FCST 2017 - Keynote
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Keynote Speakers

Keynote Speech:
Resource Management for Scalable Computations in Clouds

Prof. Albert Y. Zomaya
University of Sydney
Australia

 

ABSTRACT: The cloud is well known for its elasticity by leveraging abundant heterogeneous resources. Cloud data centres easily host thousands or even millions of multicore servers. Further, these servers are increasingly virtualized for the sake of data centre efficiency. However, the reality is that these resources are often relentlessly exploited particularly to improve applications performance. Although the elasticity facilitates achieving cost efficiency (or the performance to cost ratio), the ultimate efficiency in resource usage (or more broadly data centres) lies in scheduling and resource allocation strategies that explicitly take into account actual resource consumption. The optimization of resource efficiency in clouds is of great practical importance considering its numerous benefits in the economic and environmental sustainability. In this talk, we will discuss resource efficiency in cloud data centres with examples providing alternative approaches to constructing scalable services/computations in clouds to support large-scale distributed processing applications for data analytics applications taking advantage of the heterogeneous nature of cloud architectures.

BIO: Albert Y. Zomaya is the Chair Professor of High Performance Computing & Networking and Australian Research Council Professorial Fellow in the School of Information Technologies, Sydney University. He is also the Director of the Centre for Distributed and High Performance Computing which was established in late 2009.

Dr. Zomaya published more than 500 scientific papers and articles and is author, co-author or editor of more than 20 books. He served as the Editor in Chief of the IEEE Transactions on Computers (2011-2014) and was elected recently as a Founding Editor in Chief for the newly established IEEE Transactions on Sustainable Computing. Also, Dr. Zomaya serves as a Co-Founding Editor-in-Chief of IET Cyber-Physical Systems, Founding Editor-in-Chief of the Journal of Scalable Computing and Communications (Springer), and Associate Editor-in-Chief (Special Issues) of the Journal of Parallel and Distributed Computing. He also serves as an associate editor for 22 leading journals, such as, the ACM Computing Surveys, ACM Transactions on Internet Technology, IEEE Transactions on Cloud Computing, and IEEE Transactions on Computational Social Systems. Dr. Zomaya is the Founding Editor of several book series, such as, the Wiley Book Series on Parallel and Distributed Computing, Springer Scalable Computing and Communications, and IET Book Series on Big Data.

Dr. Zomaya has delivered more than 160 keynote addresses, invited seminars, and media briefings and has been actively involved, in a variety of capacities, in the organization of more than 700 conferences. Dr. Zomaya is the recipient of the IEEE Technical Committee on Parallel Processing Outstanding Service Award (2011), the IEEE Technical Committee on Scalable Computing Medal for Excellence in Scalable Computing (2011), and the IEEE Computer Society Technical Achievement Award (2014). He is a Chartered Engineer, a Fellow of AAAS, IEEE, and IET. Dr. Zomaya’s research interests are in the areas of parallel and distributed computing and complex systems.


Keynote Speech:
Energy Efficiency in Wireless Sensor Networks for Engineering Applications

Prof. Jiannong Cao
Hong Kong Polytechnic University
China

 

ABSTRACT: Wireless sensor networks (WSNs) contain a large collection of autonomous devices that collaborate with each other to achieve the assigned tasks. As a new form of distributed embedded system, WSNs have been applied in various areas including engineering applications such as structural health monitoring, volcanic monitoring and smart grid. In such applications, energy efficiency is regarded as one of the most important bottlenecks. To realize an energy efficient WSN system entail the design considerations from various aspects including data sampling, in-network processing, and energy harvesting. In this talk, I will describe a framework of achieving energy efficiency in WSNs and describe our research on designing energy-efficient WSNs for engineering applications.

BIO: Dr. Cao is currently a chair professor and head of the Department of Computing at Hong Kong Polytechnic University, Hung Hom, Hong Kong. His research interests include parallel and distributed computing, wireless networks and mobile computing, big data and cloud computing, pervasive computing, and fault tolerant computing.. He has co-authored 3 books, co-edited 9 books, and published over 300 papers in major international journals and conference proceedings. He is a fellow of IEEE, a senior member of China Computer Federation, and a member of ACM. He was the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society from 2012 - 2014. Dr. Cao has served as an associate editor and a member of the editorial boards of many international journals, including ACM Transactions on Sensor Networks, IEEE Transacitons on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Networks, Pervasive and Mobile Computing Journal, and Peer-to-Peer Networking and Applications. He has also served as a chair and member of organizing / program committees for many international conferences, including PERCOM, INFOCOM, ICDCS, IPDPS, ICPP, RTSS, DSN, ICNP, SRDS, MASS, PRDC, ICC, GLOBECOM, and WCNC. Dr. Cao received the BSc degree in computer science from Nanjing University, Nanjing, China, and the MSc and the Ph.D degrees in computer science from Washington State University, Pullman, WA, USA.


Keynote Speech:
Challenges and Future Research of Information Assurance in Smart Cities

Prof. Stephen S. Yau
Arizona State University
USA

 

ABSTRACT: With the rapid advances in IT and other technologies, smart cities have emerged in various parts of the world. Since all smart cities rely heavily on the quality and security of their required information to provide almost all their services, information assurance is needed for ensuring the proper functioning of smart cities. Due to the large scale and large variety of IT infrastructures involving various types of IT devices, systems and platforms, such as sensors, IoT, clouds, embedded systems and different types of networks, it is important and very challenging to achieve proper levels of information assurance required for various applications in smart cities.

In this talk, the various levels of needed information assurance in different applications in a typical smart city and the challenges of achieving such information assurance will first be identified. Current research and future research directions to meet these challenges will be discussed.

BIO: Stephen S. Yau is Professor of Computer Science and Engineering in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University (ASU), Tempe, Arizona, USA. He served as the chair of the Department of Computer Science and Engineering at ASU in 1994-2001. Previously, he was on the faculties of Northwestern University, Evanston, Illinois, and University of Florida, Gainesville.

He served as the president of the Computer Society of the Institute of Electrical and Electronics Engineers (IEEE) and was on the IEEE Board of Directors and the Board of Directors of Computing Research Association. He served as the editor-in-chief of IEEE COMPUTER magazine. He organized many major conferences, including the 1989 World Computer Congress sponsored by the International Federation for Information Processing (IFIP), and the IEEE Annual International Computer Software and Applications Conference (COMPSAC) sponsored by IEEE Computer Society. He is currently an honorary chair of IEEE World Congress on Services and co-located conferences at Honolulu, USA, June 25 – 30, 2017, and the 2017 IEEE Smart World Congress in San Francisco, USA, August 4 – 8, 2017.

His current research includes services and cloud computing systems, cyber security, trustworthy computing, software engineering, internet of things, and ubiquitous computing. He has received various awards and recognitions, including the Tsutomu Kanai Award and Richard E. Merwin Award of the IEEE Computer Society, the IEEE Centennial and Third Millennium Medals, and the Outstanding Contributions Award of the Chinese Computer Federation. He is a Life Fellow of the IEEE and a Fellow of the American Association for the Advancement of Science. He received the B.S. degree from National Taiwan University, Taipei, and the M.S. and Ph.D. degrees from the University of Illinois, Urbana, all in electrical engineering.


Keynote Speech:
Deep Learning for Big Data Applications
-Improvement and Future Directions

Prof. Yi Pan
Georgia State University
USA

 

ABSTRACT: Deep learning is a very hot area in machine learning research with many remarkable recent successes in computer vision, automatic speech recognition, natural language processing, audio recognition, and medical imaging processing. AlphaGo, the first Computer Go program to beat a professional human Go player, uses a deep learning method. Although various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to many big data applications, using deep learning to solve bioinformatics problems is still in its infancy. In this talk, I will outline the challenges and problems in existing deep learning methods when applying it to big data in general and bioinformatics in particular. I will describe a few novel architectures and algorithms recently proposed by us to improve the accuracies and learning speeds of the existing deep learning technologies. These new deep learning architectures and algorithms will be applied to several big data applications including image processing, DNA sequence annotation, long intergenic non-coding RNA detection, and gene structure prediction. The data encoding schemes, the choice of architectures and methods used will be described in details. Performance comparisons with other machine learning and existing deep learning methods will be reported. The experimental results show that deep learning is very promising for many bioinformatics applications, but requires selection of suitable models and a lot of tuning to be effective. Future research directions in this exciting area will also be outlined.

BIO: Yi Pan is a Regents’ Professor of Computer Science and an associate dean and a chair at Georgia State University, USA. He is also a visiting Changjiang Chair Professor at Central South University, China. Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan's research interests include parallel and cloud computing, wireless networks, and bioinformatics. Dr. Pan has published more than 200 journal papers with over 80 papers published in various IEEE journals. In addition, he has published over 150 papers in refereed conferences. He has also co-authored/co-edited 43 books. His work has been cited more than 8000 times. Dr. Pan has served as an editor-in-chief or editorial board member for 15 journals including 7 IEEE Transactions. He is the recipient of many awards including IEEE Transactions Best Paper Award, several other conference and journal best paper awards, 4 IBM Faculty Awards, 2 JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship. He has organized many international conferences and delivered keynote speeches at over 60 international conferences around the world.


Keynote Speech:
Advances in Mobile Robotics, Tele-surgical Robotics and Industrial Robotics

Prof. Jason Gu
Dalhousie University
Canada

 

ABSTRACT: Robotics is a fast-growing area of research. They are many traditional researches such as perception, motion planning, manipulation, control, cognition, and artificial intelligence which are focused on a specific task or application, also they are some newly developed research areas which seek to develop integrated robotic systems capable of addressing a wide range of applications in real-world environments. In this talk, Dr. Gu will start with the general introduction of robotics, the tasks of robotics and the history of robotics. Then he will summarize his contribution in robotics in various area with potential application in surgical robot, assistive robot, industrial robots, teleoperation and control. He will conclude his talk with the challenges and future directions of the robotics.

BIO: Jason Gu is a professor of Robotics and Assistive Technology in the Department of Electrical and Computer Engineering at Dalhousie University, where he also directs the robotics laboratory for biomedical, rehabilitation and assistive technology. He received his B.S degree in Electrical Engineering and Information Science at the University of Science and Technology of China in 1992 and his Master’s degree from Biomedical Engineering at Shanghai Jiaotong University in 1995 and earned his Ph.D. degree in Rehabilitation Medicine and Electrical and Computer Engineering in 2001 from University of Alberta.

Dr. Gu’s research areas include biomedical engineering, bio-signal processing, rehabilitation engineering, neural networks, robotics, mechatronics and control. Dr. Gu has published over 250 Journal, book chapters and conference papers. Dr. Gu is the IEEE member of SMC and has been the editor for Journal of Control and Intelligent Systems, Transactions on CSME, IEEE Transaction on Mechatronics, IEEE SMC Magazine, International Journal of Robotics and Automation and IEEE Access. Dr. Gu was a recipient of best paper award in ICCSE 2003. He also was awarded Faculty of Engineering Teaching award (2003), the outstanding IEEE Student Branch Councillor award (2004), Discovery Award of the Province of Nova Scotia in Canada (2005) and Faculty of Engineering Research award (2006). He was the recipient of The IEEE Canada Atlantic Section Murugan Memorial Award (2014) and won the best IEEE ICIA 2014 paper award. He received 2015 Canadian Atlantic Section distinguished service award and 2016 IEEE J. J. Eastern CANADA merit service Award. Dr. Gu is a Fellow of Engineering Institute of Canada (FEIC) and a Fellow of Canadian Academy of Engineering (FCAE).


Keynote Speech:
Enabling Efficient and Accurate Approximations on Sub-datasets with Distribution-aware Online Sampling

Prof. Jun Wang
University of Central Florida
USA

 

ABSTRACT: In this talk, we aim to enable both efficient and accurate approximations on arbitrary sub-datasets of a large dataset. Due to the prohibitive storage overhead of caching offline samples for each sub-dataset, existing offline sample based systems provide high accuracy results for only a limited number of sub-datasets, such as the popular ones. On the other hand, current online sample based approximation systems, which generate samples at runtime, do not take into account the uneven storage distribution of a sub-dataset. They work well for uniform distribution of a sub-dataset while suffer low sampling efficiency and poor estimation accuracy on unevenly distributed sub-datasets.

To address the problem, we develop a distribution aware method called Sapprox. Our idea is to collect the occurrences of a sub-dataset at each logical partition of a dataset (storage distribution) in the distributed system, and make good use of such information to facilitate online sampling. There are three thrusts in Sapprox. First, we develop a probabilistic map to reduce the exponential number of recorded sub-datasets to a linear one. Second, we apply the cluster sampling with unequal probability theory to implement a distribution-aware sampling method for efficient online sub-dataset sampling. Third, we quantitatively derive the optimal sampling unit size in a distributed file system by associating it with approximation costs and accuracy. We have implemented Sapprox into Hadoop ecosystem as an example system and open sourced it on GitHub. Our comprehensive experimental results show that Sapprox can achieve a speedup by up to a factor of 20 over the precise execution.

BIO: Dr. Jun Wang is a Professor of Computer Engineering; and Director of the Computer Architecture and Storage Systems (CASS) Laboratory at the University of Central Florida, Orlando, FL, USA. He received his Ph.D. in Computer Science and Engineering from University of Cincinnati in 2002. He is the recipient of National Science Foundation Early Career Award 2009 and Department of Energy Early Career Principal Investigator Award 2005. He has authored over 120 publications in premier journals such as IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and leading HPC and systems conferences such as VLDB, HPDC, EuroSys, IPDPS, ICS, Middleware, FAST. He has conducted extensive research in the areas of Computer Systems and High Performance Computing. His specific research interests include massive storage and file System in local, distributed and parallel systems environment. His group has secured more than four million dollars federal research fundings in last five years. At present, his group is investigating three US National Science Foundation projects, one DARPA and one NASA project. He has graduated nine Ph.D. students who upon their graduations were employed by major US IT corporations (e.g., Google, Microsoft, etc). He has served as numerous US NSF grant panelists and US DOE grant panelists and TPC members for many premier conferences. He has served on the editorial board for the IEEE transactions on parallel and distributed systems, IEEE transactions on cloud computing, and International Journal of Parallel, Emergent and Distributes Systems (IJPEDS). He is a general executive chair for IEEE DASC/DataCom/PIcom/CyberSciTech 2017, and has co-chaired technical programs in numerous computer systems conferences including the 10th IEEE International Conference on Networking, Architecture, and Storage (NAS 2015), and 1st International Workshop on Storage and I/O Virtualization, Performance, Energy, Evaluation and Dependability (SPEED 2008) held together with HPCA.


Keynote Speech:
The Internet of Things and Edge Computing

Nektarios Georgalas
BT Group PLC
UK

 

ABSTRACT: The IoT Data Hub underpins a Digital Ecosystem in a city environment. It allows multiple customers to create a value chain that enables the trade (supply and consumption) of data. This facilitates the development of innovative data-intensive services (using analytics) that can be used to drive business efficiencies. For this approach to scale in an operationally efficient manner for all involved parties (including City, Service Provider and 3rd parties), more control and flexibility needs to be introduced nearer the customer edge/premises. Specifically IoT services may need to be provided close to the ‘things’ because there are specific: 1) Latency issues – some time-critical applications cannot wait for the data to be sent to remote data centres with the latency this injects; 2) Availability & resilience issues – the applications need to be high availability and cannot be dependent on the availability of the backhaul service; 3) Cost – the service provider wants to derive essential information on the edge device rather than transmit high volumes of data, over expensive links, for processing in the data centre; 4) Customer preference – the customer has a specific preference to run their applications and services on their own premise.

In order to achieve this goal this Catalyst aims to apply Edge Computing capabilities to Data Hubs in order to provide programmable, general-purpose IoT compute environments at the customer edge in support of de-centralised control logic deployment and multi-tenancy.

This talk will describe the basic motivation stemming from the Digital Ecosystem enabled by Smart City Data Hubs, how Edge Computing can deliver the above requirements and will show innovations developed to enable Data Hubs with Edge Computing capabilities. We will also showcase a few use-cases implemented with focus on IoT SLA Management at the edge and in the cloud, and a world-first implementation of convergence of IoT Apps and NfV/SDN commonly orchestrated on Customer Premise Equipment.

BIO: Nektarios Georgalas is a Principal Researcher at British Telecom's Research and Innovation department. In his current role, he is Director of the BT/Intel and the BT/Huawei Co-labs, two collaborative research programmes with key BT partners delivering innovations in the areas of Cloud, Data Centres, Network Virtualisation and Smart Cities. During his career with BT, since 1998, he has managed numerous collaborative and internal research projects in areas such as network management, market-driven data management systems, policy-based management, distributed information systems, SOA/Web Services, Model Driven Design and Development of telecoms OSS, Cloud and NfV. Nektarios has led numerous international collaborations on the application of advanced techniques for design, development and operation of telecoms Networks and Operation Support Systems environments. In the past he was very active leading and contributing to key programmes within the TeleManagement Forum, where he established international standards teams, led Catalysts and influenced the Forum's strategy towards a model-driven and software-defined ecosystem of digital services in dynamic marketplaces; most recently he is involved in TMF’s Smart City Forum and related Catalysts. His work has been recognised several times by numerous international awards including the TMForum's "Excellence Award for Innovation" 2010, "Most Innovative Catalyst Award" 2014, "Best New Catalyst Award" 2015 and "Most Significant Contribution to Frameworx Award" 2015, “Most Innovative Catalyst – Smart X Commercial” 2016, “Outstanding Performance in the Catalyst Programme” 2017 and “Smart City Innovator of the Year” Excellence Award 2017. Other recognition accolades include Global Telecoms Business's "Business Service Innovation Award" 2010, 2012 and 2013. He has been Finalist in UK IT Industry Award for "Best IT Innovation" in 2013 and Highly Commended for the IET Innovation Award for Telecommunication in 2009. He has also achieved "Best innovation for Large Enterprise" and "Best Customer Experience Innovation" Finalists in BT Innovation Awards 2010. Nektarios has been recognised in BT's TSO "Brilliant People" 2015. Nektarios is inventor and co-inventor of 11 patents. He has also authored more than 50 papers in international journals and conferences. He has served as Programme Co-Chair, Programme Committee and Keynote Speaker and Invited Panellist in top international IEEE academic and TMForum conferences.


Keynote Speech:
Interpreting Multilayer Perceptrons Using 3-Valued Logic

Prof. Qiangfu Zhao
University of Aizu
Japan

 

ABSTRACT: Multilayer perceptrons (MLPs) have been successful in solving many problems, but in most cases, they are black boxes, and their reasoning processes are not interpretable. That is, even if an MLP may provide a correct answer for a given question, we cannot understand the reasons why it makes this certain decision. In this talk, I first review briefly some existing methods for interpreting trained MLPs, and then introduce a new method based on 3-valued logic. The three values here mean true (+1), false (-1), and unknown (0). The basic process is as follows: 1) train an MLP, 2) discretize the discretized neurons, 3) retrain the not-yet-fixed layers of the MLP, 4) induce a decision tree based on the hidden neurons, and 5) convert the decision tree to a set of 3-valued logic formulas. The proposed method is useful for automatic knowledge extraction, and in turn, can be important for autonomous growth of artificial intelligence.

BIO: Qiangfu Zhao received the B.S. degree in Computer Science from Shandong University (Jinan, China) in 1982; the M. Eng. degree in Information Engineering from Toyohashi University of Technology (Toyohashi, Japan) in 1985; and D. Eng. degree in Electronic Engineering from Tohoku University (Sendai, Japan), in 1988. He was an associate professor from 1991 to 1993 at Beijing Institute of Technology; associate professor from 1993 to 1995 at Tohoku University (Japan); associate professor from 1995 to 1999 at the University of Aizu (Japan); and tenure full professor since 1999 at the University of Aizu. He is the head of System Intelligence Laboratory; Chair of the Computer Science Division; associate editor of IEEE Transactions on Cybernetics; associate editor of the International Journal of Machine Learning and Cybernetics (Springer); and associate editor of IEEE SMC Magazine. He is the co-chair of the Technical Committee on Awareness Computing in IEEE Systems, Man, and Cybernetics Society and the Task Force on Aware Computing in IEEE Computational Intelligence Society. He has organized or co-organized several conferences, including the 19th Symposium on Intelligent Systems (FAN2009); the 2009 International Workshop on Aware Computing (IWAC2009); the 2010 International Symposium on Aware Computing (ISAC2010); and the IEEE International Conference on Awareness Science and Technology (iCAST2011, iCAST2013, iCAST2014, iCAST2015, and iCAST2017). He has published more than 190 referred journal and international conference papers related to optimal linear system design, neuro-computing, evolutionary computing, awareness computing, and machine learning.


Keynote Speech:
Towards Dataflow-based Graph Accelerator

Prof. Hai Jin
Huazhong University of Science and Technology
China

 

ABSTRACT: Existing graph processing frameworks greatly improve the performance of memory subsystem, but they are still subject to the underlying modern processor, resulting in the potential inefficiencies for graph processing in the sense of low instruction level parallelism and high branch misprediction. These inefficiencies, in accordance with our comprehensive micro-architectural study, mainly arise out of a wealth of dependencies, serial semantic of instruction streams, and complex conditional instructions in graph processing. In this talk, we propose that a fundamental shift of approach is necessary to break through the inefficiencies of the underlying processor via the dataflow paradigm. It is verified that the idea of applying dataflow approach into graph processing is extremely appealing for the following two reasons. First, as the execution and retirement of instructions only depend on the availability of input data in dataflow model, a high degree of parallelism can be therefore provided to relax the heavy dependency and serial semantic. Second, dataflow is guaranteed to make it possible to reduce the costs of branch misprediction by simultaneously executing all branches of a conditional instruction. Consequently, we make the preliminary attempt to develop the dataflow insight into a specialized graph accelerator. We believe that our work would open a wide range of opportunities to improve the performance of computation and memory access for large-scale graph processing.

BIO: Hai Jin is a Cheung Kung Scholars Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST) in China. Jin received his PhD in computer engineering from HUST in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. Jin worked at The University of Hong Kong between 1998 and 2000, and as a visiting scholar at the University of Southern California between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. Jin is the chief scientist of ChinaGrid, the largest grid computing project in China, and the chief scientists of National 973 Basic Research Program Project of Virtualization Technology of Computing System, and Cloud Security. Jin is a Fellow of CCF, senior member of the IEEE and a member of the ACM. He has co-authored 22 books and published over 800 research papers. His research interests include computer architecture, virtualization technology, cluster computing and cloud computing, peer-to-peer computing, network storage, and network security.


Keynote Speech:
Learning Autonomously from Data Streams through Empirical Data Analysis

Prof. Plamen Angelov
Lancaster University
UK

 

ABSTRACT: Autonomous learning from data and specifically from data streams [1] is now a widely recognised necessity. Indeed, the exponential growth in the scale and complexity of the data streams is now seen as an untapped resource which offers new opportunities for extracting aggregated information to inform decision-making in policy and commerce. However, currently existing methods and techniques for data mining involve a lot of prior assumptions, handcrafting and a range of other bottleneck issues: i) scalability – vast amounts of data which require high throughput automated methods (e.g. manual labelling of data samples can be prohibitive); ii) complex, heterogeneous data (including signals, images, text that may be uncertain and unstructured); iii) dynamically evolving, non-stationary data patterns, and the shortcomings of the “standard” assumptions about data distributions; iv) the need to hand craft features, parameters or set thresholds. As a result, a large proportion of the available data remains untapped. The key challenge now is to manage, process and gain value and understanding from the vast quantity of heterogeneous data without handcrafting and prior assumptions, at an industrial scale.

In this talk a newly emerging theoretical framework which we call Empirical Data Analytics [2] will be introduced and described and its relation to the probability, density, centrality, etc. Traditional disciplines of Machine Learning, Data Mining, Pattern Recognition, System Modelling and Identification are well developed. However, current tools often require a number of restrictive assumptions, or handcrafting/manual selection of features, distribution types, parameters, thresholds, etc. Existing algorithms are usually iterative, including internal cycles. In traditional statistical approaches, averages play a more important role than the individual specifics. Even rapidly emerging AI and computational intelligence approaches require ad hoc assumptions and a priori decisions (e.g. network depth/ architecture, membership function type and parameters). Furthermore, most existing algorithms assume fixed model structures. This hampers their application to dynamically evolving non-stationary data streams and dealing with shifts and drifts. In the talk this new concept will be described as well as a number of applications to various problems.

[1] P. Angelov, Autonomous Learning Systems: From Data Streams to Knowledge in Real time, John Willey and Sons, Dec.2012, ISBN: 978-1-1199-5152-0.
[2] P Angelov et al, Empirical Data Analysis: A New Tool for Data Analytics, IEEE SMC Conf., Budapest, 2016.

BIO: Prof. Angelov (MEng 1989, PhD 1993, DSc 2015) is a Fellow of the IEEE, of the IET and of the HEA. He is Vice President of the International Neural Networks Society (INNS) for Conference and member of the Board of Governors of the Systems, Man and Cybernetics Society of the IEEE. He has 25+ years of professional experience in high level research and holds a Personal Chair in Intelligent Systems at Lancaster University, UK. He leads the Data Science group at the School of Computing and Communications which includes over 20 academics, researchers and PhD students. He has authored or co-authored over 250 peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 6 patents, two research monographs (by Wiley, 2012 and Springer, 2002) cited over 5500 times (Dec. 2016) with an h-index of 36 and i10-index of 104. His single most cited paper has 760 citations. He has an active research portfolio in the area of computational intelligence and machine learning and internationally recognised results into online and evolving learning and algorithms for knowledge extraction in the form of human-intelligible fuzzy rule-based systems. Prof. Angelov leads numerous projects (including several multimillion ones) funded by UK research councils, EU, industry, UK MoD. His research was recognised by ‘The Engineer Innovation and Technology 2008 Special Award’ and ‘For outstanding Services’ (2013) by IEEE and INNS. He is also the founding co-Editor-in-Chief of Springer’s journal on Evolving Systems and Associate Editor of several leading international scientific journals, including IEEE Transactions on Fuzzy Systems (the IEEE Transactions with the highest impact factor) of the IEEE Transactions on Systems, Man and Cybernetics as well as of several other journals such as Applied Soft Computing, Fuzzy Sets and Systems, Soft Computing, etc. He gave over a dozen plenary and key note talks at high profile conferences. Prof. Angelov was General co-Chair of a number of high profile conferences including IJCNN2013, Dallas, TX; IJCNN2015, Killarney, Ireland; the inaugural INNS Conference on Big Data, San Francisco; the 2nd INNS Conference on Big Data, Thessaloniki, Greece and a series of annual IEEE Symposia on Evolving and Adaptive Intelligent Systems. Dr Angelov is the founding Chair of the Technical Committee on Evolving Intelligent Systems, SMC Society of the IEEE and was previously chairing the Standards Committee of the Computational Intelligent Society of the IEEE (2010-2012). He was also a member of International Program Committee of over 100 international conferences (primarily IEEE). More details can be found at www.lancs.ac.uk/staff/angelov.


Keynote Speech:
HPC – Rebooting Computing- The Search for Post-Moore’s Law Breakthroughs

Prof. Tarek El-Ghazawi
George Washington University
USA

 

ABSTRACT: The field of high-performance computing (HPC) or supercomputing refers to the building and using computing systems that are orders of magnitude faster than our common systems. This year, China has announced a new supercomputer, Sunway TaihuLight, which can perform close to a 100,000 trillion calculations in one second (93 PF on LINPAC). The top two supercomputers are now Chinese and the third is U.S. It is also expected that many countries will attempt to build an ExaFLOP supercomputer by 2020, a supercomputer that can perform more than one million trillion calculations per second. The top 10 supercomputers include installations in the China, the U.S., Japan, Switzerland, Saudi, and Germany. Scientists also see a "Convergence of Big Data and HPC" as processing massive data amounts become impractical without HPC. The supercomputing and the next generation computing is all at a cross road today. Some are thinking about building the next ExaFLOPS machine, others are looking at the convergence with big data, and many others are concerned that we are reaching many physical limits and we need new innovative ideas to make it to the next generation of computing. This talk will consider where we stand and where we ae going with the current state of supercomputing, and some of the ideas that scientists are looking at to re-invent computing.

BIO: Tarek El-Ghazawi is a Professor in the Department of Electrical and Computer Engineering at The George Washington University, where he leads the university-wide Strategic Academic Program in High-Performance Computing. He is a founding director of The GW Institute for Massively Parallel Applications and Computing Technologies (IMPACT) and the NSF Industry/University Center for High-Performance Reconfigurable Computing (CHREC). El-Ghazawi’s research interests include high-performance computing, computer architectures, and heterogeneous computing. He is one of the principal co-authors of the UPC parallel programming language and the first author of the UPC Texbook. El-Ghazawi has published close to 250 refereed research publications in this area. Dr. El-Ghazawi has served in many editorial roles including an Associate Editor for the IEEE Transactions on Computers. He has chaired and co-chaired many international conferences and symposia. Dr. El-Ghazawi’s research has been frequently supported by Federal agencies and industry including DARPA/DoD, NSF, DoE/LBNL, AFRL, NASA, IBM, HP, Intel, AMD, SGI, and Microsoft. Professor El-Ghazawi was elected to a Fellow of the IEEE and a Research Faculty Fellow of the IBM Center for Advanced Studies, Toronto. Professor El-Ghazawi was also awarded the Alexander von Humboldt Research Award, from Germany, which is given to 100 scientists across the world across all disciplines. El-Ghazawi was a recipient of the 2012 Alexander Schwarzkopf Prize for Technical Innovation, and has served as a Senior U.S. Fulbright Scholar.


Keynote Speech:
X-I: Personal Smart Buddies in Cyber-enabled Hyperworld

Prof. Jianhua Ma
Hosei University
Japan

 

ABSTRACT: Cyberspace has emerged as an unprecedented digital space in addition to conventional spaces, and further brought about a new global digital environment known as cyberworld. We are undergoing the revolutionary process of cyberization to form the novel cyberworld and reform existing physical, social and mental worlds towards a cyber-enabled hyperworld. Can we successfully adapt to these new worlds to truly benefit from these cyber technologies and live better in the complex and unknown cyber and cyber-integrated hyperworld environments? It appears that human abilities in perception, communication, management, control and cognition will not be sufficient to directly handle so many cyber things and cyber-conjugated physical, social and mental things. This talk presents a novel way to create a group of personal smart buddies that may help an individual’s activities in the cyber-enabled hyperworld. These smart buddies are expressed as a general notation x-I including Cyber-I, Wear-I, Robo-I, Ambi-I, Web-I, Social-I and Health-I. The main features and functions of these personal smart buddies are explained, and their future perspectives are discussed.

BIO: Jianhua Ma is a professor in the Faculty of Computer and Information Sciences, Hosei University, Tokyo, Japan. He served as the head of Digital Media Department of Hosei University in 2011-2012. His research interests include multimedia, networking, ubiquitous/pervasive computing, social computing, wearable technology, IoT, VR/AR, cyber life and cyber intelligence. Ma is a pioneer in research on Cyber World (CW), and a co-initiator of the first international symposium on Cyber World. He has conducted several unique CW-related projects including the cyber individual (Cyber-I), which was featured by and highlighted on the front page of IEEE Computing Now in 2011. He first proposed ubiquitous intelligence (UI) towards a smart world (SW), which he envisioned in 2004, and was featured in the European ID People Magazine in 2005. Ma has published more than 250 papers, co-authored/edited over 15 books and 25 journal special issues, delivered over 25 keynote speeches at international conferences, organized many international conferences and co-founded IEEE congresses on Cybermatics, Smart World (SW), Cyber Science and Technology (CyberSciTech), as well as IEEE conferences on Cyber Physical and Social Computing (CPSCom), Ubiquitous Intelligence and Computing (UIC), Pervasive Intelligence and Computing (PICom), Dependable, Trust and Secure Computing (DASC), Internet of Things (iThings), and Internet of People (IoP). He is a Chair of IEEE Technical Committee on Cybermatics, and a Chair of IEEE Technical Committee on Smart World.


Keynote Speech:
Human Behaviour Analysis for Assistive Cyber-Physical Systems within Smart Homes

Prof. Liming Chen
De Montfort University
UK

 

ABSTRACT: There is a growing ageing population, and along which there is an increasing prevalence of ageing-related physical, sensory and cognitive declines and chronic diseases. Yet available healthcare resources are dwindling, and the gap between the demand and supply of healthcare services is becoming wider and wider, not only in the developed countries but worldwide. Cyber-physical systems have offered promising solutions for addressing this global ageing problem through highly-automated closely-coupled monitoring and actuating. Nevertheless, traditional CPS research has paid little attention to human aspects. In this talk the speaker will introduce the smart home concept and the technology-driven approach to the global ageing problem, and further highlight the importance of human behaviour analysis in assistive CPSs. He will then focus on activity recognition and computational behaviour analysis – first reviewing existing approaches, methods and drawbacks, and then presenting a hybrid activity recognition framework describing a multi-phase iterative process and the corresponding underpinning methods and techniques. The speaker will show system prototyping, evaluation results as well as example funded research projects to illustrate the usage of the research. He will conclude the talk by elaborating research challenges and strategies, and sharing his insights into the future research and application.

BIO: Liming Chen is Professor of Computer Science, Head of the Context, Intelligence and Interaction Research Group and its associated Smart Lab in the School of Computer Science and Informatics, De Montfort University, UK. His current research interests include activity modelling and recognition, computational behaviour analysis, personalisation and adaptation of human-machine systems, decision support, smart environments and their application in smart homes and ambient assisted living. He is currently the coordinator of the EU Horizon2020 ACROSSING project “Advanced Technologies and Platform for Smarter Assisted Living”, and has serves as the principal investigator for the EU AAL PIA project, the MobileSage project and FP7 MICHELANGELO project, and a number of projects funded by industry and third countries. Liming has over 160 peer-reviewed publications in internationally recognised high-profile journals and conferences. He is the general chair or program chair for IEEE UIC2017, IEEE HealthCom2017, SAI Computing 2017, IEEE UIC2016, IntelliSys2016, MoMM2015/2014/2013, SAI2015/2013, IWAAL2014, UCAMI2013, and an organising chair of many workshops such as Romart-City2016 and SAGAware2015/2012, associate editor of IEEE THMS, assistant EIC for IJPCC and guest editors for IEEE THMS, PMC and IJDSN. Liming is a member of IEEE, IEEE SMC and the ETTC Task Force on Smart World, and has delivered many talks, keynote and seminars in various forums, conferences, industry and academic events.


Keynote Speech:
Towards Future Green Wireless Networks and Communication Systems

Prof. Qiang Ni
Lancaster University
UK

 

ABSTRACT: It is reported that the total energy consumed by the ICT infrastructure of wireless and wired networks takes up over 3 percent of the worldwide electric energy consumption that generated 2 percent of the worldwide CO2 emissions nowadays. It is predicted that in the future a major portion of expanding traffic volumes will be in wireless side. Furthermore, future wireless network systems (e.g., 5G/B5G) are increasingly demanded as broadband and high-speed tailored to support reliable Quality of Service (QoS) and Quality of Experience (QoE) for numerous multimedia applications. With explosive growth of high-rate multimedia applications (e.g. HDTV, UHDTV and 3DTV), more and more energy will be consumed in wireless networks to meet the QoS/QoE requirements. Specifically, it is predicted that footprint of mobile wireless communications could almost triple from 2007 to 2020 corresponding to more than one-third of the present annual emissions of the whole UK. Therefore, energy-efficient green wireless network systems are paid increasing attention given the limited energy resources and environment-friendly transmission requirements globally. This keynote speech will overview research activities and explore technological evolutionary steps and challenges in this direction towards future green wireless network systems.

BIO: Prof. Qiang Ni is a Chair Professor in Communications and Networking, and the Head of Communications Systems Research Group at InfoLab21, School of Computing and Communications at Lancaster University, UK. His main research interests are future generation communications and networking, including green network/communication systems, wireless networks, 5G and Internet of Things, cognitive radio network systems, cyber physical systems, vehicular networks, smart city, cloud networks, software defined networks and big data analytics. He has led various UK and EU projects in these fields. Prof Ni has published more than 180 papers which have attracted over 5300 citations. Prof Ni is a Fellow of IET, Fellow of Higher Education Academy and Senior Member of IEEE. He was an IEEE 802.11 Wireless Standard Working Group Voting Member and a contributor to the IEEE wireless standards. He is the Editor of IEEE Access, Journal of Security and Communication Networks, KSII Transactions on Internet and Information Systems and IEEK Transactions on Smart Processing & Computing.


Keynote Speech:
Cyberpatterns: Towards a Pattern Oriented Study of Cyberspace

Prof. Hong Zhu
Oxford Brookes University
UK

 

ABSTRACT: Cyberpatterns are patterns in cyberspace. A pattern represents a discernible regularity in the natural world or in manmade systems. From a prescriptive point of view, a pattern is a template from which instances can be created; while from a descriptive point of view, a pattern shows how phenomena repeat in a predictable manner that can be observed and recognised. Similar to theories in sciences, patterns explain and predict regularities in a subject domain. In a complicated subject domain like cyberspace, there are a large number of patterns that each describes and predicts a subset of recurring phenomena, yet these patterns can interact with each other and be interrelated and composed with each other. The pattern-oriented research method studies a subject domain by identifying the patterns, classifying and categorising them, organising them into pattern languages, investigating the interactions between them, devising mechanisms and operations for detecting and predicting their occurrences, and facilitating their instantiations. This talk illustrates this research methodology through a review of the research on software design patterns as an example of successful application of the methodology. It then proposes a general theory of patterns, including an algebra of pattern operations, and semantics of pattern instantiation and composition. Finally, the possible applications to cyberpatterns are discussed. It defines the scope of research, reviews the current state of art and identifies the key research questions on cyberpatterns.

BIO: Hong Zhu obtained his BSc, MSc and PhD degrees in Computer Science from Nanjing University, China, in 1982, 1984 and 1987, respectively. He worked at Nanjing University from 1987 to 1998. He joined Oxford Brookes University, UK, in November 1998 as a Senior Lecturer in Computing and became a Professor of Computer Science in October 2004. Prof. Zhu chairs the Applied Formal Methods Research Group of the Department of Computing and Communication Technologies. He is a senior member of IEEE Computer Society, a member of British Computer Society, ACM, and China Computer Federation. His research interests are in the area of software development methodologies, including formal methods, agent-orientation, automated software development, foundation of software engineering, software design, modelling and testing methods, Software-as-a-Service, etc. He has published 2 books and more than 180 research papers in journals and international conferences. He has been a conference program committee chair of SOSE 2012 and ICWS 2015, etc., a conference general chair of SOSE 2013, MobileCloud 2014, MS 2016, EDGE 2017, etc. He is a member of the editorial board of the journal of Software Testing, Verification and Reliability, Software Quality Journal, International Journal of Big Data Intelligence, and the International Journal of Multi-Agent and Grid Systems.


Keynote Speech:
Big Data - Big Application

Prof. Jinjun Chen
Swinburne University of Technology
Australia

 

ABSTRACT: Right now, Big Data, Data Science or Data Analytics are being on wide interest in industry and academia. During this talk, we will discuss two questions based on my research industry engagement practice.

The first one is business gain from such buzz words. This is a practical question from business. Based on my research, big data means big niche market opportunity for retail industry which can grow up to enhance major markets. For example, by analysing and generalising potential weak connection between previously sparse data sources such as flight booking data and supermarket user data, we can better expand or enhance the market for personal recommendation on flight booking.

The second is how researchers make a full potential to business. A say is "It's not who has the best algorithm that wins. It's who has the most data" by Andrew Ng (Coursea founder). Data is becoming an important resource equally important to oil. While various public datasets are available to academics or researchers for research evaluation, those datasets may not be suitable or useful and timely for researchers. One way to make full potential of big data is to intensively work with industry because they have timely data. More or less, every industry is doing data analysis yet just on their specific purposes. We will brief our research and collaboration with specific industries.

BIO: Dr Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He is the Deputy Director of Swinburne Data Science Research Institute, and the Director of Swinburne Big Data Lab. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, cloud computing, data intensive systems, data privacy and security, and related various research topics. His research results have been published in more than 130 papers in international journals and conferences, including various IEEE/ACM Transactions.

He received UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor’s Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Big Data, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, as well as other journals such as Journal of Computer and System Sciences, JNCA. He is the Chair of IEEE Computer Society’s Technical Committee on Scalable Computing (TCSC).


Keynote Speech:
5G Key Technologies and Standardized Channel Models

Prof. Cheng-Xiang Wang
Heriot-Watt University
Edinburgh, UK

 

ABSTRACT: The 5th generation (5G) wireless communication network is expected to achieve 1000 times of capacity and greatly enhanced spectral efficiency, energy efficiency, data rate, connection density, etc., in comparison with 4G networks. To meet these 5G requirements, some key technologies are briefly discussed, such as massive multiple-input multiple-output (MIMO), millimetre wave communications, high-speed train communications, vehicle-to-vehicle communications, and ultra-dense networks. Realistic channel models with good accuracy-complexity-flexibility trade-off are indispensable for the design and performance evaluation of 5G wireless systems. This talk will also discuss 8 standardized 5G channel models in terms of their capabilities and drawbacks, followed by proposing a unified framework for 5G channel models in some typical challenging 5G communication scenarios.

BIO: Prof. Cheng-Xiang Wang received the BSc and MEng degrees in Communication and Information Systems from Shandong University, China, in 1997 and 2000, respectively, and the PhD degree in wireless communications from Aalborg University, Aalborg, Denmark, in 2004.

He has been with Heriot-Watt University, Edinburgh, UK since 2005, and was promoted to a Professor in Wireless Communications in 2011. He is also a Chair Professor of Shandong University and a Guest Professor of Southeast University, China. He was a Research Fellow at the University of Agder, Grimstad, Norway, from 2001-2005, a Visiting Researcher at Siemens AG-Mobile Phones, Munich, Germany, in 2004, and a Research Assistant at Hamburg University of Technology, Hamburg, Germany, from 2000-2001. His current research interests include wireless channel modeling and (B)5G wireless communication networks. He has published 1 book, 1 book chapter, 126 journal papers, and 149 conference papers, having attracted over 6000 citations (h-index=40).

Prof. Wang served or is serving as an Editor for 9 international journals including IEEE Transactions on Vehicular Technology (since 2011), IEEE Transactions on Communications (since 2015), and IEEE Transactions on Wireless Communications (2007-2009). He was the leading Guest Editor for IEEE Journal on Selected Areas in Communications, Special Issue on Vehicular Communications and Networks. He is also a Guest Editor for IEEE Journal on Selected Areas in Communications, Special Issue on Spectrum and Energy Efficient Design of Wireless Communication Networks, and IEEE Transactions on Big Data, Special Issue on Wireless Big Data. He served or is serving as a General Chair, TPC Chair, and TPC member for over 80 international conferences. He received 9 Best Paper Awards from IEEE Globecom 2010, IEEE ICCT 2011, ITST 2012, IEEE VTC 2013-Fall, IWCMC 2015, IWCMC 2016, IEEE/CIC ICCC 2016, and IEEE WPMC 2016. He is a Fellow of the IEEE, IET and HEA.


Keynote Speech:
Good City Life

Dr. Daniele Quercia
Cambridge Bell Labs
UK

 

ABSTRACT: Quercia’s work blends urban computing with social media to create maps that improve our lives and answer fundamental research questions. Do we rethink existing mapping tools [happy-maps]? Is it possible to capture smellscapes of entire cities and celebrate good odors [smelly-maps]? And soundscapes [chatty-maps]?

[happy-maps] http://www.ted.com/talks/daniele_quercia_happy_maps
[smelly-maps] http://goodcitylife.org/smellymaps/index.html
[chatty-maps] http://goodcitylife.org/chattymaps/index.html

BIO: Daniele Quercia (www.researchswinger.org) leads the Social Dynamics team at Bell Labs in Cambridge (UK). He is interested in the relationship between online and offline worlds and his work has been focusing in the areas of urban informatics. He was General Chair for AAAI ICWSM and Track Chair for ACM WWW. He has been co-editor of Computer Communications Journal Special Issue on Online Social Networks (Elsevier) 2014 and for the Special issue on Personality in Personalized systems (UMUAI) 2014. His research has been published in leading venues including ICSE, Ubicomp, ICDM, CSCW, RecSys, WSDM, and WWW, received a best paper award from ACM Ubicomp and from AAAI ICWSM, and an honorable mention from AAAI ICWSM, and has been featured on more than 80 international news outlets. He spoke at TED, wrote for BBC, and has been named one of Fortune magazine’s 2014 Data All-Stars. He was Postdoctoral Associate at the Massachusetts Institute of Technology where he worked on social networks in a city context, and his PhD thesis at UC London was nominated for BCS Best British PhD dissertation in Computer Science. During his PhD, he was a Microsoft Research PhD Scholar and MBA Technology Fellow of London Business School, and he also interned at the National Research Council in Barcelona and at National Institute of Informatics in Tokyo. He studied at Politecnico di Torino (Italy), Karlsruhe Institute of Technology (Germany), and University of Illinois (USA).


Keynote Speech:
Optimizing Memory/Storage Systems for Big Data Applications

Prof. Zili Shao
Hong Kong Polytechnic University
China

 

ABSTRACT: Optimizing memory/storage is one of the most critical issues in big data systems as huge amount of data need to be stored/transferred/processed in memory and storage devices. In this talk, I will introduce our recent work in optimizing memory/storage systems for big data applications. In particular, I will present an approach by deeply integrating device and application to optimize flash-based key-value caching – one of the most important building blocks in modern web infrastructures and high-performance data-intensive applications. I will also briefly talk about the challenges and opportunities by utilizing the NVDIMM (Non-Volatile Dual In-line Memory Module) technologies to reduce the long I/O latency for big data workloads.

BIO: Zili Shao has been an Associate Professor with Department of Computing, The Hong Kong Polytechnic University, Hong Kong, since 2010. He received the B.E. degree in electronic mechanics from the University of Electronic Science and Technology of China, China, in 1995, and the M.S. and the Ph.D. degrees from the Department of Computer Science, University of Texas at Dallas, Texas, USA, in 2003 and 2005, respectively. His current research interests include embedded software and systems, storage systems and related industrial applications.

He is an associate Editor for IEEE Transactions on Computers, IEEE Transactions on CAD, ACM Transactions on Design Automation of Electronic Systems, ACM Transactions on Cyber-Physical Systems. He serves/served the technical program committees of many top conferences in the real-time embedded system field such as DAC and RTSS.


Keynote Speech:
Trust Models with a Defence Scheme for Distributed Systems

Prof. Keshav Dahal
University of the West of Scotland
UK

 

ABSTRACT: Optimizing memory/storage is one of the most critical issues in big data systems as huge amount of data need to be stored/transferred/processed in memory and storage devices. In this talk, I will introduce our recent work in optimizing memory/storage systems for big data applications. In particular, I will present an approach by deeply integrating device and application to optimize flash-based key-value caching – one of the most important building blocks in modern web infrastructures and high-performance data-intensive applications. I will also briefly talk about the challenges and opportunities by utilizing the NVDIMM (Non-Volatile Dual In-line Memory Module) technologies to reduce the long I/O latency for big data workloads.

BIO: Professor Keshav Dahal is the leader of the Artificial Intelligence, Visual Communication and Networks (AVCN) research centre at the University of the West of Scotland, UK. Prior to this he was with the University of Bradford and the University of Strathclyde, UK. His research interests lie in the areas of Computational intelligence, Big data, Scheduling/Optimisation, Trust/security modelling in distributed systems. His research paper on ‘reputation/trust modelling’ won the Best Paper award at an IEEE Conference. Prof. Dahal has an extensive experience in terms of research supervision and management of funded projects. He has been principal/co-investigator of 15 EU/UK/Industry funded projects. Currently, he is coordinating a consortium of 20 partners for the EU funded ‘SmartLink’ project (2014-2018). He is also a co-investigator of the EU H2020-ICT-2014-2 “SELFNET – Framework for Self-organized network management in virtualized and software defended networks” project (2015-2018). He has successfully supervised the completions of 6 Post-Doctoral and 13 PhD studies. Prof. Dahal has published over 100 peer-reviewed journals/conferences papers and 3 edited books, and has sat on organising/programme committees of over 50 reputed international conferences; including the general chair of a number of conferences. He is a senior member of the IEEE and fellow of the Higher Education Academy UK.


 

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