<abstract xmlns="http://www.w3.org/1999/xhtml">

The problem of varying length recordings is a well-known issue in paralinguistics. We investigated how to resolve this problem using the bag-of-audio-words feature extraction approach. The steps of this technique involve preprocessing, clustering, quantization and normalization. The bag-of-audio-words technique is competitive in the area of speech emotion recognition, but the method has several parameters that need to be precisely tuned for good efficiency. The main aim of our study was to analyse the effectiveness of bag-of-audio-words method and try to find the best parameter values for emotion recognition. We optimized the parameters one-by-one, but built on the results of each other. We performed the feature extraction, using openSMILE. Next we transformed our features into same-sized vectors with openXBOW, and finally trained and evaluated SVM models with 10-fold-crossvalidation and UAR. In our experiments, we worked with a Hungarian emotion database. According to our results, the emotion classification performance improves with the bag-of-audio-words feature representation. Not all the BoAW parameters have the optimal settings but later we can make clear recommendations on how to set bag-of-audio-words parameters for emotion detection tasks.
</abstract>

Using the Bag-of-Audio-Words approach for emotion recognition

<abstract xmlns="http://www.w3.org/1999/xhtml">

The concept of first KCD signless Laplacian energy is initiated in this article. Moreover, we determine first KCD signless Laplacian spectrum and first KCD signless Laplacian energy for some class of graphs and their complement.
</abstract>

Karnatak University’s Karnatak Arts College

Signless Laplacian energy of a first KCD matrix

<abstract xmlns="http://www.w3.org/1999/xhtml">

In this paper, we derive several results related to total path length and Sackin index in two classes of random recursive trees. A limiting distribution of the normalized version of the Sackin index is given by the contraction method in random recursive trees. Also, we show the normalized total path length converges in L2 and almost surely to a limiting random variable in plane-oriented recursive trees via martingales.
</abstract>

Department of Statistics, Science and Research Branch

Department of Statistics, Faculty of Basic Sciences

Imam Khomeini International University Qazvin

Limit laws for two distance-based indices in random recursive tree models

<abstract xmlns="http://www.w3.org/1999/xhtml">

In this paper we propose to create an end-to-end brain tumor segmentation system that applies three variants of the well-known U-Net convolutional neural networks. In our results we obtain and analyse the detection performances of U-Net, VGG16-UNet and ResNet-UNet on the BraTS2020 training dataset. Further, we inspect the behavior of the ensemble model obtained as the weighted response of the three CNN models. We introduce essential preprocessing and post-processing steps so as to improve the detection performances. The original images were corrected and the different intensity ranges were transformed into the 8-bit grayscale domain to uniformize the tissue intensities, while preserving the original histogram shapes. For post-processing we apply region connectedness onto the whole tumor and conversion of background pixels into necrosis inside the whole tumor. As a result, we present the Dice scores of our system obtained for WT (whole tumor), TC (tumor core) and ET (enhanced tumor) on the BraTS2020 training dataset.
</abstract>

George Emil Palade University of Medicine, Pharmacy, Science, and Technology Târgu Mureş

Department of Electrical Engineering and Information Technology

Sapientia Hungarian University of Transylvania

Cluj-Napoca Department of Electrical Engineering

U-Net architecture variants for brain tumor segmentation of histogram corrected images

<abstract xmlns="http://www.w3.org/1999/xhtml">

For a commutative ring R with identity 1, the zero-divisor graph of R, denoted by Γ(R), is a simple graph whose vertex set is the set of non-zero zero divisors Z*(R) and the two vertices x and y ∈ Z*(R) are adjacent if and only if xy = 0. In this paper, we compute the values of some graph parameters of the zero-divisor graph associated to the ring of Gaussian integers modulo n, ℤn[i] and the ring of integers modulo n, ℤn.
</abstract>

On graphs associated to ring of Guassian integers and ring of integers modulo n

<abstract xmlns="http://www.w3.org/1999/xhtml">

The transmission of a vertex u in a connected graph G is defined as σ(u) = Σv∈V(G) d(u, v) and reciprocal transmission of a vertex u is defined as <inline-formula>
<alternatives>
<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_ausi-2022-0006_eq_001.png"></inline-graphic>
<math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mi>r</mi><mi>s</mi><mo stretchy="false">(</mo><mi>u</mi><mo stretchy="false">)</mo><mo>=</mo><msub><mo>∑</mo><mrow><mi>v</mi><mo>∈</mo><mi>V</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow></mrow></msub><mrow><mfrac><mn>1</mn><mrow><mi>d</mi><mrow><mo>(</mo><mrow><mi>u</mi><mo>,</mo><mi>v</mi></mrow><mo>)</mo></mrow></mrow></mfrac></mrow></mrow></math>
<tex-math>rs(u) = \sum\nolimits_{v \in V\left( G \right)} {{1 \over {d\left( {u,v} \right)}}}</tex-math>
</alternatives>
</inline-formula>, where d(u, v) is the distance between vertex u and v in G. In this paper we define new distance based topological index of a connected graph G called transmission-reciprocal transmission index <inline-formula>
<alternatives>
<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_ausi-2022-0006_eq_002.png"></inline-graphic>
<math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mi>T</mi><mi>R</mi><mi>T</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow><mo>=</mo><msub><mo>∑</mo><mrow><mi>u</mi><mi>v</mi><mo>∈</mo><mi>E</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow></mrow></msub><mrow><mrow><mo>(</mo><mrow><mfrac><mrow><mi>σ</mi><mrow><mo>(</mo><mi>u</mi><mo>)</mo></mrow></mrow><mrow><mi>r</mi><mi>s</mi><mrow><mo>(</mo><mi>u</mi><mo>)</mo></mrow></mrow></mfrac><mo>+</mo><mfrac><mrow><mi>σ</mi><mrow><mo>(</mo><mi>v</mi><mo>)</mo></mrow></mrow><mrow><mi>r</mi><mi>s</mi><mrow><mo>(</mo><mi>v</mi><mo>)</mo></mrow></mrow></mfrac></mrow><mo>)</mo></mrow></mrow></mrow></math>
<tex-math>TRT\left( G \right) = \sum\nolimits_{uv \in E\left( G \right)} {\left( {{{\sigma \left( u \right)} \over {rs\left( u \right)}} + {{\sigma \left( v \right)} \over {rs\left( v \right)}}} \right)}</tex-math>
</alternatives>
</inline-formula> and its coindex <inline-formula>
<alternatives>
<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_ausi-2022-0006_eq_003.png"></inline-graphic>
<math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mover accent="true"><mrow><mi>T</mi><mi>R</mi><mi>T</mi></mrow><mo>¯</mo></mover><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow><mo>=</mo><msub><mo>∑</mo><mrow><mi>u</mi><mi>v</mi><mo>∉</mo><mi>E</mi><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow></mrow></msub><mrow><mrow><mo>(</mo><mrow><mfrac><mrow><mi>σ</mi><mrow><mo>(</mo><mi>u</mi><mo>)</mo></mrow></mrow><mrow><mi>r</mi><mi>s</mi><mrow><mo>(</mo><mi>u</mi><mo>)</mo></mrow></mrow></mfrac><mo>+</mo><mfrac><mrow><mi>σ</mi><mrow><mo>(</mo><mi>v</mi><mo>)</mo></mrow></mrow><mrow><mi>r</mi><mi>s</mi><mrow><mo>(</mo><mi>v</mi><mo>)</mo></mrow></mrow></mfrac></mrow><mo>)</mo></mrow></mrow></mrow></math>
<tex-math>\overline {TRT} \left( G \right) = \sum\nolimits_{uv \notin E\left( G \right)} {\left( {{{\sigma \left( u \right)} \over {rs\left( u \right)}} + {{\sigma \left( v \right)} \over {rs\left( v \right)}}} \right)}</tex-math>
</alternatives>
</inline-formula>, where E(G) is the edge set of a graph G and establish the relation between TRT(G) and <inline-formula>
<alternatives>
<inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/j_ausi-2022-0006_eq_004.png"></inline-graphic>
<math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mover accent="true"><mrow><mi>T</mi><mi>R</mi><mi>T</mi></mrow><mo>¯</mo></mover><mrow><mo>(</mo><mi>G</mi><mo>)</mo></mrow></mrow></math>
<tex-math>\overline {TRT} \left( G \right)</tex-math>
</alternatives>
</inline-formula>(G). Further compute this index for some standard class of graphs and obtain bounds for it.
</abstract>

Transmission-reciprocal transmission index and coindex of graphs

<abstract xmlns="http://www.w3.org/1999/xhtml">

In this work, the Seidel Laplacian spectrum of graphs are determined. Then new bounds are presented for the Seidel Laplacian energy of regular graphs and graphs by using their Seidel Laplacian spectrum and other techniques. Further, the Seidel Laplacian energy of specific graphs are computed.
</abstract>

On Seidel Laplacian matrix and energy of graphs

<abstract xmlns="http://www.w3.org/1999/xhtml">

Suppose that the zero-divisor graph of a commutative semi-group S, be a complete graph with an end vertex. In this paper, we determine the structure of the annihilator graph S and we show that if Z(S)= S, then the annihilator graph S is a disconnected graph.
</abstract>

Department of Basic Sciences, Sabzevar Branch

Annihilator graphs of a commutative semigroup whose Zero-divisor graphs are a complete graph with an end vertex

<abstract xmlns="http://www.w3.org/1999/xhtml">

Zero forcing is one of the dynamic vertex coloring problem. Zero forcing number is the minimum cardinality of the zero forcing sets. This parameter is the upper bound for the maximum nullity. A new class of graph where the maximum nullity is equal to the zero forcing number of the graph is defined as closed global shadow graph. Basic properties and zero forcing number of this graph class is analysed.
</abstract>

Some properties of the closed global shadow graphs and their zero forcing number

AHEAD OF PRINT

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