IEEE Computational Intelligence Magazine - May 2022 - 13

implementation to encourage future
work in the area of symbolic navigation.
The symbolic navigation performance of
humans and a robot is evaluated in a
real-world built environment. This article
concludes with a qualitative analysis of
human navigation strategies, providing
further insights into how the symbolic
navigation capabilities of robots in
unseen built environments can be im -
proved in the future. "
IEEE Transactions on Emerging
Topics in Computational
Intelligence
Deep COLA: A Deep COmpetitive
Learning Algorithm for Future Home
Energy Management Systems, by G.
Mohi-Ud-Din, A. K. Marnerides, Q.
Shi, C. Dobbins, and A. MacDermott,
IEEE Transactions on Emerging Topics in
Computational Intelligence, Vol. 5, No. 6,
December 2021, pp. 860-870.
Digital Object Identifier: 10.1109/
TETCI.2020.3027300
" A smart grid ecosystem requires
intelligent Home Energy Management
Systems (HEMSs) that allow the adequate
monitoring and control of appliance-level
energy consumption in a
given household. They should be able to:
i) profile highly non-stationary and nonlinear
measurements and ii) conduct correlations
of such measurements with
diverse inputs (e.g. environmental factors)
in order to improve the end-user experience,
as well as to aid the overall demandresponse
optimization process. However,
traditional approaches in HEMS lack the
ability to capture diverse variations in
appliance-level energy consumption due
to unpredictable human behavior and
also require high computation to process
large datasets. In this article, we go
beyond current profiling schemes by proposing
Deep COLA; a novel Deep
COmpetitive Learning Algorithm that
addresses the limitations of existing work
in terms of high dimensional data and
enables more efficient and accurate clustering
of appliance-level energy consumption.
The proposed approach
reduces human intervention by automatically
selecting load profiles and models
variations and uncertainty in human
behavior during appliance usage. We
demonstrate that our proposed scheme is
far more computationally efficient and
scalable data-wise than three popular
conventional clustering approaches
namely, K-Means, DBSCAN and SOM,
using real household datasets. Moreover,
we exhibit that Deep COLA identifies
per-household behavioral associations
that could aid future HEMSs. "
IEEE Transactions on Artificial
Intelligence
Playing Against Deep Neural NetworkBased
Object Detectors: A Novel Bidirectional
Adversarial Attack Approach, by
X. Li, Y. Jiang, C. Liu, S. Liu, H. Luo,
and S. Yin, IEEE Transactions on Artificial
Intelligence, Vol. 3, No. 1, February
2022, pp. 20-28.
Digital Object Identifier: 10.1109/
TAI.2021.3107807
" In the fields of deep learning and
computer vision, the security of object
detection models has received extensive
attention. Revealing the security vulnerabilities
resulting from adversarial attacks
has become one of the most important
research directions. Existing studies show
that object detection models can also be
threatened by adversarial examples, just
like other deep neural networks based
models, e.g., those for classification. In
this paper, we propose a bidirectional
adversarial attack method. Firstly, the
added perturbation pushes the prediction
results given by the object detectors far
away from the ground truth class while
getting close to the background class.
Secondly, a condense loss function is
designed for the region proposal network
to reduce the foreground scores.
Thirdly, the adversarial examples are
generated by a pre-trained autoencoder,
and the model is trained using an adversarial
approach, which can enhance the
similarity between the adversarial examples
and the original image and speed up
algorithm convergence. The proposed
method was verified on the most popular
two-stage detection framework (Faster
R-CNN), and 55.1% mAP-drop were
obtained. In addition, the adversarial
examples have superior transferability,
migrating which to the common onestage
detection framework (YOLOv3)
gets a 39.5% mAP-drop. "
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IEEE Computational Intelligence Magazine - May 2022

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