Realizing the full potential of the Internet of Things (IoT) requires solving technical and business challenges including the identification of things, the organization, integration and management of big data, and the effective use of knowledge-based decision systems. These challenges, and more, are the focus for the International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) international conference series.
IIKI 2020, the ninth conference in the series, provides a dedicated forum for international experts to discuss current trends, challenges, and state-of-the-art solutions in the Internet of Things.
Extended versions of invited papers from the conference will be published in major international peer-reviewed journals.
The Internet of Things, and of services and people, coupled with social networks means a huge increase in data. Analysing Big Data will become a key focus of research, competition, and innovation in the IoT. Processing of Big Data will in the cloud, and data mining will use background knowledge of societal, cultural, and personal trends. Knowledge engineering for better data mining, new approaches to cloud computing for big data, and new paradigms for Big Data processing are key topics. The topics in this track includes but are not limited to
Advances in wireless and mobile technologies have dramatically changed our lives. Many challenges exist in wireless and mobile applications including ensuring security and privacy.
The goal of this track is to explore cutting-edge research in this area.
Topics include but are not limited to：
ZhangBing Zhou ZHANGBING.ZHOU@GMAIL.COM
With the rapid development and wide adoption of the Internet of Things (IoT), traditional device-centric IoT is moving into a new era where ubiquitous IoT resources are encapsulated and represented in terms of smart IoT services. In this setting, IoT resources at a certain network region are dynamically integrated through innovative IoT services for the realization of the value of interconnected IoT resources and the satisfaction of (near) real-time, intelligent and local user demands, and consequently, for the promotion of IoT intelligence at the edge of the network. To address this challenge, this research track calls for submissions on the topics including, but are not limited to, the following:
Cyber-physical systems (CPS) are engineered systems that are built from, and depend upon, the seamless integration of computational algorithms and physical components. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and usability that will far exceed the simple embedded systems of today. CPS technology will transform the way people interact with engineered systems — just as the Internet has transformed the way people interact with information. New smart CPS will drive innovation and competition in sectors such as agriculture, energy, transportation, building design and automation, healthcare, and manufacturing. Work focused on theory, algorithms, implementation and field deployments will be of great interest to the track. Areas of interests include (but are not limited to)
Blockchain, a form of Distributed Ledger Technology, has been gaining enormous attention in areas beyond its cryptocurrency roots since more or less 2014: blockchain and IoT (the Internet of Things), Blockchain And Intellectual Property, blockchain and security, blockchain and finance, blockchain and logistics. In this track, we are looking forward novel researches in blockchain and its related work in IoT. Some possible topics are listed below and not limited to the following items:
“Big data” is endowing the traditional healthcare with mobility, intelligence and convenience, which has given birth to “E-Health & Mobile Health”. In such a mobile health environment, tasks like health monitoring of patients, information exchange between doctors and patients, intelligent diagnosis and information push, etc., can be automatically and rapidly accomplished by analyzing a large number of data collected from various mobile devices. However, such mobile applications face numerous challenges due to the voluminous data and complex procedures. Topics in this track include but are not limited to:
As The Internet of Things (IoT) continues to be envisioned as the most popular technology, the research of IoT has turned to how to drive value from IoT. While people have enjoyed a treasure trove of big data from IoT, the sheer volume of data being created by the IoT creates a big problem to analyze the deluge of data and information. Recently, the rapid development of artificial intelligence technology encounters great challenges as well as opportunities for IoT.
The proposed track provides the ground for emerging research ideas on how Artificial Intelligence (AI) can make a valuable contribution to solving problems that the Internet of Things. Contributions may come from diverse fields, including artificial intelligence; dependable computing; the Internet of Things; cyber-physical systems; mobile, wearable, and ubiquitous computing; ambient intelligence; architecture.
Original technical submissions on, but not limited to, the following topics are invited:
With the development of wireless communications and networking, various communication models, interference models and channel models appeared, which lead to the effect that algorithmic design becomes more and more important and challenging. On the other hand, for the well-known reasons, centralized algorithms may not be the best choice to implement in large-scale wireless and heterogeneous networks, especially for the IoT, and distributed solutions are more desirable and appealing. Therefore, it is necessary to pay more efforts on designing distributed algorithms and protocols for solving problems from all layers of wireless networks. Areas of interest in this track include all the following aspects in wireless networks (but are not limited to)
JIE GAO 11694820@QQ.COM
As the wide application of information technology in the military and civil fields, information superiority has become the key factor to determine the outcome of all kinds of competition. Electromagnetic and optical sensing of target and environment, and the corresponding processing and analysis techniques are playing an increasingly important role. Therefore, the data acquisition of electromagnetic and optical characteristics, data analysis and database construction will become the key topics. The topics in this track includes but not limited to:
Ubiquitous Sensing and Intelligent Media, including environment sensing, Internet of Things (IoT), data or multimedia acquisition, intelligent media processing, harmonious human-computer interaction and pervasive computing have attracted huge interests from researchers. Accordingly, there are a variety of potential application in Ubiquitous Sensing and Intelligent Media. The aim of this track is to survey a state of art of methodologies, algorithms and systems in advanced research into Ubiquitous Sensing and Intelligent Media, which may involve any types of media data such as visual (including 2D, 3D and RGB data), audial, Electroencephalography (EEG)/MRI/CT and touch sensory data etc.
The digital economy is booming over the world, especially emerging markets in recent years. The new digital paradigms include a vast array of enabling technologies, such as the Internet of Things, Big Data, Artificial Intelligence, Cloud Computing, and Augmented and Virtual Reality, Smart Manufacturing, etc. Novel business models are emerging day by day. In the times of the digital economy, the platform economy supported by algorithms, big data, and computing power is replacing the traditional development model where companies are the primary unit. The three elements of digital models (algorithm + big data + computing power) are gradually becoming the new production factors of the intelligent economy. There is surely a list of challenges for digital economics as a brand-new model which is completely different from traditional ones, such as the digital transitioning of enterprises, the flatization of organizational structure, the operation of digital business models, the fully usage of the big data capital, and the smart algorithms to realize the intelligentalization, among many others.
This track is intended to bring together scholarly contributions from a variety of disciplines that will shed light on technological and social change in the digital time.
Topics include, but are not limited to, the following:
Big data is an emerging paradigm in almost all industries. Finance Big Data (FBD) is currently becoming one of the most promising area for finance managing and governing. It is significantly changing business models in financial companies. Some researchers argue that big data is fuelling the transformation of finance and even business in unpredictable ways.
Due to the 4V characters of Big Data: Volume (large data scale), Velocity (real-time data streaming), Variety (different data forms), and Veracity (data uncertainty), a long list of challenges for FBD management, analytics, and applications exist. These challenges include 1) to organize and manage FBD in effective and efficient ways, 2) to find novel business models from FBD analytics, 3) to handle traditional finance issues like high frequency trading, sentiments, credit risk, financial analysis, risk management and regulation, etc, in creative big-data driven ways, 4) to integrate variety heterogeneous data from different sources, and 5) to ensure the security and safety of the finance systems and to protect the individual privacy in view of big data. To meet these challenges, we need more fundamental research on both data analytics technology and finance business and more efforts from not only academia but also industries.
The track aims at bringing together research efforts focused on the development of methods, tools and techniques for the handling of various aspects of finance big data from academia and industries.
The solicited original papers include, but are not limited to, the topical areas listed below:
Xuegang Cui firstname.lastname@example.org
Advances in technology are dramatically promoting the efficiency of accounting works and open a brand-new paradise for this traditional area. Tedious and repetitive tasks are out while high-value and transformative new roles are in. With the practice and application of a vast array of enabling technologies, such as the Internet of Things, Big Data, Artificial Intelligence, Cloud Computing, Block Chain, etc, automation, intelligence and sharing in the field of accounting and finance has become an irresistible trend.
In 2020, the global economy will face unprecedented difficulties and risks under the impact of COVID-19. Whether in China or other countries, enterprises are faced with great difficulties in financing, operation, sales and so on when facing the impact of unpredictable public crisis. The participation of intelligent accounting and finance is needed to get rid of difficulties and turn crises into opportunities. This track is intended to bring together scholarly contributions from a variety of disciplines that will shed light on the intelligent transformation of accounting and finance. Topics include, but are not limited to, the following:
We welcome both qualitative and quantitative empirical research, including but not limited to archival studies, case studies, experiments, surveys, simulation studies and historical reflections.
Digital currency makes use of cryptography to regulate the creation and transactions of the exchange unit, which is called cryptocurrency. Many cryptocurrencies have been proposed recently, which are completely decentralized without any central authority and cloud, so they are immune to any central bank’s interference. Cryptocurrencies have become an important research topic recently, and the excitements are mainly brought by bitcoins. At the core of this new advancement is blockchain, which is a distributed consensus protocol. Blockchain is a public ledger that acts as the underlying infrastructure to record electronic transactions online. Many technical challenges arise with the rapid development of the new technologies such as cryptocurrency and blockchain. Industrial companies and academic researchers have recognized that blockchain technology can be used to solve meet the challenges especially in economic areas. Therefore, it is interesting to apply blockchain technology to exciting financial application scenarios together with digital currencies.
This track aims to promote research and reflect the security and privacy issues in digital currencies and payment systems. Related topics such as digital currency and blockchain technology will be also considered, and we are accepting original theoretical or empirical research articles but not limited to the following topics:
COVID-19 has had a disastrous impact on the global economy and society. During the deadly epidemic, people’s behavior patterns, including living habits, shopping activities, and ways of working was extremely restricted and had to be changed. A vast array of emerging technologies, such as Big Data, Artificial Intelligence, Cloud Computing, Internet of Things, etc, has helped to accurately prevent and control the epidemic as well as to enhance people’s living quality and operation models of companies. Under the impact, the fin-tech and digital economy have played a vital role in fighting the epidemic and ensuring the normal operation of the economy and society. Mobile payment, financial risk prevention, insurance, fin-tech enterprises and the popularization of novel business types, such as online shopping, online medical care, and online education, online working, etc, have dramatically alleviated the negative impacts brought by the epidemic. The epidemic brings many challenges. The potential of fin-tech and digital economy highlighted in the epidemic will promote the advanced development of fin-tech and accelerate the penetration of digital economy into various industries.
This track is intended to bring together scholarly contributions from a variety of disciplines that will shed light on the development of fin-tech and digital economy in the context of COVID-19.This track will collaborate with the special issue on “The Impact of COVID-19 on Financial Markets, Banking Systems, and the Overall Economy”. please see the detail at https://www.journals.elsevier.com/finance-research-letters/call-for-papers/the-impact-of-covid-19-on-financial-markets.
Topics include, but are not limited to, the following:
In recent years, quantitative trading methods represented by statistical arbitrage, paired trading, algorithmic execution, CTAs, and high-frequency trading have made great progress in financial markets. The frontiers of quantitative finance are still changing rapidly, driven by a variety of evolving quantitative methods and technologies. With the increasing abundance of hedging instruments and the increasing complexity of investments, how to handle the laws of the market through big data and AI technology has become a common concern for researchers. Robo-Advisory, for example, has become an innovative service that combines quantitative investment and wealth management businesses. Quantitative investment will lead the future of finance and promote a new pattern in the field of technology finance. This is the result of the comprehensive application of cutting-edge technology represented by AI in Fintech. Topics include, but are not limited to, the following:
– Paper Submission Deadline:
August 31，2020 September 30，2020
– Paper Acceptance Notification:
September 30, 2020 October 31, 2020
– Camera-ready Paper Submissions: TBD
– Conference Date: November 27-29, 2020
We encourage submission of full papers and position papers presenting novel ideas that may lead to insightful technical discussions.
Papers should contain original contributions that have not been published or submitted elsewhere, and references to related state-of-the-art research.
Please submit your papers at
Please contact email@example.com for further enquiries.
Biography: Dr. Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia.The Network with Head quarters in Seattle, USA has currently more than 1,000 scientific members from over 100 countries.As an Investigator / Co-Investigator, he has won research grants worth over 100+ Million US$ from Australia, USA, EU, Italy, Czech Republic, France, Malaysia and China.Dr. Abraham works in a multi-disciplinary environment involving machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, data mining and applied to various real world problems. In these areas he has authored / coauthored more than 1,300+ research publications out of which there are 100+ books covering various aspects of Computer Science. One of his books was translated to Japanese and few other articles were translated to Russian and Chinese. About 1000+ publications are indexed by Scopus and over 800 are indexed by Thomson ISI Web of Science. Some of the articles are available in the ScienceDirect Top 25 hottest articles. He has 700+ co-authors originating from 40+ countries. Dr. Abraham has more than 30,000+ academic citations (h-index of 82as per google scholar). He has given more than 100 plenary lectures and conference tutorials (in 20+ countries). For his research, he has won seven best paper awards at prestigious International conferences held in Belgium, Canada Bahrain, Czech Republic, China and India. Since 2008, Dr. Abraham is the Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (which has over 200+ members) and served as a Distinguished Lecturer of IEEE Computer Society representing Europe (2011-2013). Currently Dr. Abraham is the editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves/served the editorial board of over 15 International Journals indexed by Thomson ISI. He is actively involved in the organization of several academic conferences, and some of them are now annual events. Dr. Abraham received Ph.D. degree in Computer Science from Monash University, Melbourne, Australia (2001) and a Master of Science Degree from Nanyang Technological University, Singapore (1998).
More information at: http://www.softcomputing.net/
Topic: Industry 4.0 and Society 5.0: Role of AI and Data Sciences
We are blessed with the sophisticated technological artifacts that are enriching our daily lives and the society. Industry 4.0 is the current trend of automation and data exchange in manufacturing technologies, which also includes a close integration of cyber-physical systems, the Internet of things and cloud computing. In this talk, the concept of Industry 4.0 and Society 5.0 will be presented and then various research challenges from several applications perspective will be illustrated. Some real world applications involving the analysis of complex data / applications would be the key focus.
J. Christopher Westland is Professor in the Department of Information & Decision Sciences at the University of Illinois. He holds a BA in Statistics and an MBA in Accounting and received his PhD in Computers and Information Systems from the University of Michigan. He has professional experience as a systems consultant, statistician and certified public accountant in the US, Europe, Latin America and Asia. He has consulted, taught and conducted academic research in electronic commerce, accounting, innovation, information technology and statistics and machine learning. His most recent book Audit Analytics provides a road-map and software for the application of statistical and machine learning in the automation of public accounting. He is the author of numerous academic papers and books, including Financial Dynamics (Wiley 2003); Valuing Technology (Wiley 2002); Global Electronic Commerce (MIT Press 2000); Global Innovation Management (Palgrave Macmillan 2008; Springer 2017); Red Wired: China’s Internet Revolution (Marshall Cavendish, 2010); Structural Equation Modeling (Springer 2015, 2nd edition 2019) and Audit Analytics (Springer 2020). He has served on the editorial boards of numerous scholarly journals and is current Editor-inChief of the premier Electronic Commerce Research journal. He has served on the faculties at the University of Michigan, University of Southern California, Hong Kong University of Science and Technology, Tsinghua University, Nanyang Technological University, and other academic institutions. He has been designated by China as a High-level Foreign Expert under the 1000-Talents Plan. He has advised on valuation and technology strategy for Microsoft, Intel, Motorola, V-Tech, Aerospace Corporation, IBM, Pacific Bell, and other technology firms.
Topic:Advances in Machine Learning for Finance
Advances in machine learning over the past decade have tended to focus on image, text or voice recognition and processing, with impressive results. Machine learning applications in finance, economics and accounting require different methods, and advances here have been less successful. This talk surveys machine learning methods suitable specifically for finance, and reviews one particularly successful application addressing credit card fraud using Sarbanes-Oxley data and Fama-French risk factors. A systematic tuning of hyperparamters for a suite of machine learning models, starting with a random forest, an extremely-randomized forest, a random grid of gradient boosting machines (GBMs), a random grid of deep neural nets, a fixed grid of general linear models where assembled into two trained stacked ensemble models optimized for F1 performance; an ensemble that contained all the models, and an ensemble containing just the best performing model from each algorithm class. Tuned GBMs performed best under all conditions. Without Fama-French risk factors, models yielded an AUC of 99.3% and closeness of the training and validation matrices confirm that the model is robust. The most important predictors were firm specific, as would be expected, since control weaknesses vary at the firm level. Audit firm fees were the most important non-firm-specific predictors. Adding Fama-French risk factors to the model rendered perfect prediction (100%) in the trained confusion matrix and AUC of 99.8%. The most important predictors of credit card fraud were the Fama-French coefficient for the High book-to-market ratio Minus Low factor. The second most influential variable was the year of reporting, and third most important was the Fama-French 3-factor model coefficient of determination. Together these described most of the variance in credit card fraud occurrence.
Betty J. Simkins, Ph.D., is the Williams Companies Chair, Regents Professor of Finance, and Head of the Department of Finance at Oklahoma State University’s Spears School of Business. She has a Ph.D. in Finance from Case Western Reserve University, an MBA from Oklahoma State University (OSU), and a BS in Chemical Engineering from the University of Arkansas. Prior to getting her PhD, Dr. Simkins (Betty) began her career as a chemical engineer in the oil and gas industry for Conoco and as an engineer and planning analyst for Williams Companies. Currently, in addition to teaching energy finance courses at OSU and conducting research that she presents internationally, she also teaches executive education for energy companies globally. In June 2018, she was appointed to the Market Risk Advisory Committee for the Commodity Futures Trading Commission (CFTC).Betty has received a number of teaching awards and research awards at OSU including the Regents Distinguished Teaching Award, the Regents Distinguished Research Award, the Outreach Excellence Award, and the Outstanding OSU MBA Faculty Award. She was also nominated for Regents Professor, the highest academic award at OSU and the appointment will become official July 1, 2019. Her primary area of research is risk management, including energy risk, but she also conducts research in energy finance and corporate governance, among other areas. She has coauthored more than 70 journal articles and book chapters and has won best paper awards for her research. She has published in the Journal of Finance, Journal of Banking and Finance, Financial Management, Journal of Futures Markets, among others. In addition, she has published extensively on energy finance and enterprise risk management including three books titled: Energy Finance and Economics: Analysis and Valuation, Risk Management, and the Future of Energy (2013);, Enterprise Risk Management: Insights and Analysis on Today’s Leading Research and Best Practices (2010); and Implementing Enterprise Risk Management: Case Studies and Best Practices (2014), all published by Wiley.Betty is very active professionally. She is editor (and founding co-editor) of the Journal of Commodity Markets and serves on the editorial boards of 12 other academic journals including the Journal of Banking and Finance, International Review of Financial Analysis, British Accounting Review, Finance Research Letters, and the Journal of Applied Corporate Finance. She is past president of the Eastern Finance Association and Vice President of Education for the Financial Management Association International. She is a member of several energy industry associations including the NGEAO (Natural Gas and Energy Association of Oklahoma), the Society of Petroleum Engineers (SPE), and the International Association for Energy Economics (IAEE).
Dr. FRED PHILLIPS is currently a Professor at University of New Mexico. He is the 2017 winner of the Kondratieff Medal, awarded by the Russian Academy of Sciences. He is a Senior Fellow (and formerly Research Director) at the IC2 Institute of the University of Texas at Austin, and a PICMET Fellow.
Earlier, Fred was Distinguished Professor at Yuan Ze University Professor in Taiwan; Program Chair at State University of New York at Stony Brook; Visiting Scientist at the Chinese Academy of Sciences, Beijing; Vice Provost for Research at Alliant International University; Associate Dean at Maastricht School of Management (Netherlands); and Dean of Management at Oregon Graduate Institute of Science & Technology. In the USA and overseas, he has been a leader in developing graduate management curricula for employees of international and high-tech companies. His contributions in operations research include “Phillips’ Law” of longitudinal sampling, and the first parallel computing experiments with Data Envelopment Analysis. He is co-recipient of grants totaling $5 million from the Air Force Office of Scientific Research for the study of Japanese technology management practices. He brought many other grants to IC2, OGI, MSM, YZU and Stony Brook, and was co-principal investigator on a $1 million NSF project, developing advanced information systems for the US Forest Service. He has won several awards for outstanding research. Dr. Phillips is Editor-in-Chief of Elsevier’s international journal Technological Forecasting & Social Change (impact factor 5.846). He authored the textbook Market-Oriented Technology Management (Springer 2001), the popular title The Conscious Manager: Zen for Decision Makers (General Informatics 2003), a book on high-tech economic development, The Technopolis Columns (Palgrave 2006), and What About the Future? (Springer 2019). In earlier years he held teaching, research, honorary, or management positions at the Universities of Aston and Birmingham in England, General Motors Research Laboratories, Market Research Corporation of America, Battelle-Pacific Northwest National Laboratories, CENTRUM Católica in Peru, and National Chengchi University in Taiwan.
Dr. Phillips has been a consultant to such organizations as Intel, Texas Instruments, and Frito-Lay Inc., and has consulted worldwide on technology based regional development. Through his consulting firm, General Informatics LLC, he and his team have worked on projects for World Bank, UNESCO, and the US Environmental Protection Agency. Fred is a founder of the Austin Technology Council, and was also a Board member for the Software Association of Oregon. He is a popular op-ed columnist and panel member in forums dealing with trends in management, technology, higher education, and economic development.
Dr. Phillips attended The University of Texas and Tokyo Institute of Technology, earning the Ph.D. at Texas (1978) in mathematics and management science. Married to Sue Phillips since 1979 and with two grown daughters, Fred enjoys his mission as an educator. His avocational passions are aikido, Argentine tango, travel and writing.
Dr Ning Wang works as a Data Scientist and Senior Research Fellow at the Oxford-NIE financial Big Data Lab, Mathematical Institute, University of Oxford. He also works as Research Fellow at the St Hugh’s College, University of Oxford. His research is driven by a deep interest in analysing a wide range of social and economic problems by exploiting data science approaches. His research interest lies in the broad areas of artificial intelligence, blockchain, fintech, behavioural finance, and social networks. More specially, he is interested in machine/deep learning in finance, social media and mobile trading platform, online sentiment analysis and financial market, trading behaviours and performance evaluation.
Prior to Oxford, he was a postdoctoral researcher at the Computer Laboratory, University of Cambridge. He was involved in research to analyse a large number of real-world data on social networks in an effort to optimise decentralised communications.He has been invited as a speaker to attend various prestigious conferences and actively participate in organising many AI and Fintech events, including Wuzhen World Internet Conference, China International Big Data Industry Expo, Shanghai Pujiang Forum, China International Big Data Conference, Tencent Big Data Summit, Alibaba New Economic Think Tank Conference, FinTech Frontier Forum, Sino-British Artificial Intelligence High-end Dialogue and other top-level meetings. He has been invited to give a talk at Hong Kong University of Science and Technology, Peking University, Tsinghua University and Shanghai Jiaotong University, etc. He is also an honorary advisor to Tencent Internet Research Institute and blockchain companies Vechain and Trias, a senior data scientist in Hong Kong Financial Data Ltd., a specially invited expert for Guosen Securities, a committee member of the China Quantitative Trading Competition.
Biography: Omer F. Rana is Professor of Performance Engineering at Cardiff University, with research interests in high performance distributed computing, data analysis/mining and multi-agent systems. He was formerly the deputy director of the Welsh eScience Centre and had the opportunity to interact with a number of computational scientists across Cardiff University and the UK. He is a fellow of Cardiff
University’s multi-disciplinary “Data Innovation” Research Institute. Rana has contributed to specification and standardisation activities via the Open Grid Forum and worked as a software developer with London-based Marshall Bio-Technology Limited prior to joining Cardiff University, where he developed specialist software to support biotech instrumentation. He also contributed to public understanding of science, via the Wellcome Trust funded “Science Line”, in collaboration with BBC and Channel 4. Rana holds a PhD in “Neural Computing and Parallel Architectures” from Imperial College (London Univ.), an MSc in Microelectronics (Univ. of Southampton) and a BEng in Information Systems Eng. from Imperial College (London Univ.). He serves on the editorial boards (as Associate Editor) of IEEE Transactions on Parallel and Distributed Systems, (formerly) IEEE Transactions on Cloud Computing and ACM Transactions on Internet Technology. He is a founding-member and associate editor of ACM Transactions on Autonomous & Adaptive Systems.
Topic: AI at the “Edge”: Service Orchestration & Enactment Across IoT, Edge & Cloud Resources
Many Internet of Things (IoT) applications today involve data capture from sensors that are close to the phenomenon being measured, with such data subsequently being transmitted to Cloud data centre for analysis and storage. Currently devices used for data capture often differ from those that are used to subsequently carry out analysis on such data. Increasing availability of storage and processing devices closer to the data capture device, perhaps over a one-hop network connection or even directly connected to the IoT device itself, requires more efficient allocation of processing across such edge devices and data centres. Scalability in this context needs to consider both cloud resources and initial processing on edge resources closer to the user. We refer to these as “vertical workflows” – i.e. workflows (a combined set of services) which are enacted across resources that can vary in: (i) type and behaviour; (ii) processing and storage capacity; (iii) latency and security profiles. Understanding how a workflow can be enacted across these resource types is outlined, motivated through multiple application scenarios. The overall objective considered is the completion of the workflow within some deadline and security constraints — but with flexibility on where data processing is carried out.
Biography: Professor Xueqi Cheng is the vice president of the Institute of Computing Technology, Chinese Academy of Sciences (CAS).His main research focus on data science, big data analysis technology, Web search and data mining, big data infrastructure system and information security applications. He is the CCF Fellow, IEEE senior member and the general secretary of CCF Task Force on Big Data, the chair of the Special Interesting Group of Information Retrieval of CIPS, and the vice chair of CSIAM Task Force on big data and artificial intelligence. He was awarded the NSFC Distinguished Youth Scientist (2014), State Department special allowance. His research achievements are published in the top conference, such as SIGIR, WWW, CIKM, IJCAI, and the prestigious journals, including IEEE TKDE, IEEE TIST, etc. He has more than 300 publications, and has more than 17000 citations according to Google Scholar. The developed system such as large-scale distributed machine learning system（EasyML），Text and NLP toolset（MatchZoo）, Bid graph computing engine（SQLGraph）, have wide influence in the international open source communities. His works in query understanding, information retrieval and ranking learning won “Best Paper Award” of the top academic conferences five times (e.g. ACM SIGIR、ACM CIKM、PKDD etc.). The big data analysis system and key technologies formed by the research results have been applied on a large scale. He was awarded the second-class National Prize for Progress in Science and Technology in 2004, 2012 and 2017. He was awarded six provincial and ministerial Awards.
Topic: Rethinking about Data Science and Computing Intelligence
Several high-quality international journals will carry selected papers from the conference in specially commissioned theme issues. Note: Most accepted papers in IIKI2018 have been recommended to SCI or EI indexed journals for publication, including Personal and Ubiquitous Computing (SCI indexed), International Journal of Distributed Sensor Networks (SCI indexed) and so on.
The Proceedings of IIKI2020 will be published by Elsevier Journal- Procedia Computer Science (EI indexed (CA)).
Procedia Computer Science(ISSN: 1877-0509)is an electronic product focusing entirely on publishing high quality conference proceedings. Procedia Computer Science enables fast dissemination so conference delegates can publish their papers in a dedicated online issue on ScienceDirect, which is then made freely available worldwide.
Extended version of selected papers from the conference will be recommended to the following journals for publication.
Personal and Ubiquitous Computing (ISSN: 1617-4909, SCI IF: 1.735) for longer contributions and expended papers on ubiquitous computing.
Electrotechnical Review/Elektrotehniški vestnik(ISSN: 0013-5852) is journal of electrical engineering and computer science. The journal has respectful publishing tradition and celebrated 80th volume in 2013. Elektrotehniški vestnik is indexed in EI Compendex. We can recommend 1-2 articles from IIKI 2020 to each issue of this journal for publication after extension.
IEEE Access is an award-winning (SCI IF: 3.745) , multidisciplinary, all-electronic archival journal, continuously presenting the results of original research or development across all of IEEE’s fields of interest.
The special session focus on the latest results over computer vision for augmented reality(SCI IF:2.814).Please contact IIKI@bnu.edu.cn if you hope your article to be recommended to this journal before August 15, 2020.
Journal Scope: the methodology and practice of technological forecasting and future studies as planning tools as they interrelate social, environmental and technological factors.(ISSN:0040-1625;SCI IF:5.846), We will recommend several top articles from IIKI2020 to TS&SC for publication after journal reviewment.
Zhuhai Jinghuayuan Hotel (International Exchange Center) 珠海京华苑大酒店（国际交流中心）
Address: Beijing Normal University International Exchange Center, No. 20, Jinfeng Road, Tangjiawan , Zhuhai, Guangdong
Foreign citizens must obtain a Chinese visa before entry into China, with the exception of visa-free entry based on relevant agreements or regulations. An Business Visa (F Visa) is issued to foreign citizens who are invited to China for a visit, research, lecture, business, exchanges in the fields of science, technology or culture, advanced study, or internship for a period of no more than 6 months.
Zengquan Fang, Beijing Normal University, China
Lizhen Cui，Shandong University, China
Yunchuan Sun, Beijing Normal University, China
Andrej Kos, University of Ljubljana, Slovenia
Houbing Song, Embry-Riddle Aeronautical University, USA,
Xuegang Cui, Beijing Normal University, China
Chenglei Yang, Shandong University
Antonio J. Jara, University of Applied Sciences Western Switzerland (HES-SO), Switzerland
Yufeng Shi， Shandong University, China
Yuan Gao, Academy of military science of the PLA
Huansheng Ning, University of Science & Technology Beijing, China
Zhiwen Zhao, Beijing Normal University(Zhuhai), China
Xin Zhong, Beijing Normal University(Zhuhai), China
Zhengjun Zhang, University of Wisconsin Madison
Hao Jiao, Beijing Normal University
Xiao Bai, Beihang University, China
Dongxiao Yu, Shandong University
Hefeng Tong, Tecent
Haodi Wang, Beijing Normal University, China
Zefeng Mu, Beijing Normal University, China
Zhipeng Cai, Georgia State University
Qinghe Du, Xi’an Jiaotong University, China
Zhangbing Zhou, China University of Geosciences (Beijing), China & TELECOM SudParis, France
Lin Meng, Ritsumeikan University, firstname.lastname@example.org
Yu Bai，California State University, Fullerton
Zhihan Lv, University College London (UCL), UK
Fatos Xhafa, Universitat Politecnica de Catalunya, Spain
Andrej Zemva, University of Ljubljana, Slovenia
College of artificial intelligence, Beijing Normal University
Business School, Beijing Normal University
Tecent Research Institute
Shandong Big Data Research Association
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