|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ioana Branescu, Octavian Grigorescu and Mihai Dascalu
Effectively understanding and categorizing vulnerabilities is vital in the ever-evolving cybersecurity landscape, since only one exposure can have a devastating effect on the entire system. Given the increasingly massive number of threats and the size of...
ver más
|
|
|
|
|
|
|
|
|
|
|
Mohamad Mahmoud Al Rahhal, Yakoub Bazi, Hebah Elgibreen and Mansour Zuair
Zero-shot classification presents a challenge since it necessitates a model to categorize images belonging to classes it has not encountered during its training phase. Previous research in the field of remote sensing (RS) has explored this task by traini...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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 ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Andrea Rondinelli, Lorenzo Bongiovanni and Valerio Basile
Topic classification is the task of mapping text onto a set of meaningful labels known beforehand. This scenario is very common both in academia and industry whenever there is the need of categorizing a big corpus of documents according to set custom lab...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yajing Xu, Haitao Yang, Si Li, Xinyi Wang and Mingfei Cheng
Visual relationship detection (VRD), a challenging task in the image understanding, suffers from vague connection between relationship patterns and visual appearance. This issue is caused by the high diversity of relationship-independent visual appearanc...
ver más
|
|
|
|
|
|