TY  - JOUR
AU  - Gnjatović, Milan
AU  - Maček, Nemanja
AU  - Saračević, Muzafer
AU  - Adamović, Saša
AU  - Joksimović, Dušan
AU  - Karabašević, Darjan
PY  - 2023
UR  - http://jakov.kpu.edu.rs/handle/123456789/1514
AB  - This paper introduces a heuristic for multiple sequence alignment aimed at improving
real-time object recognition in short video streams with uncertainties. It builds upon the idea of
the progressive alignment but is cognitively economical to the extent that the underlying edit dis-
tance approach is adapted to account for human working memory limitations. Thus, the proposed
heuristic procedure has a reduced computational complexity compared to optimal multiple sequence
alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation
conducted in real-life settings demonstrated a significant improvement in number recognition accu-
racy in short video streams under uncertainties caused by noise and incompleteness. The second line
of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing
of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine
learning approaches, which are typically not tailored to the task of object sequence recognition from a
limited number of frames of incomplete data recorded in a dynamic scene situation
PB  - Basel, Switzerland : MDPI
T2  - Axioms
T1  - Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties
VL  - 12
IS  - 1
SP  - 3
DO  - 10.3390/axioms12010003
ER  - 

@article{
author = "Gnjatović, Milan and Maček, Nemanja and Saračević, Muzafer and Adamović, Saša and Joksimović, Dušan and Karabašević, Darjan",
year = "2023",
abstract = "This paper introduces a heuristic for multiple sequence alignment aimed at improving
real-time object recognition in short video streams with uncertainties. It builds upon the idea of
the progressive alignment but is cognitively economical to the extent that the underlying edit dis-
tance approach is adapted to account for human working memory limitations. Thus, the proposed
heuristic procedure has a reduced computational complexity compared to optimal multiple sequence
alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation
conducted in real-life settings demonstrated a significant improvement in number recognition accu-
racy in short video streams under uncertainties caused by noise and incompleteness. The second line
of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing
of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine
learning approaches, which are typically not tailored to the task of object sequence recognition from a
limited number of frames of incomplete data recorded in a dynamic scene situation",
publisher = "Basel, Switzerland : MDPI",
journal = "Axioms",
title = "Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties",
volume = "12",
number = "1",
pages = "3",
doi = "10.3390/axioms12010003"
}

Gnjatović, M., Maček, N., Saračević, M., Adamović, S., Joksimović, D.,& Karabašević, D.. (2023). Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties. in Axioms
Basel, Switzerland : MDPI., 12(1), 3.
https://doi.org/10.3390/axioms12010003

Gnjatović M, Maček N, Saračević M, Adamović S, Joksimović D, Karabašević D. Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties. in Axioms. 2023;12(1):3.
doi:10.3390/axioms12010003 .

Gnjatović, Milan, Maček, Nemanja, Saračević, Muzafer, Adamović, Saša, Joksimović, Dušan, Karabašević, Darjan, "Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties" in Axioms, 12, no. 1 (2023):3,
https://doi.org/10.3390/axioms12010003 . .

TY  - JOUR
AU  - Barzut, Srđan
AU  - Milosavljević, Milan
AU  - Adamović, Saša
AU  - Saračević, Muzafer
AU  - Maček, Nemanja
AU  - Gnjatović, Milan
PY  - 2021
UR  - http://jakov.kpu.edu.rs/handle/123456789/1511
AB  - Modern access controls employ biometrics as a means of authentication to a great extent.
For example, biometrics is used as an authentication mechanism implemented on commercial devices
such as smartphones and laptops. This paper presents a fingerprint biometric cryptosystem based on
the fuzzy commitment scheme and convolutional neural networks. One of its main contributions
is a novel approach to automatic discretization of fingerprint texture descriptors, entirely based on
a convolutional neural network, and designed to generate fixed-length templates. By converting
templates into the binary domain, we developed the biometric cryptosystem that can be used in
key-release systems or as a template protection mechanism in fingerprint matching biometric systems.
The problem of biometric data variability is marginalized by applying the secure block-level Bose–
Chaudhuri–Hocquenghem error correction codes, resistant to statistical-based attacks. The evaluation
shows significant performance gains when compared to other texture-based fingerprint matching
and biometric cryptosystems.
PB  - Basel, Switzerland : MDPI
T2  - Mathematics
T1  - A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks
VL  - 9
IS  - 7
SP  - 730
DO  - 10.3390/math9070730
ER  - 

@article{
author = "Barzut, Srđan and Milosavljević, Milan and Adamović, Saša and Saračević, Muzafer and Maček, Nemanja and Gnjatović, Milan",
year = "2021",
abstract = "Modern access controls employ biometrics as a means of authentication to a great extent.
For example, biometrics is used as an authentication mechanism implemented on commercial devices
such as smartphones and laptops. This paper presents a fingerprint biometric cryptosystem based on
the fuzzy commitment scheme and convolutional neural networks. One of its main contributions
is a novel approach to automatic discretization of fingerprint texture descriptors, entirely based on
a convolutional neural network, and designed to generate fixed-length templates. By converting
templates into the binary domain, we developed the biometric cryptosystem that can be used in
key-release systems or as a template protection mechanism in fingerprint matching biometric systems.
The problem of biometric data variability is marginalized by applying the secure block-level Bose–
Chaudhuri–Hocquenghem error correction codes, resistant to statistical-based attacks. The evaluation
shows significant performance gains when compared to other texture-based fingerprint matching
and biometric cryptosystems.",
publisher = "Basel, Switzerland : MDPI",
journal = "Mathematics",
title = "A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks",
volume = "9",
number = "7",
pages = "730",
doi = "10.3390/math9070730"
}

Barzut, S., Milosavljević, M., Adamović, S., Saračević, M., Maček, N.,& Gnjatović, M.. (2021). A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks. in Mathematics
Basel, Switzerland : MDPI., 9(7), 730.
https://doi.org/10.3390/math9070730

Barzut S, Milosavljević M, Adamović S, Saračević M, Maček N, Gnjatović M. A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks. in Mathematics. 2021;9(7):730.
doi:10.3390/math9070730 .

Barzut, Srđan, Milosavljević, Milan, Adamović, Saša, Saračević, Muzafer, Maček, Nemanja, Gnjatović, Milan, "A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks" in Mathematics, 9, no. 7 (2021):730,
https://doi.org/10.3390/math9070730 . .