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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Pechlivanis, Dimitrios Didaskalou, Stylianos Kaldoudi, Eleni Drosatos, George School of Medicine Democritus University of Thrace Alexandroupoli68100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

Ultrasound imaging is a widely used diagnostic method in various clinical contexts, requiring efficient and accurate data preparation workflows for artificial intelligence (AI) tasks. Preparing ultrasound data presents challenges such as ensuring data privacy, extracting diagnostically relevant regions, and associating contextual metadata. This paper introduces a standalone application designed to streamline the preparation of ultrasound DICOM files for AI applications across different medical use cases. The application facilitates three key pro- cesses: (1) anonymisation, ensuring compliance with privacy standards by removing sensitive metadata;(2) cropping, isolating relevant regions in images or video frames to enhance the utility for AI analysis;and (3) tagging, enriching files with additional metadata such as anatomical position and imaging purpose. Built with an intuitive interface and robust backend, the application optimises DICOM file processing for efficient inte- gration into AI workflows. The effectiveness of the tool is evaluated using a dataset of Deep Vein Thrombosis (DVT) ultrasound images, demonstrating significant improvements in data preparation efficiency. This work establishes a generalizable framework for ultrasound imaging data preparation while offering specific insights into DVT-focused AI workflows. Future work will focus on further automation and expanding support to ad- ditional imaging modalities as well as evaluating the tool in a clinical setting. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Ultrasonic imaging

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Data management in Ethereum DApps: A cost and performance analysis

Data management in Ethereum DApps: A cost and performance an...

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作者： Kostamis, Periklis Sendros, Andreas Efraimidis, Pavlos S. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece Institute for Language and Speech Processing Athena Research Center Xanthi67100 Greece

The cost of interacting with a blockchain and the time required to search and retrieve information from it are important factors in the design of Decentralized Applications (DApps). The corresponding approaches for data management in Ethereum vary from pure on-chain solutions to hybrid architectures. In this work, we thoroughly examine a wide range of data management methods, including unconventional ones, and evaluate them in terms of storage cost and retrieval latency while considering the impact of recent hard forks. More precisely, we elaborate on the use of Smart Contract (SC) storage as a data store for DApps and in view of the prohibitive cost inherent to this approach, we present and compare methods for low-cost data handling in Ethereum, namely the event-logs, the transaction payload, and the almost surprising exploitation of unused function arguments. In addition, we study hybrid schemes by using IPFS and Swarm as storage platforms and Ethereum as a timestamping proof mechanism. Such schemes are especially effective when large chunks of data have to be managed. The upload and retrieval performance of these platforms and the cost associated with storing (on-chain) the identifiers of the uploaded data are of utmost importance and therefore are investigated in detail. © 2023

关键词： Smart contract

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Sample-Efficient Unsupervised Domain Adaptation of speech Recognition Systems: A Case Study for Modern Greek

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IEEE/ACM Transactions on Audio speech and language processing 2024年 32卷 286-299页

作者： Paraskevopoulos, Georgios Kouzelis, Theodoros Rouvalis, Georgios Katsamanis, Athanasios Katsouros, Vassilis Potamianos, Alexandros National Technical University of Athens Graduate School of Electrical and Computer Engineering Athens10682 Greece Athena Research Center Institute for Speech and Language Processing Marousi15125 Greece National Technical University of Athens Faculty of Electrical and Computer Engineering Athens10682 Greece

Modern speech recognition systems exhibit rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where the diversity of training data is limited. In this work, we propose M2DS2, a simple and sample-efficient fine-tuning strategy for large pre-trained speech models, based on mixed source and target domain self-supervision. We find that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a 120-hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a test-bed for multi-domain evaluation of Greek ASR systems. In our experiments, we find that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environment, M2DS2 yields significant improvements for cross-domain adaptation, even when only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we find that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines. © 2014 IEEE.

关键词： speech recognition

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The Greek podcast corpus: Competitive speech models for low-resourced languages with weakly supervised data

arXiv

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arXiv 2024年

作者： Paraskevopoulos, Georgios Tsoukala, Chara Katsamanis, Athanasios Katsouros, Vassilis Institute for Speech and Language Processing Athena Research Center Athens Greece

The development of speech technologies for languages with limited digital representation poses significant challenges, primarily due to the scarcity of available data. This issue is exacerbated in the era of large, data-intensive models. Recent research has underscored the potential of leveraging weak supervision to augment the pool of available data. In this study, we compile an 800-hour corpus of Modern Greek from podcasts and employ Whisper large-v3 to generate silver transcriptions. This corpus is utilized to fine-tune our models, aiming to assess the efficacy of this approach in enhancing ASR performance. Our analysis spans 16 distinct podcast domains, alongside evaluations on established datasets for Modern Greek. The findings indicate consistent WER improvements, correlating with increases in both data volume and model size. Our study confirms that assembling large, weakly supervised corpora serves as a cost-effective strategy for advancing speech technologies in under-resourced languages. © 2024, CC BY.

关键词： speech recognition

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Asia Minor Greek in Contact (AMGiC): Towards a dialectal treebank comprising contact-induced grammatical changes 20

Asia Minor Greek in Contact (AMGiC): Towards a dialectal tre...

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20th International Workshop on Treebanks and Linguistic Theories, TLT 2021

作者： Sampanis, Konstantinos Prokopidis, Prokopis Boğaziçi University Turkey Institute for Language and Speech Processing Athena RC

ISBN: (纸本)9781955917162

In this contribution we briefly present methodological and theoretical aspects of the "Asia Minor Greek in Contact" (AMGiC) treebank. AMGiC is a project in preparation that comprises annotated sentences of contact-induced morphosyntactic change observed in Greek varieties spoken in the region of Cappadocia in Anatolia until the beginning of the 20th century. The treebank is being compiled in accordance with the Universal Dependency annotation scheme and incorporates a geodemographic and a sociolinguistic component in its metadata so that it serves as a tool for comprehensive research in the domain of language contact. © TLT 2021 International Workshop on Treebanks and Linguistic Theories

关键词： Forestry

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Welocalize-Arc/NKUA's Submission to the WMT 2022 Quality Estimation Shared Task 7

Welocalize-ARC/NKUA's Submission to the WMT 2022 Quality Est...

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7th Conference on Machine Translation, WMT 2022

作者： Zafeiridou, Eirini Sofianopoulos, Sokratis Welocalize Inc FrederickMD United States National and Kapodistrian University of Athens Athens Greece Institute for Language and Speech Processing - Athena RC Athens Greece

ISBN: (纸本)9781959429296

This paper presents our submission to the WMT 2022 quality estimation shared task and more specifically to the quality prediction sentence-level direct assessment (DA) subtask. We build a multilingual system based on the predictor-estimator architecture by using the XLM-RoBERTa transformer for feature extraction and a regression head on top of the final model to estimate the z-standardized DA labels. Furthermore, we use pretrained models to extract useful knowledge that reflect various criteria of quality assessment and demonstrate good correlation with human judgements. We optimize the performance of our model by incorporating this information as additional external features in the input data and by applying Monte Carlo dropout during both training and inference. © 2022 Association for Computational Linguistics.

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Empowering Knowledge Discovery from Scientific Literature: A novel approach to Research Artifact Analysis 3

Empowering Knowledge Discovery from Scientific Literature: A...

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3rd Workshop for Natural language processing Open Source Software, NLP-OSS 2023

作者： Stavropoulos, Petros Lyris, Ioannis Manola, Natalia Grypari, Ioanna Papageorgiou, Haris Institute for Language and Speech Processing Athena R.C. Greece Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece OpenAIRE AMKE Greece

ISBN: (纸本)9798891760455

Knowledge extraction from scientific literature is a major issue, crucial to promoting transparency, reproducibility, and innovation in the research community. In this work, we present a novel approach towards the identification, extraction and analysis of dataset and code/software mentions within scientific literature. We introduce a comprehensive dataset, synthetically generated by ChatGPT and meticulously curated, augmented, and expanded with real snippets of scientific text from full-text publications in Computer Science using a human-in-the-loop process. The dataset contains snippets highlighting mentions of the two research artifact (RA) types: dataset and code/software, along with insightful metadata including their Name, Version, License, URL as well as the intended Usage and Provenance. We also fine-tune a simple Large language Model (LLM) using Low-Rank Adaptation (LoRA) to transform the Research Artifact Analysis (RAA) into an instruction-based Question Answering (QA) task. Ultimately, we report the improvements in performance on the test set of our dataset when compared to other base LLM models. Our method provides a significant step towards facilitating accurate, effective, and efficient extraction of datasets and software from scientific papers, contributing to the challenges of reproducibility and reusability in scientific research. © 2023 Association for Computational Linguistics.

关键词： Extraction

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Cleansing and expanding the HURTLEX(EL) with a multidimensional categorization of offensive words 6

Cleansing and expanding the HURTLEX(EL) with a multidimensio...

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6th Workshop on Online Abuse and Harms, WOAH 2022

作者： Stamou, Vivian Alexiou, Iakovi Klimi, Antigone Molou, Eleftheria Saivanidou, Alexandra Markantonatou, Stella Institute for Language and Speech Processing Athena R.C. Greece Faculty of Philology University of Athens Greece

ISBN: (纸本)9781955917841

We present a cleansed version of the Modern Greek branch of the multilingual lexicon HURTLEX.1 The new version contains 737 offensive words. We worked bottom-up in two annotation rounds and developed detailed diagnostics of "offensiveness" by cross-classifying words on three dimensions: context, reference, and thematic domain. Our work reveals a wider spectrum of thematic domains concerning the study of offensive language than those identified in the Greek lexicographic literature as well as social and cultural aspects that are not included in the original HURTLEX categories. © 2022 Association for Computational Linguistics.

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Meltemi: The first open Large language Model for Greek

arXiv

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arXiv 2024年

作者： Voukoutis, Leon Roussis, Dimitris Paraskevopoulos, Georgios Sofianopoulos, Sokratis Prokopidis, Prokopis Papavasileiou, Vassilis Katsamanis, Athanasios Piperidis, Stelios Katsouros, Vassilis Institute for Speech and Language Processing Athena Research Center Artemidos 6 & Epidavrou Athens Greece

We describe the development and capabilities of Meltemi 7B, the first open Large language Model for the Greek language. Meltemi 7B has 7 billion parameters and is trained on a 40 billion token Greek corpus. For the development of Meltemi 7B, we adapt Mistral, by continuous pretraining on the Greek Corpus. Meltemi 7B contains up-to-date information up to September 2023. Furthermore, we have translated and curated a Greek instruction corpus, which has been used for the instruction-tuning of a chat model, named Meltemi 7B Instruct. Special care has been given to the alignment and the removal of toxic content for the Meltemi 7B Instruct. The developed models are evaluated on a broad set of collected evaluation corpora, and examples of prompts and responses are presented. Both Meltemi 7B and Meltemi 7B Instruct are available1 under the Apache 2.0 license. © 2024, CC BY.

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Introducing the Digital language Equality Metric: Technological Factors

Introducing the Digital Language Equality Metric: Technologi...

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2022 Towards Digital language Equality Workshop, TDLE 2022

作者： Gaspari, Federico Gallagher, Owen Rehm, Georg Giagkou, Maria Piperidis, Stelios Dunne, Jane Way, Andy ADAPT Centre School of Computing Dublin City University Ireland Germany Institute for Language and Speech Processing Research Centre "Athena" Greece

ISBN: (纸本)9782493814036

This paper introduces the concept of Digital language Equality (DLE) developed by the EU-funded European language Equality (ELE) project, and describes the associated DLE Metric with a focus on its technological factors (TFs), which are complemented by situational contextual factors. This work aims at objectively describing the level of technological support of all European languages and lays the foundation to implement a large-scale EU-wide programme to ensure that these languages can continue to exist and prosper in the digital age, to serve the present and future needs of their speakers. The paper situates this ongoing work with a strong European focus in the broader context of related efforts, and explains how the DLE Metric can help track the progress towards DLE for all languages of Europe, focusing in particular on the role played by the TFs. These are derived from the European language Grid (ELG) Catalogue, that provides the empirical basis to measure the level of digital readiness of all European languages. The DLE Metric scores can be consulted through an online interactive dashboard to show the level of technological support of each European language and track the overall progress toward DLE. © European language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

关键词： Digital language Equality Europe language Resources Technological Factors Technologies Tools

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