IEEE Computational Intelligence Magazine - November 2021 - 14

Spotlight
Publication
Haibo He, University of Rhode Island, USA
Jon Garibaldi, University of Nottingham, UK
Carlos A. Coello Coello, CINVESTAV-IPN, MEXICO
Julian Togelius, New York University, USA
Yaochu Jin, University of Surrey, UK
Yew-Soon Ong, Nanyang Technological University, SINGAPORE
Hussein Abbass, University of New South Wales, AUSTRALIA
CIS Publication Spotlight
IEEE Transactions on Neural
Networks and Learning Systems
A Survey on Learning-Based Approaches
for Modeling and Classification of
Human-Machine Dialog Systems, by F.
Cui, Q. Cui, and Y. Song, IEEE Transactions
on Neural Networks and Learning
Systems, Vol. 32, No. 4, April
2021, pp. 1418-1432.
Digital Object Identifier: 10.1109/
TNNLS.2020.2985588
" With the rapid development from
traditional machine learning (ML) to
deep learning (DL) and reinforcement
learning (RL), dialog system equipped
with learning mechanism has become
the most effective solution to address
human-machine interaction problems.
The purpose of this article is to provide
a comprehensive survey on learningbased
human-machine dialog systems
with a focus on the various dialog models.
More specifically, we first introduce
the fundamental process of establishing
a dialog model. Second, we examine the
features and classifications of the system
dialog model, expound some representative
models, and also compare the
advantages and disadvantages of different
dialog models. Third, we comb the
commonly used database and evaluation
metrics of the dialog model. Furthermore,
the evaluation metrics of these
dialog models are analyzed in detail.
Finally, we briefly analyze the existing
issues and point out the potential
Digital Object Identifier 10.1109/MCI.2021.3108300
Date of current version: 13 October 2021
future direction on the human-machine
dialog systems. "
Numerical Spiking Neural P Systems,
by T. Wu, L. Pan, Q. Yu, and K. C.
Tan, IEEE Transactions on Neural Networks
and Learning Systems, Vol. 32,
No. 6, June 2021, pp. 2443-2457.
Digital Object Identifier: 10.1109/
TNNLS.2020.3005538
" Spiking neural P (SN P) systems are
a class of discrete neuron-inspired computation
models, where information is
encoded by the numbers of spikes in
neurons and the timing of spikes. However,
due to the discontinuous nature of
the integrate-and-fire behavior of neurons
and the symbolic representation of
information, SN P systems are incompatible
with the gradient descent-based
training algorithms, such as the backpropagation
algorithm, and lack the
capability of processing the numerical
representation of information. In this
work, motivated by the numerical
nature of numerical P (NP) systems in
the area of membrane computing, a
novel class of SN P systems is proposed,
called numerical SN P (NSN P) systems.
More precisely, information is
encoded by the values of variables, and
the integrate-and-fire way of neurons
and the distribution of produced values
are described by continuous production
functions. The computation power
of NSN P systems is investigated. We
prove that NSN P is Turing universal as
number generating devices, where the
production functions in each neuron are
linear functions, each involving at most
14 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2021
one variable; as number accepting devices,
NSN P systems are proved to be universal
as well, even if each neuron
contains only one production function.
These results show that even if a single
neuron is simple in the sense that it
contains one or two production functions
and the production functions in
each neuron are linear functions with
one variable, a network of simple neurons
are still computationally powerful.
With the powerful computation power
and the characteristic of continuous
production functions, developing learning
algorithms for NSN P systems is
potentially exploitable. "
IEEE Transactions on
Fuzzy Systems
Soft Overlapping Community Detection
in Large-Scale Networks via Fast Fuzzy
Modularity Maximization, by S. Yazdanparast,
T.C. Havens, and M.
Jamalabdollahi, IEEE Transactions on
Fuzzy Systems, Vol. 29, No. 6, June
2021, pp. 1533-1543.
Digital Object Identifier: 10.1109/
TFUZZ.2020.2980502
" Soft overlapping clustering is one
of the notable problems of community
detection. Extensive research has been
conducted to develop efficient methods
for nonoverlapping and crisp-overlapping
community detection in largescale
networks. In this article, fast fuzzy
modularity maximization (FFMM) for
soft overlapping community detection
is proposed. FFMM exploits novel
iterative equations to calculate the
IEEE Computational Intelligence Magazine - November 2021

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