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检索条件"机构=Dept. of Computer Engineering and Informatics University of Patras"

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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Content Creation for Touristic Portals Using ChatGPT 15

Content Creation for Touristic Portals Using ChatGPT

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Lampros, Papantoniou Eleni, Voyiatzaki Ioannis, Hatzilygeroudis University of Patras Computer Engineering and Informatics Dept Patras Greece

ISBN: (纸本)9798350368833

Our research presents the development of an innovative web-based application designed to assist hoteliers in creating and managing multilingual content for their tourist accommodations for tourism portals. To address this need, our application leverages the advanced capabilities of OpenAI's ChatGPT for automated content generation and DeepL's API for high-quality translations, integrated within a user-friendly interface. Through an integrated development process, we leveraged the capabilities of combining natural language processing with artificial intelligence to meet the specific needs of the tourism and hospitality industry. Our findings indicate that the application significantly streamlines the content creation and translation process, offering hoteliers a competitive edge in a globally connected marketplace. By automating these tasks, the system not only saves time and resources but also ensures consistency and quality in accommodation listings across different languages. The application's impact is further evidenced by its adaptability to various accommodation types and the potential for enhancing guest satisfaction through improved information accessibility. The benefits of our application are supported by a medium scale evaluation. © 2024 IEEE.

关键词： Tourism

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

引用

24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

引用

9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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A Framework Leveraging Robotics and Machine Learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops 4

A Framework Leveraging Robotics and Machine Learning Technol...

引用

4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kounalakis, Nikolaos Kalykakis, Emmanouil Kosmopoulos, Dimitrios Fasoulas, John Sfakiotakis, Michael Heraklion Greece University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350300918

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework. © 2023 IEEE.

关键词： Machine learning

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eXtended Reality Services for Outdoor Cultural Spaces: System Design and Evaluation Framework Using Unbiased Biometric Data 15

eXtended Reality Services for Outdoor Cultural Spaces: Syste...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Koukopoulos, Zois Koukopoulos, Dimitrios Fidas, Christos University of Patras Interactive Technologies Lab Dept. of Electrical & Computer Engineering Patras Greece University of Patras Dept. of History-Archaeology Patras Greece University of Patras Dept. of Electrical & Computer Engineering Patras Greece

ISBN: (纸本)9798350368833

The concept of enriching the visible reality with more information has led to the development of the eXtended Reality technology, where digital elements like text, images, 3D representations and virtual scenarios, are embedded to the real world and become visible to humans via mobile devices. Cultural Heritage is embracing eXtended Reality and tries to find new ways to harvest its merits in an endless struggle to disseminate cultural information to the public and the next generations. In this paper, we are presenting an innovative intelligent Environment, operating in outdoor cultural spaces and offering personalized eXtended Reality cultural experiences to visitors. The Environment aims to support the financial sustainability of cultural heritage institutes by attracting visitors and especially younger ones, as well as create new sources of revenue for cultural heritage professionals by allowing them to create XR experiences, without the help of IT professionals and exploiting them in an online market of XR cultural experiences. We propose a novel framework to evaluate the user experience by collecting biometric data (brain activity and eye behavior) from the visitors of outdoor cultural spaces and exploring their cognition and emotions while using the new Environment. © 2024 IEEE.

关键词： Biometric Data eXtended Reality Outdoor Cultural Spaces Sustainability

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UniNetProbe: A Comprehensive 5G Research Testbed Framework 29

UniNetProbe: A Comprehensive 5G Research Testbed Framework

引用

29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Papaioannou, P. Tziavas, G. Giannopoulos, D. Tranoris, C. Denazis, S. University of Patras Dept. of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9798350377644

A well-defined and well-designed experimentation process is crucial for meaningful research outcomes. This paper introduces a framework aimed at streamlining the experimentation process in networking testbeds, consisting of five key components: i) the experiment driver, containing available experiment tools, ii) the control tools to enhance capabilities and create complex scenarios, iii) the monitoring implementation, providing researchers with meaningful insights into gathered metrics, iv)the database component, which will be the main repository for storing experimental results and v) the analytics component, which could provide insights on patterns and hidden knowledge found in the extracted data, with the help of algorithms and artificial intelligence. The framework can be applied to various networking testbeds and is followed by descriptions of two Proof of Concepts (PoCs) demonstrating its implementation. © 2024 IEEE.

关键词： 5G mobile communication systems

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Fault Detection of Permanent Magnet Synchronous Motors Employing DQ Current Features and Isolation Forest

Fault Detection of Permanent Magnet Synchronous Motors Emplo...

引用

2024 International Conference on Electrical Machines, ICEM 2024

作者： Koutrakos, Konstantinos Mitronikas, Epameinondas University of Patras Electrical and Computer Engineering Dept. Rion-Patras Greece

ISBN: (纸本)9798350370607

Early and automated fault detection is necessary for proper condition monitoring and operation management of electric motor and drive systems. This paper investigates fault detection of Permanent Magnet Synchronous Motors (PMSM) using Motor Current Signature Analysis (MCSA), d-q Transform and Isolation Forest (iForest). This approach utilizes statistical indicators for feature extraction from dq-transformed current measurements and Isolation Forest, for outlier and fault detection. The proposed methodology is tested in a PMSM test bench under multiple operating conditions. © 2024 IEEE.

关键词： Permanent magnets

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Zero-Shot Sentiment Analysis Exploring BART Models 8

Zero-Shot Sentiment Analysis Exploring BART Models

引用

8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350373615

The BART model is an advanced adaptation of transformers introduced by Facebook. It has incorporated elements from both BERT and GPT transformers, enabling significant advancements in language understanding and general speech processing. Utilizing both encoder and decoder components, BART proves versatile for various tasks, including translation, text completion, automatic sentence generation, entity recognition, sentiment analysis, and more. In this study, we focus on the study of pretrained models, BART and a modified version called distilbart, in the context of Zero-Shot Text Classification. In the experimental study we dive into the Zero-Shot technique applied to various pretrained Transformers. Our analysis demonstrates that, depending on the Model we utilize, we can achieve F1 scores of up to 88%, showcasing the model's effectiveness in discerning classes for this Sentiment Analysis problem using the Zero-Shot Text Classification technique. © 2023 IEEE.

关键词： Sentiment analysis

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Velocity-aided Channel Estimation for Spatially Selective mmWave Massive MIMO Communications 31

Velocity-aided Channel Estimation for Spatially Selective mm...

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Vlachos, Evangelos Mavrokefalidis, Christos Berberidis, Kostas Industrial Systems Institute ATHENA Research Center Patras Greece Dept. of Computer Engineering and Informatics University of Patras Patras Greece

ISBN: (纸本)9789464593600

This paper focuses on addressing the challenge of estimating multiple-input multiple-output (MIMO) channels for wireless communication between a ground base-station and a moving vehicle. One recently recognised model for time-varying channels incorporates spatial selectivity, which is referred to as beam squint, and is particularly relevant in the millimeter-wave (mmWave) range. In such scenarios, it is essential to account for the beam squint when attempting to recover channel parameters using a training sequence. However, the use of a training sequence alone may be insufficient for this purpose. To overcome this issue, in this work, we propose a channel estimation approach that exploits information provided by the control module of the vehicle, namely its velocity. The estimation problem that is designed, regards the channel both in a parametric and a non-parametric form and the alternating direction method of multipliers is utilised to efficiently solve it. It is demonstrated via simulations that considerable gains can be achieved if information from the control unit of the vehicle can be appropriately introduced and exploited. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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