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IEEE Conference on Decision and control

作者： Jerzy Baranowski Waldemar Bauer Karolina Dukała Dorota Mozyrska Małgorzata Wyrwas Department of Automatic Control and Robotics AGH University of Science & Technology Kraków Poland SWPS University of Social Sciences and Humanities Katowice Poland Faculty of Computer Science Bialystok University of Technology Bialystok Poland

In this paper we propose a consensus model using fractional calculus, which is an emerging topic in multi-agent modeling. Fractional models have infinite memory and can be understood as a relatively simple extension of traditional calculus. We propose a model structure motivating it by psychological research. For such model we also provide a stability analysis allowing results on possibilities of consensus arising in the modelled group of agents. To achieve this, we use fractional difference equations, which illustrate our considerations for agent groups of increasing complexity.

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Towards Automated Detection of Adversarial Attacks on Tabular Data 18

Towards Automated Detection of Adversarial Attacks on Tabula...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Biczyk, Piotr Wawrowski, Lukasz Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka Gliwice44-100 Poland Ukasiewicz Research Network Institute of Innovative Technologies Emag Ul. Leopolda 31 Katowice40-189 Poland Qed Software Sp. Z O.o. Mazowiecka 11/49 Warsaw00-052 Poland

ISBN: (纸本)9788396744784

The paper presents a novel approach to investigating adversarial attacks on machine learning classification models operating on tabular data. The employed method involves using diagnostic parameters calculated on an approximated representation of a model under attack and analyzing differences in these diagnostic parameters over time. The hypothesis researched by the authors is that adversarial attack techniques, even if attempting a low-profile modification of input data, influence those diagnostic attributes in a statistically significant way. Thus, changes in diagnostic attributes can be used for detecting attack events. Three attack approaches on real-world datasets were investigated. The experiments confirm the approach as a promising technique to be further developed for detecting adversarial attacks. © 2023 Polish Information Processing Society.

关键词： Information use

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Improving the detection of noisy labels in image datasets using modified Confidence Learning

Improving the detection of noisy labels in image datasets us...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Adam Popowicz Krystian Radlak Slawomir Lasota Karolina Szczepankiewicz Michal Szczepankiewicz Faculty of Automatic Control Silesian University of Technology Electronics and Computer Science Gliwice Poland Warsaw University of Technology Warsaw Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596

The effectiveness of machine learning algorithms, including deep neural networks (DNN) for classifying image data, depends on proper preparation of the training dataset. Erroneously labeled images in the training data will degrade algorithmic efficiency and cause unpredictable model behavior, thus reduce its safety. Verifying labels in the numerous available databases remains a complicated and laborious task. In this article, we present a MultiNET approach that allows for efficient verification of labeled image datasets. We adapt a state-of-the-art technique, namely Confidence Learning, extending its flexibility and improving the effectiveness by combining outcomes from various DNN architectures. Thanks to the proposed modification, it is possible to automatically detect incorrect labels while minimizing the number of false positives, thus making the verification process much less burdensome. The technique may be of use for researchers and software engineers dealing with externally supplied image datasets.

关键词： Deep learning Training Machine learning algorithms Databases Training data computer architecture Data models

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Detection of Early Pregnancy in Ultrasound Images Using YOLOv3 *

Detection of Early Pregnancy in Ultrasound Images Using YOLO...

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Mediterranean Conference on control and Automation (MED)

作者： Oana Mihaela Teodor Monica Mihaela Cirstoiu Loretta Ichim Dan Popescu Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Bucharest Romania Carol Davila University of Medicine and Pharmacy Bucharest

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

automatic interpretation of morphological metrics recently gained great interest in medical imaging applications. For ultrasound image analysis various artificial intelligence algorithms emerged with the aim to overcome drawbacks related to measurement variability and image quality. Advanced methods of automatic analysis can meet the need to centralize a large amount of information from these images, to accurately interpret the medical data, and to minimize the rate of human error. In the current paper, we propose a solution designed for automating the detection of early pregnancy from endovaginal ultrasound scans. A YOLOv3 convolutional neural network was configured to detect the gestational sac, the yolk sac, and the embryo given the potential to facilitate ultrasound diagnostic in obstetrics by automating the detection of these early pregnancy elements. The database created for this research included 349 images evaluating incipient pregnancies with gestational age ranging from 40 to 70 days. The results obtained support the use of YOLOv3 for the precise detection of ultrasound elements specific to an incipient pregnancy.

关键词： Pregnancy Image quality Ultrasonic imaging Embryo Databases Ultrasonic variables measurement Current measurement

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Cyber Physical Systems Architecture for Collaborative Services 23

Cyber Physical Systems Architecture for Collaborative Servic...

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23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Pop, Eugen Gifu, Daniela Stanescu, Aurelian Mihai Moisescu, Mihnea Alexandru Sacala, Ioan Stefan RandD Institute for Automation IPA SA ICT Department Bucharest Romania Alexandru Ioan Cuza University of Iasi Institute of Computer Science Romanian Academy Faculty of Computer Science Iasi Branch Romania Politehnica University of Bucharest Faculty of Automatic Control and Computers Romania

ISBN: (纸本)9781665439398

Cyber Physical Systems (CPS) represents an autonomous system which integrates sensing devices, actuators, hardware equipments and software applications, having also communication functionality. A CPS can realize data transmission and cooperation with remote systems and IoT networks. Within smart factories, having the ability of involving business scenarios, CPS autonomously exchange data, control and monitor processes, perform actions in the Industry 4.0 context. Correlated with CPS interoperability capabilities, a new paradigm in manufacturing appeared, based on collaborative, service-oriented, customer-centric industries. Various new business models involve a large, exponentially growing number, of interconnected devices. In this paper is presented a CPS services framework, which integrates an e-business service group within a collaborative infrastructure, thus offering to the users the possibility to valorify on the market, through services integration and collaboration, the remarkable technical advantages of CPS. As e-business example, an e-payment service, developed in a Service Oriented Architecture (SOA), is considered. The data transactions are realized on mobile and wireless communication channels, through Soap and Rest Web services, which are accessed by mobile client software application, installed on Android OS Smartphones. © 2021 IEEE.

关键词： Cyber Physical System

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HISTORIAE, history of socio-cultural transformation as linguistic data science. A humanities use case 3

HISTORIAE, history of socio-cultural transformation as lingu...

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3rd Conference on Language, Data and Knowledge, LDK 2021

作者： Armaselu, Florentina Apostol, Elena-Simona Khan, Anas Fahad Liebeskind, Chaya McGillivray, Barbara Truică, Ciprian-Octavian Oleškevičiene, Giedre Valūnaite University of Luxembourg Luxembourg Department of Computer Science and Engineering Faculty of Automatic Control and Computer University Politehnica of Bucharest Romania Institute for Computational Linguistics «A. Zampolli» National Research Council of Italy Pisa Italy Department of Computer Science Jerusalem College of Technology Israel Theoretical and Applied Linguistics Faculty of Modern and Medieval Languages and Linguistics University of Cambridge United Kingdom The Alan Turing Institute London United Kingdom Department of Computer Science and Engineering Faculty of Automatic Control and ComputerUniversity Politehnica of Bucharest Romania Institute of Humanities Mykolas Romeris University Vilnius Lithuania

ISBN: (纸本)9783959771993

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for detecting semantic change in multilingual historical corpora and generating diachronic ontologies as linguistic linked open data (LLOD). Initiated as a use case (UC4.2.1) within the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, the study will explore emerging trends in knowledge extraction, analysis and representation from linguistic data science, and apply the devised methodology to datasets in the humanities to trace the evolution of concepts from the domain of socio-cultural transformation. The paper will describe the main elements of the methodological framework and preliminary planning of the intended workflow. © Florentina Armaselu, Elena-Simona Apostol, Anas Fahad Khan, Chaya Liebeskind, Barbara McGillivray, Ciprian-Octavian Truică, and Giedre Valūnaite Oleškevičien e;licensed under Creative Commons License CC-BY 4.0

关键词： Linguistics

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ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Motion Model Applicable to VR/AR Devices 21st

ELSA: Euler-Lagrange Skeletal Animations - Novel and Fast Mo...

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21st International Conference on Computational science, ICCS 2021

作者： Wereszczyński, Kamil Michalczuk, Agnieszka Foszner, Pawel Golba, Dominik Cogiel, Michal Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland KP Labs Gliwice Poland

ISBN: (纸本)9783030779764

Euler Lagrange Skeletal Animation (ELSA) is the novel and fast model for skeletal animation, based on the Euler Lagrange equations of motion and configuration and phase space notion. Single joint’s animation is an integral curve in the vector field generated by those PDEs. Considering the point in the phase space belonging to the animation at current time, by adding the vector pinned to this point and multiplied by the elapsed time, one can designate the new point in the phase space. It defines the state, especially the position (or rotation) of the joint after this time elapses. Starting at time 0 and repeating this procedure N times, there is obtained the approximation, and if the N→ ∞ the integral curve itself. Applying above, to all joint in the skeletal model constitutes ELSA. © 2021, Springer Nature Switzerland AG.

关键词： Animation

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On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition

arXiv

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arXiv 2025年

作者： Cosma, Adrian Cǎtrunǎ, Andy Rǎdoi, Emilian University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania

Gait recognition from video streams is a challenging problem in computer vision biometrics due to the subtle differences between gaits and numerous confounding factors. Recent advancements in self-supervised pretraining have led to the development of robust gait recognition models that are invariant to walking covariates. While neural scaling laws have transformed model development in other domains by linking performance to data, model size, and compute, their applicability to gait remains unexplored. In this work, we conduct the first empirical study scaling on skeleton-based self-supervised gait recognition to quantify the effect of data quantity, model size and compute on downstream gait recognition performance. We pretrain multiple variants of GaitPT — a transformer-based architecture — on a dataset of 2.7 million walking sequences collected in the wild. We evaluate zero-shot performance across four benchmark datasets to derive scaling laws for data, model size, and compute. Our findings demonstrate predictable power-law improvements in performance with increased scale and confirm that data and compute scaling significantly influence downstream accuracy. We further isolate architectural contributions by comparing GaitPT with GaitFormer under controlled compute budgets. These results provide practical insights into resource allocation and performance estimation for real-world gait recognition systems. © 2025, CC BY-NC-ND.

关键词： Budget control

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How capable are state-of-the-art language models to cope with sarcasm?

How capable are state-of-the-art language models to cope wit...

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International Conference on control Systems and computer science (CSCS)

作者： Alexandru -Costin Băroiu Ştefan Trăuşan-Matu Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania Romanian Academy Research Institute for Artificial Intelligence “Mihai Drăganescu” Academy of Romanian Scientists Bucharest Romania

Sarcasm detection has established itself as one of the more difficult Natural Language Processing tasks, due to the complex nature of sarcasm. This paper aims to benchmark the performance of state-of-the-art models like BERT, RoBERTa, ALBERT and GPT-3 when faced with this task. The dataset selected is MUStARD, which has increased in popularity in recent years, especially for multimodal tasks, and is one of the most qualitative and data rich dataset. An untuned GPT-3 based model was selected as the baseline and all the other models were fine-tuned using the textual data present in MUStARD, mainly the context and utterance information. The best performer was found to be the GPT-3 fine-tuned model, with an F1 score of 77. This is in line with the reported feats of GPT-3 based models that have popularized in recent months and reaffirms the superiority of GPT-3. Future avenues of research are then presented and explored, and the conclusions are drawn.

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automatic detection of vehicles in outdoor parking lots from zenith perspective using neural networks

Automatic detection of vehicles in outdoor parking lots from...

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2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： García, Juan Manuel Carrera Piorno, Joaquín Recas Mata-Garcia, María Guijarro Department of Computer Architecture and Automatic Control Faculty of Computer Science Universidad Complutense de Madrid Spain

ISBN: (纸本)9781713819462

Nowadays there are a variety of methods to assist parking users in finding free sites in parking lots. However, there is no automatic system that takes into account the size of the car looking for a space or whether the cars adjacent to the free spaces are correctly parked. This paper presents a new method for detecting and calculating the area of vehicles in images taken from a zenith plane using computer vision and machine learning techniques that will help to create a vehicle-oriented search algorithm dedicated to finding the optimal spaces for vehicles entering an outdoor parking lot based on its characteristics. Results with scaled-down and real vehicles show that this new method can detect the area of the vehicles in an image with an average accuracy of 97.98%. ©2020 Society for Modeling & Simulation International (SCS)

关键词： Vehicles

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