Workshop 1
Summary:
With
the
advancement
of
sensing,
communication,
and
networking,
autonomous
vehicular
networks
are
expected
to
play
a
vital
role
in
a
variety
of
areas,
including
industry
4.0,
smart
logistics,
smart
transportation,
and
public
safety.
The
application
of
artificial
intelligence
(AI)
technologies
can
provide
significant
benefits
for
automating
sensing,
computing,
and
communication
tasks
in
autonomous
vehicular
networks.
However, in order to realize real-time perception and autonomous control, AI-enabled autonomous vehicular networks will need to be more complex and heterogeneous than before. AI-enabled autonomous vehicular networks will be extremely challenging in terms of security and privacy due to complex features such as high mobility of nodes, unreliable link connections, vulnerable terminal equipments, limited resources, and heterogeneous topologies. For example, distributed AI models are very important in autonomous vehicular networks with multiple self-organizing vehicles. However, malicious attacks on AI models trained on edge devices are still an important problem to be solved in AI-enabled autonomous vehicular networks.
This workshop specifically focuses on the latest advances, challenges, and approaches to the secure integration of AI and autonomous vehicular networks. We encourage original and high-quality contributions that address both the theoretical and practical aspects of the above challenges.
However, in order to realize real-time perception and autonomous control, AI-enabled autonomous vehicular networks will need to be more complex and heterogeneous than before. AI-enabled autonomous vehicular networks will be extremely challenging in terms of security and privacy due to complex features such as high mobility of nodes, unreliable link connections, vulnerable terminal equipments, limited resources, and heterogeneous topologies. For example, distributed AI models are very important in autonomous vehicular networks with multiple self-organizing vehicles. However, malicious attacks on AI models trained on edge devices are still an important problem to be solved in AI-enabled autonomous vehicular networks.
This workshop specifically focuses on the latest advances, challenges, and approaches to the secure integration of AI and autonomous vehicular networks. We encourage original and high-quality contributions that address both the theoretical and practical aspects of the above challenges.
Keywords: autonomous vehicular networks, artificial intelligence, machine learning, security, privacy, risk assessment, data fusion
Zhiquan
Liu
is
a
full
professor
with
the
College
of
Cyber
Security,
Jinan
University.
In
recent
years,
he
has
published
more
than
80
SCI/EI
papers
on
authoritative
journals
and
conferences,
such
as
IEEE
JSAC,
IEEE
TIFS,
IEEE
TDSC,
IEEE
TMC,
IEEE
TKDE,
IEEE
TPAMI,
IEEE
TITS,
IEEE
IOTJ,
IEEE
TVT,
IEEE
TII,
IEEE
TCC,
IEEE
Network,
Science
China
Information
Sciences,
Information
Fusion,
Information
Sciences,
IEEE
ICWS,
IEEE
WCNC,
ACISP,
and
Chinese
Computer
Journal
(including
more
than
40
papers
on
CCF-A/JCR-1/TOP
journals,
4
best
papers
on
international
conferences,
2
most
popular
papers
on
international
journals,
and
1
ESI
highly
cited
paper),
and
has
applied
for/been
authorized
more
than
100
invention
patents
and
PCT
patents.
He
currently
serves
as
the
associate
editors
of
IEEE
Internet
of
Things
Journal,
IEEE
Network,
Computer
Networks,
etc.,
as
well
as
the
editor-in-cheif
of
Advances
in
Transportation
and
Logistics.
His
homepage
is
https://www.zqliu.com/.
Workshop 2
Summary:
As
AI
technologies
rapidly
evolve,
their
integration
into
healthcare
settings
heralds
a
transformative
era
with
the
potential
to
significantly
improve
patient
outcomes,
streamline
operations,
and
amplify
the
capabilities
of
healthcare
professionals.
This
workshop
is
dedicated
to
exploring
the
cutting-edge
AI
technologies
that
are
reshaping
healthcare
by
enhancing
patient
care,
optimizing
operations,
and
empowering
healthcare
professionals.
We invite leading researchers, practitioners, and policymakers to join us in examining the theoretical advancements, practical applications, and promising future of AI in the healthcare sector. The workshop will cover the following key themes (but is not limited to them):
• Future of AI in Healthcare: Participants will explore state-of-the-art research and trends shaping healthcare's future. Discussions will focus on AI's role in diagnosing diseases earlier, optimizing treatment plans, predicting patient outcomes, and its implications for healthcare delivery.
•Trustworthy AI for Healthcare: This segment will delve into the ethical considerations, importance of data privacy, and strategies for ensuring transparency and accountability. It is crucial for building reliable, ethical AI systems that align with public health goals and maintain patient trust.
•Explainable AI in Healthcare and Medicine: Explainability is vital for clinical acceptance. This theme will cover the importance of developing AI systems whose actions are interpretable by human experts, enhancing transparency and fostering trust among healthcare providers.
•Novel AI Methods and Practices for Healthcare: This new theme will highlight cutting-edge AI methodologies and their current applications within healthcare. It will showcase innovative practices and how they are being implemented to address complex medical challenges, with a focus on emerging technologies such as deep learning, natural language processing, and robotics.
We look forward to engaging discussions, collaborative opportunities, and shared insights that will shape the future of AI in healthcare.
We invite leading researchers, practitioners, and policymakers to join us in examining the theoretical advancements, practical applications, and promising future of AI in the healthcare sector. The workshop will cover the following key themes (but is not limited to them):
• Future of AI in Healthcare: Participants will explore state-of-the-art research and trends shaping healthcare's future. Discussions will focus on AI's role in diagnosing diseases earlier, optimizing treatment plans, predicting patient outcomes, and its implications for healthcare delivery.
•Trustworthy AI for Healthcare: This segment will delve into the ethical considerations, importance of data privacy, and strategies for ensuring transparency and accountability. It is crucial for building reliable, ethical AI systems that align with public health goals and maintain patient trust.
•Explainable AI in Healthcare and Medicine: Explainability is vital for clinical acceptance. This theme will cover the importance of developing AI systems whose actions are interpretable by human experts, enhancing transparency and fostering trust among healthcare providers.
•Novel AI Methods and Practices for Healthcare: This new theme will highlight cutting-edge AI methodologies and their current applications within healthcare. It will showcase innovative practices and how they are being implemented to address complex medical challenges, with a focus on emerging technologies such as deep learning, natural language processing, and robotics.
We look forward to engaging discussions, collaborative opportunities, and shared insights that will shape the future of AI in healthcare.
Keywords: Artificial Intelligence in Healthcare, Medical Imaging, Biomedical Data Analysis, Explainable AI, Healthcare Innovation
Haoxi
Zhang
is
an
Associate
Professor
at
Chengdu
University
of
Information
Technology,
China.
He
holds
a
Ph.D.
in
Knowledge
Engineering
from
the
University
of
Newcastle,
Australia
(2013),
and
a
Master’s
in
Software
Engineering
from
the
University
of
Electronic
Science
and
Technology
of
China.
Prof.
Zhang's
research
intricately
melds
artificial
intelligence
with
healthcare,
focusing
on
the
development
of
advanced
computational
models
and
machine
learning
algorithms
for
biomedical
data
analysis,
particularly
medical
imaging
and
AIoT
for
healthcare,
to
enhance
medical
decision-making.
His
work
emphasizes
developing
multimodal
learning
algorithms
to
integrate
multi-scale
biomedical
data
for
comprehensive
disease
management,
constructing
real-world
learning
systems
for
creating
robust,
trustworthy
representations
from
imperfect
medical
data,
and
innovating
causality-driven
learning
algorithms
to
boost
interpretability
and
safety
in
healthcare
applications.
Prof.
Zhang
has
published
over
40
refereed
papers
in
leading
journals
and
conferences.
Workshop 3
Summary:
With
the
rapid
development
of
science
and
technology
and
the
rapid
rise
of
Internet
information
technology,
people
get
more
and
more
effective
information
from
the
Internet,
and
people's
life
has
been
greatly
facilitated.
With
the
gradual
innovation
and
development
of
information
technology,
artificial
intelligence
has
been
paid
more
attention
and
applied
in
people's
life.
Artificial
intelligence
technology
is
developed
by
analyzing
the
law
of
people's
activities
through
intelligent
technology.
It
has
a
great
degree
of
application
function
in
robots,
control
systems
and
simulation.
In
artificial
intelligence
technology,
the
application
of
big
data
technology
can
analyze
the
potential
law
of
data
from
a
large
amount
of
data,
to
summarize
a
certain
development
law,
and
then
promote
the
further
development
of
artificial
intelligence.
This workshop aims to bring together the latest research progress of academic and industry researchers, such as big data science and foundations, big data infrastructure, big data management, big data search and mining, big data learning and analytics, data ecosystem, big data applications, artificial intelligence and technology, natural language processing, expert systems, multi-agent systems, knowledge engineering, neural network theory and architectures, artificial intelligence in modeling and simulation, pattern recognition, complex system, intelligent system, intelligent control, speech recognition, and synthesis, machine translation, computer perception, machine learning, intelligent robot, image processing and computer vision and so on. We encourage prospective authors to submit related distinguished research papers on the subject of big data and artificial intelligence.
This workshop aims to bring together the latest research progress of academic and industry researchers, such as big data science and foundations, big data infrastructure, big data management, big data search and mining, big data learning and analytics, data ecosystem, big data applications, artificial intelligence and technology, natural language processing, expert systems, multi-agent systems, knowledge engineering, neural network theory and architectures, artificial intelligence in modeling and simulation, pattern recognition, complex system, intelligent system, intelligent control, speech recognition, and synthesis, machine translation, computer perception, machine learning, intelligent robot, image processing and computer vision and so on. We encourage prospective authors to submit related distinguished research papers on the subject of big data and artificial intelligence.
Keywords: Big Data, Artificial Intelligence, Data Mining, Data Science, Natural Language Processing, Expert Systems, Multi-Agent Systems, Knowledge Engineering, Neural Network, Pattern Recognition, Complex System, Intelligent System, Intelligent Control, Speech Recognition and Synthesis, Machine Translation, Computer Perception, Machine Learning, Intelligent Robot, Image Processing and Computer Vision
Shan
Liu
is
Professor
and
Chair
of
the
Department
of
Intelligent
Science
at
the
Communication
University
of
China.
She
received
her
Ph.D.
degree
in
Electrical
Engineering
from
Texas
A&M
University,
College
Station,
Texas,
USA,
in
2013.
She
is
a
committee
member
of
the
Chinese
Association
for
Artificial
Intelligence,
the
Chinese
Institute
of
Electronics,
the
Beijing
Society
of
Image
and
Graphics,
the
Association
of
Fundamental
Computing
Education
in
Chinese
Universities,
the
China
Automation
Society,
the
National
Alliance
of
Artificial
Intelligence
and
Big
Data
Innovation
Industry
in
Colleges
and
Universities,
and
the
Institute
of
Electrical
and
Electronics
Engineers.
She
has
served
as
a
peer
reviewer
for
a
series
of
IEEE
Transactions
and
other
high-level
SCI
journals.
Her
current
research
areas
include
complex
networks,
intelligent
tags,
big
data,
and
artificial
intelligence.
Prof.
Liu
has
been
invited
to
the
University
of
California,
the
National
Laboratory
of
the
United
States,
and
other
countries
such
as
France
and
Belgium
to
give
lectures
and
speeches
and
has
gained
broad
academic
influence.
She
has
presided
over
more
than
30
research
projects,
such
as
the
National
Natural
Science
Foundation
of
China,
the
National
Social
Science
Foundation
of
China,
and
the
National
Key
Research
and
Development
Program
of
China,
published
more
than
100
high-level
academic
papers,
and
received
multiple
best
paper
awards.
Workshop 4
Summary:
In
the
context
of
the
rapid
development
of
global
informatization,
big
data
has
become
a
strategic
resource.
The
frequency,
breadth,
and
complexity
of
decision-making
activities
in
various
industries
are
fundamentally
different
from
before.
The
uncertainty
factors
in
the
decision-making
process
increase,
and
the
difficulty
of
decision-making
analysis
continues
to
increase.
Traditional
data
analysis
methods
and
decision-making
based
on
human
experience
are
no
longer
able
to
meet
the
decision-making
needs
of
the
big
data
era.
Big
data-driven
intelligent
decision-making
will
become
the
main
theme
of
decision-making
research.
We
need
to
combine
the
characteristics
of
big
data
and
analyze
some
potential
research
directions
from
aspects
such
as
intelligent
decision
support
systems,
uncertainty
processing,
information
fusion,
correlation
analysis,
and
incremental
analysis.
As
a
rapidly
developing
open
discipline,
big
data
intelligent
decision-making
still
requires
more
research
and
practice
in
terms
of
connotation
and
extension,
model
theory,
technical
methods,
and
implementation
strategies.
Keywords: Big Data, Intelligent Decision-making, Information Fusion, Incremental Learning
Xin
Nie
is
a
professor
and
master's
supervisor
at
the
School
of
Computer
Science
and
Engineering,
Wuhan
Institute
of
Technology.
His
research
interests
include
intelligent
optimization
algorithms,
machine
learning,
and
other
fields.
He
is
currently
the
director
of
the
Software
Engineering
Teaching
and
Research
Office,
a
member
of
the
China
Computer
Federation,
a
member
of
the
Chinese
Association
for
Artificial
Intelligence,
and
a
committee
of
the
Chinese
Institute
of
Command
and
Control.
Workshop 5
Summary:
This
workshop
welcomes
theoretical,
simulation
and
experimental
contributions
in
the
complex
systems
and
complex
networks.
Areas
of
interest
include,
but
are
not
limited
to,
the
following
topics:Link
prediction;Information
dissemination
and
epidemiological
information
dissemination;Community
detection;Network
mining;Methods
for
the
analysis
of
network
structures;Complex
network
modeling,
structure
and
function
analysis;Dynamics
on
complex
networks:
synchronization,
propagation,
game
playing,
etc.Network
control,
multi-agent
system
control
and
stability;Biological
networks,
systems
biology,
biological
dynamic
systems,
etc.Network
analysis
of
social,
economic
and
technical
networks;Basic
theory
and
application
of
network
security;Complex
network
and
big
data
analysis,
artificial
intelligence
computing;Complex
network
applications:
link
prediction
and
recommendation
algorithms,
traffic,
routing,
etc;Swarm
dynamics,
human
behavior
dynamics;The
intersection
of
complex
systems
with
AI
and
other
disciplines
and
their
applications;
Fractional
networks,
higher-order
networks
and
dynamics
analysis.
Keywords: Complex network; Social network; Machine learning; Deep learning; Data mining
Dr.
Xiaoyang
Liu
is
a
full
professor
of
Computer
Science
at
the
Chongqing
University
of
Technology.
He
has
won
the
second
prize
of
the
National
Teaching
Achievement
Award.
He
is
a
high-level
innovative
talent
of
Elite
Plan
of
Banan
District.
He
received
Ph.D.
degree
in
communication
and
information
systems
from
Northwestern
Polytechnical
University.
He
has
completed
his
Postdoctoral
fellow
in
Chongqing
University
and
The
University
of
Alabama,
USA.
He
is
a
Distinguished
Member
of
CCF,
and
a
member
of
IEEE
and
ACM.
His
main
research
interests
are
in
the
areas
of
social
network,
information
diffusion,
complex
network,
data
mining,
computer
application.
He
has
presided
research
on
more
than
50
national
and
provincial
projects
such
as
the
National
Natural
Science
Foundation,
the
National
Social
Science
Foundation,
Science
and
Technology
projects
of
the
National
Bureau
of
Statistics
and
the
Ministry
of
Education.
In
IEEE
Transactions
on
Network
Science
and
Engineering,
IEEE
Transactions
on
Computational
Social
Systems,
Nonlinear
Dynamics,
Applied
Intelligence
and
other
important
journals
and
conferences
at
home
and
abroad
more
than
100
academic
papers
(SCI
included);
more
than
40
national
invention
patents
have
been
authorized.
He
has
published
4
academic
monographs.
Workshop 6
Workshop 7
Workshop 8
Workshop 9
Workshop 10
Workshop 11
Workshop 12
Workshop 13
Workshop 14
Summary:
This
workshop
aims
to
explore
the
transformative
impact
of
artificial
intelligence
on
various
scientific
fields.
Participants
will
delve
into
cutting-edge
AI
methodologies
and
their
applications
in
research,
from
data
analysis
and
modeling
to
predictive
analytics
and
automation.
Through
a
series
of
expert-led
sessions,
hands-on
demonstrations,
and
collaborative
discussions,
attendees
will
gain
valuable
insights
into
how
AI
is
revolutionizing
scientific
inquiry,
enabling
faster
discoveries,
and
solving
complex
problems.
Join
us
to
learn
how
to
leverage
AI
tools
to
enhance
your
research
capabilities
and
drive
innovation
in
science.
Keywords: AI4Science
From
September
2022
to
present,
Professor,
School
of
Computer
and
Electronic
Information/School
of
Artificial
Intelligence,
Nanjing
Normal
University
Associate Professor, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, from November 2016 to August 2022
From October 2013 to September 2016, lecturer at the School of Computer Science and Technology, Nanjing Normal University
Associate Professor, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, from November 2016 to August 2022
From October 2013 to September 2016, lecturer at the School of Computer Science and Technology, Nanjing Normal University
Workshop 15
Summary:
The
emergence
of
edge
and
fog
computing
has
significantly
alleviated
the
heavy
burden
on
the
Internet-of-Things
(IoT)
networks
by
utilizing
a
decentralized
architecture
with
caching
and
storing
massive
amount
of
IoT
data.
This
computing-enabled
IoT
architecture
ensures
data
is
delivered
and
processed
in
close
promixity
to
end
terminals,
resulting
in
short,
deterministic
delay,
and
high
reliability.
These
improvements
enable
real-time
applications,
enhanced
security,
and
better
user
experiences.
Despite
these
advancements,
many
important
technical
challenges
remain.
It
is
crucial
to
improve
the
collaboration
efficiency,
reliability,
and
security
of
IoT
endpoints,
especially
when
utilizing
constrained
computing
resources.
Addressing
these
challenges
involves
leveraging
various
in-network
processing
technologies,
such
as
caches,
storage,
advanced
network
protocols
and
control,
efficient
network
measurement,
federated
learning,
and
blockchain,
all
while
maintaining
low
costs.
This
workshop
seeks
submissions
that
advance
the
theories,
technologies,
experiments,
and
practices
in
this
field.
- In-network processing for IoTs
- Edge/fog computing for IoTs
- IoT-enabled efficiency collaboration between edge/fog and cloud
- Future network architecture for IoTs
- Reliability technologies for IoT collaboration
- Blockchain technology for IoTs
- Distributed IoT data storage and management
- Network protocols for IoTs
- Security and privacy-preserving technolgoies for IoT collaboration
- Access authorization for IoT collaboration
- IoT big data management and predictive analysis
- Efficiency data caching and storage for IoTs
Keywords: IoT, edge computing, fog computing, in-network processing
Boyang
Zhou
is
a
research
expert
at
Research
Center
for
High-Productivity
Computing
System,
Zhejiang
Lab,
and
serves
as
an
adjunct
associate
professor
at
the
School
of
Intelligent
Science
and
Technology,
UCAS
Hangzhou
Institute
for
Advanced
Study.
He
received
his
doctoral
degree
in
Computer
Science
and
Technology
from
Zhejiang
University
in
2014.
He
has
authored
over
40
papers
in
esteemed
journals
such
as
IEEE
IoTJ,
IEEE
TII,
IEEE
COMMAG,
IEEE
TPDS,
IEEE
ICC,
INFOCOM,
and
USENIX
ONS.
His
intellectual
property
portfolio
boasts
30
PCT
and
national
patents
in
China.
His
recent
contributions
include
pioneering
the
Disruption
Resilient
Hop-by-Hop
Transport
Protocol
(DRTP)
and
the
establishment
of
the
Industrial
Internet
Endogenous
Security
Testbed.
He
has
served
as
a
workshop
chair
and
TPC
member
for
more
than
30
conferences.
He
has
received
three
provincial
and
society
awards
for
scientific
and
technological
progress
in
China,
as
well
as
three
best
paper
awards.
His
research
scope
encompasses
Distributed
Systems,
Computer
Networks,
High-Performance
Computing,
Smart
Grid
Communications,
and
Industrial
Internet
Security.
Workshop 16
Workshop 17
Summary:
This
workshop
will
focus
on
the
integration
of
edge
computing
and
artificial
intelligence
(AI)
to
enable
real-time
data
processing
and
intelligent
applications.
As
the
demand
for
low-latency
and
high-efficiency
data
processing
increases,
edge
computing
has
emerged
as
a
crucial
technology
to
complement
cloud
computing
by
bringing
computation
and
data
storage
closer
to
the
data
sources.
This
proximity
reduces
latency,
enhances
data
security,
and
allows
for
real-time
decision-making.
Keywords: edge computing； artificial intelligence (AI)；real-time data processing；multi-source data fusion
Gao
Pan,
Ph.D.,
professor
and
doctoral
supervisor
at
Shihezi
University,
currently
serves
as
Vice
Dean
of
the
School
of
Information
Science
and
Technology
at
Shihezi
University,
Deputy
Director
of
the
Provincial
Key
Laboratory
of
Computing
Intelligence
and
Network
Information
Security,
and
Director
of
the
Software
and
Digital
Technology
Innovation
Center
of
Xinjiang
Production
and
Construction
Corps;
In
recent
years,
key
technologies
and
product
research
and
application
have
been
carried
out
in
industries,
agriculture,
and
other
fields
using
artificial
intelligence
and
big
data
technology.
Hosted
2
National
Natural
Science
Foundation
projects,
15
provincial-level
and
above
scientific
research
projects,
published
62
papers,
authorized
14
patents,
published
5
monographs,
and
won
6
various
scientific
and
technological
awards
such
as
the
first
prize
of
provincial-level
scientific
and
technological
progress.
Workshop 18
Summary:
QoE-adaptive
task
offloading
in
multi-tier
computing
networks
is
an
urgent
demand
for
the
popularization
of
intelligent
perception
and
ubiquitous
Internet
of
Things
(IoT)
applications.
However,
it
still
faces
the
issues
in
theoretical
and
technical
mismatch
between
QoE
supply
and
demand.
Namely,
the
IoT
equipment’s
lacking
the
ability
to
perceive
the
fog
environment,
the
fog
node’s
selfish
and
uncoordinated
tendency,
and
the
cloud
server’s
ineffective
coordination
in
the
relationship
between
resource
heterogeneity
and
network
environment
dynamics.
To
this
end,
this
workshop
is
dedicated
to
publishing
research
papers
related
to
“QoE-adaptive
task
offloading
in
multitier
computing
network:
from
the
perspective
of
artificial
intelligence”,
with
the
aim
of
publishing
innovative
and
cutting-edge
theories
and
applications
in
this
field.
Potential
topics
include
but
are
not
limited
to
the
following:
- Theory and technology of task offloading in multi-tier computing network;
- Applications of the technology of task offloading in the field of IoT;
- Combination of artificial intelligence with multi-tier computing network;
- Intelligent-reflecting-surface-assisted wireless communications in WSNs;
- Deep-learning-based technologies in WSNs;
- Energy efficient task offloading mechanism in Heterogeneous IoT;
- Data offloading technology in hybrid fog-clouding computing
Keywords: Fog computing; Task offloading; Energy efficiency; Cloud computing; IoT.
IEEE
Member,
ACM
Member,
CCF
Member,
and
CIC
Member,
he
received
the
D.E
degree
in
Computer
System
Architecture
from
Xidian
University,
China
in
2019
respectively.
He
is
currently
with
School
of
Electrical
and
Electronic
Engineering,
Nanyang
Technological
University,
Singapore,
and
with
School
of
Software,
Nanchang
University,
China,
and
School
of
Electrical
and
Electronic
Engineering,
Shanghai
Jiao
Tong
University,
China.
Besides,
He
worked
as
a
visiting
researcher
in
School
of
Electronic
and
Electrical
Engineering,
University
of
Leeds,
UK
from
Oct.,
2017
to
Oct.,
2018.
His
research
interests
cover
WSNs,
IoTs,
and
Fog
Computing.
Workshop 19
Workshop 20
Workshop 21
Summary:
Service
robots
represent
a
transformative
application
of
robotics
that
profoundly
impacts
human
life,
spanning
domains
from
healthcare
to
industry.
These
robots
serve
as
lifesavers
and
support
systems,
alleviating
humans
from
strenuous
tasks
and
repetitive
work
that
might
compromise
accuracy
in
job
execution.
According
to
ISO
8373:2012,
service
robots
encompass
two
main
types:
personal
service
robots,
designed
for
use
outside
manufacturing,
and
professional
service
robots,
catering
to
non-commercial
and
commercial
purposes.
These
robots
operate
on
a
spectrum
from
semi-autonomous
to
fully
autonomous,
gradually
gaining
acceptance
as
invaluable
human
assistants
across
diverse
applications
and
professions.
Industries
are
increasingly
integrating
service
robots
into
their
production
lines,
marking
a
pivotal
shift
within
the
context
of
the
industrial
revolutions.
The
first
revolution
brought
mechanization,
followed
by
the
second
revolution
powered
by
electricity.
Industry
4.0,
however,
intertwines
digital
and
internet
technologies,
propelling
further
innovation
and
evolution
in
the
realm
of
technology.
Within
this
discourse,
the
focus
narrows
to
AGV
(Automated
Guided
Vehicles)
and
MIR
(Mobile
Industrial
Robots)
as
exemplary
service
robots.
The
discussion
delves
into
the
modeling
steps
and
simulation
processes
involved
in
their
creation.
Additionally,
it
scrutinizes
the
performance
of
designed
AGVs
employing
various
algorithms.
This
analysis
aims
to
serve
as
a
guide
for
researchers,
offering
insights
and
practical
implementations
for
diverse
control
systems
within
modeled
systems.
Keywords: Service robot, AGV, Vision Systems , Machine Perception
Dr.
Ata
Jahangir
Moshayedi
is
an
Associate
Professor
at
Jiangxi
University
of
Science
and
Technology,
China,
holding
a
PhD
in
Electronic
Science
from
Savitribai
Phule
Pune
University,
India.
With
a
distinguished
career
spanning
academia
and
research,
Dr.
Moshayedi
is
recognized
as
an
IEEE
Senior
Member
and
holds
esteemed
memberships
including
the
Instrument
Society
of
India
as
a
Life
Member
and
Lifetime
Member
of
the
Speed
Society
of
India.
Dr.
Moshayedi's
expertise
extends
beyond
membership,
as
he
actively
contributes
to
the
advancement
of
knowledge
in
his
field.
He
serves
on
the
editorial
boards
of
several
esteemed
conferences
and
journals,
including
Biomimetic
Intelligence
and
Robotics
(BIRob),
EAI
Endorsed
Transactions
on
AI
and
Robotics,
International
Journal
of
Robotics
and
Control,
JSME,
Bulletin
of
Electrical
Engineering
and
Informatics,
and
International
Journal
of
Physics
and
Robotics
Applied
Electronics.Throughout
his
illustrious
career,
Dr.
Moshayedi
has
made
significant
scholarly
contributions,
with
more
than
80
papers
published
in
national
journals
and
conferences.
Additionally,
he
has
authored
three
books,
further
solidifying
his
reputation
as
a
thought
leader
in
his
field.
His
innovative
spirit
is
reflected
in
his
ownership
of
two
patents
and
12
copyrights,
demonstrating
his
commitment
to
pushing
the
boundaries
of
knowledge
and
technology.Dr.
Moshayedi's
research
interests
are
diverse
and
impactful,
focusing
on
Robotics
and
Automation,
Sensor
Modeling,
Bio-inspired
Robots,
Mobile
Robot
Olfaction,
Plume
Tracking,
Embedded
Systems,
Machine
Vision-based
Systems,
Virtual
Reality,
and
Artificial
Intelligence.
His
multidisciplinary
approach
and
dedication
to
excellence
continue
to
inspire
colleagues
and
students
alike,
shaping
the
future
of
robotics
and
technology.
Workshop 22
Summary:
6G
wireless
networks
are
envisioned
to
revolutionize
the
evolution
of
wireless
communications
from
connected
things
to
connected
intelligence.
Artificial
Intelligence
(AI)
has
become
the
core
driving
force
for
the
new
round
of
industrial
transformation,
and
has
already
demonstrated
great
potential
in
various
aspects
of
wireless
networks
from
resource
allocation
to
network
optimization.
Integrating
AI
into
6G
promises
unprecedented
levels
of
efficiency,
reliability,
and
intelligence,
while
there
remain
various
challenges,
including
scalable
AI
algorithms
to
handle
massive
data
volumes
and
real-time
processing,
endogenous
security
and
privacy,
and
AI
models
that
can
adapt
to
highly
dynamic
and
heterogeneous
network
architecture.
It
still
needs
plenty
of research
efforts
to
explore
the
intersection
of
AI
and
6G
and
to
unlock
more
new
opportunities.
This workshop aims to provide a platform for researchers from both academia and industry to exchange ideas and bring together novel contributions to explore the opportunities, challenges, and solutions related to approaches on AI-driven 6G wireless networks. We solicit high-quality original technical papers on the following topics (but not limited to)
This workshop aims to provide a platform for researchers from both academia and industry to exchange ideas and bring together novel contributions to explore the opportunities, challenges, and solutions related to approaches on AI-driven 6G wireless networks. We solicit high-quality original technical papers on the following topics (but not limited to)
- New research paradigm and fundamental limits of 6G native AI
- AI for 6G network architecture, e.g., resources/spectrum/interference management, network slicing and virtualization, autonomous operations
- AI for wireless communication systems, e.g., deep learning (DL)/machine learning (ML)/federated learning (FL) modeling, design, implementation for signaling, waveform, coding, modulation, beamforming, propagation design
- AI for state-of-the-art wireless technologies, e.g., reconfigurable intelligent surface (RIS), non-orthogonal multiple access (NOMA), orthogonal time frequency space (OTFS), integrated sensing and communication (ISAC), near-filed communications (NFC), movable/fluid antenna, massive MIMO, mmWave/THz/optical communications, and semantic communications
- AI for emerging wireless networks, e.g., ambient Internet of Things (AIoT), cell-free networks, edge computing/learning, body area network, vehicular-to-everything (V2X), unmanned aerial vehicle (UAV), low earth orbit (LEO), and integrated satellite-air-terrestrial-maritime networks
- AI for wireless network security, e.g., encryption/authentication protocol, cryptography, blockchain, security/privacy/trustworthy in cyber-physical systems, anti-jamming/spoofing, physical layer security, quantum security
- System-level simulation, prototyping, and experimentation on AI for 6G
Keywords: Artificial intelligence, 6G wireless networks, wireless communications, Internet of Things, security and privacy.
Tong-Xing
Zheng
received
the
B.S.
degree
in
Information
Engineering
and
Ph.D.
degree
in
Information
and
Communications
Engineering
from
Xi’an
Jiaotong
University,
Xi’an,
in
2010
and
2016,
respectively.
From
2017
to
2018,
he
was
a
Visiting
Scholar
with
the
School
of
Electrical
Engineering
and
Telecommunications,
University
of
New
South
Wales,
Sydney,
Australia.
He
is
currently
an
Associate
Professor
with
Xi’an
Jiaotong
University.
His
research
interests
include
B5G/6G
wireless
networks,
physical
layer
security,
covert
communications,
reconfigurable
intelligent
surface,
integrated
sensing
and
communications.
He
has
published
three
book/chapters
and
100+
papers
in
telecommunications
journals
and
conference
proceedings.
He
was
a
recipient
of
the
Excellent
Doctoral
Dissertation
Award
of
Shaanxi
Province
in
2019,
the
First
Prize
of
Science
and
Technology
Award
in
Higher
Institution
of
Shaanxi
Province
in
2019
and
2024,
the
First
Prize
of
Air
Force
First
Aviation
Creative
Challenge
in
2023,
and
the
Best
Paper
Award
at
the
2023
International
Conference
on
Ubiquitous
Communication.
He
was
honored
as
an
Exemplary
Reviewer
of
IEEE
TRANSACTIONS
ON
COMMUNICATIONS
in
2017,
2018,
and
2021.
He
served
as
an
Associate
Editor
of
IET
ELECTRONIC
LETTERS
from
2020
to
2023.
He
served
as
a
Session
Chair
for
the
IEEE
ICC
2022
and
IoTCIT
2024.
He
has
served
as
a
TPC
member
for
various
IEEE
sponsored
conferences,
including
IEEE
GLOBECOM,
ICC,
WCNC,
VTC,
and
PIMRC.
Yiliang
Liu
received
the
B.E
and
M.Sc
degrees
in
Computer
Science
and
Communication
Engineering,
Jiangsu
University,
Zhenjiang,
China,
and
the
Ph.D.
degree
in
the
School
of
Electronics
and
Information
Engineering,
Harbin
Institute
of
Technology,
Harbin,
China,
in
2012,
2015,
and
2020,
respectively.
He
was
a
Visiting
Research
Student
with
the
Department
of
Engineering
Science,
National
Cheng
Kung
University,
Tainan,
Taiwan,
from
2014
to
2015,
and
the
Department
of
Electrical
and
Computer
Engineering,
University
of
Waterloo,
Waterloo,
ON,
Canada,
from
2018
to
2019.
He
is
currently
an
assistant
professor
with
the
School
of
Cyber
Science
and
Engineering,
Xi'an
Jiaotong
University,
Xi'an,
China.
His
research
interests
include
the
security
of
wireless
communications,
physical
layer
security,
and
intelligent
connected
vehicles.
Dr.
Liu
is
a
recipient
of
the
Outstanding
Doctoral
Dissertation
Award
from
China
Education
Society
of
Electronics
in
2020
and
a
recipient
of
Best
Paper
Award
of
IEEE
Systems
Journal
in
2021.
Cong
Li
received
the
doctor
degree
from
Xidian
university,
Xi’an,
China,
in
2018.
He
is
an
senior
engineer
at
China
Academy
of
Space
Electronic
Information
Technology-Xi’an,
where
he
leads
the
research
team
of
“intelligent
satellite
system”.
He
has
led
and
participated
in
multiple
national
level
projects,
covering
topics
such
as
satellite
communication,
multifunctional
software
defined
satellites
and
human-machine
hybrid
intelligence.
He
translated
a
machine
learning
book,
and
published
more
than
15
journals
and
conferences.
His
research
interests
include
6G,
intelligent
satellite
communication,
satellite
intelligent
applications
and
so
on.
Yang
Yang
received
the
M.S.
degree
in
Circuits
and
Systems
and
Ph.D.
degree
in
Communications
and
Information
Systems
from
Beijing
University
of
Posts
and
Telecommunications,
Beijing,
in
2012
and
2015,
respectively.
From
2015
to
2017,
he
was
a
postdoctoral
researcher
at
the
Department
of
Electronic
Engineering,
Tsinghua
University,
Beijing,
China.
From
2023
to
2024,
he
was
also
a
visiting
scholar
at
Sungkyunkwan
University
and
Yonsei
University
in
South
Korea,
respectively.
Now
he
is
currently
an
Associate
Professor
with
School
of
Artificial
Intelligence,
Beijing
University
of
Posts
and
Telecommunications,
Beijing,
China.
His
research
interests
include
intelligent
wireless
network
optimization,
6G
mobile
network
scheduling,
integrated
satellite-terrestrial
networks.
He
has
published
two
books
and
over
60
papers
in
IEEE
journals
and
conferences.
He
was
a
recipient
of
the
‘CCF-Baidu’
scholar
fellowship
in
2022,
the
excellent
instructor
of
China
Graduate
Student
Electronic
Design
Competition
in
2018,
and
the
Best
Paper
Award
at
the
2019
International
Conference
on
Computer
and
Communications,
Chengdu,
China.
He
was
honored
as
an
Exemplary
Reviewer
of
IEEE
TRANSACTIONS
ON
COMMUNICATIONS
in
2018.
He
served
as
a
Guest
Editor
of
SENSORS
and
ELECTRONICS
from
2022
to
2024.
He
has
served
as
a
TPC
member
for
various
IEEE
sponsored
conferences,
including
the
IEEE
GLOBECOM,
ICC,
VTC,
and
PIMRC.
Workshop 23
Workshop 24
Workshop 25
Summary:
Currently,
data
elements
are
gradually
becoming
the
core
and
fundamental
driving
force
of
the
new
quality
productive
forces.
There
are
still
many
theoretical
and
engineering
problems
in
how
to
solve
the
controllable,
reliable,
and
accurate
data
flow
among
various
entities
in
government,
industry,
and
group
enterprises.
At
the
same
time,
the
relevant
foundational
theories
and
technologies
supporting
the
data
element
market
are
also
within
the
scope
of
this
research
topic.
Keywords: Data Integration; Data Access Control; Intelligent Analytics; Data Elements
Deqian
Fu
received
Ph.D.
from
the
University
of
Suwon
(USW),
Korea,
in
2014,
and
M.S
from
Shandong
University
of
Science
and
Technology
(SDUST),
China,
in
2005.
He
is
presently
a
professor
in
the
department
of
information
science
and
engineering,
Linyi
University,
China.
Dr.
Fu
published
more
than
40
academic
papers.
His
research
interests
include
data
elements,
machine
learning,
computer
vision,
information
systems.
Yang
Qu,
received
a
Ph.D.
degree
in
Economics
from
Xi’an
Jiao
Tong
University,
is
currently
a
postdoctoral
researcher
of
School
of
Information
Management
Wuhan
University.
He
has
been
focusing
on
research
on
emerging
technologies
(such
as
Cloud
Computing,
Artificial
Intelligence,
etc.)
embracing
financial
applications.
As
one
of
the
earliest
scholars
in
the
world
to
conduct
quantitative
research
on
the
benefits
of
emerging
technology
from
a
micro
perspective,
he
published
a
quantitative
study
on
FinTech
and
credit
risk
in
Pacific-Basin
Finance
Journal
(JCR
Q1)
in
2020,
which
has
been
cited
more
than
300
times
to
date
and
was
selected
as
one
of
the
Most
Cited
Papers
in
this
journal.
After
that,
he
published
several
papers
in
several
SSCI
journals
such
as
International
Review
of
Financial
Analysis
(JCR
Q1
Top;
ABS
3stars)
and
Journal
of
Financial
Stability
(JCR
Q1;
ABS
3stars).
His
research
was
recognized
by
the
Shaanxi
Provincial
Government
and
Xi’an
Jiao
Tong
University.
At
present,
he
is
also
paying
attention
to
the
coupling
of
emerging
technologies
and
data
elements.
Workshop 26
Summary:
In
the
burgeoning
field
of
artificial
intelligence,
Approximate
Nearest
Neighbor
(ANN)
algorithms
have
become
indispensable
for
high-dimensional
similarity
search
across
applications
like
computer
vision,
NLP,
and
recommendation
systems.
With
the
ever-growing
volume
of
data,
the
quest
for
efficient
and
reliable
ANN
algorithms
is
more
critical
than
ever.
This workshop is dedicated to the exploration and advancement of ANN algorithms, focusing on their efficiency, scalability, and integration with emerging technologies. We will delve into the latest innovations, theoretical developments, and practical implementations that enhance the performance of ANN search systems.
Our goal is to foster a collaborative environment where researchers and practitioners can share breakthroughs, discuss challenges, and showcase their work in the field of ANN. We are particularly interested in submissions that address the following themes:
Join us for a dynamic event that brings together the brightest minds to drive the future of ANN research. We welcome contributions from academia and industry alike, encouraging an open exchange of ideas and fostering the development of the next generation of ANN solutions.
This workshop is dedicated to the exploration and advancement of ANN algorithms, focusing on their efficiency, scalability, and integration with emerging technologies. We will delve into the latest innovations, theoretical developments, and practical implementations that enhance the performance of ANN search systems.
Our goal is to foster a collaborative environment where researchers and practitioners can share breakthroughs, discuss challenges, and showcase their work in the field of ANN. We are particularly interested in submissions that address the following themes:
- Novel ANN algorithms and their theoretical underpinnings.
- The synergy of ANN with cutting-edge hardware like GPUs and TPUs.
- Interdisciplinary approaches that combine ANN with other AI technologies.
Join us for a dynamic event that brings together the brightest minds to drive the future of ANN research. We welcome contributions from academia and industry alike, encouraging an open exchange of ideas and fostering the development of the next generation of ANN solutions.
Keywords: Vector index, similarity search, Approximate Nearest Neighbors
Ruini
Xue,
received
his
master
and
Ph.D.
degrees
in
computer
science
and
technology
from
Tsinghua
University.
He
worked
as
an
associated
professor
at
the
School
of
Computer
Science
and
Engineering,
University
of
Electronic
Science
and
Technology
of
China.
His
research
interests
include
graph
computing,
distributed
database
and
system
architecture.
He
participated
in
the
National
Natural
Science
Foundation
and
Chinese
National
Programs
for
High
Technology
Research
and
Development,
etc.
He
published
more
than
30
papers
and
about
20
patents
have
been
granted.
Workshop 27
Workshop 28
Summary:
In
many
scenarios
such
as
target
recognition,
defect
detection,
image
and
text
generation,
intelligence
analys,
and
intelligent
Q&A,
it
is
difficult
to
ensure
the
accuracy
of
recognition,
detection,
or
answering
using
a
single
modal
data
modeling
due
to
complex
and
variable
environments,
strong
background
noise,
and
insufficient
information.
To
improve
the
modeling
effect,
it
is
necessary
to
fully
utilize
the
complementary
advantages
of
multi-modal
data,
integrate
data
from
various
modalities
such
as
RGB
images,
infrared
images,
ultrasound,
text,
and
electromagnetic
signals,
and
construct
a
unified
feature
representation
model.
This
workshop
will
focus
on
several
academic
issues
in
multi-modal
fusion
in
artificial
intelligence,
including
but
not
limited
to:
object
recognition,
defect
detection,
image
and
text
generation,
intelligence
analys,
intelligent
Q&A,
and
other
aspects.
Keywords: Multi-modal fusion, Artificial intelligence, Feature representation
As
the
project
leader
or
main
participant,
Professor
Chen
participated
in
many
projects
such
as
863,
NSFC.
In
recent
years,
He
has
published
more
than
100
academic
papers
in
many
important
academic
journals
or
international
conferences
such
as
Physics
Report,
Knowledge
Based
Systems,
and
Information
Sciences.
He
Received
the
second
prize
of
the
Natural
Science
Award
of
the
CCF
in
2014
and
the
third
prize
of
the
Sichuan
Provincial
Science
and
Technology
Progress
Award
in
2022.
Workshop 29
Workshop 30
Summary:
ntelligent
manufacturing
is
the
key
direction
of
the
future
development
of
the
manufacturing
industry.
By
means
the
integration
of
advanced
information
technology,
automation
technology,
big
data
analysis
and
artificial
intelligence,
it
realizes
the
intelligent,
flexible
and
personalized
production
process.
The
application
of
artificial
intelligence
in
intelligent
manufacturing
is
mainly
reflected
in
the
following
aspects:
The research focus of artificial intelligence in intelligent manufacturing includes:
- 1.Predictive maintenance: The machine learning algorithm is used to analyze equipment operation data, predict equipment failure and maintenance requirements, so as to reduce downtime and improve production efficiency.
- 2.Quality control: Through image recognition and pattern recognition technology, real-time monitoring of the production process, automatically detect product defects, to ensure product quality.
- 3.Supply chain optimization: Artificial intelligence is used to conduct demand prediction, inventory management and logistics optimization to reduce inventory costs and improve the response speed and flexibility of the supply chain.
- 4.Adaptive production: The AI system can automatically adjust the production plan and production line configuration according to the changes of market demand, and realize rapid switching and personalized customization.
- 5.Intelligent robots: Deploy intelligent robots on the production line that can perform complex tasks and cooperate with human workers to improve production efficiency and safety.
- 6.Virtual simulation: Use artificial intelligence to carry out virtual simulation of product design and production process, optimize product design, and reduce the actual trial and error costs.
The research focus of artificial intelligence in intelligent manufacturing includes:
- 1.Deep learning and pattern recognition: Develop more accurate algorithms to improve the ability to analyze and identify production data.
- 2.Natural language processing: It enables machines to understand and process natural language instructions and simplify the process of human-computer interaction.
- 3.Edge calculation: Conduct data processing at the source of data generation to reduce latency and improve real-time performance.
- 4.Human-machine collaboration: Study how to make the artificial intelligence system to better cooperate with human workers to improve work efficiency and safety.
- 5.Security and privacy: To ensure that the application of artificial intelligence system in intelligent manufacturing will not leak sensitive information and ensure production safety.
- 6.Interpretability and transparency: Improve the interpretability of ai decision-making process and enhance users' trust in the system.
Keywords: Artificial Intelligence，Intelligent Manufacturing，Application，Research
Research
interests:
Intelligent
Manufacturing,
Modern
Mechanical
Design,
Electromechanical,
Hydraulic
and
Pneumatic
Integration
Technology
2021-present: Professor, President of School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, China
2013-2021: Professor, Vice-President of School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, China
2009-2013. Deputy Director of Academic Affairs Office, Jingdezhen Ceramic Institutes, China
1992-2009,Teacher, School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institutes, China
2021-present: Professor, President of School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, China
2013-2021: Professor, Vice-President of School of Mechanical and Electronic Engineering, Jingdezhen Ceramic University, China
2009-2013. Deputy Director of Academic Affairs Office, Jingdezhen Ceramic Institutes, China
1992-2009,Teacher, School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institutes, China
Workshop 31
Summary:
Today,
big
data
has
become
a
kind
of
capital.
The
world’s
largest
technology
companies
are
all
based
on
big
data,
which
they
continuously
analyze
to
improve
operational
efficiency
and
develop
new
products.
Although
it
has
been
around
for
a
long
time,
the
utilization
of
big
data
is
just
beginning.
The
value
of
big
data
lies
in
its
application.
It
can
help
people
understand
data
more
intuitively
and
conveniently,
and
can
further
mine
other
valuable
data.
The aim of this workshop is to bring together the latest research results in the field of Big Data Application provided by researchers from academia and the industry. We encourage prospective authors to submit related distinguished research papers on the following topics. Please name the title of the submission email with “paper title_workshop title”. Big Data Applications and Software in Science, Engineering, Healthcare, Finance, Business, Transportation, Telecommunications, etc.
Big Data Analytics in Small Business Enterprises, Public Sector and Government.
Big Data Industry Standards.
Development and Deployment Experiences with Big Data Systems.
The aim of this workshop is to bring together the latest research results in the field of Big Data Application provided by researchers from academia and the industry. We encourage prospective authors to submit related distinguished research papers on the following topics. Please name the title of the submission email with “paper title_workshop title”. Big Data Applications and Software in Science, Engineering, Healthcare, Finance, Business, Transportation, Telecommunications, etc.
Big Data Analytics in Small Business Enterprises, Public Sector and Government.
Big Data Industry Standards.
Development and Deployment Experiences with Big Data Systems.
Keywords: big data application, big data analysis, big data industry standard, big data system
Jianjun
Zhang, received
a
Ph.D.
degree
in
Computer
Science
and
Technology
from
Hunan
University.
He
worked
as
an
associate
professor
at
the
College
of
Engineering
and
Design,
Hunan
Normal
University.
His
research
interests
include
Big
Data
Application
and
Information
Security.