The 13th International Conference on Computer and Communications Management
Invited Speakers
Prof. Takumi Miyoshi
Shibaura Institute of Technology, Japan
Takumi Miyoshi received his B.Eng., M.Eng., and Ph.D. degrees
in electronic engineering from the University of Tokyo, Japan,
in 1994, 1996, and 1999, respectively. He started his career as
a research associate at Global Information and Telecommunication
Institute, Waseda University, from 1999 to 2001. He is presently
a professor at Department of Electronic Information Systems,
College of Systems Engineering and Science, Shibaura Institute
of Technology, Japan. He was a visiting scholar in Laboratoire
d’Informatique de Paris 6 (LIP6), Sorbonne Université, Paris,
France, from 2010 to 2011. He received the IEICE Young
Researcher’s Award in 2004, the Best Regional Paper Award in
IEEE ICCE 2024, the Best Paper Award in APNOMS 2016 and 2020,
the Best Short Paper Award in IEICE ICETC 2020, the 20th Best
Paper Award in Transactions of Human Interface Society in 2020.
His research interests include overlay networks, mobile ad hoc
and sensor networks, machine learning, smart cities, and digital
twin. He is a member of IEEE and a senior member of IEICE.
Assoc. Prof. Jie Gong
Sun Yat-sen University, China
Jie Gong received his B.S. and Ph.D. degrees in Department of
Electronic Engineering in Tsinghua University, Beijing, China,
in 2008 and 2013, respectively. From Jul. 2012 to Jan. 2013, he
visited University of Edinburgh, Edinburgh, UK. From Jul. 2013
to Oct. 2015, he worked as a postdoctoral scholar in Tsinghua
University. He is currently an associate professor in School of
Computer Science and Engineering, Sun Yat-sen University,
Guangzhou, China. He is serving as an editor for IEEE Trans.
Green Commun. Netw., IEEE/CIC ICCC 2022 Workshop Co-Chair and
Publicity Co-chair for Workshop on Intelligent Computing and
Caching at the Network Edge in IEEE WCNC since 2018. He was a
co-recipient of the Best Paper Award from IEEE Communications
Society Asia-Pacific Board in 2013, and the Best Paper Award of
the 5th EAI International Conference on IoT as a Service in
2019. His research interests include Age of Information,
reinforcement learning, mobile edge computing and green
communications and networking.
Prof. Pascal Lorenz
University of Haute Alsace, France
Pascal Lorenz received his M.Sc. (1990) and Ph.D. (1994) from
the University of Nancy, France. He is a professor at the
University of Haute-Alsace, France, since 1995. His research
interests include QoS, wireless networks and high-speed
networks. He is the author/co-author of 3 books, 3 patents and
200 international publications in referred journals and
conferences. He is associate Editor for International Journal of
Communication Systems (IJCS-Wiley), Journal on Security and
Communication Networks (SCN-Wiley) and International Journal of
Business Data Communications and Networking, Journal of Network
and Computer Applications (JNCA-Elsevier). He is a senior member
of the IEEE, IARIA fellow and member of many international
program committees. He has organized many conferences, chaired
several technical sessions and gave tutorials at major
international conferences. He was IEEE ComSoc Distinguished
Lecturer Tour during 2013-2014.
Speech Title: "Architectures of Next
Generation Wireless Networks"
Abstract: Internet Quality of Service (QoS) mechanisms are expected to
enable wide spread use of real time services. New standards and
new communication architectures allowing guaranteed QoS services
are now developed. We will cover the issues of QoS provisioning
in heterogeneous networks, Internet access over 5G networks and
discusses most emerging technologies in the area of networks and
telecommunications such as IoT, SDN, Edge Computing and MEC
networking. We will also present routing, security, baseline
architectures of the inter-networking protocols and end-to-end
traffic management issues.
Assoc. Prof. Taku Yamazaki
Shibaura Institute of Technology, Japan
Taku Yamazaki received the B.E. and M.S. degrees in electronic
information systems from Shibaura Institute of Technology,
Tokyo, Japan, in 2012 and 2014, respectively. He received the
D.E. degree in computer science and communications engineering
from Waseda University, Tokyo, Japan, in 2017. He was a research
associate at Department of Communications and Computer
Engineering, School of Fundamental Science and Engineering,
Waseda University, from 2015 to 2018. He was an adjunct
researcher at Global Information and Telecommunication
Institute, Waseda University, Tokyo, Japan from 2018 to 2021. He
is presently an assosiate professor at Department of Electronic
Information Systems, College of Systems Engineering and Science,
Shibaura Institute of Technology, Saitama, Japan. He received
IEICE Network Systems Research Award in 2014, the CANDAR/ASON
Best Paper Award in 2014, IEICE Young Researcher's Award in
2015, IEICE Network Software Best Poster Award in 2016, IEICE
Network Systems Young Researcher's Encouragement Award in 2018.
He is a member of IEEE, ACM, IEICE, and JSEE.
Assoc. Prof. Pavel Loskot
ZJU-UIUC Institute, China
Pavel Loskot joined the ZJU-UIUC Institute in January 2021 as
an Associate Professor after being 14 years with Swansea
University in the UK. He received his PhD degree in Wireless
Communications from the University of Alberta in Canada, and the
MSc and BSc degrees in Radioelectronics and Biomedical
Electronics, respectively, from the Czech Technical University
of Prague in the Czech Republic. In the past nearly 30 years, he
was involved in numerous collaborative research and development
projects, and also held a number of paid consultancy contracts
with industry. He is the Senior Member of the IEEE, Fellow of
the Higher Education Academy in the UK, and the Recognized
Research Supervisor of the UK Council for Graduate Education.
His current research interests focus on mathematical and
probabilistic modeling, statistical signal processing and
classical machine learning for multi-sensor data.
Speech Title: "Exploiting Approximations in
Signal Processing and Machine Learning"
Abstract: The real-world signals often have complex
dependencies, so they cannot be easily described by simple
models. Approximating signals is one of the basic strategies in
devising sensible signal processing methods with sufficient
performance. For instance, the regression fitting of models to
data is a very common task in machine learning. Other examples
include approximating complex multi-dimensional distributions in
Bayesian inferences, interpolating values to increase the
resolution, extrapolating values in forecasting, and defining
learnable functions under additional, e.g., continuity
constraints. The approximating functions are usually defined as
sequences of parameterized models, which can guarantee a
convergence arbitrarily close to the function being
approximated, which may not actually be even known. In this
talk, I will discuss approximations, and introduce the
underlying mathematical foundations that can inspire the
existing as well as future methods in signal processing and
machine learning.
Assoc. Prof. Antoine Bossard
Kanagawa University, Japan
Antoine Bossard is a Professor of the Graduate School of
Science, Kanagawa University in Japan. He received the BS and MS
degrees from Université de Caen Basse-Normandie, France in 2005
and 2007, respectively, and the Ph.D. degree from Tokyo
University of Agriculture and Technology, Japan in 2011. Amongst
others, he is in charge of the computer architecture and
functional programming lectures for undergraduate students, and
of a graph theory lecture for master students. He also is
responsible for the functional and logic programming lecture at
Tokyo University of Agriculture and Technology.
Regarding research activities, Antoine mostly focuses on the
following two subjects: interconnection networks (network
topologies, routing problems, fault tolerance) and information
representation and processing of Chinese characters (e.g.
fingerprinting). He is the author of multiple papers in these
fields, papers published in international journals and
conference proceedings. He has also written several books, for
instance for his students of computer architecture and
functional programming, and on Chinese characters, with notably
a commented translation of the first part of the Dictionarium
anamitico-latinum of Jean-Louis Taberd.
Dr. Rajkumar Singh Rathore
Cardiff Metropolitan University, UK
Dr. Rajkumar Singh Rathore is the Head of Cyber Security for
Connected and Autonomous Systems CINC and Head of Cyber Physical
and Networked Systems CeRISS at Cardiff Metropolitan University
in the UK. He holds a doctorate, dual master’s degrees, and a
bachelor’s in computer science and engineering. A Fellow of the
Higher Education Academy (HEA) UK, Dr. Rathore has extensive
experience in teaching and research. His research has been
supported by Nottingham Trent University and Manchester
Metropolitan University. He has co-authored textbooks for BSc
and MSc students and received multiple 'Best Teacher' awards.
Dr. Rathore is active in international organizations, serving as
a editor, reviewer for peer-reviewed top tier journals and
chairing on technical committees for conferences. His research
areas include Wireless Communications, IoT, Cyber Security,
Connected Vehicles, EV Charging Management, and AI/ML
applications. He is a founding member of the IEEE Trustworthy
Internet of Things Working Group and also serves on the ACM
Europe Technology Policy Committee.
Speech Title "Intelligent Caching Techniques for Popular Content
in Vehicular Networks"
Abstract: In my talk, I will explore the innovative approach of
Information-Centric Networking (ICN) in vehicular networks
(VNs), which facilitates data caching at each node to enhance
performance and reduce content delivery delays, especially
during high-traffic scenarios. I will address the significant
challenges faced by existing content popularity prediction
methods in dynamic environments, particularly regarding the
rapidly changing preferences in vehicular traffic. To tackle
these issues, I propose an intelligent caching strategy that
utilizes Deep Transfer Learning (DTL) to increase the cache hit
rate of popular data, thereby minimizing system costs and
content delays. The proposed method incorporates collaborative
caching with social interactions among clusters to share the
most popular content (MPC) and features a time-varying mechanism
for accurate content popularity prediction. Additionally, I will
present our innovative content update method designed for
cooperative caching environments, along with a thorough analysis
of simulation results that confirm our approach exceeds the
performance of existing baseline methods.
Dr. Muhammad Waqas
University of Greenwich, UK
Dr. Muhammad Waqas is a leading researcher in wireless
communications, vehicular networks, cybersecurity, and machine
learning. Currently a Senior Lecturer at the University of
Greenwich, UK, he has held academic and research positions at
institutions in China, Bahrain, Pakistan, and Australia. His
research includes over 150 publications in high-impact journals
and conferences, accumulating over 3000 citations, with an
h-index of 31 and an i10-index of 89.
Dr. Waqas has secured multiple funded research projects and
serves as an Associate Editor and Guest Editor for several
reputed Journals. His research interests are Wireless
Communication, vehicular networks, cybersecurity and Machine
Learning. He is recognised as a Global Talent in Wireless
Communications by UK Research and Innovation and a Professional
Member of Engineer Australia. He is a senior member of IEEE, a
Professional Member of ACM, an IEEE Young Professional, a Member
of the Pakistan Engineering Council and PhD approved supervisor
by the Higher Education Commission of Pakistan.