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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Smădu, Răzvan-Alexandru Ion, David-Gabriel Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia National University of Science and Technology POLITEHNICA Bucharest Faculty of Automatic Control and Computers Bucharest Romania National Institute for Research & Development in Informatics - ICI Bucharest Bucharest Romania Academy of Romanian Scientists Bucharest Romania Paris 1 Panthéon-Sorbonne University Paris France University of Bucharest Bucharest Romania

ISBN: (纸本)9798891761643

Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5turbo and GPT-4o, in the CWI, LCP, and MWE settings. We evaluate zero-shot, few-shot, and fine-tuning settings and show that LLMs struggle in certain conditions or achieve comparable results against existing methods. In addition, we provide some views on meta-learning combined with prompt learning. In the end, we conclude that the current state of LLMs cannot or barely outperform existing methods, which are usually much smaller. © 2024 Association for Computational Linguistics.

关键词： Zero-shot learning

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Constrained Predictive Current control in dq Frame for a Permanent Magnet Synchronous Machine

Constrained Predictive Current Control in dq Frame for a Per...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Madalin Costin Corneliu Lazar Dept.of Automatic Control and Applied Informatics "Gheorghe Asachi" Technical University of Iasi Iasi Romania Dept. of Automatic Control and Electrical Engineering "Dunarea de Jos" University of Galati Galati Romania

In the paper, the design and Simulink implementation of a predictive current control structure in dq frame for a permanent magnet synchronous machine (PMSM) are presented. Starting from the multivariable model of the currents in the dq reference frame, first it is decoupled and through the rejection of the disturbance introduced by the back-EMF, two single-input single-output (SISO) systems result whose dynamics are generated by R-L circuits. For these SISO systems, the model predictive control (MPC) algorithms are designed that allow the consideration of physical limitations through constraints The performances of the current control structure with MPC algorithms are compared with those obtained with the conventional PI controllers.

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SERVICE QUALITY EVALUATION USING ROBOTIC PROCESS AUTOMATION TOOLS

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

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2023年第3期85卷 93-108页

作者： Negoiţă, Radu Florin Borangiu, Theodor Doctoral School of Automatic Control and Computers University POLITEHNICA of Bucharest Romania Dept. of Automation and Industrial Informatics University POLITEHNICA of Bucharest Romania

Service quality is a vital component of the overall customer experience and should therefore be measured to know how the business’ service measures up to the customer’s expectations. The paper describes a Robotic Process Automation software tool for service quality control created for information-based service systems. The RPA bots are extended with artificial intelligence techniques used in the end-to-end automation of future service organizations. The software bots automate repetitive, time consuming human tasks (customer feedback evaluation, content analysis, data extraction) and speed up complex computations: OCR, data-driven analysis, support to intelligent decisions. Experiments performed with the Blue Prism tool are reported. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Robotics

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Analysis of message flow transmissions for an inter-vehicle communication scenario 14

Analysis of message flow transmissions for an inter-vehicle ...

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14th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2022

作者： Militaru, Andreea V. Lazar, Razvan G. Caruntu, Constantin F. Comsa, Ciprian R. Bogdan, Ion Gheorghe Asachi Technical University of Iasi Department of Telecommunications and Information Technologies Iasi Romania Gheorghe Asachi Technical University of Iasi Department of Automatic Control and Applied Informatics Iasi Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

Mobility in urban areas evolved massively in recent decades, leading in a continuous manner to traffic difficulties and significantly increasing the number of accidents, especially in crowded intersections. While much attention has been paid to controlling individual vehicles, recently the wireless communication networks were integrated as main ingredients for cooperation between vehicles with the other traffic participants and smart infrastructure. Thus, the fifth-generation (5G) communication technology promises an increase of the capacity for the existent cellular network, a faster connection, an ultra-low latency, and higher reliability. Moreover, device-to-device (D2D) communication is an essential feature when a large amount of devices have to be connected. The main advantage of D2D communication is the direct exchange of information between devices, without having to send the signal through the base station. Hence, this paper uses the Simu5G simulator to explore the D2D communication in a vehicle communication scenario by analysing the quality of the network. For this, the network performance was evaluated by monitoring the Channel Quality Indicator (CQI) and the message flow during the movement of the cars. The obtained results are in accordance with the real-time operation of the 5G technology. © 2022 IEEE.

关键词： 5G mobile communication systems

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Pneumonia Detection Using Federated Learning

Pneumonia Detection Using Federated Learning

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International Conference on control Systems and Computer Science (CSCS)

作者： Camelia-Raluca Farkaş Radu-Ioan Ciobanu Ciprian Dobre Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania National Institute for Research and Development in Informatics Bucharest Romania

Pneumonia is a major cause of illness and death among older adults, and hospitalization rates for pneumonia in patients aged 65 and older have been increasing. However, detecting pneumonia automatically is challenging because it requires large amounts of data from many patients, making traditional machine learning solutions impractical in real life, despite the good results they can achieve. To address this issue, our paper focuses on using federated learning, a new method for applying machine learning techniques in a distributed environment while maintaining patients’ privacy and reducing communication and storage overhead at the processing servers. We propose a federated learning system that can analyze X-rays of patients with and without pneumonia, present our approach, and demonstrate promising results.

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A Computation Offloading Framework for Android Devices in a Mobile Cloud

A Computation Offloading Framework for Android Devices in a ...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Vlad-Florin Ilie Mihail-Andrei Bănică Radu-Ioan Ciobanu Ciprian Dobre Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania National Institute for Research and Development in Informatics Bucharest Romania

Mobile devices have become a widespread commodity with an increasing number of applications. The resources needed by those applications have also been increasing disproportionately with the mobile phones’ battery capacity and computation power. The solution for this problem is to offload computation to both neighboring mobile devices and to remote servers. The Drop Computing paradigm proposes a few ways of implementing such a solution. The objective of this paper is thus to implement the Drop Computing paradigm as an Android framework that can then be used by third-party developers in their applications in order to leverage computation offloading. The experimental results performed on multiple Android devices show that the framework fairs well with computation problems that have small-sized or constant inputs.

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Ensuring Data Privacy in AC Optimal Power Flow with a Distributed Co-Simulation Framework

arXiv

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arXiv 2024年

作者： Dai, Xinliang Kocher, Alexander Kovačević, Jovana Dindar, Burak Jiang, Yuning Jones, Colin Çakmak, Hüseyin Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Automatic Control Laboratory École Polytechnique Fédérale de Lausanne Switzerland

During the energy transition, the significance of collaborative management among institutions is rising, confronting challenges posed by data privacy concerns. Prevailing research on distributed approaches, as an alternative to centralized management, often lacks numerical convergence guarantees or is limited to single-machine numerical simulation. To address this, we present a distributed approach for solving AC Optimal Power Flow (OPF) problems within a geographically distributed environment. This involves integrating the energy system Co-Simulation (eCoSim) module in the eASiMOV framework with the convergence-guaranteed distributed optimization algorithm, i.e., the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). Comprehensive evaluations across multiple system scenarios reveal a marginal performance slowdown compared to the centralized approach and the distributed approach executed on single machines—a justified trade-off for enhanced data privacy. This investigation serves as empirical validation of the successful execution of distributed AC OPF within a geographically distributed environment, highlighting potential directions for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Data privacy

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Improvement of Random Undersampling to Avoid Excessive Removal of Points from a Given Area of the Majority Class 21st

Improvement of Random Undersampling to Avoid Excessive Remov...

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21st International Conference on Computational Science, ICCS 2021

作者： Bach, Malgorzata Werner, Aleksandra Department of Applied Informatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783030779665

In this paper we focus on class imbalance issue which often leads to sub-optimal performance of classifiers. Despite many attempts to solve this problem, there is still a need to look for better ones, which can overcome the limitations of known methods. For this reason we developed a new algorithm that in contrast to traditional random undersampling removes maximum k nearest neighbors of the samples which belong to the majority class. In such a way, there has been achieved not only the effect of reduction in size of the majority set but also the excessive removal of too many points from the given area has been successfully prevented. The conducted experiments are provided for eighteen imbalanced datasets, and confirm the usefulness of the proposed method to improve the results of the classification task, as compared to other undersampling methods. Non-parametric statistical tests show that these differences are usually statistically significant. © 2021, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Multivariable control in General Anesthesia: A Comparative Study of Model Predictive and Fractional-Order Strategies for Effect-Site Concentration and Hemodynamic Regulation

Multivariable Control in General Anesthesia: A Comparative S...

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International Conference on Computational Cybernetics (ICCC)

作者： Erwin T. Hegedüs Anca Maxim Marcian D. Mihai Isabela R. Birs Nicoleta Badau Cristina I. Muresan Department of Automation Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Automatic Control and Applied Informatics “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

Managing anesthesia and hemodynamic regulation during surgical procedures poses significant challenges due to patient-specific variability and the complex interactions of administered drugs. While advanced monitoring systems and Target-controlled Infusion (TCI) devices enhance precision in drug delivery, their reliance on manual adjustments limits real-time adaptability. This study investigates two advanced control strategies–centralized Model Predictive control (MPC) and decentralized fractional-order control–within a multivariable framework designed to regulate hypnosis, analgesia, and hemodynamic stabilization. Virtual patient simulations were used to evaluate these approaches under diverse anesthetic and, notably, hemodynamic conditions. The findings reveal that MPC delivers superior precision in maintaining the Bispectral Index (BIS) but tends to apply more aggressive adjustments to hemodynamic variables. In contrast, fractional-order control ensures smoother responses, though its ability to handle disturbances is less robust in certain scenarios. This study highlights the trade-offs between these strategies and advocates for further exploration of hybrid solutions to enhance patient safety and surgical outcomes.

关键词： Stability criteria Surgery Anesthesia Regulation Real-time systems Hemodynamics Safety Reliability Transient analysis Predictive control

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ML-based Expert Products Scoring System

ML-based Expert Products Scoring System

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Progress in Applied Electrical Engineering (PAEE)

作者： Patryk Mendoń Mariusz Pelc Aleksandra Kawala-Sterniuk Mariusz Gola Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Mathematics Physics and Computer Science University of Opole Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

In this paper the authors propose a system architecture which allows product scoring based on sentiment analysis and utilising Machine Learning (ML) and Expert systems technologies. This solution presented in this paper allows decoupling the AI-based part, which is very capable in sentiment analysis but is lacking some flexibility to actually put the sentiment analysis results in a wider context to make a better sense as to what the results actually mean. This task in our solution is performed by the Expert system where the expert may provide some decision making rules in order to determine the final system *** results confirm discrepancy between reviews and star ratings.

关键词： Knowledge engineering Fuzzy logic Sentiment analysis Reviews Systems architecture Stars Machine learning Cognition Expert systems System analysis and design

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