institute of Computing Technology, Chinese Academy of Sciences. She worked as an assistant professor in the Institute of Software, Chinese Academy of Sciences from 2007 to 2009. Between June 2006 and Dec. 2006, she was a visiting researcher at Platforms and Devices Centre in Microsoft Research Asia. She is active in academic community. She was Assistant Editor for Health Information Science Journal (Springer) from 2012 to 2014 and helped launch the first issue in Dec. 2012. She had served as Publication Chair of WISE'2013, WISE'2012, HIS'2013 and HIS'2012, workshop Co-Chair for 4 workshops joint with WISE'2013, APWeb'2012, APWeb'2008 and MCDM'2009. She achieved APWeb'2012 Best Local Arrangement Chair Award and HIS'2012 Best Workshop Chair Award.

Special Session 08: Neuromanagement and Neuromarketing

Felisa M. Córdova, Faculty of Engineering, University Finis Terrae, Chile (fcordova@uft.cl)

Rogers Atero, Faculty of Engineering, University Finis Terrae, Chile (rogers.atero@uft.cl)

Objectives and Motivation
Human decision making can depend on many factors, some of them are the result of our present life history, and others depend on our changing dynamic preferences. Efforts are made to apply engineer to neuro cognitive processes, assuming that there are prior knowledge and experience about the physiological and neurological elements and/or components of the systems which we aim to highlight. Since the discovery of the neurobiological basis that rule the process of learning and memory, and the explosive development of neuroscience through what has been called the era of the brain, the amount of knowledge that is being accumulated and comprehended progressively, makes unavoidable not to consider brain functioning and knowledge about how it works of great and useful value. On the other hand, the vertiginous development of technology involving brain stimulation and scanning, functional brain imaging and image analysis have collaborated in the development of this new discipline.

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

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

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

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

  • Theoretical and practical solutions to capture human physiological activity during decision-making processes and purchase decisions.
  • Systems and information technology applications in decision-making processes.
  • Software and systems based on neuromanagement and neuromarketing.
  • Qualitative and quantitative methods used in new applications.
  • Process of learning about customer purchasing preferences.
  • Simulation process involved in decision making.
  • Trends in neuromanagement and neuromarketing.


Special Session 09: Researches and Cases in Artificial Intelligent Systems Application

Sungbum Park, Hoseo University, Korea, (parksb@hoseo.edu)

Jongwon Lee, Hoseo University, Korea, (jweel@hoseo.edu)

Hee-Woon Cheong, Faculty of Engineering, University Finis Terrae, Chile (cheong1221@gmail.com)

This session focuses on exchanging information relating to various artificial intelligent systems applied in industry, government, and universities worldwide. It also deals with the application of the artificial intelligent systems, and also to provide practical guidelines in the development and management of these systems. Thus, the objective of the proposed session is to highlight the ongoing research on the artificial intelligent systems’ application in the areas of, but not limited to: finance, accounting, engineering, marketing, auditing, law, procurement and contracting, project management, risk assessment, information management, information retrieval, crisis management, stock trading, strategic management, network management, telecommunications, space education, intelligent front ends, intelligent database management systems, medicine, chemistry, human resources management, human capital, business, production management, archaeology, economics, energy, and defense. Papers specialized in MLP systems, information retrieval, neural networks, autonomous driving, and related algorithms will also be encouraged.

Special Session 10: Strategic Planning of ZET-projects

Milan Zeleny, Thomas Bata University in Zlin, Czech Republic and Fordham University, New York, USA , (mzeleny@fordham.edu)

Prof. Maila Zeleny is a Global Professor, Economist, Scientist, Bata-system promoter, Business and Government consultant, ZET-Foundation and Human Systems Management Founder. This special session is about the business and startups projects offered by the ZET-Foundation, Czech Republic. Prof. Milan Zeleny will intrduce a seris of the Zet projects to Omaha local bisnessmen and startups so that they can partcipante such a meaningful international projects. The seesion also welcome the ITQM particiapnts submit the related papers for exchange and discussion. These ZET projects include, but not limited to:

1) ZET-Town Network., based on the experience of Bata industrial cities; we coordinate the construction of complex, integrated production entities, often only partially completed, for export to economically challenged areas, as close as possible to the final customer, renewing autonomous communities – and thus alleviating forced migrations, external and internal – while using the newest digital, modular, material and robotic technologies. At the same time we aim at the electro-solar complex of distributed autonomous energy sourcing.
2) ZET-Tech-Share centers, support establishment of shared-technology centers, for the purpose of training and educating according to current enterprise needs in usage and improvement of modern technology. Our goal is the improvement and flourishing of local and regional autonomous economies and communities. Consortium of companies thus affords a study and use of even the latest and most expensive economies - to increase cooperation and competitiveness of companies and institutions in the ZET network.
3) ZET-cubator Startup develops education and training of the entrepreneurial talent through entrepreneurial universities and innovation centers. Both innovation “cubators” and startups, also with foreign participation, not only renews and strengthens the legacy of Czech Bata-system, but also raises it to the levels of current and modern problems, context, technology and knowledge.
4) ZET-camps represent opportunities for the young, often disadvantaged or handicapped youth, directed towards awakening of talent, encouraging the zest for life, and establishing the goal-directed, ethical and creative habits and skills during the earliest times of forming the character, abilities and goals of younger generations. We emphasize entrepreneurship, cooperation, English and searching for one´s own road to success. We are preparing an integration of corporate camps within ZET-Network, emphasizing early entrepreneurial thinking.
5) Entrepreneurial ZET-Impulse, specific entrepreneurship corridor (Ostrava-Zlin-Brno-Breclav-Bratislava), conceived as a network of cooperating entrepreneurial universities, open to local as well as foreign talent, distinguished by specific and original tasks – like new companies creation, integrated productive towns and unique startups – renewing the traditions, heritage and abilities of the best within us, in the world around us - as it is required by our times, the new contexts and unprecedented era of change.
6) ZET-authority, allowing for the possibilities of accreditation alternatives in the areas of entrepreneurial education for both state and private universities. The cooperation of these institutions is critical for the early establishment of innovation, entrepreneurial and strategic habits. Small and medium enterprises are the foundation for developing new, re-localized economies. Freedom and autonomy of private initiatives and institutions should be preserved.
7) Entrepreneurial University is the vision of Jan A. Bata (genius Czech entrepreneur), seeking massive preparation of high-quality, and professionally and ethically anchored entrepreneurs capable of creating jobs, realize needed innovations and strengthen local economies and communities. Entrepreneurship cannot be learned by reading books on entrepreneurship. Entrepreneurs learn by action: founding companies, analyzing markets, creating teams and mastering technologies. Entrepreneurship is knowledge (action), not information (description of action). There is plenty of information and critical shortage of knowledge. Information is not knowledge.
8) ZET- solutions, address the problem of growing number and intensity of conflicts, in the society and business, while noting the decline in human abilities to solve them. Traditional solutions do not remove conflicts, but rather bring forth the new ones. The emerging „trumpism “ pretends to solve problems through direct confrontation of opposing sides, in search of compromise or consensus. In contrast to such „compromise“ – which is harming both sides, ZET offers conflict „dissolution“, instead of THE traditional resolution, through seeking out the prominent alternative, assuring both sides better position than traditional compromise.
9) Self-Renewing ZET-Corporation. In the era of accelerated change, both survival and legacy of traditionally organized corporation are increasingly at risk. The ZET-Corporation cannot function as one, unitary and singly organized complex enterprise anymore. Modern enterprise will have to function as a triune shifting organization of sections of the past (leaving), present (being) and future (becoming). Essentially, the goal is to secure the process of continuous and accelerating innovation. Individual organizational sections must be sufficiently autonomous in order to adapt to different contexts of their entrepreneurial function.
10) ZET -university. In the era of Change Acceleration, self-taught courses and self-studies will become indispensable accompaniments of traditional school and university education. Instead of passive lectures, instead of simple information, the emphasis will shift to knowledge. To know of something does not mean to be able to know how to do anything. Concrete assignments of unsolved problems will prevail and be approached though problem-solving teams.

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