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Jier Xi and Xiufen Ye
There are many challenges in using side-scan sonar (SSS) images to detect objects. The challenge of object detection and recognition in sonar data is greater than in optical images due to the sparsity of detectable targets. The complexity of real-world u...
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Maryan Rizinski, Andrej Jankov, Vignesh Sankaradas, Eugene Pinsky, Igor Mishkovski and Dimitar Trajanov
The task of company classification is traditionally performed using established standards, such as the Global Industry Classification Standard (GICS). However, these approaches heavily rely on laborious manual efforts by domain experts, resulting in slow...
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Jaroslaw Kurek, Tomasz Latkowski, Michal Bukowski, Bartosz Swiderski, Mateusz Lepicki, Grzegorz Baranik, Bogusz Nowak, Robert Zakowicz and Lukasz Dobrakowski
In the evolving realities of recruitment, the precision of job?candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-Min...
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Hana Alostad, Shoug Dawiek and Hasan Davulcu
The Kuwaiti dialect is a particular dialect of Arabic spoken in Kuwait; it differs significantly from standard Arabic and the dialects of neighboring countries in the same region. Few research papers with a focus on the Kuwaiti dialect have been publishe...
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Sergiu Zaharia, Traian Rebedea and Stefan Trausan-Matu
The research presented in the paper aims at increasing the capacity to identify security weaknesses in programming languages that are less supported by specialized security analysis tools, based on the knowledge gathered from securing the popular ones, f...
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Luigi Gianpio Di Maggio, Eugenio Brusa and Cristiana Delprete
The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using a G...
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Kirill Tyshchuk, Polina Karpikova, Andrew Spiridonov, Anastasiia Prutianova, Anton Razzhigaev and Alexander Panchenko
Embeddings, i.e., vector representations of objects, such as texts, images, or graphs, play a key role in deep learning methodologies nowadays. Prior research has shown the importance of analyzing the isotropy of textual embeddings for transformer-based ...
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Elie Saad, Marcin Paprzycki, Maria Ganzha, Amelia Badica, Costin Badica, Stefka Fidanova, Ivan Lirkov and Mirjana Ivanovic
There are many areas where conventional supervised machine learning does not work well, for instance, in cases with a large, or systematically increasing, number of countably infinite classes. Zero-shot learning has been proposed to address this. In gene...
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Shuai Dong, Zhihua Yang, Wensheng Li and Kun Zou
Conveyors are used commonly in industrial production lines and automated sorting systems. Many applications require fast, reliable, and dynamic detection and recognition for the objects on conveyors. Aiming at this goal, we design a framework that involv...
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Konstantinos Demertzis and Lazaros Iliadis
Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely cos...
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