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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Truica, Ciprian-Octavian Istrate, Neculai-Ovidiu Apostol, Elena-Simona University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350341270

automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem;(2) perform an in-depth analysis of our architecture in terms of accuracy and scalability;(3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets. © 2023 IEEE.

关键词： Ecosystems

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EQUILIBRIA AND STABILITY OF ONE MESSENGER RNA AND TWO MICRO RNA DYNAMICS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2022年第4期84卷 3-8页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

In this paper we study an ODE model on the microRNA-mRNA dynamics. We prove the existence of two equilibrium points (one with strictly positive compo-nents) and obtain a biologically consistent, sufficient asymptotic stability condition for the strictly positive equilibrium. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Asymptotic stability

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FUSION-BASED ALGORITHM FOR X-RAY PHASE CONTRAST IMAGING TECHNIQUES

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第3期86卷 157-176页

作者： Rasinar, Cezara Safca, Nicoleta Cotorceanu, Elena Ciobanu, Ionut Stefanoiu, Dan Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Magurele Romania National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Chemical Engineering and Biotechnologies National University of Science and Technology POLITEHNICA Bucharest Romania

Phase Contrast X-ray Imaging represents a technique that has shown remarkable potential in the research field, by providing better visualization of soft tissue, high-contrast images, and high spatial resolution. In the phase contrast imaging technique, the extraction of the three images represented by attenuation, differential phase contrast, and dark-field contrast is essential, providing, in a specific way, information about the nature of the analyzed tissue. This paper introduces a new method based on the three acquired images merging while adjusting the weighting factors, aiming to identify the interest object and to extract the information provided by each contrast channel. In this regard, a fusion-based algorithm was applied to images acquired, using a 5.66 m long Interferometer Talbot-Lau with ultrahigh sensitivity of 0.84 µradians and mean energy of 30 keV. The obtained results are quite promising, as shown in the end of the article. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Interferometers

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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Optimizing Artificial Neural Networks Trough Weight Adjustments 28th

Optimizing Artificial Neural Networks Trough Weight Adjustme...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Akber, Syed Muhammad Abrar Szczesna, Agnieszka Kazmi, Sadia Nishat Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Longer training times pose a significant challenge in artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network. © 2024 The Authors.

关键词： Deep neural networks

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UPB at IberLEF-2023 AuTexTification: Detection of Machine-Generated Text using Transformer Ensembles

UPB at IberLEF-2023 AuTexTification: Detection of Machine-Ge...

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2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Preda, Andrei-Alexandru Cercel, Dumitru-Clementin Rebedea, Traian Chiru, Costin-Gabriel Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest060042 Romania

This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Deep learning

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BIOPHYSICAL SIGNAL PROCESSING FOR automatic ANXIETY CLASSIFICATION IN A VIRTUAL REALITY EXPOSURE THERAPY SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第2期86卷 73-84页

作者： Jinga, Nicolae Petrescu, Cătălin-Dumitru Mitruț, Oana Moldoveanu, Alin Moldoveanu, Florica Petrescu, Livia Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Automatic Control and Systems Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Faculty of Biology University of Bucharest Romania

This paper presents a signal processing framework for automatic anxiety level classification in a virtual reality exposure therapy system. Two types of biophysical data (heart rate and electrodermal activity) were recorded, pre-processed and passed through a feature extraction procedure that provided input for the real-time anxiety level classification algorithm. We showcase the mathematical and engineering techniques behind the procedures and conclude with the challenges encountered in our research and future development ideas. The proposed method provides a good estimate of the level of anxiety while having at the same time a reduced level of complexity, allowing implementation on equipment with limited computing resources. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Classification (of information)

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Massive Natural Language Processing in Distributed Environment 20th

Massive Natural Language Processing in Distributed Environm...

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20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Kielce Poland

ISBN: (纸本)9783031383175

Natural Language Processing (NLP) models are one of the most promising topics nowadays. Applications like ChatGPT uncover the power of such models and their broad applications. However, developing such models requires an extensive amount of training data sets to operate on. As such data sets grow, it becomes impossible to process them on a single machine, even using parallel computations. In this paper, the problem of distributing the NLP computations is taken into consideration. The Scalable Distributed Two-Layer Data Structures were used to organize the distributed environment. Stanford Core NLP library was used to perform accurate NLP computations. The experiments focused on the efficiency of the computation in the distributed environment and the accuracy of the predictions in relation to the scale of the distributions. The results clearly show that an increase in performance can be obtained without a significant decrease in accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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Real-Time Communication Model for IoT Systems 18

Real-Time Communication Model for IoT Systems

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

作者： Deniziak, Stanislaw Plaza, Miroslaw Arcab, Lukasz Kielce University of Technology Faculty of Electrical Engineering Automatic Control and Computer Science Kielce Poland

ISBN: (纸本)9788396744784

Internet of Things solutions typically involve interaction between sensors, actuators, the cloud, embedded systems and user applications. Often in such cases, there are time constraints specifying the maximum response time to a request. This time depends on the calculation time and transmission time. Existing Internet communication solutions do not ensure the implementation of transmissions in a way that guarantees meeting the set time constraints. This paper proposes a new model of Internet communication dedicated to real-time Internet of Things systems, which includes a communication protocol, as well as a transmission scheduling and routing method. The protocol takes into account information about transmission time constraints, which is used for packet scheduling by routers, allowing to increase quality of service. In addition, the proposed static routing mechanism makes it possible to parallelize transmissions if time constraints are still exceeded. Also presented are preliminary results of experiments showing to what extent the proposed methods allow improving the quality of service in real-time Internet of Things systems. © 2023 Polish Information Processing Society.

关键词： Internet of things

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