IEEE Computational Intelligence Magazine - February 2022 - 12

IEEE Transactions on Cognitive
and Developmental Systems
Explanation as a Social Practice: Toward
a Conceptual Framework for the Social
Design of AI Systems, by K. J. Rohlfing,
P. Cimiano,
IEEE Transactions on Emerging
Topics in Computational
Intelligence
I. Scharlau, T.
Matzner, H. M. Buhl, H. Buschmeier,
E. Esposito, A. Grimminger, B.
Hammer, R. Häb-Umbach, I. Horwath,
E. Hüllermeier, F. Kern, S.
Kopp, K. Thommes, A.-C. Ngonga
Ngomo, C. Schulte, H. Wachsmuth,
P. Wagner, and B. Wrede, IEEE Transactions
on Cognitive and Developmental
Systems, Vol. 13, No. 3, September
2021, pp. 717-728.
Digital Object Identifier: 10.1109/
TCDS.2020.3044366
" The recent surge of interest in
explainability in artificial intelligence
(XAI) is propelled by not only technological
advancements in machine learning
but also by regulatory initiatives to
foster transparency in algorithmic decision
making. In this article, we revise
the current concept of explainability
and identify three limitations: passive
explainee, narrow view on the social
process, and undifferentiated assessment
of explainee's understanding. In order
to overcome these limitations, we present
explanation as a social practice in
which explainer and explainee co-construct
understanding on the microlevel.
We view the co-construction on a
microlevel as embedded into a macrolevel,
yielding expectations concerning,
e.g., social roles or partner models: typically,
the role of the explainer is to provide
an explanation and to adapt it to
the current level of explainee's understanding;
the explainee, in turn, is
expected to provide cues that direct the
explainer. Building on explanations
being a social practice, we present a
conceptual framework that aims to
guide future research in XAI. The
framework relies on the key concepts of
monitoring and scaffolding to capture
the development of interaction. We
relate our conceptual framework and
our new perspective on explaining to
transparency and autonomy as objectives
considered for XAI. "
Realizing Behavior Level Associative
Memory Learning Through ThreeDimensional
Memristor-Based Neuromorphic
Circuits, by H. An, Q. An, and
Y. Yi, IEEE Transactions on Emerging
Topics in Computational Intelligence, Vol.
5, No. 4, August 2021, pp. 668-678.
Digital Object Identifier: 10.1109/
TETCI.2019.2921787
" Associative memory is a widespread
self-learning method in biological livings,
which enables the nervous system to
remember the relationship between two
concurrent events. The significance of
rebuilding associative memory at a behavior
level is not only to reveal a way of designing
a brain-like self-learning neuromorphic system
but also to explore a method of comprehending
the learning mechanism of a
nervous system. In this paper, an associative
memory learning at a behavior level is realized
that successfully associates concurrent
visual and auditory information together
(pronunciation and image of digits). The
task is achieved by associating the largescale
artificial neural networks (ANNs)
together instead of relating multiple analog
signals. In this way, the information carried
and preprocessed by these ANNs can be
associated. A neuron has been designed,
named signal intensity encoding neurons
(SIENs), to encode the output data of the
ANNs into the magnitude and frequency
of the analog spiking signals. Then, the
spiking signals are correlated together with
an associative neural network, implemented
with a three-dimensional (3-D) memristor
array. Furthermore, the selector
devices in the traditional memristor cells
limiting the design area have been avoided
by our novel memristor weight updating
scheme. With the novel SIENs, the 3-D
memristive synapse, and the proposed
memristor weight updating scheme, the
simulation results demonstrate that our
proposed associative memory learning
method and the corresponding circuit
implementations successfully associate the
pronunciation and image of digits together,
which mimics a human-like associative
memory learning behavior. "
12 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2022
IEEE Transactions on
Artificial Intelligence
Learn2Evade: Learning-Based Generative
Model for Evading PDF Malware
Classifiers, by H. Bae, Y. Lee, Y. Kim,
U. Hwang, S. Yoon, and Y. Paek, IEEE
Transactions on Artificial Intelligence, Vol.
2, No. 4, August 2021, pp. 299-313.
Digital Object Identifier: 10.1109/
TAI.2021.3103139
" Recent research has shown that a
small perturbation to an input may forcibly
change the prediction of a machine
learning (ML) model. Such variants are
commonly referred to as adversarial
examples. Early studies have focused
mostly on ML models for image processing
and expanded to other applications,
including those for malware classification.
In this article, we focus on the problem of
finding adversarial examples against MLbased
portable document format (PDF)
malware classifiers. We deem that our
problem is more challenging than those
against ML models for image processing
because of the highly complex data structure
of PDF and of an additional constraint
that the generated PDF should
exhibit malicious behavior. To resolve our
problem, we propose a variant of generative
adversarial networks that generate
evasive variant PDF malware (without
any crash), which can be classified as
benign by various existing classifiers yet
maintaining the original malicious behavior.
Our model exploits the target classifier
as the second discriminator to rapidly
generate an evasive variant PDF with our
new feature selection process that includes
unique features extracted from malicious
PDF files. We evaluate our technique
against three representative PDF malware
classifiers
(Hidost'13, Hidost'16, and
PDFrate-v2) and further examine its
effectiveness with AntiVirus engines from
VirusTotal. To the best of our knowledge,
our work is the first to analyze the performance
against the commercial AntiVirus
engines. Our model finds, with great
speed, evasive variants for all selected seeds
against state-of-the-art PDF malware classifiers
and raises a serious security concern
in the presence of adversaries. "
IEEE Computational Intelligence Magazine - February 2022

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